An Administrative Claims Model Suitable for Profiling Hospital Performance Based on 30-Day Mortality Rates Among Patients With Heart Failure

Abstract

Background-A model using administrative claims data that is suitable for profiling hospital performance for heart failure would be useful in quality assessment and improvement efforts. Methods and Results-We developed a hierarchical regression model using Medicare claims data from 1998 that produces hospital risk-standardized 30-day mortality rates. We validated the model by comparing state-level standardized estimates with state-level standardized estimates calculated from a medical record model. To determine the stability of the model over time, we used annual Medicare cohorts discharged in 1999 -2001. The final model included 24 variables and had an area under the receiver operating characteristic curve of 0.70. In the derivation set from 1998, the 25th and 75th percentiles of the risk-standardized mortality rates across hospitals were 11.6% and 12.8%, respectively. The 95th percentile was 14.2%, and the 5th percentile was 10.5%. In the validation samples, the 5th and 95th percentiles of risk-standardized mortality rates across states were 9.9% and 13.9%, respectively. Correlation between risk-standardized state mortality rates from claims data and rates derived from medical record data was 0.95 (SEϭ0.015). The slope of the weighted regression line from the 2 data sources was 0.76 (SEϭ0.04) with intercept of 0.03 (SEϭ0.004). The median difference between the claims-based state risk-standardized estimates and the chart-based rates was Ͻ0.001 (25th percentileϭϪ0.003; 75th percentileϭ0.002).

Sex and Race Differences in the Utilization and Outcomes of Coronary Artery Bypass Grafting Among Medicare Beneficiaries, 1999–2014

“…We identified comorbidities included in the Centers for Medicare & Medicaid Services 30‐day mortality and readmission measures for acute myocardial infarction and heart failure,17, 18 including cardiovascular risk factors (hypertension, diabetes mellitus, atherosclerotic disease, unstable angina, previous myocardial infarction, previous heart failure, peripheral vascular disease, stroke, and other cerebrovascular diseases), geriatric conditions (dementia, functional disability, and malnutrition), and other conditions (renal failure, chronic obstructive pulmonary disease, pneumonia, respiratory failure, liver disease, cancer, major psychiatric disorders, depression, and trauma). We determined comorbidities from a combination of secondary diagnosis codes for the index hospitalization and principal and secondary diagnosis codes for all hospitalizations over 12 months preceding the index hospitalization.…”

Current trends in heart failure readmission rates: analysis of medicare data

“…This approach is consistent with methods used in the analysis of large public databases. 11 Readmissions in the 2 quarters after the initial HF hospitalization were identified. Again, follow-up times between initial hospitalization and readmissions could not be precisely determined.…”

Derivation and Validation of an In‐Hospital Mortality Prediction Model Suitable for Profiling Hospital Performance in Heart Failure

“…Krumholz et al developed and validated an administrative claims–based risk‐adjustment model for HF with the purpose of characterizing hospital quality. This model, which is used by the Centers for Medicare and Medicaid Services (CMS) to compare mortality rates across hospitals for public reporting purposes, produces results that are very similar to medical records–based models 10, 11, 19, 20. There are several key differences between our model and the CMS model.…”

“…To compare RSMRs derived from the Premier and LAPS models, we first examined the distribution of RSMRs using histograms. We then used linear regression to model the association between the 2 rates, weighting each hospital by number of observations 11. An intercept close to 0 and slope close to 1 would indicate similar RSMRs by the 2 models.…”