A clinical score to predict mortality in patients after acute heart failure from Japanese registry

Abstract Aims Clinical scores that consider physical and social factors to predict long‐term observations in patients after acute heart failure are limited. This study aimed to develop and validate a prediction model for patients with acute heart failure at the time of discharge. Methods and results This study was retrospective analysis of the Kitakawachi Clinical Background and Outcome of Heart Failure Registry database. The registry is a prospective, multicentre cohort of patients with acute heart failure between April 2015 and August 2017. The primary outcome to be predicted was the incidence of all‐cause mortality during the 3 years of follow‐up period. The development cohort derived from April 2015 to July 2016 was used to build the prediction model, and the test cohort from August 2016 to August 2017 was used to evaluate the prediction model. The following potential predictors were selected by the least absolute shrinkage and selection operator method: age, sex, body mass index, activities of daily living at discharge, social background, comorbidities, biomarkers, and echocardiographic findings; a risk scoring system was developed using a logistic model to predict the outcome using a simple integer based on each variable's β coefficient. Out of 1253 patients registered, 1117 were included in the analysis and divided into the development (n = 679) and test (n = 438) cohorts. The outcomes were 246 (36.2%) in the development cohort and 143 (32.6%) in the test cohort. Eleven variables including physical and social factors were set into the logistic regression model, and the risk scoring system was created. The patients were divided into three groups: low risk (score 0–5), moderate risk (score 6–11), and high risk (score ≥12). The observed and predicted mortality rates were described by the Kaplan–Meier curve divided by risk group and independently increased (P