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

Singh, Gurpreet; Hussain, Yasin; Xu, Zhuoran; Sholle, Evan; Michalak, Kelly; Dolan, Kristina; Lee, Benjamin C.; Rosendael, Alexander R. van; Fatima, Zahra; Peña, Jessica M.; Wilson, Peter W.F.; Gotto, Antonio M.; Shaw, Leslee J.; Baskaran, Lohendran; Al’Aref, Subhi J.

doi:10.1371/journal.pone.0239934

Cited by 31 publications

(18 citation statements)

References 49 publications

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“…Of the 17 custom RDRs live as of July 2021, academic output includes but is not limited to that from Cardiac Imaging, 30 , 31 Digestive Care, 32 Mental Health, 33 , 34 Myeloproliferative Neoplasms, 35 , 36 Pulmonary and Critical Care, 37 , 38 and Stroke. 39 Largely driven by investigators with grant funding, RDR projects have generated data marts to address specific clinical research questions (eg, predictors of outcomes in hospitalized cirrhotic patients) while also yielding generalizable resources for the institution, such as an i2b2 eye exam ontology from Ophthalmology and surgical pathology report NLP from Urology.…”

Section: Resultsmentioning

confidence: 99%

An architecture for research computing in health to support clinical and translational investigators with electronic patient data

Campion

Sholle

Pathak

et al. 2021

Journal of the American Medical Informatics Association

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Objective Obtaining electronic patient data, especially from electronic health record (EHR) systems, for clinical and translational research is difficult. Multiple research informatics systems exist but navigating the numerous applications can be challenging for scientists. This article describes Architecture for Research Computing in Health (ARCH), our institution’s approach for matching investigators with tools and services for obtaining electronic patient data. Materials and Methods Supporting the spectrum of studies from populations to individuals, ARCH delivers a breadth of scientific functions—including but not limited to cohort discovery, electronic data capture, and multi-institutional data sharing—that manifest in specific systems—such as i2b2, REDCap, and PCORnet. Through a consultative process, ARCH staff align investigators with tools with respect to study design, data sources, and cost. Although most ARCH services are available free of charge, advanced engagements require fee for service. Results Since 2016 at Weill Cornell Medicine, ARCH has supported over 1200 unique investigators through more than 4177 consultations. Notably, ARCH infrastructure enabled critical coronavirus disease 2019 response activities for research and patient care. Discussion ARCH has provided a technical, regulatory, financial, and educational framework to support the biomedical research enterprise with electronic patient data. Collaboration among informaticians, biostatisticians, and clinicians has been critical to rapid generation and analysis of EHR data. Conclusion A suite of tools and services, ARCH helps match investigators with informatics systems to reduce time to science. ARCH has facilitated research at Weill Cornell Medicine and may provide a model for informatics and research leaders to support scientists elsewhere.

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Section: Resultsmentioning

confidence: 99%

An architecture for research computing in health to support clinical and translational investigators with electronic patient data

Campion

Sholle

Pathak

et al. 2021

Journal of the American Medical Informatics Association

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show abstract

“…Machine learning provides an improved performance of modeling and outcome prediction in cardiovascular medicine. Several studies have developed machine learning methods to better estimate LDL-C levels ( 14 – 16 ). Lee et al ( 14 ) developed a DNN model for estimating LDL-C including 180 perceptrons, which was motivated by the novel method from the standard lipid profile (TC, HDL-C, and TG).…”

Section: Discussionmentioning

confidence: 99%

“…Lee et al ( 14 ) developed a DNN model for estimating LDL-C including 180 perceptrons, which was motivated by the novel method from the standard lipid profile (TC, HDL-C, and TG). Singh et al ( 16 ) proposed a machine learning method utilizing random forests for LDL-C estimation using a direct LDL-C as a reference value. Tsigalou et al ( 15 ) suggested a machine learning model to estimate LDL-C using shallow and deep machine learning methods.…”

Section: Discussionmentioning

confidence: 99%

Comparison of a Machine Learning Method and Various Equations for Estimating Low-Density Lipoprotein Cholesterol in Korean Populations

Kwon

Lee

Baik

et al. 2022

Front. Cardiovasc. Med.

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BackgroundLDL-C is the primary target of lipid-lowering therapy and used to classify patients by cardiovascular disease risk. We aimed to develop a deep neural network (DNN) model to estimate LDL-C levels and compare its performance with that of previous LDL-C estimation equations using two large independent datasets of Korean populations.MethodsThe final analysis included participants from two independent population-based cohorts: 129,930 from the Gangnam Severance Health Check-up (GSHC) and 46,470 participants from the Korean Initiatives on Coronary Artery Calcification registry (KOICA). The DNN model was derived from the GSHC dataset and validated in the KOICA dataset. We measured our proposed model's performance according to bias, root mean-square error (RMSE), proportion (P)10–P20, and concordance. P was defined as the percentage of patients whose LDL was within ±10–20% of the measured LDL. We further determined the RMSE scores of each LDL equation according to Pooled cohort equation intervals.ResultsOur DNN method has lower bias and root mean-square error than Friedewald's, Martin's, and NIH equations, showing a high agreement with LDL-C measured by homogenous assay. The DNN method offers more precise LDL estimation in all pooled cohort equation strata.ConclusionThis method may be particularly helpful for managing a patient's cholesterol levels based on their atherosclerotic cardiovascular disease risk.

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“…But our study demonstrated lesser correlation coefficient (0.90) in the group of patients with triglyceride >400 mg/dL for the random forests model. 25…”

Section: Discussionmentioning

confidence: 99%

“…24 Random forests model was used in a study to predict LDL-C, and the performance was found to better than Friedewald and Martin formulas. 25…”

Section: Introductionmentioning

confidence: 99%

Machine learning predictive models of LDL-C in the population of eastern India and its comparison with directly measured and calculated LDL-C

et al. 2021

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Background LDL-C is a strong risk factor for cardiovascular disorders. The formulas used to calculate LDL-C showed varying performance in different populations. Machine learning models can study complex interactions between the variables and can be used to predict outcomes more accurately. The current study evaluated the predictive performance of three machine learning models—random forests, XGBoost, and support vector Rregression (SVR) to predict LDL-C from total cholesterol, triglyceride, and HDL-C in comparison to linear regression model and some existing formulas for LDL-C calculation, in eastern Indian population. Methods The lipid profiles performed in the clinical biochemistry laboratory of AIIMS Bhubaneswar during 2019–2021, a total of 13,391 samples were included in the study. Laboratory results were collected from the laboratory database. 70% of data were classified as train set and used to develop the three machine learning models and linear regression formula. These models were tested in the rest 30% of the data (test set) for validation. Performance of models was evaluated in comparison to best six existing LDL-C calculating formulas. Results LDL-C predicted by XGBoost and random forests models showed a strong correlation with directly estimated LDL-C (r = 0.98). Two machine learning models performed superior to the six existing and commonly used LDL-C calculating formulas like Friedewald in the study population. When compared in different triglycerides strata also, these two models outperformed the other methods used. Conclusion Machine learning models like XGBoost and random forests can be used to predict LDL-C with more accuracy comparing to conventional linear regression LDL-C formulas.

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

Cited by 31 publications

References 49 publications

An architecture for research computing in health to support clinical and translational investigators with electronic patient data

An architecture for research computing in health to support clinical and translational investigators with electronic patient data

Comparison of a Machine Learning Method and Various Equations for Estimating Low-Density Lipoprotein Cholesterol in Korean Populations

Machine learning predictive models of LDL-C in the population of eastern India and its comparison with directly measured and calculated LDL-C

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