Prediction of 90 day mortality in elderly patients with acute HF from e-health records using artificial intelligence

Abstract

Aims

Mortality risk after hospitalization for heart failure (HF) is high, especially in the first 90 days. This study aimed to construct a model automatically predicting 90 day post-discharge mortality using electronic health record (EHR) data 48 h after admission and artificial intelligence.

Methods

All HF-related admissions from 2015 to 2020 in a single hospital were included in the model training. Comprehensive EHR data were collected 48 h after admission. Natural language processing was applied to textual information. Deaths were identified from the French national database. After variable selection with least absolute shrinkage and selection operator, a logistic regression model was trained. Model performance [area under the receiver operating characteristic curve (AUC)] was tested in two independent cohorts of patients admitted to two hospitals between March and December 2021.

Results

The derivation cohort included 2257 admissions (248 deaths after hospitalization). The evaluation cohorts included 348 and 388 admissions (34 and 38 deaths, respectively). Forty-two independent variables were selected. The model performed well in the derivation cohort [AUC: 0.817; 95% confidence interval (CI) (0.789–0.845)] and in both evaluation cohorts [AUC: 0.750; 95% CI (0.672–0.829) and AUC: 0.723; 95% CI (0.644–0.803]), with better performance than previous models in the literature. Calibration was good: ‘low-risk’ (predicted mortality ≤8%), ‘intermediate-risk’ (8–12.5%) and ‘high-risk’ (>12.5%) patients had an observed 90 day mortality rate of 3.8%, 8.4% and 19.4%, respectively.

Conclusions

The study proposed a robust model for the automatic prediction of 90 day mortality risk 48 h after hospitalization for decompensated HF. This could be used to identify high-risk patients for intensification of therapeutic management.