Acknowledgements

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

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

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

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

Associations between process measures (PM) and mortality • Mixed evidence in regards to the associations between process measures and mortality • Acute coronary syndrome1 • AMI2 • Heart failure3 • 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

Objective • Measure associations between the 4 current CMS heart-failure process measures and 1-year mortality • H1: Hospital-level process measures will be associated with patient-level mortality

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

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

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

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

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

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

Selected Baseline Characteristics (N=22,451)

Hospital PM Adherence Rates (N=178)

Associations between hospital-level process measures and patient mortality

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

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

Strengths • First known study to link clinical registry data with CMS data to examine associations between process measures and long-term outcomes • Generalizeable to Medicare fee-for-service heart failure patients1 • Models • Include both patient and hospital-level covariates • Account for clustering 1: Curtis et al., Abstract Proceedings at AHA, 2007

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

Acknowledgements