The Multicenter Perioperative Outcomes Group:
Using Automated Data Sources for Outcomes Research and Quality
Improvement
Sachin Kheterpal, MD MBA
sachinkh@med.umich.edu
Introduction:
Adverse events following surgery are a major source of mortality, morbidity, and health care
expenditures in the United States. Over 40 million surgical procedures are performed annually, associated with
over 100,000 postoperative deaths and more than $100 billion in unnecessary costs. The wide variation in
risk-adjusted morbidity and mortality across centers and providers suggests opportunities for quality
improvement. Historically, anesthesiology has focused its research efforts on preventing rare, catastrophic
intraoperative mishaps. However, a growing body of animal studies and case series demonstrate that
anesthesiologists may influence common perioperative adverse outcomes by modulating the surgical injury,
perioperative stress, and inflammatory responses.
Difficulties in blinding, ethical randomization, and recruitment challenge the use of randomized
controlled trials in surgical patients, resulting in a paucity of large, multicenter perioperative trials. As a result,
basic perioperative decisions such as the choice of anesthesia technique, hemodynamic thresholds, or
transfusion triggers lack compelling data. Attempts to correlate intraoperative management with postoperative
outcomes in observational studies are hampered by the absence of structured patient outcome data in
perioperative clinical information systems. We sought to develop a scalable, multicenter infrastructure for
automatically aggregating observational data across disparate anesthesia information management systems
(AIMS). In addition, the infrastructure must enable research using de-identified, HIPAA compliant patient
datasets, while maintaining the ability to link the data to national data sources.
The Multicenter Perioperative Outcomes Group (MPOG http://mpog.med.umich.edu) was formed to
develop the necessary policies, procedures, and technical infrastructure required for multicenter perioperative
outcomes research. It has already aggregated 2 million operative encounters across 14 medical centers
across the US and Europe with detailed risk adjustment, process of care, and outcome data for observational
outcomes research.
Current State:
The Multicenter Perioperative Outcomes Group (MPOG) is a consortium of 37 anesthesiology and
surgical departments. Each uses a commercially available Anesthesia Information Management System
(AIMS) – a structured electronic medical record that replaces the paper pre-, intra-, and post-operative
anesthesia records. The MPOG consortium has already obtained performance site, coordinating center, and
analysis of data IRB approvals. A configurable interface that abstracts data from multiple vendors and
institutions into the standardized MPOG content and database has been developed. Disparate data sources
are translated into standardized MPOG data structures:
 AIMS: Anesthesia information management system data are extracted into eleven distinct data structures
o Patient: stores a single record for each unique patient in a given institution’s database. Static
information about the patient that cannot change over time is stored in this structure: race, sex, and
hashed codes based upon patient identifiers
o PreoperativeCaseInfo: Multiple records for each preoperative attribute, including preoperative
history and physical information. This includes ASA physical status, home medications, height,
weight, allergies, baseline antibiotic resistance precautions, baseline vital signs (heart rate, blood
pressure, SpO2), and all comorbidities. Key elements from the surgical scheduling or nursing
1

documentation system, such as primary CPT code or wound classification, are also stored in this
table. Because date of birth is removed from the extract to limit PHI, the patient’s age on the date
of the operation is stored with each operation. This age is HIPAA compliant at extremes (old or
young)
o IntraoperativeCaseHeader: a single record for each operative case which summarizes basic
information about the structure of care: surgical center type (free standing ambulatory, ambulatory
associated with acute care hospital, acute care hospital, offsite procedure area), procedure area
type (offsite, main operative room, etc), and admission type
o IntraoperativeMedicationDetails: many records per operation, with each dose of each medication
and infusion rate change documented. Rather than free text documentation, this table is based
upon structured data elements, with each medication and infusion mapped to an MPOG medication
system number which is then referenced to RxNorm.
o IntraoperativeFluidDetails many records per operation, with each fluid administration or output
(urine, estimated blood loss) stored as a distinct record.
