Equation Disagreement as a Zero-Cost Uncertainty Signal for LDL-C Classification: An Interpretable Regime-Aware Calibration Model Rescues Misclassification at Treatment Thresholds.

Abstract

Background: Three LDL cholesterol (LDL-C) estimation equations (Friedewald, Sampson/NIH, and Martin-Hopkins) can be calculated from every standard lipid panel, yet laboratories typically report only one. We tested whether disagreement identifies misclassification risk at clinical thresholds and whether simple calibration improves accuracy in this subgroup.

Methods: We analyzed 10 799 All of Us lipid panels using direct LDL-C as the reference standard. At 70, 100, and 130 mg/dL [1.81, 2.59, and 3.36 mmol/L], panels were classified as Agree if all 3 equations fell on the same side and Disagree otherwise. A regime-aware calibration model was compared with 7 machine learning (ML) methods using 5-fold cross-validation and tested in 14 549 hospital-based Medical Information Mart for Intensive Care IV panels.

Results: When equations agreed (86%-92% of panels), accuracy was 92% to 96%; when they disagreed (8%-14%), accuracy fell to 48%-61%. The model's mean absolute error was 8.98 mg/dL (0.232 mmol/L), matching the best ML ensemble (9.01 mg/dL [0.233 mmol/L]; 95% CI for the difference, -0.35 to 0.29 mg/dL [-0.009 to 0.008 mmol/L]). In external validation, among split-threshold panels, the model outperformed the best-performing individual equation at each threshold by 3.5 to 9.0 percentage points and the majority vote by 18.5 to 25.2 percentage points. Calibration improved accuracy by 19 to 25 percentage points; hybrid routing achieved 90%-93% accuracy internally and 90%-94% externally.

Conclusions: Equation disagreement is a zero-cost uncertainty signal identifying patients at highest misclassification risk. A simple, interpretable calibration model improves classification while matching the best-performing ML ensemble.