Construction of a Prognostic Risk Prediction Model for Multiple Myeloma Patients after Bortezomib Treatment Based on Decision Tree Algorithm.
10.19746/j.cnki.issn.1009-2137.2025.05.022
- Author:
Tao JIANG
1
;
Yuan LUO
2
;
Huan WANG
1
;
Hui LI
1
Author Information
1. Department of Hematology, The People's Hospital of Jianyang City, Jianyang 641400, Sichuan Province, China.
2. Department of Hematology, Langzhong People's Hospital, Langzhong 637400, Sichuan Province, China.
- Publication Type:Journal Article
- Keywords:
multiple myeloma;
bortezomib;
prognosis;
decision tree model
- MeSH:
Humans;
Bortezomib/therapeutic use*;
Multiple Myeloma/diagnosis*;
Decision Trees;
Prognosis;
Algorithms;
Risk Factors;
Male;
Female;
Middle Aged
- From:
Journal of Experimental Hematology
2025;33(5):1386-1391
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To explore the influencing factors on the prognosis of patients with multiple myeloma (MM) after bortezomib treatment, and construct a decision tree risk prediction model based on the influencing factors.
METHODS:One hundred and seventy MM patients admitted to the People's Hospital of Jianyang City from January 2019 to June 2022 were selected as research subjects, and divided into poor prognosis group and good prognosis group according to the prognosis after bortezomib treatment. The clinical data of the patients were analyzed, univariate and logistic regression analysis were used to screen influencing factors, SPSS Modeler software was used to construct a decision tree prediction model, and the diagnostic performance of the decision tree risk prediction model was analyzed.
RESULTS:The incidence of poor prognosis in 170 MM patients after bortezomib-based chemotherapy was 21.18%. Kappa light chain level≥19.4 mg/L, platelet count (PLT) ≤100×109/L, homocysteine (Hcy) >22 μmol/L, serum creatinine (Scr) ≥176 μmol/L, lactate dehydrogenase (LDH) ≥300 U/L, serum ferritin (SF) >500 mg/L, and β2-microglobulin (MG) >6 μg/L were independent risk factors for poor prognosis in MM patients after bortezomib treatment (all P < 0.05). The decision tree model selected 7 explanatory variables (Kappa light chain level, LDH, PLT, SF, β2-MG, Scr, and Hcy) as nodes of the model, among which Kappa light chain level was the most important predictor. In addition, the area under the ROC curve (AUC) values of the decision tree model and logistic regression model were 0.895 and 0.881, respectively. The prediction performance of the decision tree model was better than that of the logistic regression model ( Z=3.325, P =0.005).
CONCLUSION:The decision tree model has high value in predicting the prognosis after bortezomib treatment in MM patients, which can screen high-risk factors that affect poor prognosis, providing practical references for clinical healthcare professionals to take preventive treatment for high-risk MM patients.