o IntraoperativePhysiologic: thousands of records per operation, with each vital sign (from the
physiologic monitors) and device parameter (from the ventilator and anesthesia machine) recorded
every sixty seconds. Structured data elements map each record to a specific clinical concept rather
than free text. Where applicable, SNOMED codes are used to define physiologic concepts.
o IntraoperativeEvents: contains dozens to hundreds of records per operation, depending upon the
duration of the operation. All non-pharmacologic interventions, such as intubation, placement of a
forced-air warmer, nasogastric suctioning, peripheral nerve blockade, etc are stored as discrete
rows in this table. In addition, process times (in hospital, in room, anesthesia start, etc), anesthetic
technique performed, airway management technique, associated minor procedures (peripheral
nerve block, arterial line, epidural), and free text documentation is stored.
o IntraoperativeStaffing: contains a record for each anesthesia care team provider participating
during the anesthesia services. Includes the provider’s role (CRNA, attending, resident),
experience level for residents, and care-handoffs. In addition, a surgical attending identifier is also
recorded for each case.
o PerioperativeLaboratoryValues: contains a record for each laboratory value of interest within 365
days. Specific laboratory values are extracted from the AIMS or hospital laboratory system:
hemoglobin, hematocrit, platelets, white blood cell count, prothrombin time, partial thromboplastin
time, creatinine, troponin, etc. In addition, all intraoperative point of care testing such as
hemoglobin, coagulation studies, and pH is stored.
o VenousAccess: contains a record for each venous access site used or documented during the
intraoperative anesthetic
o ProfessionalCharge: For each operative episode, the primary surgical CPT, anesthesia CPT, and
operative ICD codes are stored. Anesthesiology modifiers and flat-fee CPT (for line placement,
TEE interpretation, etc) are also stored. These elements allow structured analysis of the primary
surgical procedure performed, an essential component of outcomes research risk adjustment. No
actual financial charges (dollar amounts) are collected or analyzed.
o HospitalCharge: For each inpatient or outpatient stay, the ICD discharge diagnoses and
procedures used for hospital billing are also collected. These diagnoses are an important source of
risk adjustment and outcome data.
Surgical registries: The ACS-NSQIP database at participating sites is incorporated into the MPOG
schema.
o Preoperative: One record for each operation, and stores all 44 preoperative comorbidity elements
tracked by ACS-NSQIP methodology, ranging from coronary artery disease to cancer status
o PreoperativeLab: One record for each operation with 13 preoperative lab variables and the date
the value was observed. Includes elements such as creatinine, albumin, hematocrit, platelet count,
prothrombin time, partial thromboplastin time, etc
o IntraoperativeSummary: Contains 6 clinical elements, the most important of which is the
preoperative wound classification, and basic process variables such as seniority of the surgery
assistant and a unique surgeon ID
2
o

AdverseEvents: If a patient experiences any of 3 intraoperative or 26 postoperative events within
30 days of the operation, each event and descriptive information regarding it is recorded.
Outcomes range from SSI to myocardial infarction and renal failure
o DischargeOutcome: one record for each operation and indicates the patients status at
postoperative day 30. The discharge location, needed for reoperation, and vital status are
recorded.
Administrative sources: The reliable 30-day outcomes collected using ACS-NSQIP are of great interest.
However, access to longer-term outcomes currently requires the use of administrative data sources. Using
the Blinded Record Index technology, we are able to connect detailed perioperative clinical data with the
following administrative data elements
o All cause-mortality: Integration of the social security death master file using the Blinded National
Death Service™ (BliNDS) allows each institution to identify all-cause mortality beyond the 30 day
time period for all of its perioperative patients. Although all-cause mortality and the social security
death master file have limitations, data for patients from 2003 and beyond demonstrate a 95%
sensitivity and 98% specificity using BliNDS.
o Discharge ICD9 Through the use of administrative data linked with EHR, we are able to provide
highly specific complication data for outcome analyses.
