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

PLoS One. 2020 Sep 30;15(9):e0239934. doi: 10.1371/journal.pone.0239934. eCollection 2020.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Cholesterol, HDL / blood
  • Cholesterol, LDL / blood*
  • Data Interpretation, Statistical
  • Female
  • Humans
  • Hyperlipidemias / blood
  • Hyperlipidemias / pathology
  • Machine Learning*
  • Male
  • Middle Aged
  • Triglycerides / blood

Substances

  • Cholesterol, HDL
  • Cholesterol, LDL
  • Triglycerides

Grant support

The research reported in this manuscript was supported by the Dalio Institute of Cardiovascular Imaging (New York, NY, USA). No funding was provided for this study.The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.