Machine Learning Estimation of Low-Density Lipoprotein Cholesterol in Women With and Without HIV.
Journal
Journal of acquired immune deficiency syndromes (1999)
ISSN: 1944-7884
Titre abrégé: J Acquir Immune Defic Syndr
Pays: United States
ID NLM: 100892005
Informations de publication
Date de publication:
01 03 2022
01 03 2022
Historique:
received:
30
04
2021
accepted:
19
10
2021
pubmed:
24
11
2021
medline:
9
3
2022
entrez:
23
11
2021
Statut:
ppublish
Résumé
Low-density lipoprotein cholesterol (LDL-C) is typically estimated from total cholesterol, high-density lipoprotein cholesterol, and triglycerides. The Friedewald, Martin-Hopkins, and National Institutes of Health equations are widely used but may estimate LDL-C inaccurately in certain patient populations, such as those with HIV. We sought to investigate the utility of machine learning for LDL-C estimation in a large cohort of women with and without HIV. We identified 7397 direct LDL-C measurements (5219 from HIV-infected individuals, 2127 from uninfected controls, and 51 from seroconvertors) from 2414 participants (age 39.4 ± 9.3 years) in the Women's Interagency HIV Study and estimated LDL-C using the Friedewald, Martin-Hopkins, and National Institutes of Health equations. We also optimized 5 machine learning methods [linear regression, random forest, gradient boosting, support vector machine (SVM), and neural network] using 80% of the data (training set). We compared the performance of each method using root mean square error, mean absolute error, and coefficient of determination (R2) in the holdout (20%) set. SVM outperformed all 3 existing equations and other machine learning methods, achieving the lowest root mean square error and mean absolute error, and the highest R2 (11.79 and 7.98 mg/dL, 0.87, respectively, compared with those obtained using the Friedewald equation: 12.45 and 9.14 mg/dL, 0.87). SVM performance remained superior in subgroups with and without HIV, with nonfasting measurements, in LDL <70 mg/dL and triglycerides > 400 mg/dL. In this proof-of-concept study, SVM is a robust method that predicts directly measured LDL-C more accurately than clinically used methods in women with and without HIV. Further studies should explore the utility in broader populations.
Identifiants
pubmed: 34813572
doi: 10.1097/QAI.0000000000002869
pii: 00126334-202203010-00011
doi:
Substances chimiques
Cholesterol, HDL
0
Cholesterol, LDL
0
Triglycerides
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
318-323Informations de copyright
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors have no conflicts of interest to disclose.
Références
Ference BA, Ginsberg HN, Graham I, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2017;38:2459–2472.
Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18:499–502.
Martin SS, Blaha MJ, Elshazly MB, et al. Comparison of a novel method vs the Friedewald equation for estimating low-density lipoprotein cholesterol levels from the standard lipid profile. JAMA. 2013;310:2061–2068.
Sampson M, Ling C, Sun Q, et al. A new equation for calculation of low-density lipoprotein cholesterol in patients with normolipidemia and/or hypertriglyceridemia. JAMA Cardiol. 2020;5:540–548.
Schneider EE, Sarkar S, Margolick JB, et al. Comparison of calculated low-density lipoprotein cholesterol (LDL-C) values in HIV-infected and HIV-uninfected men using the traditional Friedewald and the novel Martin–Hopkins LDL-C equations. AIDS Res Hum Retrovir. 2020;36:176–179.
Rana MN, Kim CH, Sullivan CE, et al. Performance of methods to estimate low-density lipoprotein cholesterol in women with and without HIV infection. J Acquir Immune Defic Syndr. 2021;87:750–754
Osegbe I, Ugonabo M, Chukwuka C, et al. Comparison of calculated versus directly-measured low-density lipoprotein-cholesterol: an evaluation of ten formulas for an HIV-positive population in Sub-Saharan Africa. J Lab Phys. 2017;9:111.
Rana MN, Kim CH, Sullivan C, et al. Performance of methods to estimate low-density lipoprotein cholesterol in women with and without HIV infection. JAIDS. 2021;87:750–754.
Mosepele M, Molefe-Baikai OJ, Grinspoon SK, et al. Benefits and risks of statin therapy in the HIV-infected population. Curr Infect Dis Rep. 2018;20:20.
Funderburg NT, Mehta NN. Lipid abnormalities and inflammation in HIV inflection. Curr HIV/AIDS Rep. 2016;13:218–225.
Grundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart association Task Force on clinical practice guidelines. J Am Coll Cardiol. 2019;73:e285–e350.
Périard D, Telenti A, Sudre P, et al. Atherogenic dyslipidemia in HIV-infected individuals treated with protease inhibitors. Circulation. 1999;100:700–705.
Feinstein MJ, Hsue PY, Benjamin LA, et al. Characteristics, prevention, and management of cardiovascular disease in people living with HIV: a scientific statement from the American Heart Association. Circulation. 2019;140:e98–e124.
Al-Kindi SG, Zidar DA, McComsey GA, et al. Gender differences in statin prescription rate among patients living with HIV and hepatitis C virus. Clin Infect Dis. 2016;63:993–994.
Bacon MC, Von Wyl V, Alden C, et al. The Women's Interagency HIV Study: an observational cohort brings clinical sciences to the bench. Clin Diagn Lab Immunol. 2005;12:1013–1019.
Barkan SE, Melnick SL, Preston-Martin S, et al. The Women's interagency HIV study. WIHS Collaborative Study Group. Epidemiology. 1998;9:117–125.
Study. WsIH. Section 10: Laboratory Specimen Collection and Processing Procedures. WIHS Manual of Operations; 2017:2–4
Diagnostics Q. LDL Cholesterol. Quest Diagnostics. 2020. Available at: https://testdirectory.questdiagnostics.com/test/test-guides/TS_LDL_Cholesterol/ldl-cholesterol . Accessed November 24, 2020.
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available at: http://www.R-project.org/ . Accessed November 24, 2020
Caret Package: Available at: https://cran.r-project.org/web/packages/caret/index.html . Accessed November 24, 2020
Keras Package: Available at: https://cran.r-project.org/web/packages/keras/index.html . Accessed November 24, 2020
Ggplot2 Package: Available at: https://cran.r-project.org/web/packages/ggplot2/index.html . Accessed November 24, 2020
Singh G, Hussain Y, Xu Z, et al. Comparing a novel machine learning method to the Friedewald formula and Martin-Hopkins equation for low-density lipoprotein estimation. PLoS One. 2020;15:e0239934.