Fourteen institutions – The University of Michigan, Columbia University, Oregon Health & Science
University, University Medical Center of Utrecht, Netherlands, University of Colorado, University of Florida,
University of Oklahoma, University of Tennessee, University of Vermont, University of Virginia, University of
Utah, University of Washington, Vanderbilt University, and Washington University (St Louis) have successfully
deployed the MPOG data interfaces and were able to extracts vital signs, physiologic parameters, procedures,
interventions, and medications from the intraoperative period. Five leading vendors of AIMS have been
successfully integrated into the MPOG schema: Epic (Optime) General Electric (Centricity), Philips
(Compurecord), Picis (Anesthesia Manager), and iMDSoft (Metavision). Across these fourteen institutions,
more than 2 million operative records have been extracted into a single database with detailed, structured
minute-to-minute vital signs, medication administration, anesthesia technique, laboratory records, and
procedural intervention data.
These detailed, structured AIMS data can be combined with complementary data sources to enable
outcomes research to address critical perioperative interventions. These data have already been integrated
with preoperative and postoperative laboratory data, national all-cause mortality data, and ACS-NSQIP. The
use of secure hashing algorithms combined with the social security death master file allows the derivation of
all-cause mortality without the communication of protected health information. This novel record-linking
strategy allows us to merge patient-specific data without using patient identifiers, minimizing privacy risk. In
addition, a “de-identification” algorithm that is used to remove protected health information from free-text entry
fields is used to reduce the risk of protected health information transmission. Using US Census Bureau data
regarding common names and institution-specific provider and patient data, identifiers are removed using
automated routines. Finally, we have mapped intraoperative medications and physiologic vital signs to
internationally accepted lexicons such as RxNorm and SNOMED-CT to allow comparison of data from multiple
institutions in a single repository.
The MPOG database is also supported by the leading EHR vendor in the US – Epic Systems. Epic
systems developed, supports, and distributes an MPOG interface that allows any EPIC customer to submit
their data to the MPOG research consortium. Other leading vendors, such as GE and SIS, are now developing
support structures as well.
Next steps
With the successful deployment of the AIMS interfaces at these fourteen initial institutions, two major
initiatives are the focus of MPOG efforts: performing specific hypothesis-driven research projects using the 2
million operations already collected, and deploying the interfaces to other MPOG members. The deep
observational research expertise of the MPOG members enables rigorous analyses focused on important
patient-centric outcomes. These analyses are focused on healthcare associated infections, perioperative
myocardial infarction, acute kidney injury, stroke, airway management, and optimal anesthesia techniques.
3
Each contributing institution has equal access to the centralized repository of data stored at the MPOG
coordinating center, The University of Michigan. Proposals are reviewed and refined by a multicenter
Perioperative Clinical Research Committee (PCRC) that ensures that scientifically valid analyses are
performed on sound underlying observational data. Nineteen proposals have already been reviewed,
improved, and are now in the process of data extraction or statistical analysis. Four manuscripts using MPOG
data or describing the MPOG methodology have already been published.
Second, the MPOG consortium is ready to accelerate adoption at other MPOG member institutions that
use the same AIMS and surgical outcome registries. Because each of these interfaces was designed to be
configurable and reusable at subsequent sites, minimal incremental effort is needed to unlock the outcomes
research potential of remaining MPOG members. There are no monetary fees associated with MPOG
membership, lowering the hurdle for participation from a broad range of institutions.
Finally, the MPOG consortium is now spreading from a US-focused effort to inclusion of medical
centers around the world. We are actively receiving data from Academic Medical Center (AMC), Amsterdam
and University Medical Center of Utrecht, Netherlands. Our next goal is to have active software development
in place from centers in Germany, Israel, and Canada. The variant care patterns in these other countries will
be a fertile ground for observational research.
4