Factors Influencing FDA Clearance Time for Medical Device 510(k) Applications MASCUSTSNTTT MASSACHUSETTS INSTffUE Andrew J Koert OF TECHNOLOGY B.S. Mechanical Engineering University of Virginia, Charlottesville, 2002 OCT 2 1 2010 Masters of Business Administration MIT Sloan School of Management, Cambridge, 2009 LIBRARIES ARCHIVES Submitted to the Harvard-MIT Division of Health Sciences and Technology in Partial Fulfillment of the Requirements of the Degree of MASTER OF SCIENCE IN HEALTH SCIENCES AND TECHNOLOGY at the OF TECHNOLOGY INSTITUTE MASSACHUSETTS September 2010 @2010 Andrew J Koert. All rights reserved. The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. Signature of A uthor .................................... And(ew J Koert Biomedical Enterprise Program Harvard-MIT Division of Health Sciences and Technology - . ........-.... Certified by ............................................................... T. (Teo) Forcht Dagi, MD, MPH, MBA Senior Lecturer, Harvard-MIT Division of Health Sciences & Technology Thesis Supervisor Ce rtified by ........................................ dk 11 u A p t -eU y ...... Farzad Parsaie Chief Operating Officer, Levitronix LLC Thesis Supervisor ........................................... Xam'asiseKnaran, -n.D. Director, Harvard-MIT Division of Health Sciences & Technology / Edward Hood Taplin Professor of Health Sciences & Technology and Biological Engineering Factors Influencing FDA Clearance Time for Medical Device 510(k) Applications By Andrew J Koert Submitted to the Harvard-MIT Division of Health Sciences and Technology in partial fulfillment for the Degree of Master of Science in Health Sciences and Technology Abstract Since the FDA Modernization Act of 1997, recent legislation has sought to establish performance goals and consistency in the regulatory review and clearance of new medical devices. Despite these goals, regulatory review times have varied dramatically for devices seeking clearance through the 510(k) pathway; devices that are by definition "substantially equivalent" to other devices previously cleared by the FDA and currently on the market. The goal of this thesis is to investigate the factors that impact the ability for a company to obtain a 510(k) clearance in a predictable timeframe and assess the most important factors that influence time to clearance. The Medical Device industry is a dynamic and rapidly evolving sector that presents complex challenges to regulatory oversight. The 510(k) program and larger device regulatory process has endured the test of time and is well structured to address these challenges. This research focuses on the 7,481 510(k) "substantially equivalent" clearances during the period from October 1, 2007 through June 30, 2010 (FY08 - Q3FY10) in order to analyze the variables that impact clearance times. By testing various hypotheses about the most important factors I aim to provide the device community with the data and suggestions for improving confidence around a 2 particular 510(k) submission's likely approval time. I performed subset analyses on the dataset, segmenting clearances by the type of 510(k) (traditional, abbreviated, and special), clinical category, fiscal year of clearance, use of 3 rd party review, submission volume, and product diversity according to clinical category. The results show a distinct increase in clearance time from FY08 to FY10 and suggest that recent scrutiny of the FDA premarket review process may be affecting the efficiency of the Agency FDA review process for medical devices. Further, the 3 rd party review program is under-utilized with only 14%, on average, of eligible devices gaining clearance through this process. Through a discussion of two specific critiques of the 510(k) process, namely a recent Government Accountability Office study and an article by the Public Citizen (a public advocacy group), the potential implications of regulatory reform is explored from both a policy and industry perspective. Thesis Co-Supervisor: T. (Teo) Forcht Dagi, MD, MPH, MBA Senior Lecturer, Harvard-MIT Division of Health Sciences & Technology Thesis Co-Supervisor: Farzad Parsaie Chief Operating Officer, Levitronix LLC Acknowledgements The Biomedical Enterprise Program (BEP) at MIT has provided a tremendous growth experience allowing me to further explore my deep passion for healthcare and the life sciences. The deep emersion into ground breaking medical research, health sciences, biomedical industry dynamics, and clinical practice are unmatched in other similar programs. My appreciation for the unique interplay between the healthcare industry, clinical care providers, scientific research, government, and the very patients themselves is forever heightened. For their passion in creating and supporting the BEP program, I would like to especially thank the following people: Drs. Ernst Berndt and Richard Cohen, BEP Program Directors and the driving force behind its creation. Drs. Fred Schoen, Shiv Pillai, Bobby Padeira, and Rick Mitchell who expanded and challenged my understanding of medical science through their stimulating courses at Harvard Medical School. Drs. Rox Anderson and William Zapol for pulling back the veil on the inner workings of Mass General Hospital and connecting me with many of the leading researchers across the clinical departments of MGH. All of my fellow BEP classmates for their support in challenging my ideas and friendship, they truly inspired me with their diverse and unique accomplishments. Finally, Dr. Teo Dagi and Farzad Parsaie, whose support, encouragement, and advice were invaluable in guiding me toward completion of the research and writing of this thesis. To my family and friends, for their support, encouragement, and inspiration, thank you from the bottom of my heart. Table of Contents List of Figures ............................................................................................... 8 List of Tables.................................................................................................... 9 Chapter 1: Introduction ................................................................................. 10 Basis of Research.................................................................................................. 10 Scope of Research................................................................................................ 12 The FDA and the 510(k) ......................................................................................... 13 Regulating for Safety & Effectiveness while Promoting Innovation...................... 13 Significance of the Study ...................................................................................... 15 Chapter 2: Background ................................................................................. 17 The Medical Device Industry................................................................................ 17 Industry Statistics and Trends.............................................................................. 17 Medical Device Innovation .................................................................................. 18 The Challenges to Commercialization................................................................. 19 Medical Device Regulation.................................................................................. 20 Overview of Medical Device Regulation in the U.S...................... ...................... 20 Device Classification and the Risk-based Regulatory Framework...................... 21 The Impact of the 510(k) System ......................................................................... 23 Safe Medical Devices Act of 1990 ....................................................................... 24 FDA Modernization Act of 1997 ....................................... ....25 Medical Device User Fee and Modernization Act (MDUFMA) ...... .......... 26 The Many Forms of the Premarket Notification 510(k) System ......................... 26 Traditional 510(k)............................................................................................... 26 Abbreviated 510(k).............................................................................................. 29 Special 510(k) ...................................................................................................... 30 "De Novo" 510(k).................................................................................................. 30 Third Party Review .............................................................................................. 31 Chapter 3: Methods ..................................................................................... 33 Sum m ary.................................................................................................................... Research Database ................................................................................................ Definition of Terms ............................................................................................. Filtering the Dataset ............................................................................................. Developm ent of Hypotheses................................................................................ Data Analysis Tools............................................................................................. Clearance Tim e Calculation .................................................................................. Chapter 4: Results ......................................................................................... Dataset Overview .................................................................................................. Clearances by Fiscal Year..................................................................................... 510(k) Type ................................................................................................................ Advisory Com mittee ............................................................................................. Advisory Com mittee Volume ................................................................................ Advisory Com mittee Product Diversity................................................................ 3 rd Party Review ..................................................................................................... 3 rd Party Review Statistics, FY08-Q3FY10 ......................................................... 33 33 34 37 38 42 43 44 44 46 49 50 50 56 57 58 Party Review Adoption among 3rd Party Eligible 510(k) Clearances .............. 61 Average 3 rd Party Clearance Times.................................63 67 Chapter 5: Discussion and Conclusions ..................................................... 67 Sum mary of Findings ........................................................................................... 67 510(k) T y pe ............................................................................................................. 68 Time FY08-Q3FY10 ........................................................................................... 68 Advisory Com mittee Volume & Product Diversity ............................................... 69 Review Program .................................................................................. rd Party 3 70 Rationale for Data Sub-set Analyses .................................................................. 70 Clearance Time Calculation ................................................................................ 70 Clearance Decision and 510(k) Type ................................................................... Fiscal Year versus Calendar Year........................................................................ 71 71 Lim itations ................................................................................................................. Deficiencies in the FDA Database........................................................................ 72 73 Need for FDA Transparency ................................................................................ 73 FDA versus Industry Perspective ....................................................................... Constraints Inherent to the Study........................................................................ 74 75 Areas for Future Research.................................................................................. 79 Implications ............................................................................................................... Government Oversight: Recent GAO Study on the 510(k) process....................79 81 Public Scrutiny of the Regulatory Process .......................................................... Conclusions............................................................................................................... 88 510(k) Clearance Time Factors............................................................................ 88 90 Device Regulatory Policy .................................................................................... 93 Appendix ....................................................................................................... 93 510(k) Requirem ents: 21 CFR 807.87 ................................................................... 96 2009 510(k) Dataset Sum mary .............................................................................. References...............................................................................................................173 3 rd List of Figures Figure 1: Medical Devices Industry Revenue Forecast, 2003-2013................17 Figure 2: Average Review Time in Days by Fiscal Year and 510(k) Type ..... 47 Figure 3: Distribution of Traditional 510(k) Clearance Times .......................... 48 Figure 4: Number of 510(k)s by Clinical Category and Type (FY08 - Q3FY10) .51 Figure 5: Product Code Diversity Within Clinical Categories (as measured by number of unique product codes / total product codes cleared) .............. 56 Figure 6: Number of 3rd Party 510(k) Clearances by Clinical Category..........59 Figure 7: Average Clearance Time (Days) for Traditional 510(k)s Stratified by 3 rd Party Review Eligibility............................................................................ 63 Figure 8: Unique Class I Device Recalls, 2003-2009..................................... 87 List of Tables Table 1: Medical Device Innovation Profile .................................................... 15 Table 2: Top Medical Equipment & Supplies Manufacturers by Market Share ... 18 Table 3: Medical Device Regulatory Classification Structure.......................... 22 Table 4: MDUFMA Performance Goals for 510(k) Applications......................26 32 Table 5: Summary of Major FDA Regulatory Policy Reforms ......................... 36 Table 6: FDA Advisory Committee Codes ...................................................... Table 7: Overall Dataset Composition (incl. 3 rd Party Submissions)...............45 Table 8: 510(k) Clearances and Review Times by Fiscal Year.......................46 Table 9: Percentage of Clearances Above or Below Specified Thresholds ........ 49 Table 10: Effect of 510(k) Type on Clearance Time....................................... 50 Table 11: Number and % of Total 51 0(k)s by Clinical Category and Type.......52 Table 12: Advisory Committee 510(k) Volume Impact on Clearance Time.........53 Table 13: Number of 51 0(k)s Above or Below Review Time Thresholds........55 57 Table 14: Impact of Product Code Diversity on 510(k) Clearance Time ...... Distribution........60 Code Product and Clearances Party Table 15: Number of 3rd Table 16: 3rd Party Eligibility vs. Actual 3rd Party Clearances ....................... 62 Table 17: Average Time for 3 rd Party Eligible Clearances by Clinical Category..65 Table 18: 21 CFR 807.87 510(k) submission requirements........................... 