Derivation and validation of a machine learning-based risk prediction model for in-hospital mortality in patients with acute heart failure

Abstract

Abstract Background Although risk stratification is important in patients with acute heart failure (AHF) to predict patient prognosis, pre-existing risk models have not often been used due to its complexity. Recently, machine learning methods have been presented as an alternative approach to analyzing the predictive probability of large clinical datasets. Purpose The aim of this study is to develop a user-friendly risk score developed by one of machine learning methods and compare the performance of the new risk score to the existing conventional risk models. Methods A machine-learning-based risk model was developed using least absolute shrinkage and selection operator (LASSO) regression by identifying predictors of in-hospital mortality in the derivation cohort (REALITY-AHF) and externally validating and comparing its performance with two pre-existing risk models: the Get With The Guidelines risk score incorporating brain natriuretic peptide and hypochloremia (GWTG-BNP-Cl-RS) and the acute decompensated heart failure national registry (ADHERE) risk model. Results In-hospital deaths in the derivation and validation (NARA-HF) cohorts were 76 (5.1%) and 61 (4.9%), respectively. The risk score comprised four variables (systolic blood pressure, blood urea nitrogen, serum chloride, and C-reactive protein) and was developed according to the results of the LASSO regression weighting the coefficient for selected variables using a logistic regression model (4V-RS). Even though 4V-RS comprised fewer variables, In the validation cohort, it showed a higher area under the receiver operating characteristic curve (AUC) than the ADHERE risk model (AUC, 0.783 vs. 0.740; P=0.059) and a significant improvement in net reclassification (0.359; 95% CI, 0.10–0.67; p=0.006). 4V-RS performed similarly to GWTG-BNP-Cl-RS in terms of discrimination (AUC, 0.783 vs. 0.759; p=0.426) and net reclassification (0.176; 95% CI, −0.08–0.43; p=0.178). Conclusions The 4V-RS model comprising only four readily available data points at the time of admission performed similarly to the more complex pre-existing risk model in patients with AHF. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Cardiovascular Research Fund