Machine learning-based infection prediction model for newly diagnosed multiple myeloma patients

Abstract

Objective: To understand the infection characteristics and risk factors for infection by analyzing multicenter clinical data of newly diagnosed multiple myeloma (NDMM) patients.

Methods: This study reviewed 564 NDMM patients from 2 large tertiary hospitals from January 2018 to December 2021, of whom 395 comprised the training set and 169 comprised the validation set. Thirty-eight variables from first admission records were collected, including patient demographic characteristics, clinical scores and characteristics, laboratory indicators, complications, and medication history, and key variables were screened using the Lasso method. Multiple machine learning algorithms were compared, and the best performing algorithm was used to build a machine learning prediction model. The model performance was evaluated using the AUC, accuracy, and Youden's index. Finally, the SHAP package was used to assess two cases and demonstrate the application of the model.

Results: In this study, 15 important key variables were selected, namely, age, ECOG, osteolytic disruption, VCD, neutrophils, lymphocytes, monocytes, hemoglobin, platelets, albumin, creatinine, lactate dehydrogenase, affected globulin, β2 microglobulin, and preventive medicine. The predictive performance of the XGBoost model was significantly better than that of the other models (AUROC: 0.8664), and it also performed well for the expected dataset (accuracy: 68.64%).

Conclusion: A machine learning algorithm was used to establish an infection prediction model for NDMM patients that was simple, convenient, validated, and performed well in reducing the incidence of infection and improving the prognosis of patients.

Keywords: diagnosis; infection; machine learning; multiple myeloma; prediction model.