93 Chapter 1: Introduction Basis of Research The purpose of this thesis is to investigate the attributes of 510(k) submissions that impact the amount of time required to obtain FDA clearance. The genesis of this research was borne out of a common misconception around regulatory clearance of medical devices that consist of technology that are "substantially equivalent" to that already cleared by the FDA and are in clinical use. The misconception is that the regulatory pathway for these devices, called the "510(k)" pathway, has a mandated 90-day review period and therefore a dossier will either receive clearance or denial within this time period. While in the strictest sense, the FDA does have to respond to a traditional 510(k) submission within 90 days, often and with growing frequency the response is a request for more information - a response that resets the review clock and requires additional time and effort by the submitting company. Inaddition, there are several different types of 510(k) submissions, which can have different mandated review periods, as well as variations in the level of supporting data that are required. As a result, the true time required for FDA clearance of a 510(k) dossier can vary dramatically, from a few days to several years. A vast majority of medical devices launched in the U.S. every year are cleared through the 510(k) process. Large companies and start-ups both commit significant resources to the development and launch of a new device and 10 accurate timing is paramount to ensuring resources are not wasted. This challenge is especially critical in a start-up with limited financial resources where very tight control over burn-rate can dictate survival or failure of the company. A start-up that ramps up sales force personnel and manufacturing operations in anticipation of a regulatory clearance only to become mired in months of additional delays can quickly burn through cash reserves. Conversely, delaying the ramp-up of product launch activities can cost a start-up a great deal in lost time and revenue if a regulatory clearance comes through quickly. As of this writing, very few explorations into the 510(k) review process have been conducted from the industry perspective with the goal of informing and improving corporate decision-making and strategy. While a similar investigation into PMA review times has been conducted, there is not a great deal of literature focused on attributes of the 510(k) process itself.'Annual FDA performance reports focus on review division's initiatives and overall statistics on device approvals. High level statistics however do not inform business decisions in a regulatory process as diverse as the 510(k) pathway where therapeutic indication, submission type, technology standards, and many other variable all potentially impact the amount of time required to ensure a device's safety and efficacy. Scope of Research The following thesis endeavors to characterize the factors that impact the length of time needed for FDA clearance of a 510(k) application. To accomplish this, a retrospective analysis of 510(k) submissions cleared from FY08 to Q3FY2010 is constructed using the FDA's online 510(k) Medical Device Database. The FDA operates on a fiscal year from October to September. As a result, the analysis period runs from 10/1/2007 to 6/30/2010t. The resulting dataset therefore includes 510(k) submissions from previous years provided the clearance occurred between FY08 and Q3FY2010. There are several limitations in the data publicly available from the FDA. Mid-review FDA requests for further information and FDA's "not substantially equivalent" (NSE) decisions are not captured in the FDA database. These limitations are further described in later sections. As a result, this analysis is limited to timelines associated with successful 510(k) submissions and does not lend direct insight into submissions that do not receive clearance. A further discussion regarding the usefulness of published FDA data on device approvals will follow in Chapter 5. Fiscal years 2008, 2009, and Q1-Q3 2010 were chosen for the dataset for three reasons: (1)This is the most recent period from which to sample data and should best represent current timelines in the review process. (2) The Medical Device User Fee and Modernization Act (MDUFMA) was extended in 2007 by Congress i As of this writing in July 2010, only 510(k) clearances through 6/30/2010 were available for download from the FDA's website. 12 with additional funding to further build on performance goals. Selecting FDA clearances after MDUFMA II should capture the impact of this additional CDRH funding. (3) The large number of 510(k) clearances every year allows for a sufficient level of statistical significance and allow for sub-group analyses. The dataset was further filtered to include only "substantially equivalent" decisions, the reasons for which will be discussed in further detail in Chapter 3: Methods. As with most retrospective analyses, the results do not necessarily predict future events. The 510(k) regulatory pathway is currently under a great deal of scrutiny from Congress as well as the FDA itself and there is potential for significant changes to the system in the coming years. Therefore, the results stemming from this investigation should serve as a window into regulatory timelines. Without foresight into the changes on the horizon the applicability of conclusions drawn from this investigation to future years is uncertain. A discussion of the recent criticisms of the 510(k) process by both the GAO and the Public Citizen will follow in Chapter 5: Discussion. The FDA and the 510(k) Regulating for Safety & Effectiveness while Promoting Innovation When discussing the regulation of medical devices in the U.S., the focus invariably turns to the Pre-market Approval, or PMA process, because it is the system responsible for evaluating breakthrough technologies and devices that pose significant inherent risk to patients. However, the PMA process is only a small subset of the overall regulation of medical devices handled by the FDA. The 510(k) regulatory pathway on the other hand has served as the starting point for roughly 90% of medical devices authorized for marketing in the US today. In 2009, the FDA cleared more than 3,000 medical devices via the 510(k) process from 19 different clinical categories. As a result of this broad diversity of devices, the regulatory system must be sufficiently flexible to accommodate thorough scientific evaluation while ensuring timely availability of new healthcare technologies. The framework that makes up the 510(k) process has remained largely unchanged since it was conceived and has proven well suited to handle the enormous spectrum of medical devices that fall under its umbrella. A principal reason this system has endured over the last 30 years is its ability to deal with the highly iterative nature of medical device innovation. Unlike drugs, which have a fixed chemical or biologic composition, the launch of a new medical device represents just the beginning of a series of design improvements and modifications to follow - many of them resulting directly from clinical practitioner's use of the product and subsequent feedback to industry. AdvaMed nicely summarizes the traits of medical device innovation in Table 1 below: 4 Table 1: Medical Device Innovation Profile While the structure of the 510(k) process itself will be dealt with in more detail in Chapter 2, one could argue that the system has succeeded in addressing the challenge of risk-appropriate regulation across a very large spectrum of medical device technologies. Through a closer inspection of device and process traits that impact the regulatory review time, this thesis aims to understand the factors that improve or hinder timely clearance of the technological innovations that define the medical device industry. Significance of the Study With the extension of the Medical Device User Fee and Modernization Act in 2007 promoting further 510(k) process improvements, and significant changes to the process anticipated in late 2010, an investigation of the state of the 510(k) process from 2008-2010 is both relevant and necessary. The conclusions drawn from this study are relevant to industry stakeholders across the board: entrepreneurs, investors, and policy makers. Medical device entrepreneurs can benefit from more accurate forecasting of regulatory clearance timelines and an understanding of how selection of clinical indications and product categories can impact time to market. Device industry investors, with a clearer picture of the regulatory challenges, can make more informed investment decisions and spot sources of risk due to these challenges. Further, policy-makers within the FDA can gain perspective on the implications of their policies and internal performance metrics as compared to the real timelines experienced by the device industry. The economic implications of a delay, regulatory or otherwise, can be devastating for a small company with limited resources. As a result, of critical importance is the FDA's ability to effectively balance their mandate to regulate for safety and effectiveness while maintaining a system that fosters innovation and ensures medical technology advances reach patients in a timely manner. This thesis is intended to stimulate discussion on how to optimize this process with both patient safety and continued innovation as paramount goals. Chapter 2: Background The Medical Device Industry Industry Statistics and Trends The U.S. medical device and supply industry comprises roughly $89.4 billion in annual revenue and is estimated to be growing at a 9%CAGR '6 . While almost 40% of this revenue flows into the 4 largest firm's coffers, over 75% of the roughly 12,000 device companies are small firms with fewer than 50 employees5'6 . Figure 1 illustrates the revenue growth forecast for the Medical Device Industry6 . Figure 1: Medical Devices Industry Revenue Forecast, 2003-2013 Medical Device's Industry: Revenue Forecasts (U.S.), 2003-2013 140.0 120,0 1C0.0 ED.0 S60,0 S40,0 20.0 0,0 003 2CD4 205 2005 20J7 21B 209 2910 2311 2)12 2313 Year Table 2 details the largest Medical Device companies and their corresponding market share in the US5 . Table 2: Top Medical Equipment & Supplies Manufacturers by Market Share Johnson &Johnson 13.5% General Electric Company 10.2% Medtronic, Inc. 8.9% Baxter International Inc. 7.0% Covidien Plc. Boston Scientific Corporation Becton, Dickenson and Company Stryker Corporation 6.3% 0.9% 0.8% 0.6% Competition in the Medical Device industry hinges to a large extent on technological innovation and continuous introduction of new products and technologies. As a result, R&D investment currently stands at around 12% of sales, more than four times the average for manufacturing costs overall 5. Medical Device Innovation In 2009, Venture Capital firms invested $2.5 billion in the Medical Device industry comprised of 313 deals.7 While this number has fallen dramatically in the current economic climate from the $3.4 billion invested in 2008, Venture Capital still remains as one of the primary drivers for medical device innovation second only to R&D investment by large device companies (approx $4.2b by the largest 5 firms alone). In addition, M&A has served as the primary exit for medical device 18 companies over the last five years and large firms have augmented their internal product innovation through numerous mergers and acquisitions. This is especially true in the current economic climate, as the IPO market has virtually disappeared for medical device startups (annual average of 73 IPOs in 20042007 versus 6 IPOs in 2008)7. The FDA's Centers for Devices and Radiological Health (CDRH) estimates there are roughly 80,000 medical devices marketed in the U.S. Whether developed within the large device firms or small startups, medical device innovation is comprised of short product life cycles with rapid iterative design improvements. This is evidenced by the fact that there are roughly 8,000 new devices marketed in the US each year, 10% of the total number of devices quoted by the CDRH. 8 The Challenges to Commercialization Despite this M&A activity, consolidation in the device industry remains very low and a large number of the new devices seeking FDA approval are developed within small firms. With IPO activity drying up and the M&A as the only form of exit for small, venture backed firms, successfully getting a device onto the market with demonstrable revenues can spell the difference between a healthy return on investment and complete failure. A device startup with validated clinical value, either through successful clinical trials or recurring sales revenue is much more valuable to a potential acquirer because the revenues will directly impact 19 earnings. Conversely, a startup with merely a promising technology requires a financial and time investment by the acquirer as well as justification as to how the technology can be leveraged across the larger firm; therefore resulting in lower valuations, if a potential acquirer emerges at all. Provided a startup is successful at developing a technology that meets a clinical need, the next major hurdle lies in navigating the regulatory process and obtaining U.S. marketing authorization from the FDA. While guidelines and general timetables exist for the various approval pathways, significant variations in approval times persist. For a capital constrained startup firm, an unanticipated regulatory delay can spell the difference between survival and failure. The following sections with provide a background introduction to the current FDA medical device regulatory process. Medical Device Regulation Overview of Medical Device Regulation in the U.S. The FDA's authority to regulate medical devices in the US was quite limited for most of the 20 th century. In 1969, President Nixon appointed Dr. Theodore Cooper of the National Institutes of Health (NIH) to chair a committee investigating medical devices with the goal of providing recommendations on an appropriate regulatory framework. Up to this point, Congress had been promoting the extension of the drug provisions in existing law to cover devices. 