Machine Learning Estimation of Low-Density Lipoprotein Cholesterol in Women With and Without HIV.

Abstract

Low-density lipoprotein cholesterol (LDL-C) is typically estimated from total cholesterol, high-density lipoprotein cholesterol, and triglycerides. The Friedewald, Martin-Hopkins, and National Institutes of Health equations are widely used but may estimate LDL-C inaccurately in certain patient populations, such as those with HIV. We sought to investigate the utility of machine learning for LDL-C estimation in a large cohort of women with and without HIV. We identified 7397 direct LDL-C measurements (5219 from HIV-infected individuals, 2127 from uninfected controls, and 51 from seroconvertors) from 2414 participants (age 39.4 ± 9.3 years) in the Women's Interagency HIV Study and estimated LDL-C using the Friedewald, Martin-Hopkins, and National Institutes of Health equations. We also optimized 5 machine learning methods [linear regression, random forest, gradient boosting, support vector machine (SVM), and neural network] using 80% of the data (training set). We compared the performance of each method using root mean square error, mean absolute error, and coefficient of determination (R2) in the holdout (20%) set. SVM outperformed all 3 existing equations and other machine learning methods, achieving the lowest root mean square error and mean absolute error, and the highest R2 (11.79 and 7.98 mg/dL, 0.87, respectively, compared with those obtained using the Friedewald equation: 12.45 and 9.14 mg/dL, 0.87). SVM performance remained superior in subgroups with and without HIV, with nonfasting measurements, in LDL <70 mg/dL and triglycerides > 400 mg/dL. In this proof-of-concept study, SVM is a robust method that predicts directly measured LDL-C more accurately than clinically used methods in women with and without HIV. Further studies should explore the utility in broader populations.