Prediction of risk factors associated with the development of multidrug-resistant tuberculosis in patients with tuberculosis

Haiyan Chen^1*Zhaowei Tong²Jianfeng Zhong²Yong Tong²Qingqiu Zeng²Bin Shen²Qun Song²Fuchu Qian³Xin Xiao^4*

• ¹Department of Infection,, Huzhou Central Hospital, Huzhou, China
• ²Department of Infection, Huzhou Central Hospital, Huzhou, China
• ³Huzhou Key Laboratory of Precision Medicine Research and Translation for Infectious Disease, Huzhou Central Hospital, Huzhou, China
• ⁴Huzhou Key Laboratory of Precision Medicine Research and Translation for Infectious Diseases, Huzhou Central Hospital, Huzhou, China

This study aimed to develop and validate a reliable nomogram based on clinical factors to predict development of multidrug-resistant tuberculosis (MDR-TB) associated with individuals with tuberculosis ( TB ) , so as to reduce the harm and economic burden caused by disease progression.The study included 4,251 individuals with TB who received treatment at Huzhou Central Hospital between January 2016 and December 2023, excluding 87 individuals because of infection with non-TB mycobacterium or incomplete information (including missing laboratory or clinical data). A total of 4,164 individuals (2,261 sputum smear-positive and 1,903 sputum smear-negative patients) were ultimately included in the analysis. This analysis incorporated clinical baseline presentations, demographic information, imaging findings, laboratory results, and clinical diagnoses to develop a predictive model for MDR-TB.We constructed a predictive model to examine the risk factors associated with the development of multidrug-resistant tuberculosis (MDR-TB) in individuals with tuberculosis(TB).This study demonstrated that sex, age, a history of anti-TB therapy, body mass index(BMI) ≤18.5, smoking history, occupation, previously diagnosed TB, pulmonary cavitation, comorbidities, poverty, and C-reactive protein(CRP)≥37.3 mg/L were major risk factors for MDR-TB in patients with TB. The area under the receiver operating characteristic(ROC)curve was 0.902 for the training group and 0.909 for the validation group. Calibration curve analysis revealed that the predicted and actual incidence rates of MDR-TB in the two groups were in good agreement, whereas decision curve analysis (DCA) indicated that the predictive model resulted in better clinical benefit.The nomogram model established in this study included 11 clinical characteristics and demographic features of patients with TB, which may be valuable for predicting MDR-TB.