20 The "Cooper Committee" report, issued in 1970, stressed that the breadth and diversity of medical devices required a regulatory approach much more adaptable than the one applied to drug development. The committee recommended a "risk-based" approach to evaluating devices. These recommendations formed the basis for the Medical Device Amendments of 1976 which formally gave the FDA comprehensive regulatory authority over medical devices. The Center for Devices and Radiological Health (CDRH) was the division of the FDA created to oversee the regulatory review of medical devices. Device Classification and the Risk-based Regulatory Framework The concept of a "risk-based" regulatory framework held that the level of regulatory oversight should be aligned with the level of risk posed to the patient by a device for a specific indication, and requiring pre-market approval only for those devices posing the greatest risk to patients. All medical devices developed after 1976 fall into one of three classes in accordance with the level of risk posed to the patient. The Table on the following page details the different device classes. Table 3: Medical Device Regulatory Classification Structure * "General controls" pertain to misbranding, adulteration, registration, and listing of a device. ** "Special controls" include promulgation of performance standards, patient registries, development and distribution of guidance documents, and any additional actions the FDA determines necessary to ensure the safety and effectiveness of a medical device. As the Table above outlines, class I devices are those posing the least risk to patients and for which a great deal of technical and clinical know-how already exists. Class I devices are those that neither support nor sustain human life nor that pose an unreasonable risk of injury. Devices in this class can generally ensure safety and effectiveness through general controls and adherence to quality system regulations (QSR). Class 11devices are those for which general controls are not sufficient to ensure safety and effectiveness but do have a great deal of technical and clinical know22 how surrounding the device and application. All but a few class 11devices require 510(k) clearance.t Class Ill devices pose the most risk of injury or illness to the patient and require individual review under the premarket approval process to ensure safety and effectiveness. These devices are generally life-sustaining or encompass cutting edge technologies for which a sufficient knowledge base exists regarding the clinical application. The Impact of the 510(k) System The FDA estimates that 90% of all devices are originally evaluated and classified via the 510(k) system. From this starting point, roughly 10% fall into class Ill and must follow the stringent premarket approval (PMA) process. Another 30% are class I devices and are only subject to "general controls" which only in select cases includes a 510(k) submission. The remaining 60% of medical devices that seek marketing approval are class 11devices and are subject to the 510(k) process including any applicable performance standards and "special controls".9 This fundamental risk-classification system has remained unchanged since it was designed and all subsequent legislation enacted by Congress has built on this foundation. t For the purposes of this paper, when referencing class 11devices I will only be discussing the devices that require a 510(k) for marketing clearance. 23 NOTE: While medical device PA applications are "approved" by the FDA, 510(k) applications are "cleared" for marketing. This distinction in terminology highlights the difference with whdevice res are subjected to regulatory review according to risk. Further, the device itself is not cleared or approved, but the device's intended therapeutic indication is cleared resulting in a private license to market the device with a particular label. Accordingly, I make every effort to remain consistent in my use of this terminology. Safe Medical Devices Act of 1990 The Safe Medical Devices Act (SMDA) was signed into law in 1990 with the goal of strengthening the FDA's authority over medical device regulation, including post-market actions such as recalling devices that caused undue risk to patients. An incredibly important aspect of this bill was a change in the 90-day timeline for 51 0(k) review. Prior to SMDA, companies could begin marketing their device 90 days after submission of the 51 0(k) dossier. SMDA prevented such market launch without explicit written clearance from the FDA. This change is a key shift in regulatory policy that an important impact on medical device development and clearance times. A second key shift resulting from SMDA was the FDA's ability to require clinical evidence for some 51 0(k)s in order to prove substantial equivalence. As a result, some 510(k)s have begun to more closely resemble PMAs. FDA Modernization Act of 199710 The FDA Modernization Act of 1997 (FDAMA) codified into law many systems developed within the CDRH during the 1990s in response to unacceptable backlogs of pending 510(k) applications. These systems were designed to streamline the efficiency of device reviews and had been effective in essentially eliminating the 510(k) application backlog by 1997. The FDAMA attempted to refocus the FDA on the "risk-based" approach to regulation and follow a "least burdensome" approach to device evaluation. Specifically, a section of FDAMA stated: "...we cannot afford an overly complex, bureaucratic, timeconsuming, and expensive regulatory system. Nor can we afford an adversarial relationship between the FDA and the industries it regulates or an agency pursuing so many agendas that it lacks a clear-cut mission and sphere of responsibility."1 The evolution of the "Special 510(k)" and the "Abbreviated 510(k)" as well as Third Party Review are three of the systems arising from the FDAMA which will be described in detail in later sections of this Chapter. Lastly, FDAMA created a system for reclassifying devices into class I or Il after receiving an "automatic class Ill designation" due to a Not Substantially Equivalent (NSE) finding. Particularly important in cases where a new device did not resemble a technology already on the market, but which did not present a significant risk to the patient and for which a PMA would prove overly burdensome. 25 Medical Device User Fee and Modernization Act (MDUFMA) MDUFMA and MDUFMA II were signed into law in 2002 and 2007 respectively. The latter was an extension of MDUFMA, which had been set to expire in 2007. The development of performance benchmarks for PMA and 510(k) reviews were a significant outcome to reduce review times. In return, the FDA was authorized to charge user fees to review PMA and 510(k) applications, creating resources that would then be used to hire additional review personnel and augment resources needed for regulation of new devices. The performance goals established by MDUFMA are shown in the table below 12 : Table 4: MDUFMA Performance Goals for 510(k) Applications FDA Decision (SE, NSE) 90 days 75% 75% 80% 90% 90% 80% 90% 70% 60 days Second or later action *MDUFMA Il extended MDUFMA I and increased the performance goals for 510(k) applications to a decision in 90-days for 90% of applications and 150-days for 98% of applications. The Many Forms of the Premarket Notification 510(k) System Traditional 510(k) The term 510(k) arises from section 510, item "k"in the Medical Device Amendments. The FDA defines a 510(k) as: "Asubmission made to FDA to demonstrate that a device is at least as safe and effective as another legally marketed device."12 As the definition implies, 510(k) device applications must 26 demonstrate that the new device does not raise any new questions about the safety and effectiveness as compared to a "predicate" device on the market. In addition, the predicate device must not be subject to a PMA (a device approved through the PMA process cannot then be used as a predicate for a new class Ill device). Most class 11devices, non-exempt class I devices, and "preamendment"t class III devices may seek clearance through the 510(k) process. In essence, the 510(k) provides a framework upon which a series of laws, regulations, and guidance documents are built. This structure has proven very adaptable to the constantly changing device technological landscape allowing for new guidance documents, performance standards, and special controls to be created to ensure device safety and effectiveness. A new device is either found by the FDA to be "substantially equivalent" (SE) to the predicate(s) or "not substantially equivalent" (NSE). The former serving as clearance to begin marketing the device while the latter automatically classifies the device as class IlIl, thus requiring a PMA submission. In response to a NSE decision, a submitter in some cases can submit a second 510(k) with new data, file for reclassification, or request class I or 11designation through the "de novo" process. t Pre-amendment class IlIl devices are those devices that were on the market prior to the 1976 Medical Device Amendments. 27 According to the FDA: A device is substantially equivalent if, in comparison to a predicate it: e has the same intended use as the predicate; and . has the same technological characteristics as the predicate; or e has the same intended use as the predicate; and . has different technological characteristics and the information submitted to FDA; o does not raise new questions of safety and effectiveness; and o demonstrates that the device is at least as safe and effective as the legally marketed device. A majority of 510(k)s submitted annually to the FDA follow the traditional format. The information required in a 510(k) application is outlined in 21 CFR 807.87 and is reprinted in the Appendix. Abbreviated 510(k) Both the Abbreviated and Special 510(k) were formalized in 1998 just after passage of FDAMA as part of "The New 510(k) Paradigm"t. In an effort to streamline the review process, the Abbreviated 510(k) allows an applicant to adhere to consensus standards when developing specific types of devices. Consensus standards inthis case are developed by independent organizations such as the International Organization for Standardization (ISO) or the Association for the Advancement of Medical Instrumentation (AAMI).* The FDA then reviews these standards. If the FDA accepts the standard, a company can choose to adhere to the standard in the development of their device and submit an abbreviated 510(k) referencing the standards. The manufacturer then has the option of submitting a summary report describing how the standards (guidance documents) were applied in the device development process and the associated risks. The principal advantage of the Abbreviated 510(k) is a shorter, simplified submission dossier; however, it does not offer any review time advantage (the FDA still has 90-days to review). The alternative would be to submit a traditional 510(k) and include the extensive test verification & validation documentation demonstrating safety of the device for the given indication. t "The New 510(k) Paradigm: Alternate Approaches to Demonstrating Substantial Equivalence in Premarket Notifications, Final Guidance," prepared by CDRH, March 20, 1998. *<http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceD ocuments/ucm080187.htm> 29 Special 510(k) The Special 510(k) provides an avenue to manufacturers to make design modifications to a previously cleared device and obtain more rapid regulatory clearance than submitting a new traditional 510(k). The FDA recognized that Quality System Regulations (QSR) could be relied upon to ensure safety and efficacy in the case of certain device design modifications. This pathway requires that the modification does not alter the fundamental scientific technology of the device or change its intended use. For example, changing a bone screw from stainless steel to titanium would be a change that would be acceptable under a Special 510(k). A Special 510(k) submission essentially consists of a risk analysis of the design modification and verification and validation summary to prove continued safety and efficacy of the device. The approach allows FDA to focus resources on new technologies and less on minor changes to previously cleared devices. As such, the review time performance goal for review of Special 510(k)s is 30-days which certainly offers an advantage to manufacturers and lowers the barrier to making truly beneficial improvements to marketed devices. "De Novo" 510(k) Another system resulting from the "least burdensome" approach of the FDAMA was the development of the "de novo" 510(k) pathwaytt. Occasionally, a device is developed for which there is no legally marketed predicate, but which t Section 207 of FDAMA, "Evaluation of Automatic Class III Designation," amended the FD&C Act to add section 513(f)(2). 30 otherwise would be placed in class I or Il based on the risk posed by use of the device. The resulting NSE decision results in an "automatic class Ill designation." In this instance, the manufacturer has 30-days to submit an argument for class I or Il consideration. The FDA then has 60-days to reclassify the device or determine the device belongs in class 111. While not that common (in 2009 there was one "de novo" 510(k) clearance), the system provides an important avenue for manufacturers of new, low risk devices to avoid the costly, and time-consuming PMA process when such an undertaking would prove an overly burdensome path to market. Third PartyReview The Third Party Review system is not a unique 510(k) pathway in and of itself, but it does represent an important variation on traditional 510(k) applications and therefore merits discussion. Also called the "Accredited Persons Program", Third Party Reviews were part of FDAMA and have grown in popularity as the process has become better understood over the last decade. The process allows a device manufacturer to submit a 510(k) to an accredited third party reviewer. The reviewer then sends the 510(k) with a clearance recommendation (SE or NSE) to the FDA. The FDA then has 30 days to respond to the manufacturer with a final decision. According to the FDA, the system has resulted in submissions receiving clearance an average of 77 days after initial submission to the accredited reviewer, a 29% decrease in time compared to submission directly to the FDA. There are however a couple of caveats: There is a list of eligible devices which qualify for third part review (see Appendix for detailed list), and the third party reviewers are able to charge their own fees. Aside from the FDA evidence of reduced review time, another benefit is the manufacturer does not have to pay user fees to the FDA for the summary review of the third party recommendation. In addition, most accredited persons have specialized expertise in areas that can be of tremendous benefit to device manufacturers. The initial cohort of 3rd party eligible devices established by FDAMA was expanded in 2001 and now includes 1,626 product codes. Table 5: Summary of Major FDA Regulatory Policy Reforms Chapter 3: Methods Summary The database constructed for this research consists of the 51 0(k)s cleared during fiscal years 2008, 2009 and Q1-Q3 2010 publicly available on the FDA CDRH website. The data was then filtered (as described later in this chapter) to result in a total dataset containing 7,481 510(k)s from well over 2,000 applicants and representing 941 unique product codest. A list of the 510(k) numbers is included in the appendix along with select information from each. I performed the analysis using the statistics package available in Excel 2007 and tested various hypotheses about individual factors that may impact clearance times. Further breakout of data by type of submission (Traditional, Abbreviated, Special) to control for differences in mandated review clocks allowed for subset comparisons between the submission types. Inaddition, a subset analysis was performed on the submissions that were 3rd party review eligible and either did or did not gain clearance through this pathway. Research Database The US FDA website contains downloadable databases for releasable 510(k) device clearances in 5-year increments from 1980 to 1995 and a single database t Product codes are three letter codes assigned by the FDA to generic categories of devices. According to the FDA: "The Product Code assigned to a device is based upon the medical device product classification designated under 21 CFR Parts 862-892." <http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/Overview/ClassifyYourDevice/ucm051 637. htm> 33 file for 1996-present. In addition, it is possible to view clearances by month for each of the years from 1999 to 2010 directly on the FDA website. In building the database, I downloaded the "1996-current" file, which included 510(k) clearances through the third quarter of 2010 (a total of 49,903 510(k) clearances). Definition of Terms The following fields are included in the downloadable 510(k) FDA database and were included in the dataset used in this research: 510(k) Number: The FDA assigns a unique identifier number to every 510(k) submission consisting of the letter "K"followed by six digits. Applicant: The company name or entity submitting the 510(k). Contact: The contact name of the individual submitting the 510(k) on behalf of the Applicant. Street: Mailing address of the applicant. City: see above. State (or Country): see above. Zip code (or postal code): see above. Device Name: The name of the device submitted for clearance. Date Received: Calendar date associated with FDA receipt of the 510(k) application. Decision Date: The date corresponding to an FDA decision whether or not to clear the device. Decision: The decision code representing the FDA's decision for the device in question (only substantial equivalence "SE" codes were analyzed in this work; however, definition for common decision codes is provided later in this section). Advisory Committee: Two letter code corresponding to one of the 19 different FDA advisory committees organized by clinical specialty to which the device belongs. See definitions of the 19 categories in Table 6 below. Unlike the PMA process, the a 510(k) is reviewed by a lead reviewer from one of the 19 clinical departments. The Advisory Committee is involved in defining the clinical category a particular product code belongs to only and is not the same as the advisory panels that are employed to review some PMA submissions. Advisory Panels do not exist in the current 510(k) review process. Product Code: Three letter code assigned to each device based on a generic device category according to 21 CFR 862892. 