Comparing a novel machine learning method to the Friedewald formula and Martin-Hopkins equation for low-density lipoprotein estimation
PLoS ONE, vol. 15, pp. e0239934
Abstract
BACKGROUND: Low-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). OBJECTIVES: We 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. METHODS: The 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. RESULTS: Between 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. CONCLUSIONS: An 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.
Authors 13
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Affiliation as printed
Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
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Affiliation as printed
Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, United States of America
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Affiliation as printed
Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
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Affiliation as printed
Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, United States of America
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Affiliation as printed
Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
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Affiliation as printed
Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
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Leiden University Medical Center
Affiliation as printed
Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
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Affiliation as printed
Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
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Affiliation as printed
Emory Clinical Cardiovascular Research Institute, Emory University, Atlanta, Georgia, United States of America
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Affiliation as printed
Weill Cornell Medicine, New York, New York, United States of America
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Affiliation as printed
Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
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National Heart Centre Singapore · Weill Cornell Medicine
Affiliation as printed
Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
Department of Cardiovascular Medicine, National Heart Centre, Singapore, Singapore
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Subhi Jamal Alaref corresponding
Affiliation as printed
Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York, United States of America
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