ArticleBiomedicines2022
Accuracy and Clinical Impact of Estimating Low-Density Lipoprotein-Cholesterol at High and Low Levels by Different Equations.
Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- New Methods for Calculating LDL-Cholesterol and Related Biomarkers of Atherosclerotic Cardiovascular Disease Risk.Current atherosclerosis reports · 2026Review
- Cardiovascular disease lipids and lipoproteins biomarker standardization.Critical reviews in clinical laboratory sciences · 2025Review
- Machine learning-based prediction of LDL cholesterol: performance evaluation and validation.PeerJ · 2025Article
- An improved method for estimating low LDL-C based on the enhanced Sampson-NIH equation.Lipids in health and disease · 2024Article
- Assessment of three equations to calculate plasma LDL cholesterol concentration in fasting and non-fasting hypertriglyceridemic patients.Clinical chemistry and laboratory medicine · 2024Article
- Performance of the enhanced Sampson-NIH equation for VLDL-C and LDL-C in a population with familial combined hyperlipidemia.Atherosclerosis · 2023Article
- Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project.Cardiovascular diabetology · 2023Observational
- Approach to the Patient With a Suboptimal Statin Response: Causes and Algorithm for Clinical Management.The Journal of clinical endocrinology and metabolism · 2023Review
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Authors and funding
9 authors.
Funding
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Abstract
New more effective lipid-lowering therapies have made it important to accurately determine Low-density lipoprotein-cholesterol (LDL-C) at both high and low levels. LDL-C was measured by the β-quantification reference method (BQ) (N = 40,346) and compared to Friedewald (F-LDL-C), Martin (M-LDL-C), extended Martin (eM-LDL-C) and Sampson (S-LDL-C) equations by regression analysis, error-grid analysis, and concordance with the BQ method for classification into different LDL-C treatment intervals. For triglycerides (TG) < 175 mg/dL, the four LDL-C equations yielded similarly accurate results, but for TG between 175 and 800 mg/dL, the S-LDL-C equation when compared to the BQ method had a lower mean absolute difference (mg/dL) (MAD = 10.66) than F-LDL-C (MAD = 13.09), M-LDL-C (MAD = 13.16) or eM-LDL-C (MAD = 12.70) equations. By error-grid analysis, the S-LDL-C equation for TG > 400 mg/dL not only had the least analytical errors but also the lowest frequency of clinically relevant errors at the low (<70 mg/dL) and high (>190 mg/dL) LDL-C cut-points (S-LDL-C: 13.5%, F-LDL-C: 23.0%, M-LDL-C: 20.5%) and eM-LDL-C: 20.0%) equations. The S-LDL-C equation also had the best overall concordance to the BQ reference method for classifying patients into different LDL-C treatment intervals. The S-LDL-C equation is both more analytically accurate than alternative equations and results in less clinically relevant errors at high and low LDL-C levels.
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