35 Summary or Statement: Whether the decision file available from the FDA includes a "summary" of safety and effectiveness or a "statement" of safety and effectiveness is available from the 510(k) applicant. Type of 510(k): The type of 510(k) - "Traditional", "Special", "Abbreviated", or "Evaluation of Automatic Class Ill Designation". Whether or not the application was reviewed by a Third Party: third party. Expedited Review: Whether or not the application was granted expedited review status. Table 6: FDA Advisory Committee Codes RA Radiology TX Clinical Toxicology Inaddition, I created the category "DAYS" which signified the number of days between submission and decision. "DAYS" was calculated in Excel by taking the difference between the decision and submission date. It is important to point out that the FDA defines their review time performance goals in terms of "FDA Days" which include only FDA workdays (excluding government holidays and weekends). Therefore a separate category "FDA DAYS" was created which only counts the workdays (excl. holidays) between the submission and decision dates. "FDA DAYS" was calculated using the "NETWORKDAYS" function in Excel along with a reference table containing Federal Holidays for 2007-2009 (some 510(k)s cleared in the dataset time period were submitted as early as 2003). Filtering the Dataset Filtering the database by "Decision Date" then allowed for only those 510(k)s cleared in FY08, FY09, and FY10 to be pulled out. As mentioned in Chapter 1, clearances during the FDA's fiscal years 2008, 2009, and 2010 were chosen because they represent clearances that fall under the performance goals of MDUFMA II that took effect in September of 2007. Therefore, the cohort should provide the most relevant insight into the current state of the regulatory review process. Further, the analysis was limited to 510(k)s receiving a "substantial equivalent" decision. In FY08 - 3QFY10, there were 8,176 FDA clearances of 510(k)s. From these data, 7,482 were "SE" decisions. The remaining 694 decisions are distributed as follows: 610 CS "SE", CLIA submission 56 SU "SE"with Limitations 22 SK "SE" - Kit 1 SD "SE"with Drug 5 AN Cleared for Marketing, Automatic Class Ill Designation The 610 "CS" decisions were removed from the dataset because they include diagnostic technologies and represent a class of devices quite different from traditional devices such as surgical equipment, orthopedic implants, or endoscopes. The remaining 84 clearances were relatively rare and, due to the type of decision, represented potential confounders to the subsequent analysis. The "AN" decision clearly represents a technology without a predicate device (De Novo). Similarly, the devices including a kit or a drug could experience review procedures unique to other 510(k)s receiving mere "SE" decisions. Development of Hypotheses Based on my research into the structure of the 510(k) regulatory system and the clearance data made available by the FDA, I developed several hypotheses 38 regarding factors that impact 510(k) review times. These hypotheses were further refined through interviews with representatives from industry as well as within the FDA CDRH. The resulting hypotheses are discussed below in the context of the data analysis approach used to examine each. 510(k) Type: Special 510(k)s are subject to a shorter review period (30 days) and therefore should have significantly shorter review periods versus traditional and abbreviated 51 0(k)s. Further, I expect Abbreviated 51 0(k)s to have shorter review periods versus Traditional 510(k)s because the submissions are both shorter in length and represent devices around which there is a great deal of technical knowledge in the form of consensus standards. A shorter submission dossier with a sound rationale for compliance with consensus standards should allow for a quicker review by FDA. Time FY08-FY10: It could logically be argued that clearance times should decrease on average from FY08 to FY10 as the FDA continues to implement processes and resources to achieve MDUFMA II performance goals. However, there is growing consensus in recent literature suggesting that 39 Government and Public scrutiny of the 510(k) process has lead to a more conservative clearance process. As a result, I expect to see clearance times increasing between FY08 and FY10. Advisory Committee Volume: The number of submissions received varies dramatically between clinical categories. Orthopedic (OR), Cardiovascular (CV), and General & Plastic Surgery (SU) make up over 300 clearances in 2009 each, over 6 times more 510(k)s than Pathology (PA), Hematology (HE), or Ear Nose & Throat (EN). While fewer submissions might result in a smaller workload burden on FDA staff in those categories, it also results in a smaller body of technical knowledge passing through the FDA annually. I expect that the "high traffic" categories will have shorter review times due to the breadth of technical and scientific knowledge of the reviewers resulting from the volume of annual submissions. The data will be split into the "High" and "Low" volume bins based on the volume of traditional, non-third party 510(k)s in each category and identifying a natural drop off in the number of clearances. If a natural break is not identified, then the categories will 40 be split with the largest 9 categories classified as "high volume" and the remaining 10 categories as "low volume". Advisory Committee Product Diversity: Similarly, I expect that diversity of product submissions in each clinical category will impact review times. Looking at the number of unique product codes compared to the number of submissions can be used as a proxy for the diversity of technical innovation occurring in each clinical category. Categories with a very low percentage of product codes to the total number of clearances should have shorter average & overall review times as reviewers' familiarity with product indications and clinical use increases. Third Party Review: The FDA promotes the Third Party Review program as shortening review times by 29% on average (77 days from initial receipt by the Accredited Person to FDA decision).t The statistic is based on a FY2002 cohort of approximately 110 submissions. In FY2008 and FY2009, t <www.fda.gov/medicaldevices/deviceregulationandguidance/howtomarketyour device/premarketsubmissions/thirdpartyreview/default.htm> 41 Third Party reviews increased to 270 and 251 respectively, more than double the FY2002 level. I expect the impact to be even more pronounced as the pace of technological innovation has accelerated and numbers and experience of Accredited Persons grows. To my knowledge, a subset analysis has not been published looking at 3rd party eligible devices that still pursue the traditional pathway. Many 3 rd party eligible devices have a substantial number of guidance documents and established standards that should result in faster review times regardless of pursuit of 3rd party review. This would represent a stringent comparator group. A subset analysis of 3 rd party eligible 51 0(k)s cleared from FY08 through Q3FY1 0 may show that these products experience shorter review times across the board with or without 3rd party review. Data Analysis Tools The principal tool used in analyzing the database was Excel 2007. Graphical representations of the data as well as statistical tests were conducted within Excel 2007 and the Data Analysis add-in. The F-test for two-sample variance and the t-test for both equal and unequal variances were used in evaluating the 42 hypotheses. These tests are generally accepted tools for measuring the equality in the means of two samples of data. The F-test was used to measure statistical equivalence between the variances of the data which then dictated which of the two t-tests mentioned above should be used to evaluate the samples. Significance was defined as a p-value less than 0.05. Clearance Time Calculation When analyzing average clearance time for overall categories, I will only include non-3rd party reviewed clearances. This decision was made because the submission date and clearance date reported by the FDA only concern their receipt and clearance of the 510(k). Therefore, the 3 rd party clearances will have some bias in the average clearance time because the time for review by the Accredited person (3 rd party) is not counted. An analysis of clearance times for 3rd party reviews will be conducted separately. Therefore, unless specifically stated, all average clearance times for a given category (510(k) type, clinical category, 3rd party eligible, etc) will be calculated from the non-3rd party 510(k) dataset of 6,809 clearances from FY08 to Q3FY10. Chapter 4: Results The following sections will present the dataset sliced so as to highlight each of the hypotheses discussed in the previous chapter. Generally summary statistics will summarize the subset of data followed by graphical and statistical results. A summary and discussion of the results will follow in Chapter 5. Dataset Overview A total of 7,482 510(k) applications were cleared in FY08, FY09, and Q1-Q3 2010. Only one 510(k) was removed from the dataset, K043617, because there was an identifiable error in the data: The submission and clearance date were both 12/21/2007, the application was not 3 rd party reviewed, and the dossier number "K04..." indicates submission occurred in 2004. Since the exact submission date cannot be ascertained, this data point was removed from the analysis dataset. The remaining 7,481 510(k)s are listed along with select details in the Appendix. The table below highlights some of the attributes of the analysis dataset. Table 7: Overall Dataset Composition (incl. 3 rd Party Submissions) From the table above, traditional 51 0(k)s account for about three fourths of all 510(k)s cleared in the study period. Further, Special and Abbreviated 510(k)s accounted for 23% and 4% of all clearances, respectively. Nine percent of all clearances followed the 3 rd party review pathway (3 rd party reviews will be dealt with in detail later in this chapter). The most active clinical categories, as measured by advisory committee classification, were Orthopedic (16.2% of all clearances), General & Plastic Surgery (14.3%), Cardiovascular (13.6%), 45 Radiology (11.1%), and Dental (9.8%). Conversely, the least active clinical categories were Pathology (.03%), Immunology (0.2%), Clinical Toxicology (0.2%), Microbiology (0.4%), and Hematology (0.5%). Clearances by Fiscal Year The following table breaks down the clearances by fiscal year (excluding submissions). Removing the 672 3 rd 3 rd party party reviewed 510(k)s results in a dataset containing 6,809 submissions. T ble 8: 510(k) Clearances and Review Tir s by Fiscal Year * FY10 ** FDA only includes data from the FDA's Q1-Q3, 10/1/2009 through 6/30/2010. DAYS is defined as: Weekdays excluding Federal Holidays. 46 The relative distribution of 510(k) types remained consistent across fiscal years. The data range was quite large, from a minimum of 3 days to a maximum of 1,554 days for obtaining 510(k) clearance. Figure 2: Average Review Time in Days by Fiscal Year and 510(k) Type Average 510(k) Review Days 180 160 140 120 0 * FY2008 100 - FY2009 60 - FY2010 S80 40 20 0 Traditional Abbreviated Special There is a statistically significant increase in mean clearance time from FY08 (M=135.3, SD=104.6, N=1756) to FY10 (M=157.1, SD=96.5, N=1246) using a two-sample t-test for unequal variances {t(2803)=-5.88, p<0.001}. Further, there was a significant increase in the average review days in FY10 for Special 510(k)s {t(775)=-5.83, p<0.001}; however, the increased mean time for Abbreviated 510(k)s was not significant {t(137)=-1.41, p=0.16}. t An F-test for two sample variances resulted in F=1.17, p=0.001 47 Due to the large variance of clearance times, the averages alone don't provide a complete picture. The histogram below highlights the dramatic variation in the average number of days for approval. Figure 3: Distribution of Traditional 510(k) Clearance Times 500 V 450 400 I - 350 300 250 200 150 100 50 0 --- o Days - - oooooooooooooooooooooooooooooooooo00000ooo SN CM Lt C) 1- Co0 W M M C) N CO IMLO C0 r- W M o 04 M It LO C.0 1- 0o N ) Co Number of Days CO M LO CD I, C\ M It LDO Co M o r 00 0) O N M,; U) CD I, N (M 'I MDCD The Table below breaks out the number of clearances using 30-, 60-, 90-, 180-, and 270-day bins. Table 9: Percentage of Clearances Above or Below Specified Thresholds 96 2% 1080 64% 601 36% 16% 45 3% 438 72% 255 42% 23% 1135 63% 226 13% 36 99 16% 411 64% 215 34% 32% 944 76% 194 16% 71 8% 118 27% 235 53% 134 30% 25% 3089 64% 619 All Traditional 435 9% 1193 All Special 22 1% 79 5% 305 FY08 Traditional 147 8% 394 22% 1042 60% 288 5 1% 22 4% 14% 151 8% 406 3 1% 22 3% 141 11% 402 14 3% 35 FY08 Special FY09 Traditional FY09 Special FY10 Traditional FY10 Special 88 18% 13% 2% 6% Table 8 also highlights the increase in 510(k) clearance time from FY2008 to FY 201 0.Traditional 51 0(k)s taking more than 90 days to receive clearance jumped from 64% in FY08 to 76% in FY10 as a percentage of all traditional 510(k)s cleared. Similarly, Special 510(k)s taking less than 60 days for clearance dropped from 72% in FY08 to 53% in FY10. 510(k) Type The hypothesis that Abbreviated 510(k)s should have shorter average clearance times than Traditional 51 0(k)s was tested using a t-test for samples with equal variances (F=1.01, p=0.45). 