Circulation Cardiovascular Quality and Outcomes · 2008 · 507 citations · 16 references
Health ProfessionsHealth AdministrationHealth Services AdministrationHealthcare AdministrationHealthcare Quality ImprovementHospital AdministrationClaims ModelingHealthcare Performance MeasurementHeart FailureQuality Of CareReadmission AnalysisReadmission Outcomes30-Day ReadmissionAdministrative Claims DataHospital Performance MetricsHospital Readmission MeasurementHospital Readmission RatesReadmission Rate AnalysisReadmission RatesThirty-day Readmission
Readmission soon after discharge is an expensive and often preventable event for patients with heart failure. The authors present a National Quality Forum–approved model for public reporting of hospital‑level 30‑day all‑cause readmission rates for heart‑failure patients by CMS. They developed and validated a hierarchical logistic regression model using 2004 Medicare claims data and medical record data to calculate risk‑standardized 30‑day readmission rates for heart‑failure admissions. The model, comprising 37 variables, achieved a c‑statistic of 0.60, discriminated 15–37% across deciles, produced hospital risk‑standardized rates of 23.1–24.0% (25th–75th percentile), and showed that hospitals 1 SD above average had 1.30 times the odds of readmission, with state‑level rates correlating 0.97 with a medical‑record model, indicating the claims‑based estimates can serve as surrogates.
Background— Readmission soon after hospital discharge is an expensive and often preventable event for patients with heart failure. We present a model approved by the National Quality Forum for the purpose of public reporting of hospital-level readmission rates by the Centers for Medicare & Medicaid Services. Methods and Results— We developed a hierarchical logistic regression model to calculate hospital risk-standardized 30-day all-cause readmission rates for patients hospitalized with heart failure. The model was derived with the use of Medicare claims data for a 2004 cohort and validated with the use of claims and medical record data. The unadjusted readmission rate was 23.6%. The final model included 37 variables, had discrimination ranging from 15% observed 30-day readmission rate in the lowest predictive decile to 37% in the upper decile, and had a c statistic of 0.60. The 25th and 75th percentiles of the risk-standardized readmission rates across 4669 hospitals were 23.1% and 24.0%, with 5th and 95th percentiles of 22.2% and 25.1%, respectively. The odds of all-cause readmission for a hospital 1 standard deviation above average was 1.30 times that of a hospital 1 standard deviation below average. State-level adjusted readmission rates developed with the use of the claims model are similar to rates produced for the same cohort with the use of a medical record model (correlation, 0.97; median difference, 0.06 percentage points). Conclusions— This claims-based model of hospital risk-standardized readmission rates for heart failure patients produces estimates that may serve as surrogates for those derived from a medical record model.
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Discharge Education Improves Clinical Outcomes in Patients With Chronic Heart Failure
Todd M. Koelling, Monica L. Johnson, Robert Cody et al. · Circulation · 2005 · 571 citations · Full text