Development and internal validation of a LASSO-based clinical prediction model for nontuberculous mycobacterial pulmonary disease versus pulmonary tuberculosis

Background: The rising global incidence of nontuberculous mycobacterial pulmonary disease (NTM-PD) and its significant overlap with pulmonary tuberculosis (PTB) in symptoms and imaging pose a major diagnostic challenge, often leading to misdiagnosis and inappropriate treatment. A reliable pre-culture predictive tool is urgently needed. Methods: = 206) from January 2021 to December 2023. Demographic, clinical, comorbidity, laboratory, and high-resolution CT (HRCT) data were collected. Least Absolute Shrinkage and Selection Operator (LASSO) regression with 10-fold cross-validation was used for feature selection. Selected variables were incorporated into a multivariate logistic regression model to construct a final prediction model. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), calibration (Hosmer-Lemeshow test, calibration plot), and internal validation via 1,000 bootstrap resamples. Clinical utility was assessed using decision curve analysis (DCA). Results: = 0.949). Bootstrap internal validation yielded an optimism-corrected concordance index of 0.830. DCA confirmed the model's clinical net benefit across a wide range of threshold probabilities. Conclusion: We developed and internally validated a parsimonious six-variable prediction model that effectively differentiates NTM-PD from PTB. Incorporating objective feature selection (LASSO) and rigorous validation, this tool can aid clinicians in raising early suspicion for NTM-PD, optimizing diagnostic pathways, and preventing misdiagnosis while awaiting culture results.