49 Table 10: Effect of 510(k) Type on Clearance Time The review time for Abbreviated 51 0(k)s was 5 days longer on average than Traditional 510(k)s; the opposite of the hypothesized difference. However, no statistically significant difference in the review time was seen for Abbreviated and Traditional 510(k)s (p=0.318). Advisory Committee The 510(k) clearances among the 19 clinical categories ranged from a minimum of 2 clearances in Pathology (PA) to a maximum of 1,210 clearances in Orthopedic (OR).The following sub-sections will focus on Advisory Committee Volume and Product Diversity with a third section highlighting several clinical categories. Advisory Committee Volume The following graph shows the volume of clearances in each clinical category by 510(k) type while the table beneath summarizes the volume and percentage of total clearances for each category. Both Figures are sorted by the overall number of clearances in each clinical category. Figure 4: Number of 510(k)s by Clinical Category and Type (FY08 - Q3FY10) 900 800 700 600 500 400 300 200 100 0 - I= i I I I EW Mi I I ~ - - [ - I I I I PA TX IM MI HE EN OP OB CH PM NE GU AN HO DE RA CV SU OR MTraditional MSpecial MAbbreviated Table 11: Number and % of Total 510(k)s by Clinical Category and Type 73'" Party clearances are included in the category totals and percentage calculations, but not the clearance time calculations. Traditional 510(k)s: The seven categories with the highest volume of traditional 510(k)s are: Surgical Equipment (15%), Orthopedic (15%), Radiology (13%), Dental (11 %), Cardiovascular (11 %), Hospital Equipment (10%), and Anesthesiology (7%). These seven categories will be classified as "High Volume" and the remaining twelve categories as "Low Volume" in order to test the Advisory Committee Volume hypothesis. The High Volume categories averaged 136 days for a Traditional 510(k) clearance, while the Low Volume categories averaged 155 days; an average of 19 days longer. A preliminary F-test for equivalence of variances (F=1.24, p=0.00001) indicates the variances are not equivalent. Therefore a t-test assuming unequal variances was performed. The mean review time for the High Volume group (M=136, SD=95.9, N=3810) was significantly lower than the Low Volume group (M=155, SD=107, N=990). The results validate the hypothesis that higher volume advisory committees will have shorter average review times potentially due to their familiarity with the full spectrum of clinical indications and technologies. Table 12: Advisory Committee 510(k) Volume Impact on Clearance Time High Volume (SU, OR, RA, DE, 3810 136 Days CV, HO, AN) t(1430) = -5.02, p < 0.0001 For additional confidence that the result was not biased by the selection of cutoff between the "high" and "low" volume groups, I repeated the above calculation with two alternate cutoff points. The first with "AN" included in the low volume group and the second with "GU" included in the high volume group. Both alternate results confirmed the hypothesis with a statistically significant difference in mean review times of 21 days and 24 days, respectively (a greater effect than the original calculation). Alt 1: High Volume (SU, OR, RA, DE, CV, HO): Mean Review = 135 days, N=3470 Low Volume (AN, GU, NE, PM, CH, OB, OP, EN, HE, MI, IM, TX, PA): Mean review time = 156 days, N=1330 t(2324) = -6.48, p<0.00001 Alt. 2: High Volume (SU, OR, RA, DE, CV, HO, AN, GU): Mean Review = 137 days, N=4024 Low Volume (NE, PM, CH, OB, OP, EN, HE, MI, IM, TX, PA): Mean review time = 161 days, N=776 t(1011) = -5.71, p<O.0001 ...... . ............. . .... .... .... .... ........ .. Table 13: Number of 510(k)s Above or Below Review Time Thresholds *Total 51 0(k)s do not include 3u Party review. Abbreviated 51 0(k)s were also excluded due to the low number of such clearances in each category. The values shown in bold green in Table 12 above are clinical categories where less than 25% of clearances took longer than 180 days and less than 50% took longer than 90 days. Clinical categories where 30% or more clearances took over 180 days and 70% or more took over 90 days are highlighted in bold red. This further illustrates that higher volume clinical categories (advisory committees) have shorter average review times. Radiology (RA) had the best overall review times, averaging 105 days for clearance of a traditional 510(k) and with only 12% and 44% exceeding 180 and 90 days respectively. On the 55 opposite end of the spectrum were Obstetrics/Gynecology (OB) and Ophthalmic (OP) categories averaging 196- and 199-days with 48% (>180), 91% (>90), and 45% (>180), 87% (>90) respectively. Advisory Committee Product Diversity The number of unique product codes in a given clinical category was used as a proxy for the diversity of products/technologies seen by reviewers. The table below details the number of codes as a percentage of clearances in each of the 19 clinical categories. Figure 5: Product Code Diversity Within Clinical Categories (as measured by number of unique product codes / total product codes cleared) Product Code Diversity 90% - 80/. 70% 60% 50% 40% -2- 30% - - --- - - - - -X -....- 20% 10% 0%o/ RA OR DE HO CV SU AN PM OB OP NE GU EN HE PA MI TX IM CH The largest incremental increase in the percentage of unique product codes is between AN and PM (9%increase) and between GU and EN (9%increase).The 56 first cutoff results in the same cohort of clinical categories as the "High Volume" group and therefore will also show the same significant increase in mean review time of 19 days in the "high diversity" group. Using the second cutoff point to separate the categories into "High Diversity" and "Low Diversity", the following results were calculated: Table 14: Impact of Product Code Diversity on 510(k) Clearance Time Low Diversity (SU, OR, RA, DE, CV, HO, AN, PM, OB, OP, NE, GU) 4525 141.5 Days t(306) =-0.11, p = 0.91 No statistically significant difference in mean review time results using the second cut off point. 3rd Party Review Chapter 2 introduced the 3 rd party review as an alternative pathway for eligible medical devices to pursue regulatory clearance. Using data from FY2002 including 111 3 rd party review 510(k) clearances, the FDA promotes the program as reducing clearance times 29%, an average of 77 days from submission to the 3 rd party to clearance by the FDA. This research attempts to further illuminate this program through a review of the recent clearance data. The following will look at the clinical categories that have the most 3 rd party review activity, a 57 breakdown of the types of 510(k)s going through this process, and an analysis of the eligible devices that were cleared through the traditional review pathway. There are some distinct limitations to this analysis which will be discussed in greater detail in Chapter 5. One important observation is that the 510(k) submission date to the accredited 3 rd party reviewer is not available in the FDA database. Therefore, the average clearance times calculated pertain only to the FDA review and not the overall reason the 3 rd party 3 rd party review period. This is a second key dataset was not included in the analysis of 5 10(k) clearance times in the previous sections. 3 rd Party Review Statistics, FY08-Q3FY10 Of the 672 510(k)s that were cleared through the 3 rd party review process from FY08 through Q3FY10, there were 176 unique product codes from 13 out of the 19 clinical categories. The FDA has designated 1,615 product codes (out of the 5,424 codes in total) as eligible for the 3rd party review program. Of these, 170 fall into class I (general controls) and 1,445 fall into class II (special controls). No class Ill product codes are included in the 3 rd party review process. All 19 clinical categories contain at least a few 3 rd party eligible product codes. The Chart and Table below detail the distribution of clearances and overall product codes that were subjected to the 3rd party review process between FY08 and Q3FY10: Figure 6: Number of 3rd Party 510(k) Clearances by Clinical Category 3rd Party Clearances, FY08 - Q3FY2010 300 250 200 150 100 50 0 RA CV SU HO DE AN GU NE OP *PA, HE, MI, IM, CH, and TX did not have any 3 rd OB PM EN OR party clearances Table 15: Number of 3rd Party Clearances and Product Code Distribution wI he number ot eligible codes includes all codes approved tor J' party review by the FDA. Not all approved codes were seen in the dataset. **The total number of codes includes all product codes assigned by the FDA. Not all of the codes listed above were seen in the dataset. From the data table above, the number of Traditional 510(k)s going through the 3rd party review process far outweigh the number of Special or Abbreviated 51 0(k)s. In fact, nine of the thirteen Special 51 O(k)s were from only two clinical categories: Radiology (RA) and General & Plastic Surgery (SU). Eleven of the twelve Abbreviated 510(k)s were pretty evenly distributed among the top four highest volume categories. Radiology (RA) dominated the 3rd party review data with 289 510(k)s cleared. Cardiovascular (CV), the next closest category in 3 rd party volume, had 91 clearances despite having a larger number of eligible product codes. It is interesting to note that Toxicology (TX), a very low volume clinical category with only 15 clearances from FY08 to Q3FY1 0, has the greatest number of 3 rd party eligible product codes: 222 out of 298 total codes, or 74% of all TX product codes. The two clinical categories with the longest average review times, OB and OP, were relatively equivalent to RA and CV in number of eligible product codes. However, less than twenty clearances pursued this pathway despite the fact that over 80% of all clearances in these categories were eligible for 3rd party review. 3'd Party Review Adoption among 3'd Party Eligible 510(k) Clearances Table 15 highlights the adoption of the 3rd party pathway across clinical categories by analyzing the number of 3 rd party clearances against both the total eligible clearances that did not pursue 510(k) clearances. 3rd party review as well as all non-eligible Table 16: 3rd Party Eligibility vs. Actual 3rd Par y Clearances In all clinical categories other than Orthopedic (OR), over 50% of cleared 51 0(k)s were eligible for 3rd Party review, yet on average only 14% of eligible 51 0(k)s pursued this regulatory pathway. Even in Radiology, the 289 3 rd party clearances only represented 36% of the eligible clearances in the dataset. Further, 98% of all 510(k) clearances in Radiology were eligible for 3 rd party review. On the opposite end of the spectrum, fewer than 1% of Orthopedic device clearances in the dataset were eligible for 3 rd party review. With only 3% of the existing 257 OR product codes even eligible for 3 rd party review, this does not come as a surprise. With the exception of Orthopedic, all of the "High Volume" clinical categories were the top 3rd-party categories in number of clearances. Average 3 rd Party Clearance Times The graph below highlights the average FDA review time for each of the categories in Table 15. Figure 7: Average Clearance Time (Days) for Traditional 510(k)s Stratified by 3 rd Party Review Eligibility My hypothesis that 3 rd party eligible 510(k)s that did not follow the 3 rd party review pathway would still have shorter review times that non-eligible 510(k)s proved significant overall; however, there were a few individual categories where this 63 was not the case (see Table 16). The mean review time of eligible, non-3 party traditional 510(k) clearances was 137 days (M=137, SD=92, N=2727). The mean review time of non-eligible traditional 510(k) clearances was 148 days (M=148, SD=106, N=2073). The difference in mean review time of 11 days was statistically significant {t(4121)=-3.97, p<0.00001}. The mean review time of 3 rd party traditional 510(k) clearances was 50 days, 87 days shorter than the mean review time of 3 rd party eligible traditional clearances that did not pursue the is under 3 rd 3 rd party pathway. However, the number of days a device party review is not included in the average review time, so only limited conclusions can be drawn in evaluating this part of my hypothesis and will be discussed further in the next chapter. ............. .. ...... .. . ...... ............ .... Table 17 summarizes the data from Figure 7 above: Table 17: Average Time for Party Eligible Clearances by Clinical Category 3rd *Average review times for 3ra party clearances only include FDA review, not the review time of the accredited person 3rd party reviewer). **Calculated between Eligible, non-3 Party clearances and Non-Eligible clearances. A t-test assuming unequal variances was used for each case. Significance was defined as p<0.05 and is highlights in bold green above. ***Overall calculations include clinical categories that did not have any 3 rdparty clearances but did have eligible and non-eligible clearances. Of the categories with a statistically significant difference in mean review time, only Anesthesiology (AN) had longer average clearance times for eligible as compared to non-eligible traditional 510(k)s. Further, 3 rd Party clearances in the "AN" category also took significantly longer for FDA review without including time for review by the accredited party, almost reaching the average time for noneligible devices in this clinical category. All of the other categories (CV, DE, PM) had shorter average clearance times for eligible 510(k) clearances compared to non-eligible clearances. Chapter 5: Discussion and Conclusions Summary of Findings This thesis focuses on two subjects. First, the length and variability of time required in obtaining 510(k) marketing clearance in the medical device industry. Second, it offers a closer look into the FDA regulatory process itself using 510(k) clearance data to better understand regulatory policy and its impact on industry. The following sections will first summarize the results as they pertain to my hypotheses after which I will focus a discussion around the implications of my findings and the limitations of the data analysis. 510(k) Type No statistically significant difference in the clearance time between Abbreviated and Traditional 510(k)s was seen in the data. On average, the clearance time for an Abbreviated 510(k) was 5 days longer than a Traditional 510(k), which is the opposite of the hypothesized effect. Because the data is statistically inconclusive a clearance time advantage for either type of 510(k) application cannot be concluded. While the clearance time appears to be similar for both Abbreviated and Traditional 510(k)s, there may be a distinct advantage in the time required for a company to prepare the Abbreviated 510(k) submission. This is due to the ability of a company to file an Abbreviated 510(k) prior to completing performance tests. Inessence, the Abbreviated 510(k) submission is a smaller package that promises to complete tests without deviation according to generally accepted standards. Time FY08-Q3FY10 As hypothesized, a statistically significant increase in mean review time from FY08 to Q3FY1 0 of 22 days was seen for Traditional 51 0(k)s receiving a substantial equivalence (SE) decision. In addition, a significant increase of 22 days was seen for Special 510(k) clearances between FY08 and Q3FY10. Recent scrutiny of the 510(k) process by the GAO and public advocacy groups such as the Public Citizen may be leading regulatory reviewers to be more cautious in their approach to reviewing and clearing 510(k) submissions resulting in the observed increased clearance time. In addition, the FDA itself has commissioned the Institute of Medicine to review the 510(k) process and is in the process of structuring changes to the system that may be announced as early as September of 2010. Advisory Committee Volume & Product Diversity The analysis on both the volume of submissions by clinical category (advisory committee) as well as the diversity of submissions (number of unique product codes as a percentage of clearances in a category) both resulted in the same cohort of "high volume" or "high diversity" categories. While submission volume appears to have a more robust impact on clearance time, the two factors may have a dependant effect that is difficult to analyze separately. There was a statistically significant impact on review time of between 19-24 days for the "high volume" cohort of product codes depending on the choice of cut-off for high and low volume classification (p<0.0001). The high volume cohort included the 68 categories SU (782 Traditional 510(k) clearances), OR (800), RA (401), DE (560), CV (489), HO (478), and alternately included AN (340), and GU (214) to test the sensitivity to the cutoff choice. Of all the clinical categories, Radiology (RA) was the only category with less than 50% of Traditional 510(k) clearances taking longer than 90 calendar days (44% took more than 90 days). Further, only 12% of Traditional RA 51 0(k)s took longer than 180 days to obtain clearance. The