• Plos One · Jan 2020

    Comparing a novel machine learning method to the Friedewald formula and Martin-Hopkins equation for low-density lipoprotein estimation.

    • Gurpreet Singh, Yasin Hussain, Zhuoran Xu, Evan Sholle, Kelly Michalak, Kristina Dolan, Benjamin C Lee, Alexander R van Rosendael, Zahra Fatima, Jessica M Peña, Wilson Peter W F PWF Emory Clinical Cardiovascular Research Institute, Emory University, Atlanta, Georgia, United States of America., Antonio M Gotto, Leslee J Shaw, Lohendran Baskaran, and Subhi J Al'Aref.
    • Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America.
    • Plos One. 2020 Jan 1; 15 (9): e0239934.

    BackgroundLow-density lipoprotein cholesterol (LDL-C) is a target for cardiovascular prevention. Contemporary equations for LDL-C estimation have limited accuracy in certain scenarios (high triglycerides [TG], very low LDL-C).ObjectivesWe derived a novel method for LDL-C estimation from the standard lipid profile using a machine learning (ML) approach utilizing random forests (the Weill Cornell model). We compared its correlation to direct LDL-C with the Friedewald and Martin-Hopkins equations for LDL-C estimation.MethodsThe study cohort comprised a convenience sample of standard lipid profile measurements (with the directly measured components of total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], and TG) as well as chemical-based direct LDL-C performed on the same day at the New York-Presbyterian Hospital/Weill Cornell Medicine (NYP-WCM). Subsequently, an ML algorithm was used to construct a model for LDL-C estimation. Results are reported on the held-out test set, with correlation coefficients and absolute residuals used to assess model performance.ResultsBetween 2005 and 2019, there were 17,500 lipid profiles performed on 10,936 unique individuals (4,456 females; 40.8%) aged 1 to 103. Correlation coefficients between estimated and measured LDL-C values were 0.982 for the Weill Cornell model, compared to 0.950 for Friedewald and 0.962 for the Martin-Hopkins method. The Weill Cornell model was consistently better across subgroups stratified by LDL-C and TG values, including TG >500 and LDL-C <70.ConclusionsAn ML model was found to have a better correlation with direct LDL-C than either the Friedewald formula or Martin-Hopkins equation, including in the setting of elevated TG and very low LDL-C.

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