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

Abstract

BACKGROUND:

Risk stratification is important in patients with acute heart failure (AHF), and a simple risk score that accurately predicts mortality is needed. The aim of this study is to develop a user-friendly risk-prediction model using a machine-learning method.

METHODS:

A machine-learning-based risk model using least absolute shrinkage and selection operator (LASSO) regression was developed by identifying predictors of in-hospital mortality in the derivation cohort (REALITY-AHF), and its performance was externally validated in the validation cohort (NARA-HF) and compared 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 risk (ADHERE).

RESULTS:

In-hospital deaths in the derivation and validation 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 (4 V-RS). Even though 4 V-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). 4 V-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 4 V-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.

• Authors: Kayo Misumi, Yuya Matsue, Kazutaka Nogi, Yudai Fujimoto, Nobuyuki Kagiyama, Takatoshi Kasai, Takeshi Kitai, Shogo Oishi, Eiichi Akiyama, Satoshi Suzuki, Masayoshi Yamamoto, Keisuke Kida, Takahiro Okumura, Maki Nogi, Satomi Ishihara, Tomoya Ueda, Rika Kawakami, Yoshihiko Saito, Tohru Minamino
• Journal: Journal of cardiology (J Cardiol) Vol. 81 Issue 6 Pg. 531-536 (06 2023) ISSN: 1876-4738 [Electronic] Netherlands
• PMID: 36858175 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2023 Elsevier Ltd. All rights reserved.

Chemical References

• Natriuretic Peptide, Brain

Topics

• Humans
• Risk Assessment (methods)
• Heart Failure
• Risk Factors
• Hospitalization
• Machine Learning
• Natriuretic Peptide, Brain