next closest category was Dental (DE) with 59% and 20% for Traditional clearances taking longer than 90- and 180-days, respectively. By contrast, 91 % of Obstetrics & Gynecology (OB) Traditional clearances took longer than 90 days, and 48% longer than 180-days. 3 rd PartyReview Program Overall, the 3 rd party review process was surprisingly under utilized. Even in the highest volume clinical categories, between only 8-36% of clearances came through the 3 rd 3 rd party pathway, despite 50-98% of all clearances being eligible for party review. Radiology (RA) had 289 3 rd party clearances from FY08 to Q3FY10, three times more than the next highest category, Cardiovascular (CV) with 91 3 rd party clearances. As hypothesized, third party eligible traditional 510(k)s that did not pursue the 3 rd party pathway took 11 days less, on average, than non-eligible 510(k)s (p<0.00001) lending credence to the rationale that third party eligible products include devices where a significant amount of guidance documents and performance standards already exists and are therefore more straightforward from a regulatory review perspective. The impact on overall 69 clearance time of actual third party reviewed 510(k)s could not be ascertained from the data because submission dates for the 3 rd party reviewers is not included in the FDA database. Rationale for Data Sub-set Analyses Clearance Time Calculation While the FDA calibrates regulatory review performance standards based on what it calls "FDA Days" which only count weekdays minus federal holidays, this thesis seeks to quantify clearance time in calendar days. While Table 7 did include an additional calculation for average FDA DAYS, this was done to allow for a comparison with the FDA's FY08 Annual Report and a discussion around the structure and usefulness of the FDA performance goals as it relates to industry. With industry as the primary audience for this research, I believe that measuring clearance time in calendar days is a much more useful metric to allow industry executives and regulatory personnel to make informed decisions in developing their business and regulatory strategies. Clearance Decision and 510(k) Type Only "substantially equivalent" (SE) decisions were included in the analysis. While several other decision types, such as SK (kit), SU (SE with limitations), CS (CLIA submission), occurred during FY08-Q3FY10, they were less frequent and would have introduced additional potential bias in the clearance time calculation due to the unique nature of these 51 0(k)s. Further, much of the focus was on 70 Traditional 510(k)s. This was primarily because Abbreviated 510(k)s were much fewer in number to segregate out into clinical categories and still obtain statistical significance and Special 510(k)s are subject to different review time standards. Fiscal Year versus CalendarYear The FDA fiscal year runs from October 1 st through September FDA fiscal year 2008 includes October 1 st, 3 0 th. 2007 through September As such, the 3 0 th 2008. The choice of using the FDA's fiscal year allows for a direct comparison and discussion around the FDA FY2008 Annual Report, which looks at regulatory clearance data over this time period. Further, the increased performance metrics from MDUFMA II took effect at the start of FY08 and the choice of analysis time period allowed for the largest dataset within this policy period. Lastly given the recent scrutiny of the 510(k) process, I felt it was important to include data from 2010. The most recent complete month for which data was available at the time of this writing was June 2010, which coincidentally aligned with the end of the 3 rd fiscal quarter for the FDA. Therefore, the dataset analysis included data up through the end of Q3 of FY2010. Limitations This analysis is potentially limited by three factors. Deficiencies or missing key data factors in the FDA's online database, the methodology surrounding the metrics the FDA strives to achieve versus that which is meaningful to industry, and the constraints in scope in this study. 71 Deficiencies in the FDA Database One of the key limitations in this analysis concerns the structure of the FDA 510(k) review process. During the review of a 510(k) device dossier, an FDA reviewer has the ability to formally request additional information from the applicant. When questions are sent back to the applicant, the 90-day clock by which the FDA measures its performance is stopped. Once the applicant submits the additional data a new 90-day clock begins. This ability of the reviewer to ask clarifying or probing questions ensures a thorough review and seeks to prevent 510(k)s from receiving a "not substantially equivalent" (NSE) decision simply due to lack of a particular piece of data. However, the dates, frequency, and number of questions (if any) related to each 510(k) are not captured in the publicly available FDA database. Without these critical pieces of data, the calculation of clearance time cannot distinguish between the time a particular submission spends on the reviewer's desk and the time (if any) spent by the company responding to reviewer questions. As a result, the increase in clearance times from FY08 to Q3FY10 cannot be linked directly to an increase in reviewer questions, a decrease in reviewer efficiency, or to the types of technologies crossing the reviewer's desk. While this lack of fidelity in the data does not alter the mean time to clearance from an industry perspective, it is an important piece of data that must be accounted for in order to fully illuminate the processes and politics at play behind the 510(k) review process. Hence, while recent literature suggests that clearances are taking longer because of increased conservatism at the FDA, namely in the form of increased reviewer questions, 72 the exact cause of the increase in clearance times seen from FY08 to Q3FY10 cannot be identified from the data gathered for this analysis. Need for FDA Transparency In addition to the database deficiencies identified above, the FDA also does not publish NSE decisions. Thus, only the 510(k)'s that were cleared can be analyzed and critiqued. The large number of annual clearances only highlights a subset of the overall volume of 510(k)s the FDA reviews annually. The relative rate of NSE and SE decisions could go a long way in educating the public on the diverse challenge the FDA is charged with regulating in the medical device industry. In fact, transparency is a key initiative of the CDRH under the current Director, Dr. Jeffrey Shuren. The NSE data is not currently included in the transparency initiative as described on the CDRH website; however, it would be extremely useful not only for the reasons listed above, but to allow for a more meaningful understanding by industry thus allowing for more informed business decisions. FDA versus Industry Perspective As mentioned above, the performance metrics the FDA uses to measure its ability to efficiently regulate medical devices through the 510(k) pathway are internal in nature. The goal of a 90-day review clock for traditional and abbreviated 510(k)s is reset whenever questions are asked by the reviewer - an event that is not captured in publicly available records. As a result, a general misconception exists among some in industry that FDA 510(k) review is a 90-day 73 process when in fact the true time to clearance varies drastically. So while the FDA judges its performance on a particular metric, namely turning around 510(k) submissions (whether by issuing a decision or requesting additional information) within a 90-day period, industry is left without a reliable gauge by which to organize its business strategy (such as when to ramp up a sales force or manufacturing process). Greater insight into when in the review timeline the FDA stops the regulatory clock by asking questions would be tremendously useful as well as shed some light on the frequency with which a round of questions is used to "reset the review clock" as opposed to clarify the safety or effectiveness of a device. Constraints Inherent to the Study The hypotheses developed in this work were those that could be tested with the 510(k) data available from the FDA website. However, there are several additional factors that could all play an important role in the 510(k) review time. Inthe following section discussing "areas for future research" I will detail several of these factors and my rationale behind them. They all represent a significant additional time investment in order to appropriately address and relate them to the 510(k) review process; an investment that was beyond the scope of this Master's thesis. Many of the limitations in the data discussed above could also be addressed through official FOIA requests to the FDA, an endeavor that would prove unfeasible for the -7,481 510(k) submissions analyzed in this dataset. Areas for Future Research The following represent future areas for research into factors impacting clearance time for 510(k) submissions. While not exhaustive, these factors are those that could be tested either with the available data or through additional publicly available sources. Number of 510(k) Clearances: Sponsors with greater numbers of prior 510(k) clearances likely have greater experience with the regulatory system. Established relationships with reviewers allow for more interaction and early FDA feedback prior to 510(k) submission, resulting in higher quality dossiers. In addition, there is a higher likelihood they own and have intimate knowledge of predicate technology to which to compare newer submissions. As a result, I expect applicants with greater numbers of prior 510(k)s to have shorter clearance times. Performing a search of the FDA database for each company in the FY08-Q3FY10 cohort will return the number of prior 510(k) clearances associated with that company. Third Party Reviewer: Surveys and research into 3 rd party review vendors could begin to shed light on the average 75 3rd party review time prior to receipt by the FDA for final review. Matching 510(k) dossiers with this data from accredited third parties would enable calculation of mean total review time and comparison with direct-to-FDA submissions. Company Attributes: Company size can be grouped into cohorts (ie. small cap, medium cap, large cap) and tested for its relation to clearance time. A larger company may have more experienced regulatory personnel (or a larger pool of experienced personnel) that are better able to put together high quality submissions and respond to review questions. Using market capitalization for publicly held companies or reported income multiplied by industry average P/E ratios for private companies could allow for categorization of the firms represented in this research dataset. Division of FDA Review Committees: A review of the current staffing of FDA reviewers in each of the CDRH departments would allow for an analysis of the relative number of dossiers assigned to each reviewer and analysis of review times as a function of FDA staffing and workload. 76 Timing of Reviewer Questions: As discussed in the limitations of analysis, the frequency of reviewer questions and corresponding dates were not available in the downloadable FDA 510(k) database. In addition to analyzing this data with respect to clearance time, the timing around reviewer questions could lend important insight into the substance behind the questions and incentives driving them. For instance, questions from the FDA that occur around day 60 of the review process may be driven by true deficiencies in the dossier. Alternatively, questions occurring around day 86 of the review may be less substantive in nature and instead driven by the internal performance incentives for a 90day review. Such speculation is based on qualitative discussions with industry, but could be confirmed through an analysis of the data if made public by the FDA or through FOIA requests. Product Recalls: Device recalls are grouped into class 1,11,or Ill based on the risk of injury to the patient. A class I device recall is the most serious and represents a reasonable chance of serious injury or death to the patient. Class II is less serious where there is a possibility of reversible injury or 77 remote chance of serious injury to the patient. Class Ill recalls usually are low risk and are unlikely to cause injury to the patient; however, the device does not conform to FDA law and therefore must be recalled. Comparing the product codes associated with class 1,11, and Ill recalls, review time for the associated 510(k), advisory committee, and the number of reviewer questions (if any) may illustrate whether or not there are any potential weaknesses in the review process itself or any correlations between the number of recalls in one device class and associated trends for 510(k) clearance times. Many recalls are the result of manufacturing issues or failure to follow mandated GMP and QRS requirements rather than deficiencies in the regulatory review; however, analysis into possible association between recalls and review time may reveal specific weaknesses that could be buttressed by 510(k) process modifications. Implications At various stages in this thesis, I have remarked upon the recent scrutiny the 510(k) process is under by federal as well as public advocacy groups. The FDA itself is reviewing the 510(k) process. The Institute of Medicine has been commissioned by FDA to analyze the 510(k) process and identify weaknesses (report expected in 2011). The Government Accountability Office (GAO) has recently released a report on the 510(k) system, and the Public Citizen published a critique of the system. My research both supports and draws into question some of the criticisms voiced in these reports. The conclusions of these reports and the changes to the 510(k) system that may result have potentially broad implications for device development and medical care in the United States. I summarize the recent GAO report and Public Citizen paper in the following sections and discuss the implications of each as they relate to my research. Government Oversight: Recent GAO Study on the 510(k) process14 The GAO report (GAO-09-190) and a subsequent testimony before the House of Representatives Subcommittee on Health, Committee on Energy and Commerce (GAO-09-370T) each focused on shortcomings in the FDA's regulation of medical devices and used data from FY2003 through FY2007. GAO-09-190 79 focused on the "pre-amendments" class III (high risk) devices that the FDA still permits to follow the 510(k) pathway pending official review to either reclassify to class II or require to follow the PMA process. The SMDA of 1990 mandated that the FDA address final status of these devices; however, as of the start of FY2009, twenty device types still had not been reclassified or required to follow the PMA pathway. In my dataset, I identified 39 clearances of class Ill devices from 6 distinct product codes. Nine of the clearances were Special 510(k)s, the remaining thirty clearances were Traditional 510(k)s. The devices spanned five advisory committees and one clearance followed the 3 rd Party review pathway. These clearances represent 0.5% of the total 510(k) clearances from FY08 to Q3FY10, and while completing the reclassification should continue to be a high priority for the FDA, the small number of class Ill 510(k) device clearances do not raise significant questions about the ability of the 510(k) program to provide reasonable assurance of safety & effectiveness while enabling innovative therapies to reach the patients that need them. The GAO testimony in front of the House Subcommittee of Health, Committee on Energy and Commerce (09-370T), on the other hand, went further and identified shortcomings in the postmarket surveillance of devices and inspections of manufacturing establishments. In