Representativeness of a national heart failure quality-of-care registry: comparison of OPTIMIZE-HF and non-OPTIMIZE-HF Medicare patients.
BACKGROUND
Participation in clinical registries is nonrandom, so participants may differ in important ways from nonparticipants. The extent to which findings from clinical registries can be generalized to broader populations is unclear.


RESULTS
We linked data from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure (OPTIMIZE-HF) registry with 100% inpatient Medicare fee-for-service claims to identify matched and unmatched patients with heart failure. We evaluated differences in baseline characteristics and mortality, all-cause readmission, and cardiovascular readmission rates. We used Cox proportional hazards models to examine relationships between registry enrollment and outcomes, controlling for baseline characteristics. There were 25,245 OPTIMIZE-HF patients in the Medicare claims data and 929,161 Medicare beneficiaries with heart failure who were not enrolled in OPTIMIZE-HF. Although hospital characteristics differed, patient demographic characteristics and comorbid conditions were similar. In-hospital mortality for OPTIMIZE-HF and non-OPTIMIZE-HF patients was not significantly different (4.7% versus 4.5%; P=0.37); however, OPTIMIZE-HF patients had slightly higher 30-day (11.9% versus 11.2%; P<0.001) and 1-year unadjusted mortality (37.2% versus 35.7%; P<0.001). Controlling for other variables, OPTIMIZE-HF patients were similar to non-OPTIMIZE-HF patients for the hazard of mortality (hazard ratio, 1.02; 95% confidence interval, 0.98 to 1.06). There were small but significant decreases in all-cause (hazard ratio, 0.94; 95% CI, 0.92 to 0.97) and cardiovascular readmission (hazard ratio, 0.94; 95% CI, 0.91 to 0.98).


CONCLUSIONS
Characteristics and outcomes of Medicare beneficiaries enrolled in OPTIMIZE-HF are similar to the broader Medicare population with heart failure, suggesting that findings from this clinical registry may be generalized.
Curtis
Lesley H
LH
Center for Clinical and Genetic Economics, Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.
Greiner
Melissa A
MA
Hammill
Bradley G
BG
DiMartino
Lisa D
LD
Shea
Alisa M
AM
Hernandez
Adrian F
AF
Fonarow
Gregg C
GC
D000368:Aged; D000369:Aged, 80 and over; D017507:Comoros; D005260:Female; D006333:Heart Failure / Q000401:mortality*; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data*; D017063:Outcome Assessment, Health Care / Q000379:methods* / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D011787:Quality of Health Care; D012042:Registries / Q000592:standards* / Q000706:statistics & numerical data*; D014481:United States / Q000453:epidemiology