51. Derivation and Validation of an In‐Hospital Mortality Prediction Model Suitable for Profiling Hospital Performance in Heart Failure
- Author
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Auras R. Atreya, Mohammad Amin Kashef, Mihaela S. Stefan, Peter K. Lindenauer, Gregory Valania, Tara Lagu, Meng-Shiou Shieh, Penelope S. Pekow, Mara T. Slawsky, and Quinn R. Pack
- Subjects
Male ,medicine.medical_specialty ,Time Factors ,030204 cardiovascular system & hematology ,Hospital performance ,Risk Assessment ,03 medical and health sciences ,0302 clinical medicine ,quality of care ,Data Warehousing ,Risk Factors ,medicine ,Humans ,Profiling (information science) ,Hospital Mortality ,030212 general & internal medicine ,Derivation ,Healthcare Disparities ,Quality of care ,Original Research ,Aged ,Quality Indicators, Health Care ,Heart Failure ,Aged, 80 and over ,In hospital mortality ,business.industry ,Reproducibility of Results ,Middle Aged ,Risk adjustment ,equipment and supplies ,medicine.disease ,mortality ,Outcome and Process Assessment, Health Care ,Heart failure ,Emergency medicine ,Female ,Cardiology and Cardiovascular Medicine ,business - Abstract
Background Comparing heart failure ( HF ) outcomes across hospitals requires adequate risk adjustment. We aimed to develop and validate a model that can be used to compare quality of HF care across hospitals. Methods and Results We included patients with HF aged ≥18 years admitted to one of 433 hospitals that participated in the Premier Inc Data Warehouse. This model (Premier) contained patient demographics, comorbidities, and acute conditions present on admission, derived from administrative and billing records. In a separate data set derived from electronic health records, we validated the Premier model by comparing hospital risk‐standardized mortality rates calculated with the Premier model to those calculated with a validated clinical model containing laboratory data (LAPS [Laboratory‐Based Acute Physiology Score]). Among the 200 832 admissions in the Premier Inc Data Warehouse, inpatient mortality was 4.0%. The model showed acceptable discrimination in the warehouse data (C statistic 0.75; 95% confidence interval, 0.74–0.76). In the validation data set, both the Premier model and the LAPS models showed acceptable discrimination (C statistic: Premier: 0.76 [95% confidence interval, 0.74–0.77]; LAPS: 0.78 [95% confidence interval, 0.76–0.80]). Risk‐standardized mortality rates for both models ranged from 2% to 7%. A linear regression equation describing the association between Premier‐ and LAPS ‐specific mortality rates revealed a regression line with a slope of 0.71 ( SE : 0.07). The correlation coefficient of the standardized mortality rates from the 2 models was 0.82. Conclusions Compared with a validated model derived from clinical data, an HF mortality model derived from administrative data showed highly correlated risk‐standardized mortality rate estimates, suggesting it could be used to identify high‐ and low‐performing hospitals for HF care.
- Published
- 2018