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TRUVACE RECORD VERSION
record: TRV-2026-0924
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-29T06:05:14.254562Z
status: published
lens: g_space
sector: science
headline: Development and validation of a generalizable M-protein screening model using routine laboratory indicators: a multicenter retrospective study
dek: 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 pos…
gain_title: Logistic regression screening model using routine indicators like total protein and hemoglobin achieved AUC 0.843 and provides an accessible tool for early identification of individuals at high risk of M-protein in resource-limited primary care.
problem_title: (none)
trace_subject: (none)
gain_reading: Logistic regression screening model using routine indicators like total protein and hemoglobin achieved AUC 0.843 and provides an accessible tool for early identification of individuals at high risk of M-protein in resource-limited primary care.
gain_evidence: AUC of 0.843 | offers an accessible tool to facilitate early identification of individuals at high risk of M-protein
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers developed and validated an M-protein screening model using routine laboratory indicators from 5217 participants across three Chinese hospitals. They compared eight machine learning algorithms and selected a logistic regression model incorporating sex, age, total protein, albumin, albumin/globulin ratio, and hemoglobin, achieving an AUC of 0.843 in training and 0.843, 0.801, and 0.800 in internal and two external validations with a five-tier risk stratification.

The work matters because gold standard methods SPE combined with IFE have limited accessibility, and a routine-lab-based model could support early diagnosis of clinically relevant plasma cell disorders in resource-limited primary healthcare settings. What remains uncertain is how the model performs outside the three-hospital Chinese population and whether prospective use changes clinical workflows or patient outcomes.
limitation: Model development and validation were limited to participants from three Chinese hospitals, which may constrain generalizability to other populations and healthcare settings.
tag: Evidence-backed gain
key_points: Study enrolled 5217 participants from three Chinese hospitals with derivation cohort of 3019 and two external validation cohorts of 1747 and 451. | M-protein positivity was defined by SPE combined with IFE and models used routine indicators including sex, age, total protein, albumin, albumin/globulin ratio, and hemoglobin. | Eight algorithms were tested and logistic regression was selected as optimal with internal and external AUCs of 0.843, 0.801, and 0.800 and a five-tier risk stratification based on predicted probabilities.
rundown: The authors collected demographic data and routine laboratory blood parameters and built eight models using logistic regression, k-nearest neighbors, decision tree, random forest, AdaBoost, linear discriminant analysis, quadratic discriminant analysis, and multilayer perceptron. The final models incorporated sex, age, total protein, albumin, albumin/globulin ratio, and hemoglobin.

Performance was evaluated in a derivation cohort randomly split into training and internal validation, plus two external validation cohorts. The selected LR model used a five-tier risk stratification based on predicted probabilities: 15.0%, 15.0-40.0%, 40.0-70.0%, 70.0-90.0%, and 90.0%, with reported AUCs of 0.843 internally and 0.801 and 0.800 externally.
sources:
- peer_reviewed | Clinica Chimica Acta | https://doi.org/10.1016/j.cca.2026.121307 | 2026-08-26
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