Development and validation of a generalizable M-protein screening model using routine laboratory indicators: a multicenter retrospective study.

Authors

Hou-Long Luo, Lanxin Zhou, Hua Xiao, Qinxin Zhang, Xin Li, Jiahui Ye, Youlin Liu, Jin Li, Yonggang Liang, Anping Xu, Ling Ji

Published in

Clinica chimica acta; international journal of clinical chemistry. Pages 121307. Aug 26, 2026. Epub Aug 26, 2026.

Abstract

Early detection of plasma cell disorders (PCDs) remains challenging due to limited accessibility of gold standard diagnostic methods. This study aimed to develop a simple M-protein screening model using routine laboratory indicators for clinical laboratories.
A total of 5217 participants from three Chinese hospitals were enrolled. The derivation cohort (n = 3019) was randomly divided into training and internal validation cohorts. Two external validation cohorts (n = 1747 and n = 451) were included. M-protein positivity was rigorously defined by SPE combined with IFE. Demographic data and routine laboratory blood parameters were collected for model development. Eight machine learning algorithms-Logistic regression (LR), k-nearest neighbors classifier, decision tree classifier, random forest classifier, AdaBoost classifier, linear discriminant analysis, quadratic discriminant analysis, and multilayer perceptron classifier-were used to construct M-protein screening models.
Eight M-protein screening models were established, incorporating indicators including sex, age, total protein, albumin, albumin/globulin ratio, and hemoglobin. Based on a comprehensive evaluation of models' performance and generalization ability, the LR model was identified as the optimal model, with an AUC of 0.843. A five-tier risk stratification was established based on predicted probabilities (P): ≤15.0%, 15.0-40.0%, 40.0-70.0%, 70.0-90.0%, and ≥ 90.0%. The model's performance in the internal validation cohort and two external validation cohorts also met expectations, with AUCs of 0.843, 0.801, and 0.800, respectively.
We developed and validated a practical M-protein screening model based on routine laboratory indicators. The LR model demonstrated robust predictive ability and offers an accessible tool to facilitate early identification of individuals at high risk of M-protein, thereby supporting early diagnosis of clinically relevant PCDs, particularly in resource-limited primary healthcare settings.

PMID:
42648524
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.

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