Background Heart failure (HF) inpatient mortality prediction models can help clinicians make treatment decisions and researchers conduct observational studies. Published models have not been validated in external populations, however. Methods and Results We compared the performance of seven models that predict inpatient mortality in patients hospitalized with acute decompensated heart failure (ADHF): Four HF-specific mortality prediction models developed from three clinical databases (Acute Decompensated HF National Registry [ADHERE], Enhanced Feedback for Effective Cardiac Treatment [EFFECT] Study, Get with the Guidelines-HF [GWTG-HF] Registry); two administrative HF mortality prediction models (Premier, Premier+); and a model that uses clinical data but is not specific for HF (Laboratory-Based Acute Physiology Score [LAPS2]). Using a multi-hospital electronic health record-derived (EHR) dataset (HealthFacts [Cerner Corp], 2010–2012), we identified patients ≥18 years admitted with HF. Of 13,163 eligible patients, median age was 74 years; half were women; and 27% were black. In-hospital mortality was 4.3%. Model predicted mortality ranges varied: Premier+ (0.8–23.1%), LAPS2 (0.7–19.0%), ADHERE (1.2–17.4%), EFFECT (1.0–12.8%), GWTC-Eapen (1.2–13.8%), and GWTG-Peterson (1.1–12.8%). The LAPS2 and Premier models outperformed the clinical models (c-statistics: LAPS2 0.80 [95% CI: 0.78–0.82], Premier models 0.81 [95% CI: 0.79–0.83]) and 0.76 [95% CI: 0.74–0.78]; clinical models 0.68–0.70). Conclusions Four clinically-derived inpatient HF mortality models exhibited similar performance, with c-statistics near 0.70. Three other models, one developed in EHR data and two developed in administrative data, also were predictive, with c-statistics from 0.76–0.80. Because every model performed acceptably, the decision to use a given model should depend on practical concerns and intended use.

BackgroundComparing 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 ResultsWe 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.ConclusionsCompared 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.

Current treatment of acute decompensated heart failure (ADHF) has not reduced the significant morbidity or mortality associated with this disease, and has promoted drug development aimed at neurohormonal targets. Hypervolemic hyponatremia, which is linked to the nonosmotic release of arginine vasopressin, is associated with a poor prognosis in patients with heart failure (HF). Vasopressin acts on V(2) and V(1a) receptors to cause water retention and vasoconstriction, respectively. Clinical trials have demonstrated that vasopressin receptor antagonists (VRAs) are effective in treating hypervolemic hyponatremia in ADHF without a negative impact on renal function. The small hemodynamic benefit seen with VRA use appeared to result from V(2)-receptor antagonist-induced increase in urine output rather than from a vasodilatory drug effect. VRA use in ADHF trials was associated with minimal symptomatic improvement and no impact on morbidity or mortality. At present, clinical trial evidence does not support the routine use of VRAs in ADHF. Given the favorable renal profile of VRAs, studies on the possible benefit of VRAs in ADHF patients with renal insufficiency and diuretic resistance appear warranted.