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

Authors

Jiling Zhang, Xinjun Zhang, Yandan Wu

Published in

Frontiers in medicine. Volume 13. Pages 1975168. Epub Oct 29, 2026.

Abstract

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.
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.
In the 283-specimen analysis cohort, the model achieved an OOF coefficient of determination (R²) 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 (R² = 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 (ΔMAE, -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.
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.

PMID:
43021805
Bibliographic data and abstract were imported from PubMed on 13 Nov 2026.

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