particular, the findings highlighted the inability of the FDA to "consistently review and enter the [adverse event reporting] data in a routine manner." The volume of reports, lack of objective data about device use, and underreporting of adverse events were cited as the primary problems 80 with the postmarket surveillance of devices. In addition, the GAO found that the FDA was not meeting its statutory requirement to inspect U.S. device manufacturing establishments every 2 years. There is no comparable requirement for inspections of foreign device establishments. The report found that U.S. inspections were averaging 3 and 5 years for class Ill and class II device manufacturers, respectively. Further, foreign inspections were averaging 6 and 27 years for class III and class II device makers, respectively. In all, the GAO report highlighted shortcomings that had very little to do with the actual review and clearance or approval processes of the 510(k) and PMA pathways. Given that a large percentage of device recalls are a result of manufacturing deficiencies or quality controls, an argument could be made that improving the postmarket surveillance and manufacturing inspection frequency, as called for in the GAO investigation, could go much further in improving the regulation of devices than revamping the 510(k) and PMA review process itself. Public Scrutiny of the Regulatory Process The Public Citizen issued a report in July 2010 that broadly criticized the medical device premarket review process.15 Where the GAO study focused on failures of the FDA to meet statutory requirements, the authors of this report cite eight weaknesses inthe regulation of medical devices that essentially advocates the complete dismantling of the 510(k) clearance process and restructuring of device regulation to more closely resemble the drug approval process. To illustrate their point, the authors cherry-pick examples of devices that cleared the regulatory process and either were questionably efficacious, were recalled, or stretched the definition of "same intended use" and "substantial equivalence." Given the sheer volume of device clearances and approvals annually, should one be surprised to hear of cases where a device did not perform as expected? A recent AP article1 6 cited 19 drugs pulled from the market between 1997 and 2010, a blaring reminder that even a regulatory review process as demanding as that overseen by FDA's Center for Drug Evaluation and Research (CDER) may result in approval of drugs that should not make it to commercial markets. Irrespective of how one sees the threshold of strictness for medical device clearances and approvals, I believe the position advocated by the report would not only virtually eliminate innovation and commercialization of life-saving technologies in the United States, but would bury the US economy under disproportionate costs at precisely the point in time where healthcare costs are already viewed as out of control. There are two core tenets advocated in the Public Citizen report that I will address below. The first is that the approval standards for devices are inferior to that required for drugs and do not adequately ensure the safety and effectiveness of such devices. Second, the authors contend that the device regulatory process is too loosely defined in the regulations in some areas and poorly designed in other areas and therefore allows for inappropriate discretion when evaluating individual submissions. 82 The Medical Device Amendments of 1976 recognized that medical devices are inherently different from drugs and therefore require a regulatory process tuned to dealing with the enormous diversity in technologies and applications. Consider for a moment that in 2009 there were 26 drugs approved1 7 by the FDA versus 3,001 510(k) clearances18 , 15 original PMA approvals, and 140 normal 180-day track PMA supplement approvals1 9. Simply requiring all medical devices to undergo clinical evaluation equivalent to those subjected to drugs, and further requiring that all devices follow the PMA review process as the authors suggest would massively increase costs to the regulatory system and halt device innovation in the U.S. The very GAO study discussed above highlighted the costs to the FDA of reviewing a 510(k) and a PMA: $18,200 for each 510(k) and $870,000 for each PMA. Had each of the 5,447 Traditional 51 0(k)s in my dataset been forced to follow the PMA pathway, the US Government would have seen FDA review costs skyrocket and additional $3.6 billion dollars (factoring in user fees for 510(k)s of $3,693 and PMAs of $200,725). This additionally assumes that the FDA's current staffing could handle the roughly 360x increase in PMA and corresponding IDE submissions, that industry could afford to run multiple phase randomized controlled clinical trials in support of each PMA, and that the U.S. hospital and IRB infrastructure could support the massive increase in number of trial protocols. Further, I have not even included the 1,700 Special 510(k)s that the authors contend could follow the 180-day PMA supplement pathway. Certainly a clinical trial in support of a tongue depressor, an 83 endoscope, or even a new artificial knee would present a barrier that would turn away all but the most deep-pocketed device firm. There is one certainty that such a change in regulatory process would have: those devices making it to market would be vastly more expensive than the healthcare industry is used to currently - not exactly the effect palatable to the current Healthcare Reform movement. The second tenet of the Public Citizen report points to loosely defined regulations and poorly designed programs in the 510(k) review program. Specifically, the authors contend that the De Novo process represents a "legislative loop-hole" that allows unproven devices to be cleared for marketing. Further, the regulations do not adequately define "same intended use" and criteria for controlling similarity of technological characteristics between new devices and their predicates. Again, these criticisms seem to fly in the face of the very rationale that lead to these programs in the first place. In as much as there was only one device clearance via the De Novo pathway in my dataset, 52 devices in total since the creation of the program in 1997, it can hardly be called a "loophole" through which "rejected" products can slip through to market. First, a device does not get "rejected" in the 510(k) process, but deemed "not substantially equivalent" to a currently marketed predicate. Therefore, if the sponsor is able to justify that the device and the intended use do not pose significant risk to patients, they can petition for clearance via the 510(k) 84 de novo process and request classification to class I or 11.The FDAMA created this program recognizing that there are cases where low risk devices emerge that do not have predicates for which clinical trials and the PMA process represents an excessive burden. While the example cited by the authors, TMS therapy developed by Neuronetics, has only shown questionable efficacy to date, it is important to appreciate the relatively low risk this technology poses to patients and how large the clinical need for a therapy that could help people suffering from treatment-resistant depression*. The de novo process is a good example of the FDA's efforts to balance the protection of public health with the advancement of public health via the timely introduction of new devices. Further, creating an overly narrow definition of "same intended use" and restricting discretion around technological characteristics allowed to claim a predicate does not allow the FDA to exercise the scientific judgment to evaluate a innately diverse device industry. The statistic the authors point to from the GAO study, namely that "14% of cleared 510(k) submissions have different technological characteristics than their predicates" was reported alongside the fact that none of those clearances were for an intended use different from the predicate.20 Without a detailed review of the devices themselves, a review I expect the FDA considered carefully when assessing the safety of the device for the given intended use, one cannot argue that this statistic is evidence of a faulty *This perspective balances risks, benefits, and value in ways that are beyond the scope of this research. I recognize that both the method and perspective may be open to controversy. 85 clearance process. A device made from titanium instead of stainless steel is essentially a product with "different technological characteristics" that may pose no added safety risk of loss of efficacy when used for the same intended use. On the other hand, we must acknowledge that it may. A final thought I would like to explore here: I believe the GAO has it right, that reform should focus on the areas where the FDA is not living up to its charter. The areas of postmarket surveillance of adverse events, timely onsite inspections, and classification of the 20 remaining class Ill preamendments product codes should be the focus. It is often too easy to blame the approval or clearance process itself, when the areas above may in fact do more to improve device regulation without adversely deterring innovation and commercialization of needed therapies in the U.S. If one wants to make a quick assessment of whether the rapid evolution of medical device technology is stretching the FDA's ability to effectively regulate and to protect patients from increased risk, simply look at class I device recalls. I compiled a quick look at the class I recalls over the period from 2004 to 2009 using the FDA online recall database. The results are illustrated below. 86 Figure 8: Unique Class I Device Recalls, 2003-2009 Unique* Class I Device Recalls 35 30 25 20 - 15 10 5 0 2004 2005 2006 2007 2008 2009 The online database was filtered to account for multiple product recalls by a single company that was entirely the result of a single design or manufacturing issue. For instance, a company may recall ten different product packages all due to a single manufacturing issue for a common shared component. Therefore, "unique" class I device recalls is a more appropriate measure of recall trends over time. * Inthe absence of a dramatic increase in class I recalls, one cannot subscribe to the idea that the current 510(k) premarket review program is "broken" as the authors of the Public Citizen report claim. Indeed a closer look at many of these recalls shows that several were the result of manufacturing and quality controls, not the result of unsafe and ineffective device design that slipped through the regulatory process. Conclusions The FDA has a daunting task in its mandate to regulate medical devices. I have gained an added appreciation and respect for the challenging task the FDA is charg'ed with regulating medical device commercialization in the U.S. The volume of 510(k) clearances from FY08 through Q3FY1 0 in my dataset, which does not include the total number of submissions received, illustrates this challenge. I sought to identify the attributes of 510(k) clearances found in the FDA device clearance database that impacted review time. Futher, through my analysis, I aimed to provide a context upon which a discussion around the current scrutiny the 510(k) process is under from both government and public sources could be framed. Therefore, my conclusions fall into two categories: "510(k) clearance time factors" and "device regulatory policy." 510(k) Clearance Time Factors 1. 510(k) clearance time increased an average of 22 days from FY08 to Q3FY1 0 (p<0.001) for Traditional 51 0(k)s receiving a substantially equivalent (SE) decision. The mean clearance time in FY08 was 135 days versus 157 days in the first three quarters of FY10. Clearance times for Special 510(k)s also increased an average of 22 days from a mean of 53 days in FY08 to 75 days in FY10 (p<0.001). Recent literature suggests this increase is due to a greater number of reviewer questions and general conservatism on the part of the FDA in response to recent scrutiny of the process. Without access to the dates and number of questions asked, I am unable to test this cause; however, the increase in review time is significant. 2. The number of Traditional 51 0(k)s taking longer than 180 days to gain clearance increased from 22% in FY08 to 32% in FY10. Further, Special 510(k)s taking more than 90 days increased from 14% to 27% in the same time period. 3. "High volume" FDA advisory committees, those committees with more than 300 510(k) clearances from FY08 to Q3FY10, had statistically significant clearance times faster than those in low volume categories. Orthopedic, General and Plastic Surgery, Cardiovascular, Radiology, Dental, General Hospital, and Anesthesiology issued 510(k) clearance on average 19 days faster than the remaining 11 clinical categories (136 days versus 155 days, p<0.0001). Businesses with a device technology that has several potentially beneficial applications could reach those markets falling into the high volume categories faster than if they pursued indications falling into the low volume categories. 4. Radiology (RA) had the fastest clearance times, averaging 105 days for Traditional 510(k) submissions and with only 44% and 12% of clearances taking longer than 90- and 180-days, respectively. In addition, 83% of Special 510(k)s received clearance in less than 60 days. This compares to Obstetrics & Gynecology (OB), which had an average clearance time of 196 days with 91 % and 48% of clearances taking longer than 90- and 180-days, respectively. Product development managers, medical device startups, and 89 venture capitalists should take into account regulatory clearance time and risk unique to these different clinical categories when analyzing new device development, business strategy, and investment. 5. The 3 rd Party review program for devices pursuing the 510(k) pathway is under-utilized. Radiology saw the most 3 rd party clearances from FY08Q3FY1 0 with 289 clearances; however, this represents only 36% of the total number of clearances that were eligible for 3rd party review. On average, only 14% of eligible devices are cleared through the 3 rd party review process across clinical categories. Further data is necessary to determine if the 3rd party process still offers a clearance time advantage as advertised by the FDA. It is important to note that the 3rd party eligible devices that did not follow the 3rd party pathway had significantly faster clearance times than non- eligible products (p<0.00001). Companies could potentially benefit both in terms of faster clearances and unique expertise of 3rd party reviewers by making greater use of this program. Device Regulatory Policy 1. The large number of devices seeking clearance through the 510(k) process combined with the diversity of the technologies requires a system flexible enough to review the full spectrum without unduly preventing clinically meaningful products from reaching the patients that need them. A more rigid and onerous premarket review process will not necessarily improve the safety 90 and effectiveness of devices reaching the market and will do nothing to prevent product recalls due to manufacturing and quality control issues. 2. As the GAO report highlighted, efforts should focus on shoring up deficiencies in the FDA's post-market surveillance of adverse event reporting, inspections of medical device facilities, and classification of the remaining twenty preamendment class IlIl device types. Based on my observations, the scrutiny that has been diverted to the 510(k) process itself has potentially resulted in the longer clearance times and a less efficient review process. Through my interviews with industry leaders, many remarked upon the rapid hiring of CDRH reviewers after MDUFMA II - many of whom were very academically qualified, but lacked industry experience. As these reviewers ascend the learning curve, can one be surprised that the scrutiny focused on their decisions is impacting efficiency and ability to take the least burdensome approach to regulation? 