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)

1
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)
2
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)

3
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

4
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

5
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
6
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

7
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

8
Participants

• Medicare fee-for-service HF patients matched to
  the OPTIMIZE-HF registry (N22,483)
• Excluding patients who died before discharge
• Excluding hospitals with
• missing process measures
• with less than 25 patients
• Final analytic dataset (N22,451)

9
Hospital-level single process measures (PM)

• Discharge instructions N15,142 (67)
• LVEF assessment N20,061 (89)
• ACEI or ARBs at discharge N5,457 (24)
• Smoking cessation at discharge N902 (4)
• Frequency of PM documentation
• --------------------------------------------------
  -----------
• Number of patients eligible to receive PM

10
Hospital-level combined process measures

• Composite N22,451
• Total number of processes documented
• --------------------------------------------------
  ----------
• Total number of opportunities to perform
• Defect-free N22,451
• Proportion of patients within the hospital having
  documentation for ALL the PM that they were
  eligible to receive

11
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

12
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

13
Selected Baseline Characteristics (N22,451)
Variable
Mean age (s.d) 79 (7.8)
Male 44
White 84
Black 10
Other 6
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 (N178)
Process Measure (PM) Mean Score S.D. Range
Single
Discharge Instructions 0.52 0.30 (0 1.0)
LVEF Assessment 0.87 0.12 (0.29 1.0)
ACEI / ARBs at Discharge 0.75 0.16 (0.25 1.0)
Smoking Cessation 0.57 0.35 (0 1.0)
Combined
Composite 0.72 0.15 (0.32 1.0)
Defect-free 0.54 0.22 (0 1.0)
15
Associations between hospital-level process
measures and patient mortality
HR (95 CI) HR (95 CI)
Process Measure (PM) N Unadjusted Adjusted
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
Composite 22,451 1.0 (0.99 1.03) 1.0 (0.98 1.01)
Defect-free 22,451 1.0 (0.99 1.03) 1.0 (0.99 1.01)
16
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

17
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

18
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
19
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