Acknowledgements
Q
Duke Clinical Research Institute (DCRI)
O Lesley Curtis, Ph.D.
O Adrian Hernandez, M.D.
O Bradley Hammill,
Hammill, M.S.
O Kevin Schulman, M.D.
O Eric Peterson, M.D.
Q
UCLA Medical Center
O Gregg Fonarow, M.D.
Q
Funding Sources
O Contract with GlaxoSmithKline
O Duke CERTs grant (AHRQ grant #U18HS10548)
The Relationship Between CMS Quality
Indicators and LongLong-term Outcomes Among
Hospitalized Heart Failure Patients
Mark Patterson, Ph.D., M.P.H.
PostPost-doctoral Fellow
Duke Clinical Research Institute (DCRI)
PayPay-forfor-Performance and Process Measures
Q
Q
Q
CMS Heart Failure Process Measures
Goal of PayPay-forfor-Performance: Encourage
providers to follow recommended clinical care
by providing financial incentives
Improving heart failure care remains a priority
for CMS
O Prevalence = 5 million; Cost = $30 billion
Q
4 Core Process Measures
O Providing discharge instructions
O Conducting left ventricular ejection fraction
(LVEF) assessment
O Prescribing ACE inhibitors or angiotensin
receptor blockers at discharge
O Providing smoking cessation counseling
Theory: Financial incentives Æ improve
providers’
providers’ adherence Æ improve clinical
outcomes
Process Measures: Estimate providerprovider-level
adherence to this recommended clinical care
Associations between process measures
(PM) and mortality
Q
Q
Objective
Mixed evidence in regards to the associations
between process measures and mortality
O Acute coronary syndrome1
O AMI2
O Heart failure3
Q
Measure associations between the 4 current
CMS heartheart-failure process measures and 11-year
mortality
O
Q
No evidence in regards to associations between
PM and longlong-term mortality
1: Peterson et al., JAMA, 2006
2. Bradley et al., JAMA, 2006
3. Fonarow et al., JAMA, 2007
1
H1: HospitalHospital-level process measures will be
associated with patientpatient-level mortality
Data Sources
Q
Retrospective cohort study
Q
Matched HF patients within the OPTIMIZE
registry with their Medicare Part A claims (2003 –
2004
O OPTIMIZEOPTIMIZE-HF
O Medicare Part A
O CMS denominator files
Q
Participants
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Medicare feefee-forfor-service HF patients matched to
the OPTIMIZEOPTIMIZE-HF registry (N=22,483)
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Excluding patients who died before discharge
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Excluding hospitals with
O missing process measures
O with less than 25 patients
Q
Final analytic dataset (N=22,451)
Matched on age, gender, discharge date, and
hospital
HospitalHospital-level single process measures (PM)
HospitalHospital-level combined process measures
Q
Q
Discharge instructions N=15,142 (67%)
Q
LVEF assessment N=20,061 (89%)
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ACEI or ARBs at discharge N=5,457 (24%)
Q
Smoking cessation at discharge N=902 (4%)
Total number of processes documented
-----------------------------------------------------------Total number of opportunities to perform
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Outcome and Control Variables
PatientPatient-level Mortality
O CMS denominator file
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PatientPatient-level controls
O Demographics
O Comorbities
O Clinical measures
Creatinine,
Creatinine, weight, blood pressure
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DefectDefect-free N=22,451
Proportion of patients within the hospital having
documentation for ALL the PM that they were
eligible to receive
Frequency of PM documentation
------------------------------------------------------------Number of patients eligible to receive PM
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Composite N=22,451
Statistical Analysis
HospitalHospital-level volume
O Total HF discharges
O % HF discharges of total
2
Q
Cox multivariate regressions
O Controlling for demographics, clinical measures,
selected coco-morbidities, and hospital volume
indicators
O Accounting for clustering of patients within
hospitals
Q
6 final models
O 4 Models for each single PM
O 2 Models for each combined PM
Hospital PM Adherence Rates (N=178)
Selected Baseline Characteristics (N=22,451)
Variable
Mean age (s.d
(s.d))
Male
White
Black
Other
Prior AMI
Prior PVD
Prior Hyperlipidemia
Mean Serum Creatinine (mg/dL
(mg/dL)) (s.d
(s.d))
Mean Systolic BP (mmHg) (s.d
(s.d))
Mean Weight (kg) (s.d
(s.d))
%
79 (7.8)
44%
84%
10%
6%
23%
15%
33%
1.6 (1.2)
142 (32)
77.4 (20.8)
Process Measure (PM)
Single
Discharge Instructions
LVEF Assessment
ACEI / ARBs at Discharge
Smoking Cessation
Combined
Composite
DefectDefect-free
Associations between hospitalhospital-level process measures
and patient mortality
LVEF Assessment
ACEI / ARBs at Discharge
Smoking Cessation
Combined
Composite
DefectDefect-free
N
Unadjusted
Adjusted
15,142
20,061
5,457
902
1.0 (0.99 – 1.02)
1.0 (0.96 – 1.04)
0.94 (0.89 – 0.99)
0.99 (0.96 – 1.03)
0.99 (0.98 – 1.01)
1.0 (0.96 – 1.03)
0.97 (0.93 – 1.02)
0.98 (0.93 – 1.04)
22,451
22,451
1.0 (0.99 – 1.03)
1.0 (0.99 – 1.03)
1.0 (0.98 – 1.01)
1.0 (0.99 – 1.01)
Limitations
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CrossCross-sectional design
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Unobserved factors confounding associations
O PatientPatient-level
O HospitalHospital-level
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S.D.
Range
0.52
0.87
0.75
0.57
0.30
0.12
0.16
0.35
(0 – 1.0)
(0.29 – 1.0)
(0.25 – 1.0)
(0 – 1.0)
0.72
0.54
0.15
0.22
(0.32 – 1.0)
(0 – 1.0)
Discussion
HR (95 % CI)
Process Measure (PM)
Single
Discharge Instructions
Mean Score
Q
Current CMS heart failure process measures
(PM) are not associated with 11-year mortality in
Medicare beneficiaries diagnosed with HF
Q
Explanation for null findings
O Care given at discharge may not affect 11-year
mortality
O Documentation of care does not capture the
intensity or accuracy of care
O High variation for PM may prevent ability to
detect small changes if they exist
Strengths
Documentation of process measure at discharge
may not reflect the care given over 1 year
Q
First known study to link clinical registry data
with CMS data to examine associations between
process measures and longlong-term outcomes
Q
Generalizeable to Medicare feefee-forfor-service heart
failure patients1
Q
Models
O Include both patient and hospital-level covariates
O Account for clustering
1: Curtis et al., Abstract Proceedings at AHA, 2007
3
Conclusions & Recommendations
Q
Null findings do not undermine the need to
continue providing care that is good clinical
practice
Q
Need to more firmly establish link between PM
and outcomes before broadly implementing P4P
Q
Improve the accuracy of the measures
Q
Continue evaluating the effects of PM
O Within the context of longitudinal data
O Using PM with known clinical efficacy
4