3. The FDA should take steps to better align their performance incentives with metrics that are also meaningful to industry. The current 90-day review performance metric for 510(k)s is an internal measure that do not translate into a useful benchmark that industry can use to plan business strategy and make informed decisions. The transparency initiative currently underway within the FDA should consider how such alignment of performance metrics could improve clarity and understanding among industry regarding the regulatory process. Psychology experiments have long shown that uncertainty is far more difficult to deal with than well-defined processes. 91 4. Greater transparency should additionally include more information surrounding 510(k) decisions. Reporting of data associated with the NSE decisions and review summaries would better inform industry and the public of the criteria and rigor of the 510(k) review process. Further, reporting of the dates, types, and number of questions sent to device sponsors (as well as the dates the FDA receives a response from the sponsor) would allow for a more complete picture of the state of the regulatory review process and identification of issues impacting the efficiency of the clearance process. 92 Appendix 510(k) Requirements: 21 CFR 807.87 Table 18: 21 CFR 807.87 510(k) submission requirements. Sec. 807.87 Information required in a premarket notification submission. Each premarket notification submission shall contain the following information: (a) The device name, including both the trade or proprietary name and the common or usual name or classification name of the device. (b)The establishment registration number, if applicable, of the owner or operator submitting the premarket notification submission. (c) The class in which the device has been put under section 513 of the act and, if known, its appropriate panel; or, if the owner or operator determines that the device has not been classified under such section, a statement of that determination and the basis for the person's determination that the device is not so classified. (d) Action taken by the person required to register to comply with the requirements of the act under section 514 for performance standards. (e) Proposed labels, labeling, and advertisements sufficient to describe the device, its intended use, and the directions for its use. Where applicable, photographs or engineering drawings should be supplied. (f) A statement indicating the device is similar to and/or different from other products of comparable type in commercial distribution, accompanied by data to support the statement. This information may include an identification of similar products, materials, design considerations, energy expected to be used or delivered by the device, and a description of the operational principles of the device. 93 (g)Where a person required to register intends to introduce into commercial distribution a device that has undergone a significant change or modification that could significantly affect the safety or effectiveness of the device, or the device is to be marketed for a new or different indication for use, the premarket notification submission must include appropriate supporting data to show that the manufacturer has considered what consequences and effects the change or modification or new use might have on the safety and effectiveness of the device. (h) A 510(k) summary as described in 807.92 or a 510(k) statement as described in 807.93. (i) A financial certification or disclosure statement or both, as required by part 54 of this chapter. (j) For submissions claiming substantial equivalence to a device which has been classified into class Ill under section 513(b) of the act: (1) Which was introduced or delivered for introduction into interstate commerce for commercial distribution before December 1, 1990; and (2) For which no final regulation requiring premarket approval has been issued under section 515(b) of the act, a summary of the types of safety and effectiveness problems associated with the type of devices being compared and a citation to the information upon which the summary is based (class III summary). The 510(k) submitter shall also certify that a reasonable search of all information known or otherwise available about the class Ill device and other similar legally marketed devices has been conducted (class Ill certification), as described in 807.94. This information does not refer to information that already has been submitted to the Food and Drug Administration (FDA) under section 519 of the act. FDA may require the submission of the adverse safety and effectiveness data described in the class Ill summary or citation. (k) A statement that the submitter believes, to the best of his or her knowledge, that all data and information submitted in the premarket notification are truthful and accurate and that no material fact has been omitted. (1)Any additional information regarding the device requested by the Commissioner that is necessary for the Commissioner to make a finding as to whether or not the device is substantially equivalent to a device in commercial distribution. A request for additional information will advise the owner or operator that there is insufficient information contained in the original premarket notification submission for the Commissioner to make this determination and that the owner or operator may either submit the requested data or a new premarket notification containing the requested information at least 90 days before the owner or operator intends to market the device, or submit a premarket approval application in accordance with section 515 of the act. If the additional information is not submitted within 30 days following the date of the request, the Commissioner will consider the premarket notification to be withdrawn. [42 FR 42526, Aug 23, 1977, as amended at 57 FR 18066, Apr. 28, 1992; 59 FR 64295, Dec. 14, 1994; 63 FR 5253, Feb. 2,1998] Source: www.fda.gov 95 2009 510(k) Dataset Summary 510k Number K081446 K080175 K072494 K072688 K072427 K081506 K072993 K080183 K081319 K082141 K072204 K071779 K072640 K062932 K072449 K080798 K081762 K073260 K071228 K071130 K072109 K080366 K080189 K071730 K071468 K081800 K072558 K070514 K072091 K082191 K080220 K081493 K080480 K073047 K072807 K072554 K080682 K080479 K081263 K071632 K081405 K072731 K070935 K072425 Fiscal Year FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 Clinical Category AN AN AN AN AN AN AN CH CH CH CH CH CH CH CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV DE DE DE DE DE DE DE DE DE DE DE DE DE DE 510k Type Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated 510k Number K081130 K083833 K090977 K091299 K090918 K083793 K090163 K081986 K081940 K081499 K083046 K083862 K091021 K091430 K091220 K083347 K091408 K082626 K083534 K083677 K090496 K090147 K082851 K090602 K080941 K083820 K090363 K081947 K082082 K083131 K091062 K081933 K081820 K082344 K081881 K081560 K090869 K083662 K083336 K082930 K090286 K082382 K083784 K090253 Fiscal Year FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 Clinical Category CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV 510k Type Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional K073571 K080308 K072241 K072100 K080598 K073718 K071909 K072920 K081345 K070856 K073122 K073188 K071517 K072602 K073241 K080695 K073051 K080795 K071895 K072510 K073551 K080341 K072511 K072759 K080606 K081907 K081910 K072401 K072472 K071122 K072314 K072377 K081535 K081539 K080520 K081027 K081569 K080227 K072359 K080827 K082192 K080704 K080902 K071446 K073229 K080875 K071782 K071313 K073016 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 DE DE DE DE DE GU GU HE HE HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO HO NE NE NE NE NE OB OB K090932 K082308 K081654 K082357 K082881 K083078 K081852 K081498 K081924 K090058 K091553 K090014 K081603 K082905 K082439 K090621 K082063 K081697 K083269 K090372 K090867 K090394 K091366 K082149 K090074 K090308 K082199 K082163 K082613 K081147 K091559 K090698 K090461 K083489 K090970 K082861 K081828 K081081 K082937 K081259 K091548 K092343 K083838 K082008 K082787 K082887 K083229 K090721 K081258 Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated 97 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional K072747 K070511 K080833 K070800 K072169 K073184 K081034 K072259 K073175 K072858 K070015 K072417 K071795 K073177 K073109 K081124 K073064 K072415 K080914 K073513 K080506 K070888 K070195 K080701 K073310 K081112 K073124 K073373 K081630 K082373 K081698 K072651 K081652 K072404 K071552 K071957 K081069 K081048 K081275 K072229 K072076 K081678 K073222 K081864 K072420 K073707 K081941 K081844 K073261 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 OB OB OB OB OB OP OP OP OR OR OR OR OR OR OR OR OR OR PM PM PM PM RA RA RA RA RA RA RA RA SU SU SU SU SU SU SU SU SU SU SU SU AN AN AN AN AN AN AN Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Abbreviated Special Special Special Special Special Special Special K080179 K081576 K081803 K090422 K082134 K082475 K082746 K083169 K090987 K091778 K081410 K081054 K083834 K083747 K082412 K081838 K080708 K082117 K082403 K090449 K090450 K081438 K082139 K091626 K082725 K082321 K083111 K083794 K073417 K083091 K083674 K083470 K083191 K083623 K090895 K083697 K090556 K090172 K090443 K091594 K092027 K090989 K083382 K090323 K082348 K083300 K081626 K083106 K082380 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional K080170 K080821 K080510 K080023 K072323 K072979 K080652 K071955 K082009 K071397 K080804 K072843 K072148 K071230 K080321 K072996 K073673 K081014 K080439 K081837 K072295 K072235 K072825 K073024 K080255 K081285 K081125 K081870 K081937 K071516 K080484 K081691 K081037 K081982 K072670 K082121 K082099 K081411 K080166 K072714 K080872 K072638 K072543 K081318 K081451 K073625 K080999 K081473 K082173 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN AN CH CH CH CH CH CH CH CH CH CH CV CV CV CV Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special K091358 K091397 K091513 K092576 K082421 K090040 K092372 K083139 K083343 K082385 K083657 K083596 K092299 K090610 K090801 K090886 K092635 K083148 K091908 K082959 K091505 K092486 K091304 K083135 K082718 K083535 K083321 K082599 K083043 K083681 K092719 K092161 K090664 K083083 K090999 K091137 K082074 K083735 K091563 K083051 K090355 K082532 K082649 K091252 K083473 K091632 K082285 K082171 K082434 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV DE DE DE DE DE DE DE DE DE DE Traditional Traditional Traditional 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K090727 K082971 K082408 K091020 K091887 K081811 K082791 K092053 K082985 K090079 K091186 K082158 K082652 K083201 K082137 K091096 K090434 K091706 K091852 K090718 K091826 K092020 K083796 K082325 K082973 K083059 K081268 K082591 100 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional K072735 K081776 K072446 K073397 K073398 K073630 K080289 K073445 K071691 K072248 K073100 K080415 K080500 K073434 K081046 K081045 K082356 K081394 K073709 K073146 K072507 K072724 K073400 K080392 K081467 K080737 K072824 K073025 K072765 K073472 K073318 K080264 K080925 K081417 K072800 K072748 K081944 K082186 K081454 K072233 K080232 K073094 K080141 K080292 K082279 K082061 K081359 K073140 K071889 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 FY2008 CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV CV K083739 K091494 K092319 K082115 K083398 K083304 K083438 K083516 K083766 K091019 K091106 K090817 K091735 K082943 K083062 K083512 K090167 K082768 K082753 K082744 K083652 K090106 K090826 K090941 K091300 K091705 K082071 K082198 K083036 K082594 K091035 K090911 K081965 K080906 K090567 K090933 K091702 K081960 K082556 K073121 K080559 K073368 K073645 K081900 K081128 K081806 K082200 K081751 K082639 Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special 101 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional 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Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special Special K082651 K083297 K083496 K083067 K083205 K081419 K083550 K083543 K083886 K081748 K090237 K090049 K083586 K083633 K090234 K083805 K090452 K083669 K082843 K090709 K090527 K090825 K083670 K090154 K090054 K090377 K090174 K091239 K091904 K080430 K083432 K091362 K091533 K073307 K082335 K083388 K091144 K091964 K082470 K091718 K083901 K082917 K083742 K083005 K083858 K090083 K090793 K090850 K082553 102 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE DE Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional K082383 K072624 K072572 K072679 K073032 K070228 K072878 K073141 K073628 K080286 K081005 K081186 K081950 K073220 K081669 K080622 K082206 K081542 K080956 K073399 K081004 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SU SU SU SU SU SU SU SU SU SU SU SU SU SU SU Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional Traditional K072752 K083639 K091075 K091165 K081846 K081187 K082935 K082094 K082873 K081775 K090554 K083094 K090193 K091825 K073700 K081799 K082339 K082579 K082854 K080428 K082561 K082559 K083814 K082337 K081878 K083313 K083127 K080987 K083919 K090913 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 FY2009 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Massachusetts Institute of Technology Master's Thesis, September 2007. 2 AdvaMed, "The 510(k) Process: The Key to Effective Device Regulation." 3 www.fda.gov 4 AdvaMed, "The 510(k) Process: The Key to Effective Device Regulation." 5 Kidson, Angela, "Medical Instrument and Supply Manufacturing in the US." IBISWorld Industry Report 33911 a, IBISWorld, March 2010. 6Frost & Sullivan, "US Medical Devices Industry: Investment Analysis." March 24, 2008. 7 PricewaterhouseCoopers, National Venture Capital Association Moneytree Report: www.pwcmoneytree.com. 8 David W. Feigal, Susan N. Gardner, and Mark McClellan, "Ensuring Safe and Effective Medical Devices," New England Journal of Medicine, Vol. 348, No. 3 (January 16, 2003), p. 191. 9 Gelijns, "Comparing the Development of Drugs, Devices, and Clinical Procedures," p. 165. 10 Parisian, Suzanne, MD. FDA, Inside & Out. Front Royal, VA: Fast Horse Press. 2001. "Food and Drug Administration Modernization and Accountability Act of 1997," p. 10. 12 www.fda.gov 13 <http://www.fda.gov/AboutFDA/CentersOffices/CDRH/CDRHTransparency/ default.htm>. 14 United States. Government Accountability Office. Medical Devices: Shortcomings in the FDA's Premarket Review, Postmarket Surveillance, and Inspections of Device Manufacturing Establishments. GAO-09-370T. Washington: June 18, 2009. 15 Hines JZ, Lurie P, Yu E, Wolfe S (2010) Left to Their Own Devices: Breakdowns in United States Medical Device Premarket Review. PLoS Med 7(7): e1000280. doi:10.1371/journal.pmed.1000280. 16 Marchione, Marilynn. "When is a Drug too Risky to Stay on the Market?" Forbes 30 Jun. 2010. 31 Jul. 2010 <http://www.forbes.com/feeds/ap/2010/ 06/30/general-health-care-us-med-unsafe-drugs_7734121.html>. 17 "Analyzing the FDA's 2009 Drug Approval Statistics." The Cross Border Biotech Blog. 6 Jan 2010. 31 Jul 2010. < http://crossborderbiotech.ca/2010/01/ 06/analyzing-the-fdas-2009-approval-statistics/>. 18 "Eye on the FDA 2009." Massachusetts Medical Devices Journal 29 Oct 2009. < http://www.massdevice.com/reports/eye-fda>. 19 Data gathered from the online FDA CDRH databases. < http://www. accessdata.fda.gov>. 173 20 Government Accountability Office (2009) "Medical Devices: FDA should take steps to ensure that high-risk device types are approved through the most stringent premarket review process." Washington D.C.: Government Printing Office. Publication No. GAO-09-190. < http://www.gao.gov/new.items/d09190 .pdf> 174