Abstract
Early and rapid risk stratification of patients with acute heart failure (AHF) is crucial for appropriate patient triage and outcome improvements. We aimed to develop an easy-to-use, in-hospital mortality risk prediction tool based on data collected from AHF patients at their initial presentation. Consecutive patients' data pertaining to 2006-2017 were extracted from the West Tokyo Heart Failure (WET-HF) and National Cerebral and Cardiovascular Center Acute Decompensated Heart Failure (NaDEF) registries (n = 4351). Risk model development involved stepwise logistic regression analysis and prospective validation using data pertaining to 2014-2015 in the Registry Focused on Very Early Presentation and Treatment in Emergency Department of Acute Heart Failure Syndrome (REALITY-AHF) (n = 1682). The final model included data describing six in-hospital mortality risk predictors, namely, age, systolic blood pressure, blood urea nitrogen, serum sodium, albumin, and natriuretic peptide (SOB-ASAP score), available at the time of initial triage. The model showed excellent discrimination (c-statistic = 0.82) and good agreement between predicted and observed mortality rates. The model enabled the stratification of the mortality rates across sixths (from 14.5% to <1%). When assigned a point for each associated factor, the integer score's discrimination was similar (c-statistic = 0.82) with good calibration across the patients with various risk profiles. The models' performance was retained in the independent validation dataset. Promptly determining in-hospital mortality risks is achievable in the first few hours of presentation; they correlate strongly with mortality among AHF patients, potentially facilitating clinical decision-making.