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Objective <p>Develop a diagnostic model using common hematological and immunological indicators to assist in the early screening and differential diagnosis of Multiple Myeloma (MM) in clinical settings, reducing the risk of misdiagnosis.</p> Methods <p>A retrospective analysis was conducted on 274 newly diagnosed and treated MM patients and 137 connective tissue disease patients treated at Zhejiang Provincial People’s Hospital from January 2008 to August 2023. Laboratory indicators, including complete blood count, biochemistry, coagulation function, and immunoglobulin markers, were collected. The cohort was randomly divided into a 70% training set and a 30% validation set. Relevant variables were selected through univariate and multivariate analyses in the training set. A discriminative diagnostic model was developed using a multivariate logistic regression algorithm. The model’s predictive accuracy and generalizability were evaluated by validating and conducting receiver operating characteristic (ROC) curves and calibration curves.</p> Results <p>The developed differential diagnostic model in this study included the following observed indicators: IgM, glomerular filtration rate, high-density lipoprotein, red cell distribution width, and thrombin time. The model demonstrated excellent discriminatory power and good calibration, with an area under the curve (AUC) value of 0.980 (95% CI: 0.967–0.994). Additionally, the model exhibited high sensitivity (0.963), specificity (0.938), accuracy (0.955). The validation set further confirmed the generalization and accuracy of the model, with an AUC value of 0.954 (95% CI: 0.961–0.992).</p> Conclusions <p>The constructed differential diagnostic model in this study can accurately predict and differentiate MM patients and those with elevated Ig abnormalities, thereby enhancing the efficiency of clinical diagnostic decision-making.</p>

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A differential diagnostic model based on immunological evaluation and routine laboratory tests: distinguishing multiple myeloma from other disorders with aberrant immunoglobulin elevation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月9日

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  • Manning Li,
  • Sixun Jia,
  • Han Wu,
  • Chunyi Shou,
  • Xiaolu Song,
  • Ye Peng,
  • Huafang Wang,
  • Ying Wang,
  • Xiangmin Tong,
  • Yirui Chen

摘要

Objective

Develop a diagnostic model using common hematological and immunological indicators to assist in the early screening and differential diagnosis of Multiple Myeloma (MM) in clinical settings, reducing the risk of misdiagnosis.

Methods

A retrospective analysis was conducted on 274 newly diagnosed and treated MM patients and 137 connective tissue disease patients treated at Zhejiang Provincial People’s Hospital from January 2008 to August 2023. Laboratory indicators, including complete blood count, biochemistry, coagulation function, and immunoglobulin markers, were collected. The cohort was randomly divided into a 70% training set and a 30% validation set. Relevant variables were selected through univariate and multivariate analyses in the training set. A discriminative diagnostic model was developed using a multivariate logistic regression algorithm. The model’s predictive accuracy and generalizability were evaluated by validating and conducting receiver operating characteristic (ROC) curves and calibration curves.

Results

The developed differential diagnostic model in this study included the following observed indicators: IgM, glomerular filtration rate, high-density lipoprotein, red cell distribution width, and thrombin time. The model demonstrated excellent discriminatory power and good calibration, with an area under the curve (AUC) value of 0.980 (95% CI: 0.967–0.994). Additionally, the model exhibited high sensitivity (0.963), specificity (0.938), accuracy (0.955). The validation set further confirmed the generalization and accuracy of the model, with an AUC value of 0.954 (95% CI: 0.961–0.992).

Conclusions

The constructed differential diagnostic model in this study can accurately predict and differentiate MM patients and those with elevated Ig abnormalities, thereby enhancing the efficiency of clinical diagnostic decision-making.