Toward accurate LDL-cholesterol estimation: platform-specific, population-based equations outperform Friedewald in...

Low-density lipoprotein cholesterol (LDL-C) remains a key biomarker for cardiovascular risk assessment. While the Friedewald equation is widely used for estimating LDL-C, its accuracy can vary across populations and analytical platforms. This study aimed to develop and validate population-specific, platform-adapted LDL-C equations in two independent cohorts and to compare their performance with the Friedewald equation. A retrospective analysis was conducted using lipid profiles from 31,265 individuals across two datasets: a tertiary hospital (n = 10,174; Roche Cobas platform) and a private laboratory (n = 21,091; Abbott Alinity platform). For each, two linear regression models (50:50 and 80:20 random training-validation splits) were used to develop LDL-C estimation equations using total cholesterol, high-density lipoprotein cholesterol, and triglycerides as predictors. Performance was evaluated by median absolute error (MAE), median percentage error (MPE), and agreement with direct LDL-C in clinical risk categories. The training/validation models performed nearly identically; therefore, only the 50:50 models were retained for the final analysis, with one equation generated for each platform. Both novel equations showed significantly lower MAE (-0.015 to -0.010 mmol/L) and MPE (-0.5% to -0.4%) compared to the Friedewald equation (-0.217 and -0.209 mmol/L MAE; -7.4% and -6.6% MPE) and had more balanced error distributions. The Roche Cobas-derived equation achieved higher overall classification accuracy (85.1%) than the Abbott Alinity-based model (78.6%), while both substantially outperformed Friedewald (67.1% and 65.3%) in all but the <1.03 mmol/L LDL-C category. Platform-specific, population-adapted LDL-C equations offer superior accuracy and risk classification over Friedewald. These findings further support the clinical relevance of implementing such equations; however, broader validation and formal guidance from professional bodies are needed to facilitate their integration into clinical practice.