1. The Relationship Between CMS Quality Indicators and Long-term Outcomes Among Hospitalized Heart Failure Patients Mark Patterson, Ph.D., M.P.H. Post-doctoral Fellow Duke Clinical Research Institute (DCRI)

2. Acknowledgements n Duke Clinical Research Institute (DCRI) l Lesley Curtis, Ph.D. l Adrian Hernandez, M.D. l Bradley Hammill, M.S. l Kevin Schulman, M.D. l Eric Peterson, M.D. n UCLA Medical Center l Gregg Fonarow, M.D. n Funding Sources l Contract with GlaxoSmithKline l Duke CERTs grant (AHRQ grant #U18HS10548)

3. Pay-for-Performance and Process Measures n Goal of Pay-for-Performance: Encourage providers to follow recommended clinical care by providing financial incentives n Theory: Financial incentives  improve providers’ adherence  improve clinical outcomes n Process Measures: Estimate provider-level adherence to this recommended clinical care

4. CMS Heart Failure Process Measures n Improving heart failure care remains a priority for CMS l Prevalence = 5 million; Cost = $30 billion n 4 Core Process Measures l Providing discharge instructions l Conducting left ventricular ejection fraction (LVEF) assessment l Prescribing ACE inhibitors or angiotensin receptor blockers at discharge l Providing smoking cessation counseling

5. Associations between process measures (PM) and mortality n Mixed evidence in regards to the associations between process measures and mortality l Acute coronary syndrome 1 l AMI 2 l Heart failure 3 n No evidence in regards to associations between PM and long-term mortality 1: Peterson et al., JAMA, 2006 2. Bradley et al., JAMA, 2006 3. Fonarow et al., JAMA, 2007 1: Peterson et al., JAMA, 2006 2. Bradley et al., JAMA, 2006 3. Fonarow et al., JAMA, 2007

6. Objective n Measure associations between the 4 current CMS heart-failure process measures and 1-year mortality l H 1 : Hospital-level process measures will be associated with patient-level mortality

7. Data Sources n Retrospective cohort study n Matched HF patients within the OPTIMIZE registry with their Medicare Part A claims (2003 – 2004 l OPTIMIZE-HF l Medicare Part A l CMS denominator files n Matched on age, gender, discharge date, and hospital

8. Participants n Medicare fee-for-service HF patients matched to the OPTIMIZE-HF registry (N=22,483) n Excluding patients who died before discharge n Excluding hospitals with l missing process measures l with less than 25 patients n Final analytic dataset (N=22,451)

9. Hospital-level single process measures (PM) n Discharge instructions N=15,142 (67%) n LVEF assessment N=20,061 (89%) n ACEI or ARBs at discharge N=5,457 (24%) n Smoking cessation at discharge N=902 (4%) Frequency of PM documentation ------------------------------------------------------------- Number of patients eligible to receive PM

10. Hospital-level combined process measures n Composite N=22,451 Total number of processes documented ------------------------------------------------------------ Total number of opportunities to perform n Defect-free N=22,451 Proportion of patients within the hospital having documentation for ALL the PM that they were eligible to receive

11. Outcome and Control Variables n Patient-level Mortality l CMS denominator file n Patient-level controls l Demographics l Comorbities l Clinical measures Creatinine, weight, blood pressure n Hospital-level volume l Total HF discharges l % HF discharges of total

12. Statistical Analysis n Cox multivariate regressions l Controlling for demographics, clinical measures, selected co-morbidities, and hospital volume indicators l Accounting for clustering of patients within hospitals n 6 final models l 4 Models for each single PM l 2 Models for each combined PM

13. Selected Baseline Characteristics (N=22,451) Variable% Mean age (s.d) 79 (7.8) Male44% White84% Black10% Other6% Prior AMI 23% Prior PVD 15% Prior Hyperlipidemia 33% Mean Serum Creatinine (mg/dL) (s.d) 1.6 (1.2) Mean Systolic BP (mmHg) (s.d) 142 (32) Mean Weight (kg) (s.d) 77.4 (20.8)

14. Hospital PM Adherence Rates (N=178) Process Measure (PM) Mean Score S.D.Range Single Discharge Instructions 0.520.30 (0 – 1.0) LVEF Assessment 0.870.12 (0.29 – 1.0) ACEI / ARBs at Discharge 0.750.16 (0.25 – 1.0) Smoking Cessation 0.570.35 (0 – 1.0) Combined Composite0.720.15 (0.32 – 1.0) Defect-free0.540.22 (0 – 1.0)

15. Associations between hospital-level process measures and patient mortality HR (95 % CI) Process Measure (PM) NUnadjustedAdjusted Single Discharge Instructions 15,142 1.0 (0.99 – 1.02) 0.99 (0.98 – 1.01) LVEF Assessment 20,061 1.0 (0.96 – 1.04) 1.0 (0.96 – 1.03) ACEI / ARBs at Discharge 5,457 0.94 (0.89 – 0.99) 0.97 (0.93 – 1.02) Smoking Cessation 902 0.99 (0.96 – 1.03) 0.98 (0.93 – 1.04) Combined Composite22,451 1.0 (0.99 – 1.03) 1.0 (0.98 – 1.01) Defect-free22,451 1.0 (0.99 – 1.03) 1.0 (0.99 – 1.01)

16. Discussion n Current CMS heart failure process measures (PM) are not associated with 1-year mortality in Medicare beneficiaries diagnosed with HF n Explanation for null findings l Care given at discharge may not affect 1-year mortality l Documentation of care does not capture the intensity or accuracy of care l High variation for PM may prevent ability to detect small changes if they exist

17. Limitations n Cross-sectional design n Unobserved factors confounding associations l Patient-level l Hospital-level n Documentation of process measure at discharge may not reflect the care given over 1 year

18. Strengths n First known study to link clinical registry data with CMS data to examine associations between process measures and long-term outcomes n Generalizeable to Medicare fee-for-service heart failure patients n Generalizeable to Medicare fee-for-service heart failure patients 1 n n Models l l Include both patient and hospital-level covariates l l Account for clustering 1: Curtis et al., Abstract Proceedings at AHA, 2007

19. Conclusions & Recommendations n Null findings do not undermine the need to continue providing care that is good clinical practice n Need to more firmly establish link between PM and outcomes before broadly implementing P4P n Improve the accuracy of the measures n Continue evaluating the effects of PM l Within the context of longitudinal data l Using PM with known clinical efficacy