Development of a nomogram model for predicting pulmonary tuberculosis activity

Timely and accurate identification of active pulmonary tuberculosis (APTB) is essential for effective treatment and public health control. This study aimed to develop a predictive nomogram using routine laboratory parameters to distinguish APTB from non-active pulmonary tuberculosis. A retrospective observational study was conducted at a single tertiary hospital from January 2021 to December 2024. A total of 356 newly diagnosed PTB patients were enrolled and classified into APTB (n = 225) or non-active pulmonary tuberculosis (n = 131) groups based on clinical, radiological, and microbiological criteria. Demographic, clinical, and laboratory data were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of APTB. A nomogram was constructed using 5 selected variables. Model performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis. Multivariate analysis identified mean corpuscular volume, erythrocyte sedimentation rate, serum albumin, adenosine deaminase, and monocyte-to-high-density lipoprotein cholesterol ratio as independent predictors. The nomogram demonstrated strong discrimination (area under the curve = 0.913, sensitivity = 87.68%, specificity = 95.32%) and calibration (C-index = 0.915; Hosmer-Lemeshow P = .915). Decision curve analysis confirmed the model's clinical utility. An internally validated nomogram incorporating 5 accessible laboratory indicators provides a reliable tool for predicting APTB, thereby facilitating timely diagnosis and supporting clinical decision-making.