Key result
New machine learning equation outperforms standard formulas, estimating LDL-C with ~0.04 mg/dL mean difference.
Why the study?
In the Chinese population, estimation of LDL-C concentration by the Friedewald, Martin-Hopkins, or Sampson equations is not accurate.
Does a new machine learning-based equation improve the accuracy of LDL-C estimation compared to traditional equations in a Chinese population?
Observational (n=200,186)
No
Does a new machine learning-based equation improve the accuracy of LDL-C estimation compared to traditional equations in a Chinese population?
Absolute Event Rate: 0.0424% vs -13.3647%
p-value: p=<0.001
A novel machine learning-derived equation provides more accurate estimation of LDL-C than the Friedewald, Martin, and Sampson equations, particularly in a Chinese population.
Abstract 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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Chen et al. (2023) conducted an observational in Dyslipidemia / Standard lipid panel testing (n=200,186). P equation (ternary cubic equation based on machine learning) vs. Friedewald, Martin-Hopkins, and Sampson equations was evaluated on Mean difference between calculated and measured LDL-C concentration (mg/dL) (95% CI -0.0055, 0.0903, p=<0.001). The new machine learning-based ternary cubic equation accurately estimated LDL-C concentrations with a mean difference of 0.0424 mg/dL, significantly outperforming the Friedewald, Martin, and Sampson equations.
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