Estimation of low-density lipoprotein cholesterol levels using machine learning

• Published in: International journal of cardiology 2022-04, Vol.352, p.144-149
• Main Authors: Oh, Gyu Chul, Ko, Taehoon, Kim, Jin-Hyu, Lee, Min Ho, Choi, Sae Won, Bae, Ye Seul, Kim, Kyung Hwan, Lee, Hae-Young
• Format: Article
• Language: English
• Subjects: Algorithms; Cholesterol, HDL; Cholesterol, LDL - analysis; Cost-effectiveness; Dyslipidemias - diagnosis; Humans; Hypercholesterolemia; Low-density lipoprotein cholesterol; Machine Learning; Triglycerides
• ISSN: 0167-5273; 1874-1754
• DOI: 10.1016/j.ijcard.2022.01.029

Low-density lipoprotein-cholesterol (LDL-C) is used as a threshold and target for treating dyslipidemia. Although the Friedewald equation is widely used to estimate LDL-C, it has been known to be inaccurate in the case of high triglycerides (TG) or non-fasting states. We aimed to propose a novel method to estimate LDL-C using machine learning. Using a large, single-center electronic health record database, we derived a ML algorithm to estimate LDL-C from standard lipid profiles. From 1,029,572 cases with both standard lipid profiles (total cholesterol, high-density lipoprotein-cholesterol, and TG) and direct LDL-C measurements, 823,657 tests were used to derive LDL-C estimation models. Patient characteristics such as sex, age, height, weight, and other laboratory values were additionally used to create separate data sets and algorithms. Machine learning with gradient boosting (LDL-CX) and neural network (LDL-CN) showed better correlation with directly measured LDL-C, compared with conventional methods (r = 0.9662, 0.9668, 0.9563, 0.9585; for LDL-CX, LDL-CN, Friedewald [LDL-CF], and Martin [LDL-CM] equations, respectively). The overall bias of LDL-CX (−0.27 mg/dL, 95% CI −0.30 to −0.23) and LDL-CN (−0.01 mg/dL, 95% CI -0.04–0.03) were significantly smaller compared with both LDL-CF (−3.80 mg/dL, 95% CI −3.80 to −3.60) or LDL-CM (−2.00 mg/dL, 95% CI −2.00 to −1.94), especially at high TG levels. Machine learning algorithms were superior in estimating LDL-C compared with the conventional Friedewald or the more contemporary Martin equations. Through external validation and modification, machine learning could be incorporated into electronic health records to substitute LDL-C estimation. •ML (machine learning) algorithms derived LDL-C values with higher correlation and better accuracy compared with conventional methods.•ML estimations were especially accurate even with high TG levels, an area where performance of conventional methods was inadequate.•We believe that ML algorithms could be incorporated into electronic health records, and substitute the Friedewald or Martin equations.