Informing
g a Business Case to Prevent
Infections Acquired in Acute Care
Hospitals
Yaozhu ((Juliette)) Chen,, MPA
Covance Market Access Services Inc.
Acknowledgement
• Co-authors
– Timothy Dall, MS, The Lewin Group
– Erica Moen, BS
• Special thanks to
– Vestagen Technical Textiles LLC
2
Introduction
• Hospital-acquired infections (HAIs) impose a
significant health and economic burden
– Higher medical costs, reduced quality of life, lost
productivity and premature mortality
productivity,
• A business case model to inform the cost impact
of HAIs to hospitals
– Perspective: acute-care hospital in the U.S.
– Purpose: to estimate HAI
HAI-attributed,
attributed annual increases
in mortality, LOS, and medical costs for both overall
inpatient population and selected subsets of sicker
patients
3
Model Components
• Most prevalent and costly HAIs to be modeled
– Device related
• Catheter-associated urinary tract infections (CAUTI)
• Ventilator-associated pneumonia (VAP)
• Bloodstream infections (BLS)
– Procedure related
• Surgical site infections (SSI)
– Pathogen related
• Methicillin-resistant Staphylococcus
p y
aureus ((MRSA))
• Vancomycin-resistant Enterococci (VRE)
• Discharges to be included
– Of patients who were treated in acute-care hospitals
and a few patient subsets who had service experience
in several selected ICUs during their hospital stays
4
Model Components
Oncology
VRE
Or
al
Ca
re
Ca
i
rd
Catheter Site Care
ac
o
Ne
al
t
a
n
l
PATIENT
CAUTI
5
a
ic
rg
re
Ca
VAP
Su
in
Sk
M
ed
l
a
ic
MRSA
SSI
BSI
Methods – Data Sources & Definitions
• Data sources
– 2007 Nationwide Inpatient Sample (NIS) linked to the American
Hospital Association (AHA) Annual Survey to yield a nationally
representative sample of about 5.7 million discharges from nonfederal, acute care hospitals
– HAI incidence data ((number of cases p
per 1,000
,
inpatient
p
days)
y )
from 2006-2007 National Healthcare Safety Network (NHSN)
– Apply incidence risks to discharges to generate estimated # of HAI
cases
• Then, use NIS to estimate HAI attributed increases
• Claims data define HAIs and proxies for discharges by
care unit
– HAIs: apply ICD-9-CM codes to the secondary diagnosis fields and
all procedure fields
– Discharge proxies by care unit: a combination of diagnoses
diagnoses,
procedures, and a few other criteria (e.g., age, admission reason,
LOS, discharge DRG, etc.) to identify higher-risk patients that
were likely received care from ICU
6
Diagnoses and Procedures to Define HAIs
Description
Code
Type
Details/Exclusions
Source
Notes
Urinary tract infection (UTI)
UTI - related to urinary catheter 996.64
ICD-9
UTI - unspecified
599
ICD-9
Urinary tract infection
V13.02
ICD-9
Urinary tract infection of
newborn
771.82
ICD-9
Ventilator-associated
V
til t
i t d
pneumonia
481 - 486
ICD 9
ICD-9
Mustt have
M
h
procedure
d
code
d 96.72
96 72 (i
(i.e., Buczko
k W ((2007))97
continuous ventilation for 96 hours)
Ventilator-associated
pneumonia
997.31
ICD-9
ICD-9 search;
Apply codes to secondary fields.
Needleman et al
Exclude: any primary diagnosis of
pneumonia (ICD 480-487, 507.0, 514, (2001) 96; PSI V4.0
997.3); any secondary diagnosis of
ICD 480, 481, 483, 484, or 487; MDC4
(respiratory system); AIDS;
immunocompromised
Needleman et al
(2001) 96; PSI V4.0
Apply codes to secondary fields.
Exclude: primary diagnosis, MDC 11
(kidney and urinary tract), MDC 12
(male reproductive system), MDC 13
(female reproductive system) MDC
Same exclusion criteria as above,
except include MDC=15 and exclude
age>0.
