Table 1_Development and validation of a nomogram for predicting unfavorable treatment outcomes in patients with pulmonary tuberculosis and diabetes mellitus.xlsx

ObjectiveTo develop and validate a clinical prediction model estimating individualized risk of unfavorable treatment outcomes in patients with pulmonary tuberculosis and diabetes mellitus (PTB-DM).
MethodsThis 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).
ResultsFour 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.
ConclusionWe 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.