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

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

Gurpreet Singh  1 , Yasin Hussain  2 , Zhuoran Xu  1 , Evan Sholle  3 , Kelly Michalak  1 , Kristina Dolan  1 , Benjamin C Lee  1 , Alexander R van Rosendael  4 , Zahra Fatima  1 , Jessica M Peña  1 , Peter W F Wilson  5 , Antonio M Gotto Jr  6 , Leslee J Shaw  1 , Lohendran Baskaran  1   7 , Subhi J Al'Aref  1
Affiliations

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

Gurpreet Singh et al. PLoS One. .

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.

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Conflict of interest statement

Gurpreet Singh became affiliated with GlaxoSmithKline after working on this project. Benjamin C. Lee receives consulting fees from Cleerly Inc, but has not receive that consulting fee since 2019. Leslee J. Shaw reports having an equity interest in Cleerly Inc. There are no patents, products in development or marketed products associated with this research to declare This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1
Scatter plot showing the correlation between estimated and directly measured low-density lipoprotein cholesterol (LDL-C) for the (a) overall cohort, and (b) for each of the LDL estimation models.
Fig 2
Fig 2
(A) Scatter plot showing the correlation between the ground truth LDL-C value (direct LDL-C) and estimated LDL-C value, across LDL-C subgroups, using the Weill Cornell model, Friedewald formula and Martin-Hopkins equation. (B) Correlation coefficients for each model for LDL-C subgroups.
Fig 3
Fig 3
Scatter plot showing the correlation between the ground truth LDL-C value (direct LDL) and estimated LDL-C value, across triglyceride subgroups, using the Cornell model, Friedewald formula and Martin-Hopkins method. (B) Correlation coefficients for each model for TGL subgroups. Abbreviations: TGL: triglycerides.
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Publication types

Grants and funding

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.