A predictive model for leukopenia in tuberculosis patients receiving anti-tuberculosis treatment
10.3760/cma.j.issn.1674-2397.2024.05.006
- VernacularTitle:构建肺结核患者抗结核治疗后发生白细胞减少症的预测模型
- Author:
Bin LU
1
;
Yunzhen SHI
;
Lihua WU
;
Xinling PAN
;
Xiang CHEN
Author Information
1. 温州医科大学附属东阳市人民医院感染病科，东阳322100
- Publication Type:Journal Article
- Keywords:
Pulmonary tuberculosis;
Anti-tuberculosis therapy;
Leukopenia;
Prediction model;
Machine learning model
- From:
Chinese Journal of Clinical Infectious Diseases
2024;17(5):375-382
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a nomogram model for predicting the risk of leukopenia among tuberculosis patients receiving anti-tuberculosis therapy.Methods:A total of 2 681 tuberculosis patients admitted to the affiliated Dongyang Hospital of Wenzhou Medical University from Jan 2013 to Jun 2024，were enrolled in this study. All cases received first line anti-tuberculosis treatment and were randomly divided into training（ n=1 876）and validation groups（ n=805）at a ratio of 7∶3. The endpoint was the occurrence of leukopenia during anti-tuberculosis therapy. In the training group，the predictors were screened by Lasso regression and multivariable Logistic regression analysis，and used to establish a nomogram prediction model. The discrimination power，fitness and clinical applicability were evaluated using the receiver operating characteristic（ROC）curve，calibration curve and decision curve analysis，respectively. Several machine learning models based on different methods（random forest，support vector machine，extreme gradient boosting and naive Bayes）were also constructed in the validation group. Results:There were 15.0%（273/1 876）and 15.9%（128/805）of cases developing leukopenia during anti-tuberculosis therapy in the training group and validation groups，respectively. Following Lasso regression analysis，the multivariable Logistic regression analysis showed that age ≥65 years（ OR=2.997，95% CI 2.185-4.128），alcohol consumption（ OR=4.803，95% CI 3.502-6.593）and diabetes（ OR= 5.459，95% CI 3.914-7.621）were risk factors related to the occurrence of leukopenia；while the higher levels of baseline hemoglobin（ OR=0.979，95% CI 0.971-0.987）and platelet count（ OR=0.996，95% CI 0.995-0.998）were protective factors. Based on these five factors，a nomogram prediction model was developed. The areas under ROC curve（AUCs）were 0.836（95% CI 0.810-0.863）and 0.818（95% CI 0.776-0.860）in the training group and the validation group，respectively. Moreover，this model had good fitness and clinical applicability. The discrimination power of nomogram model was comparable to those of machine learning models. Conclusion:The established nomogram model in this study has good discrimination power，calibration ability and clinical applicability for predicting the risk of leucopenia in tuberculosis patients undergoing anti-tuberculosis therapy.
