Routine CBC–based machine learning for individualized correction of lipemia-induced hemoglobin bias

Background Lipemia can interfere with photometric hemoglobin (HGB) measurement, causing spurious elevation and distortion of HGB-derived erythrocyte indices. We investigated whether routine complete blood count (CBC) parameters could continuously quantify lipemia-induced HGB bias and support individualized correction. Methods We screened 310 clinical lipemic specimens from 310 unique patients measured on a Sysmex XN-9000. Twenty-seven specimens with a pre- versus post-plasma-replacement red blood cell (RBC) difference of ≥3% failed the prespecified procedural quality-control criterion and were excluded, leaving 283 specimens from 283 patients for analysis. Plasma-replacement HGB (HGB-PR) was used as the practical reference comparator. Relative bias was defined as Bias_PR = (HGB-I−HGB-PR)/HGB-PR × 100%. A random forest model used routine RBC count (RBC-I), routine photometric HGB (HGB-I), hematocrit (HCT-I), mean corpuscular hemoglobin (MCH-I), and mean corpuscular hemoglobin concentration (MCHC-I), with nested cross-validation (5 outer and 3 inner folds). Hyperparameters minimized the mean absolute error (MAE) of machine-learning-corrected HGB (HGB_ML) versus HGB-PR. All primary estimates used out-of-fold (OOF) predictions. Benchmarking, OOF SHapley Additive exPlanations (SHAP), lipid augmentation, and coupling sensitivity analyses were performed. Results In the 283-specimen analysis cohort, the model achieved an OOF coefficient of determination (R 2 ) of 0.848 for Bias_PR prediction (MAE, 1.20 percentage points; root mean square error [RMSE], 2.06). HGB_ML agreed strongly with HGB-PR (R 2 = 0.975; MAE=1.66 g/L; RMSE=2.70 g/L; Lin concordance correlation coefficient [CCC] = 0.987), with 94.3% of errors ≤5 g/L. These favorable overall measures were not uniform across the interference range: underprediction and larger errors occurred in the high-Bias_PR tail. The random forest outperformed MCHC-only, ordinary least-squares, and Ridge models. MCHC-I dominated OOF SHAP importance (78.5%). Adding triglycerides (TG) and total cholesterol (TC) reduced HGB MAE to 1.48 g/L ( Δ MAE, −0.177 g/L; 95% confidence interval [CI], −0.318 to −0.029). Strict anti-coupling retained useful average accuracy (MAE=1.71 g/L) but was less accurate at extreme interference. Formula-corrected HGB had lower analytical error than HGB_ML. Conclusions Routine CBC parameters can support continuous estimation of lipemia-associated HGB bias and may provide an automated decision-support or reflex-testing signal. The model should not replace established analytical correction procedures, and independent multicenter prospective validation is required before clinical implementation.