In Journal of cardiology
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.
Misumi Kayo, Matsue Yuya, Nogi Kazutaka, Fujimoto Yudai, Kagiyama Nobuyuki, Kasai Takatoshi, Kitai Takeshi, Oishi Shogo, Akiyama Eiichi, Suzuki Satoshi, Yamamoto Masayoshi, Kida Keisuke, Okumura Takahiro, Nogi Maki, Ishihara Satomi, Ueda Tomoya, Kawakami Rika, Saito Yoshihiko, Minamino Tohru
2023-Feb-27
Acute heart failure, Chloride, Prediction, Risk model
