Comparison of LDL-C calculation by Martin, Sampson and old Friedewald methods in real data and synthetic data set

Gaško, Rudolf

doi:10.36290/vnl.2021.028

Cited by 4 publications

(3 citation statements)

References 18 publications

(6 reference statements)

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“…The M equation had the second-best predictive effect on LDL-C concentration, with a good fit between the predicted and measured values. However, the error in estimated LDL-C concentration was often large, and mostly underestimated, which is consistent with the findings of Ertürk et al [ 14 ] The S equation showed a similar ability to accurately calculate LDL-C concentration as the M equation, [ 15 ] but with higher convenience and lower imprecision and goodness-of-fit than the M equation. The F equation was the simplest and most widely used, [ 16 ] but its accuracy and precision in calculating LDL-C concentration were not as good as the above equations.…”

Section: Discussionsupporting

confidence: 88%

A new equation for estimating low-density lipoprotein cholesterol concentration based on machine learning

Chen,

Rong,

et al. 2024

Medicine

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Low-density lipoprotein cholesterol (LDL-C) is a crucial marker of cardiovascular system damage. In the Chinese population, the estimation of LDL-C concentration by Friedewald, Martin-Hopkins or Sampson equations is not accurate. The aim of this study was to develop a group of new equations for calculating LDL-C concentration using machine learning techniques and to evaluate their efficacy. A total of 182,901 patient samples were collected with standard lipid panel measurements. These samples were collated and randomly divided into a training set and a test set. In the training set, a new equation was constructed using polynomial ridge-regression and compared to the Friedewald, Martin/Hopkins and extended Martin/Hopkins, or Sampson equations in the test set. Subsequently, an additional set of 17,285 patient samples were collected to evaluate the performance of the new equation in clinical practice. The new equation, a ternary cubic equation, was accurate and easy to use, with a goodness-of-fit R2 of 0.9815 and an uncertainty MSE of 37.4250 on the testing set. The difference between the calculated value by the new equation and the measured value of LDL-C was small (0.0424 ± 5.1161 vs Friedewald equation: −13.3647 ± 17.9198, vs Martin/Hopkins and extended Martin/Hopkins equation: −6.4737 ± 8.1036, vs Sampson equation: −8.9252 ± 12.6522, P < .001). It could accurately calculate LDL-C concentration even at high triglyceride and low LDL-C. Furthermore, the new equation could also precisely calculate LDL-C concentration in actual clinical use (R2 = 0.9780, MSE = 24.8482). The new equation developed in this study can accurately calculate LDL-C concentration within the full concentration range of triglyceride and LDL-C, and can serve as a supplement to the direct determination of LDL-C concentration for the prevention, treatment, evaluation, and monitoring of atherosclerotic diseases, compared to the Friedewald, Martin/Hopkins and extended Martin/Hopkins, or Sampson equations.

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Section: Discussionsupporting

confidence: 88%

A new equation for estimating low-density lipoprotein cholesterol concentration based on machine learning

Chen,

Rong,

et al. 2024

Medicine

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“…However, the error in estimated LDL-C concentration was often large, and mostly underestimated, which is consistent with the ndings of Ertürk et al [13] . The S equation showed a similar ability to accurately calculate LDL-C concentration as the M equation [14] , but with higher convenience and lower imprecision and goodness-oft than the M equation. The F equation was the simplest and most widely used [15] , but its accuracy and precision in calculating LDL-C concentration were not as good as the above equations.…”

Section: Discussionmentioning

confidence: 92%

A New Equation for Estimating Low-density Lipoprotein Cholesterol Concentration Based on Machine Learning

