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

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.