Comparison of Novel Equations for Estimating Low-Density Lipoprotein Cholesterol in Patients Undergoing Coronary Angiography

比较用于估算接受冠状动脉造影患者低密度脂蛋白胆固醇的新型方程

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Abstract

AIM: The importance of precisely quantifying low-density lipoprotein cholesterol (LDL-C) has become more pronounced over the years, with the rise of metabolic syndrome in the population and the reduction in LDL-C treatment goals. This study aims to compare two novel equations indirectly estimating LDL-C and assess their compatibility with Friedewald formula, in a population with high cardiovascular risk. METHODS: This study is a retrospective analysis of the lipid profiles of 10,006 patients who underwent coronary angiography. LDL-C was calculated using Friedewald, Martin, and Sampson equations, and the compatibility between estimations was compared using methods of concordance and reclassification. RESULTS: Our findings show that Martin and Sampson equations displayed high rates of upward LDL-C reclassification (10.8% and 7.5%, respectively) when compared with Friedewald equation. In comparison to the Sampson method, Martin also reclassified 3.8 % of patients to a higher LDL-C category. The magnitude of discordance between LDL-C estimates was more pronounced in hypertriglyceridemic patients, and this increased progressively with the reduction in LDL-C. The proportion of patients with LDL-C <70 mg/dL reclassified to a higher LDL-C category reached 44% (Sampson vs. Friedewald), 65% (Martin vs. Friedewald), and 37% (Martin vs.Sampson) in those with triglyceride levels between 200 and 399 mg/dL. CONCLUSIONS: Both Martin and Sampson LDL-C estimates displayed significant proportion of upward discordance with reclassification to higher LDL-C categories compared to Friedewald formula, particularly in patients with elevated triglycerides and low LDL-C, a population in whom more accurate estimation of LDL-C is required. Further studies are warranted to validate the recently developed Sampson equation with comparison to Martin method that tended to more significantly overestimate LDL-C.

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