Needleman et al
(2001)96
ICD-9 search
ICD-9 search
Needleman listed sepsis as exclusion criteria; sepsis
diagnosis codes were identified through independent
ICD-9 search.
These two UTI code were not included in the
Needleman study - identified through independent ICD9 search (http://icd9cm.chrisendres.com/)
Pneumonia
Post-operative pneumonia
997.3
ICD-9
Bacterial pneumonia
482
ICD-9
Bronchopneumonia
485
ICD-9
Unspecified pneumonia
486
ICD-9
998.5
ICD-9
Surgical Site Infection (SSI)
Postoperative infection
Denominator includes patients who
had inpatient surgical procedures.
Codes apply to secondary diagnosis
fields. Exclude: any primary
diagnosis of 998.5 and 996.6.
Lissovoy 200946;
Needleman et al
(2001)96; National
Hospital Discharge
Survey98
997.31 identified through an independent ICD-9 search
using icd9cm.chrisendres.com. The code was added to
the ICD-9 in FY 2009. Since NIS data is from FY 2007, it
does not include this code. Exclusion criteria from
Needleman (2001) and the Patient Safety Indicator 4.0
documentation.
The pneumonia diagnosis code and exclusion criteria
were based on the information provided by the
Needleman study. We used the diagnosis codes listed in
h P
Patient
i
S
Safety
f
Indicator
I di
V4.0
V4 0 to id
identify
if
the
immunocompromised patients
Diagnosis code and exclusion criteria were taken from
the Lissovoy and Needleman studies. Analysis was
further restricted to only those patients who underwent
a surgical procedure. Surgical procedures were flagged
using the ICD-9 procedure codes listed in Table 8 of the
National Hospital Discharge Survey (Inpatient
surgeries).
Infection and inflammatory
996.6
reaction due to internal
prosthetic device
device, implant
implant, and
graft
ICD-9
Denominator includes patients who
had inpatient surgical procedures.
Exclude: Same as above
RTOP-CMS HACPOA Request for
Proposals
RTOP-CMS HAC-POA RFP included both a diagnosis
code and procedure code to identify infection related
only to orthopedic surgical procedures.
procedures Since we are
interested in infection from any surgery, we only
included the diagnosis code and did not apply any
additional procedure restrictions.
Re-admission for postoperative 418
infection
DRG
Denominator includes patients who
had inpatient surgical procedures.
Also, 998.5 must be included in
diagnoses (any position, including
primary code)
Lissovoy 200946
The 418 DRG flags for patients who are "Readmitted for
Post-Operative and Post-traumatic Infection". With
DRG 418, a primary diagnosis of surgical site infection
could indicate a hospital-acquired infection.
Surgical site infection,
mediastinitis, following CABG
ICD-9
Denominator includes patients who
had inpatient surgical procedures.
Al
Also,
mustt include
i l d one off the
th
following procedure codes: 36.1036.19
RTOP-CMS HACPOA Request for
P
Proposals
l
This set of codes only flags for the mediastinitis
infection following coronary artery bypass graft.
Secondary diagnosis field. Exclude:
AHRQ Patient
records with a primary diagnosis of Safety Indicator
(PSI) V4.0
venous-catheter-related codes
(996.63, 999.3, 999.3) and any
diagnosis codes for
immunocompromised state or cancer
Inclusion and exclusion criteria were based on the
documentation for the Patient Safety Indicator
documentation Version 4.0. Immunocompromised
states and cancer were identified using code in the PSI
V4.0
Exclude: primary diagnosis of
Clodium difficile
Schmiedeskamp
The ICD-9 code is estimated to have a sensitivity of 78%
and specificity of 99.7%.
(http://www.cdc.gov/ncidod/Eid/vol12no10/060016.htm)
519.2
(MCC)
Bloodstream infections (BSI)
Discharge prior to Oct 1, 2007
Due to other vascular device,
implant, and graft
996.63
ICD-9
Other infection
999.3
ICD-9
Discharges on or after Oct 1, 2007
Infection due to central venous 999.31
catheter
ICD-9
Clostridium difficile
difficile-associated
associated disease (CDI)
Clostridium difficile-associated 8.45
disease
ICD-9
200999
Vancomycin Resistant Enterococcus (VRE)
Infection with microorganisms
resistant to other specified
drugs
V09.8
ICD-9
Exclude: primary diagnosis of VRE
ICD-9 search
We identified the VRE code by performing an
independent online search at icd9cm.chrisendres.com.