Chen

et al. 2023

Preprint

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Background Low-density lipoprotein cholesterol (LDL-C) is a crucial marker of cardiovascular system damage. In the Chinese population, the estimation of LDL-C concentration by Friedewald, Martin-Hopkins or Sampson equations is not accurate. Objective: The aim of this study was to develop a group of new equations for calculating LDL-C concentration using machine learning techniques and to evaluate their efficacy. Methods: A total of 182,901 patient samples were collected with standard lipid panel measurements. These samples were collated and randomly divided into a training set and a test set. In the training set, a new equation was constructed using polynomial ridge-regression and compared to the Friedewald, Martin, or Sampson equations in the test set. Subsequently, an additional set of 17,285 patient samples were collected to evaluate the performance of the new equation in clinical practice. Results: The new equation, a ternary cubic equation, was accurate and easy to use, with a goodness-of-fit R2 of 0.9815 and an uncertainty MSE of 37.4250 on the testing set. The difference between the calculated value by the new equation and the measured value of LDL-C was small (0.0424 ± 5.1161 vs Friedewald equation: -13.3647 ± 17.9198, vs Martin equation: -6.2969 ± 8.1036, vs Sampson equation: -8.9252 ± 12.6522, P < 0.001). It could accurately calculate LDL-C concentration even at high TG and low LDL-C. Furthermore, the new equation could also precisely calculate LDL-C concentration in actual clinical use (R2 = 0.9780, MSE = 24.8482).Conclusion: The new equation developed in this study can accurately calculate LDL-C concentration within the full concentration range of TG and LDL-C, and can serve as a supplement to the direct determination of LDL-C concentration for the prevention, treatment, evaluation, and monitoring of atherosclerotic diseases, compared to the Friedewald, Martin, or Sampson equations.

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“…Two studies each were performed in the following populations; Korean [49,55], HIV positive [33,54], high cardiac risk [34,58 ▪▪ ], fasting participant [37,38], hyperlipidaemic [43 ▪▪ ,47], Turkish [40,60], adults [30 ▪▪ ][66] and in children [10,44]. One study each was performed in the following populations; patients with coronary artery disease [31], renal disease [48], Hungarians [31], patients from the National Health and Nutrition examination survey [36], Persian cohort study patients [39], patients with acute MI [41,44], cardiology patients [36,45 ▪▪ ], those treated with PCSK-9 inhibitors [27], South African [9 ▪ ], Italian [31], Nigerian [50], Croatian [32], Slovakian [51], Lebanese [52 ▪ ], machine learning comparison, [53], a specialized lipid cohort [59], patients with hypertriglyceridaemia [61] and patients that received alirocumab, placebo, ezetimibe or a statin [2 ▪▪ ]. These studies provide a wide range of comparisons for use of formulae in different patient groups.…”

Section: Methodsmentioning

confidence: 99%

How should low-density lipoprotein cholesterol be calculated in 2022?

Martins

Rossouw

Pillay

2022

Current Opinion in Lipidology

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Purpose of reviewThe reference method for low-density lipoprotein-cholesterol (LDL-C) quantitation is β-quantification, a technically demanding method that is not convenient for routine use. Indirect calculation methods to estimate LDL-C, including the Friedewald equation, have been used since 1972. This calculation has several recognized limitations, especially inaccurate results for triglycerides (TG) >4.5 mmol/l (>400 mg/dl). In view of this, several other equations were developed across the world in different datasets.The purpose of this review was to analyze the best method to calculate LDL-C in clinical practice by reviewing studies that compared equations with measured LDL-C.Recent findingsWe identified 45 studies that compared these formulae. The Martin/Hopkins equation uses an adjustable factor for TG:very low-density lipoprotein-cholesterol ratios, validated in a large dataset and demonstrated to provide more accurate LDL-C calculation, especially when LDL <1.81 mmol/l (<70 mg/dl) and with elevated TG. However, it is not in widespread international use because of the need for further validation and the use of the adjustable factor. The Sampson equation was developed for patients with TG up to 9 mmol/l (800 mg/dl) and was based on β-quantification and performs well on high TG, postprandial and low LDL-C samples similar to direct LDL-C.SummaryThe choice of equation should take into the level of triglycerides. Further validation of different equations is required in different populations.

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Comparison of LDL-C calculation by Martin, Sampson and old Friedewald methods in real data and synthetic data set

Cited by 4 publications

References 18 publications

A new equation for estimating low-density lipoprotein cholesterol concentration based on machine learning

A new equation for estimating low-density lipoprotein cholesterol concentration based on machine learning

A New Equation for Estimating Low-density Lipoprotein Cholesterol Concentration Based on Machine Learning

How should low-density lipoprotein cholesterol be calculated in 2022?

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