Machine learning-based prediction of elevated N terminal pro brain natriuretic peptide among US general population.

Abstract

Natriuretic peptide-based pre-heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre-heart failure, has not been well established. This study evaluated the performance of machine learning prediction models for predicting elevated N terminal pro brain natriuretic peptide (NT-proBNP) levels in the US general population. Individuals aged 20-79years without cardiovascular disease from the nationally representative National Health and Nutrition Examination Survey 1999-2004 were included. Six prediction models (two conventional regression models and four machine learning models) were trained with the 1999-2002 cohort to predict elevated NT-proBNP levels (>125pg/mL) using demographic, lifestyle, and commonly measured biochemical data. The model performance was tested using the 2003-2004 cohort. Of the 10237 individuals, 1510 (14.8%) had NT-proBNP levels >125pg/mL. The highest area under the receiver operating characteristic curve (AUC) was observed in SuperLearner (AUC [95% CI]=0.862 [0.847-0.878], P<0.001 compared with the logistic regression model). The logistic regression model with splines showed a comparable performance (AUC [95% CI]=0.857 [0.841-0.874], P=0.08). Age, albumin level, haemoglobin level, sex, estimated glomerular filtration rate, and systolic blood pressure were the most important predictors. We found a similar prediction performance even after excluding socio-economic information (marital status, family income, and education status) from the prediction models. When we used different thresholds for elevated NT-proBNP, the AUC (95% CI) in the SuperLearner models 0.846 (0.830-0.861) for NT-proBNP>100pg/mL and 0.866 (0.849-0.884) for NT-proBNP>150pg/mL. Using nationally representative data from the United States, both logistic regression and machine learning models well predicted elevated NT-proBNP. The predictive performance remained consistent even when the models incorporated only commonly available variables in daily clinical practice. Prediction models using regularly measured information would serve as a potentially useful tools for clinicians to effectively identify targets of natriuretic-peptide screening.