Enterococcal infection (not
exclusive to resistant strains)
41.04
ICD-9
Exclude: primary diagnosis of VRE
Reik 2008100
http://www.princeton.edu/~eklein/pubs/Reik.et.al.20
08.TheBurdenOfVancomycinResistantEnterococcalInfec
tions.pdf
Methicillin-resistant staphylococcus aureus (MRSA)
S. aureus septicemias
7
38.11
S. aureus pneumonias
482.41
Other S. aureus infections
41.11
Infection with microorganisms
resistant to penicillins
V09.0
ICD-9
Exclude: primary diagnosis of 038.11, Kuehnert 2005101
482.41, and 041.11
ICD-9
Exclude: primary diagnosis of V09.0, Jhung 2009102
038.11, 482.41, and 041.11
Proxy Strategy to Group Discharges by Unit
Unit Type
Neonatal
Cardiac
ICD-9 Diagnosis and Procedure Codes
Extreme immaturity: ‘765.0'; Gestational age<35 weeks:
'765.21' - '765.27'; Birth weight <1,750 g: '764.01' '764.06', '764.11' - '764.16', '764.21' - '764.26', '764.91' '764.96', '765.01' - '765.06', '765.11' - '765.16'; Other
conditions requiring NICU admission: '749' '772.1'
'772.2' '767.0' '769' '770.6' '770.1' '770.0' 777.5' '756.6'
756.77' '741'
741 '750
750.33' '745
745.22' '770
770.88'
'756
Common cardiac conditions requiring CICU admission:
410-417, 420-428, 746;
Additional Criteria
Median of ICU
days
Nationwide Children's Hospitals
Age=0, Admission
type
McMaster
M
M
University,
U i
i Division
Di i i off
Neonatology
19 days
Rhode Island Department of Human
Services
Embry M. Howell et al. 2002
Apisarnthanarak et al. 2003
Diwas KC, Christian Terwiesch 2007
2 days
Common heart surgery procedure codes: 35-37
Legacy Health
Flagged if DRG
*Range too large:
equals 082, 203, 172,
Sude KJ, Motl SE, Kuth JC 2006
0-81 days
274, 346, or 010
Oncology
Cancer diagnosis codes: '140.00' - '209.29', V10.00 V10.99;
Surgical
Ope at o s on
Operations
o various
va ous systems:
syste s: ‘01.00-16.00’,
0 .00 6.00 , ‘18.008.00
86.00’; Other diagnostic and supporting procedures
related to surgery: "00.50", "0051", "0053", "0054", "0055",
"0061", "0062", "0063", "0064", "0065", "0066" "0070",
"0071", "0072", "0073", "0080", "0081", "0082","0083",
"0084"
5 days
Critical care procedures: ‘96' '96.7', '99.0', '89.6', '11.0',
'96.0', '31.1' ,'99.62','99.15', '36.10' - '36.19'
5 days
Medical
Source
Ri h d ett al.
Richards
l 2000
The Children's Hospital
Al-Rawajfah et al. 2009
Richards et al. 2000
8
Methods – Regression Approach
• A series of multivariate regressions to isolate the
change in patient outcomes associated with the
presence of each HAI
– Logistic regression for mortality → increased inpatient death
rates associated with each type of HAIs
– Generalized
Generali ed linear model [GLM] with
ith Poisson distrib
distribution
tion for
LOS → increased LOS associated with each type of HAIs
– GLM with gamma distribution for medical cost → increased
medical costs associated with each type of HAIs
• Cost-to-charge ratios were employed, and use CPI medical
component to convert cost into 2009 $.
