NDLI: Nomogram to predict multidrug-resistant tuberculosis

Content Provider	Springer Nature : BioMed Central
Author	Wang, Saibin Tu, Junwei
Abstract	Background Multidrug-resistant tuberculosis (MDR-TB) is burgeoning globally, and has been a serious challenge in TB management. Clinically, the ability to identify MDR-TB is still limited, especially in smear-negative TB. The aim of this study was to develop a nomogram for predicting MDR-TB. Methods Demographics and clinical characteristics of both MDR-TB and drug-susceptible TB patients were utilized to develop a nomogram for predicting MDR-TB. The LASSO regression method was applied to filter variables and select predictors, and multivariate logistic regression was used to construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). Moreover, calibration analysis and decision curve analysis (DCA) of the model were performed. This study involved a second analysis of a completed prospective cohort study conducted in a country with a high TB burden. Results Five variables of TB patients were selected through the LASSO regression method, and a nomogram was built based on these variables. The predictive model yielded an AUC of 0.759 (95% CI, 0.719–0.799), and in the internal validation, the AUC was 0.757 (95% CI, 0.715–0.793). The predictive model was well-calibrated, and DCA showed that if the threshold probability of MDR-TB was between 70 and 90%, using the proposed nomogram to predict MDR-TB would obtain a net benefit. Conclusions In this study, a nomogram was constructed that incorporated five demographic and clinical characteristics of TB patients. The nomogram may be of great value for the prediction of MDR-TB in patients with sputum-free or smear-negative TB.
Related Links	https://ann-clinmicrob.biomedcentral.com/counter/pdf/10.1186/s12941-020-00369-9.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	14760711
DOI	10.1186/s12941-020-00369-9
Journal	Annals of Clinical Microbiology and Antimicrobials
Issue Number	1
Volume Number	19
Language	English
Publisher	BioMed Central
Publisher Date	2020-06-06
Access Restriction	Open
Subject Keyword	Medical Microbiology Infectious Diseases Drug Resistance Microbial Genetics and Genomics Biochemistry Parasitology MDR-TB Prediction Infection
Content Type	Text
Resource Type	Article
Subject	Microbiology (medical) Infectious Diseases
Journal Impact Factor	4.6/2023
5-Year Journal Impact Factor	5.3/2023

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