Zhongguo quanke yixue (Oct 2024)
Construction and Validation of a Predictive Model of Influencing Factors for Fluoroquinolone Resistance in Patients with Pulmonary Tuberculosis: Based on the LASSO-Logistic Regression Model
Abstract
Background Rifampicin-resistant/multidrug-resistant tuberculosis (RR/MDR-TB) is featured by challenges in the treatment, low cure rate, and high infectivity. Fluoroquinolones (FQs), as the core drugs for the treatment of RR/MDR-TB, have a severe trend of resistance. Analyzing influencing factors for FQs can help to increase the cure rate of RR/MDR-TB and to control the occurrence of the pre-extensive drug resistance (pre-XDR) and extensive drug resistance. Objective To analyze the drug resistance of FQs in hospitalized patients with pulmonary tuberculosis and the influencing factors, and to construct and validate a Nomogram prediction model for the risk factors of drug resistance of FQs. Methods A total of 583 patients with pulmonary tuberculosis who were hospitalized in Guiyang Public Health Clinical Center from January 2021 to February 2022 and tested for drug sensitivity were retrospectively selected as study subjects. They were divided into the initial treatment group (296 patients) and the retreatment group (287 patients) according to the history of previous treatment. Moreover, they were divided into the FQs-resistant group (63 patients) and FQs-sensitive group (520 patients) according to their FQs-resistance status. The distribution of total resistance to 13 antituberculosis drugs in 583 patients was analyzed, and the baseline characteristics of patients in the FQs-resistant group and FQs-sensitive group were compared. After screening the characteristic variables using least absolute shrinkage and selection operator (LASSO) regression model, multivariate Logistic regression was performed to analyze the independent risk factors for the resistance of FQs. A Nomogram prediction model was constructed, and its performance was validated by calculating the area under the curve (AUC) of receiver operating characteristic (ROC), and plotting the calibration curve. Results Among 583 patients, 520 cases were sensitive to FQs and 63 cases were resistant (resistance rate of 10.81%). The resistance rate of FQs was secondary to the total resistance rate of first-line antituberculosis drugs, including the isoniazid (36.36%), rifampicin (32.76%), streptomycin (21.61%), and ethambutol (12.86%). The resistance rates of rifampicin, isoniazid, ethambutol, streptomycin, levofloxacin, moxifloxacin and rifampicin resistance (RR), multidrug resistance (MDR), and pre-XDR were significantly higher in patients of the retreatment group than those of the initial treatment group (P<0.05). The proportions of patients with other ethnic, retreatment, acquired immunodeficiency syndrome (AIDS), history of drug abuse, cavitation, hemoptysis, history of irregular anti-TB and MDR were significantly higher in the FQs-resistance group than those of the FQs-sensitive group (P<0.05). Through LASSO regression, six variables of ethnicity, treatment history, AIDS, drug abuse history, hemoptysis, and MDR were screened out as influencing factors. Multivariate Logistic regression analysis showed that other ethnic (OR=2.313, 95%CI=1.153-4.640, P=0.018), retreatment (OR=1.892, 95%CI=1.005-3.560, P=0.048), hemoptysis (OR=1.941, 95%CI=1.087-3.465, P=0.025), and MDR (OR=3.342, 95%CI=2.398-7.862, P<0.001) were the independent risk factors for FQs resistance in patients with pulmonary tuberculosis. Logistic regression equation Logit (P) =-3.571+0.838×ethnicity+0.638×treatment history+0.663×hemoptysis+1.468×MDR. Based on which a risk Nomogram prediction model was constructed with an AUC of 0.796 (95%CI=0.717-0.876). The Bootstrap method validated the mean absolute error of 0.015, and the predictive model had good calibration ability by the Hosmer-Lemeshow goodness-of-fit test (χ2=3.426, P=0.489) . Conclusion Our findings suggest a high resistant rate of FQs in patients with pulmonary tuberculosis. Other ethnic, retreatment, hemoptysis, and MDR are independent risk factors for FQs resistance in patients. The constructed Nomogram prediction model has a good predictive value for FQs resistance in patients with pulmonary tuberculosis. Our study offers new insights into the clinical diagnosis of drug-resistant tuberculosis and the development of rational treatment regimens for RR/MDR-TB.
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