• Separate regressions employed
– By HAI type
– By setting (all inpatient settings together, each selected ICU)
• Hierarchical
Hi
hi l vs. regular
l regression
i modeling
d li
– Estimate the impact of explanatory variables at both hospital
and discharge level
9
Methods - Controlling Variables
• Patient demographics (e.g., age, sex)
• Admission type
– Elective, newborn, trauma center, urgent, emergency, and other
• Payer type
– Medicare
Medicare, Medicaid
Medicaid, private insurer
insurer, self pay
pay, no charge
charge, and
other)
• Hospital characteristics
– Ownership/control
Ownership/control, bed size,
size urban/rural
urban/rural, teaching status
status, and
Census Region)
• A series of indicator variables reflecting if each HAI type
is present during the stay (1=present
(1=present, 0=absent)
• Risk-adjustment variables created based on patient’s
primary diagnosis (Dx1)
– Average LOS for discharges with the same Dx1
– Average mortality risk for discharges with the same Dx1
– Average
e age medical
ed ca cost for
o d
discharges
sc a ges with
t tthe
e sa
same
e Dx1
10
Estimated Risk for HAI, Medical Cost, LOS, and
per HAI Case
Death Risk p
HAI
CAUTI
Measure
Neonatal
Care Unit
Surgical
Medical
All Hospital
Settings
Risk for HAI
0.40%
0.80%
0.80%
$
83 959 $
83,959
Medical cost
12 294
12,294
$
1,846
1 846 $
1.1
15.0
4.4
Length of stay
0.28%
0.30%
1.58%
Mortality risk
VAP
Risk for HAI
0.20%
0.80%
0.40%
$
64,703 $
59,686
$
3,058 $
Medical cost
0.5
18.9
6.3
Length of stay
3.16%
2.85%
5.47%
Mortality risk
SSI
Risk for HAI
0.20%
0.10%
NA
$ 170,989 $
Medical cost
33,436
NA $
NA
15 6
15.6
86
8.6
L
Length
h off stay
NA
3.49%
2.18%
Mortality risk
CLABSI Risk for HAI
0.40%
0.70%
0.70%
$ 270,840 $
80,690
$
6,429 $
Medical cost
32
3.2
19 8
19.8
11 2
11.2
Length of stay
<0.01%
2.88%
1.89%
Mortality risk
VRE
Risk for HAI
<0.1%
0.10%
0.10%
NA $ 225,028
Medical cost
$
4,882 $
1.3
NA
4.4
Length
g of stay
y
0.15%
NA
<0.01%
Mortality risk
MRSA
Risk for HAI
0.10%
0.10%
0.10%
$ 129,053 $
10,075
$
2,380 $
Medical cost
1.2
33.0
3.3
Length of stay
0
0.40%
40%
2 27%
2.27%
0 76%
0.76%
Mortality risk
Note: Risk for HAI is the average patient risk for HAI during a stay. Medical cost (in
2009 $), LOS, and mortality risk represent additional burden per case if HAI is present.
11
2.60%
5 347
5,347
2.8
0.96%
1.50%
47,487
4.0
4.62%
0.70%
36,025
89
8.9
2.10%
1.00%
79,960
12 1
12.1
1.70%
0.20%
19,760
3.8
<0.01%
0.30%
10,071
3.5
0 70%
0.70%
Strategy to Compare with An Average Hospital
• Baseline set-up in the business model
– Of an average,
average acute
acute-care
care hospital in 2007
• With work volume of 30,000 inpatient days and 4,700
discharges
• Model users can generate estimates
– If use default values
• For this average-risked hospital, what are the HAI-associated
outcomes in form of attributed medical costs, inpatient days,
and
d premature
t
d
deaths
th ffor allll iinpatient
ti t settings
tti
as a whole
h l and
d
by selected ICUs separately
– If use customized entries in admission volume and
inpatient days
• Can get customized results on HAI-attributed outcomes
12
Selected Outcomes from Business Model
Outcomes by Type
Discharge Outcomes
Average discharge volume
An Average,
Average inpatient days
Acute-Care
# of HAI Cases per acute-care
Hospital
hospital with average risk
National discharge volume
Inpatient days
National Total
# of HAI Cases of all acute-care
hospitals
HAI-Attributed Discharge Outcomes
Attributed medical costs
An Average,
Att ib t d iinpatient
Attributed
ti t d
days
Acute-Care
Hospital
Attributed premature mortality
Attributed medical costs
National Total Attributed inpatient days
Attributed premature mortality
Discharge by ICU Experience
Neonatal
Surgical
Medical
All Hospital
Settings
140
1,260
2
170
2,950
8
200
2,530
5
4,700
30,100
181
703,000
6,130,000
8,000
811,000
14,321,000
36,900
972,000
12,295,000
26,500
22,682,000
146,010,000
879,000
$315,000
36
$692,000
126
$45,000
16
$6,782,000
1 134
1,134
0.04
0.6
0.2
4.7
$1 53 billion $3.36
$1.53
$3 36 billion
$0 22 billion
$0.22
$32 94 billion
$32.94
177,000
609,000
80,000
5,506,000
200
2,800
800
23,000
Note: The color highlighted areas are the outcomes relevant to the average, acute-care hospital;
in particular, light green areas allow model users to do customized data entry.
