Development and validation of a nomogram for predicting unfavorable treatment outcomes in patients with pulmonary tuberculosis and diabetes mellitus

Objective To develop and validate a clinical prediction model estimating individualized risk of unfavorable treatment outcomes in patients with pulmonary tuberculosis and diabetes mellitus (PTB-DM). Methods This retrospective study enrolled 110 inpatients with PTB-DM, categorized into favorable ( n = 55) and unfavorable ( n = 55) outcome groups. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the most relevant predictors from clinical and laboratory data. A multivariate logistic regression model was built based on these predictors to construct a nomogram. The model’s performance was evaluated by its discrimination (Area Under the Curve, AUC), calibration (Hosmer-Lemeshow test and calibration curve), and clinical utility (Decision curve analysis). Internal validation was performed using bootstrap resampling (1,000 repetitions). Results Four variables were selected by LASSO regression for model construction: Age, Body Mass Index (BMI), pulmonary cavity, and the Glucose-to-Lymphocyte Ratio (GLR). The multivariate model confirmed these as independent risk factors. The nomogram demonstrated excellent discrimination, with an AUC of 0.885 (95% CI: 0.826–0.944) and a bootstrap-corrected AUC of 0.858. Good calibration was indicated by a non-significant Hosmer-Lemeshow test ( P = 0.856). Decision curve analysis confirmed the model’s clinical net benefit across a wide range of risk thresholds. Conclusion We developed and internally validated a nomogram that accurately predicts the risk of unfavorable outcomes in PTB-DM patients by integrating four readily available clinical parameters. This tool shows robust performance and holds promise for aiding clinicians in identifying high-risk individuals for personalized management strategies.