13
HAI Prevention
• Implementing
p
g
prevention can lead to
up to a 70% reduction
in HAIs
• Challenges
HAI Infection
H
n Rate
High
Hospital
P f
Performance
Low
Higher
potential for
HAI reduction
Average
Lo
ow
Lower
potential for
HAI reduction
High
0%
14
HAI Prevention Potential
100%
– As HAIs are reduced,,
the cost of detecting
each event will become
increasingly
gyg
great
– Implementation of
interventions designed
to move towards the
target will require more
resources
HAI Severity by Acute-Care Hospital
350
Num
mber of US A
Acute Care H
Hospitals
300
250
200
150
100
50
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
Hospital Acquired Infections per 1000 Inpatient Days
Note: Measured by # of hospitals at different HAI risk level. Source: NIS 2007.
15
39
41
43
Use of This Business Model
• Offer hospitals with an easy-to-apply approach to
estimate the potential benefits from implementing
prevention & control approaches against HAIs
• It is a benchmark natured exercise
exercise, may not be
specific to the unique risks of each hospital
• Can be used to estimate potential medical savings
• But may under-represent total hospital benefits
– Hospital reputation and avoidable litigation costs
– Hospital management: bed turnaround time, revenue
from reimbursement, isolation cost
– Hospital staff: increased work load for hospital staff,
i
increased
d chances
h
off staff
t ff burn-out
b
t
16
Business Model: Current and Future Versions
• Our analysis indicates that opportunities exist for
substantial improvements in patient outcomes and
h
hospital
it l performance
f
th
through
h effective
ff ti HAI prevention
ti
and control
• Based on this model,
model hospitals may compare
alternative approaches to reduce HAI cases
– Compare costs of each prevention approach vs. potential
benefits to help reduce
red ce the HAI b
burden
rden
• Next steps in model development: continue to build
more dynamic
y
evaluation tools
– Budget-impact analysis: to estimate the financial consequences
of adopting one or a series of intervention approaches, and
measure hospital’s
hospital s resource reallocation
– Cost-effectiveness analysis: to compare the relative costs and
effects of two or more interventions, and can accommodate
outcomes like life years gained and QALY
17
Relevance to Hospital Management
• Establish greater consistency of HAI-related estimates
– Increase reliable estimates of HAIs as economic base for
prevention and feasibility assessment
• Relate model to hospital Value-Based Purchasing (VBP)
plan methodology
– Help hospitals implement CMS performance-based payment for
hospitals, which include measures of HAI prevention and
outcomes as a basis for payment
• Multi-modal infection control program needed
–
–
–
–
Surveillance or monitoring
g of incidence
Limiting use of invasive procedures and devices
Staff training and education
Limiting the risk of disease transmission through appropriate
hygiene and environmental decontamination
– Improve antimicrobial use
18
F further
For
f th information,
i f
ti
please
l
contact:
t t
Yaozhu (Juliette) Chen,
yaozhu.chen@covance.come