A parsimonious machine-learning model using routine laboratory data to differentiate nontuberculous mycobacterial disease from tuberculosis: Development and independent external validation

In tuberculosis-endemic settings, suspected mycobacterial disease may reflect either nontuberculous mycobacteria (NTM) or Mycobacterium tuberculosis (MTB), yet routine clinical findings do not reliably distinguish them before pathogen-specific confirmation. We developed an eight-variable Extra Trees model in Hospital A (n = 1,090; 130 NTM) and independently evaluated the frozen model in Hospital B (n = 1,160; 128 NTM). Internal out-of-fold (OOF) AUC/AUPRC were 0.892/0.588; external AUC/AUPRC were 0.849/0.505. Calibrated Brier scores were 0.072 and 0.075. At the Hospital A-derived threshold of 0.13179, sensitivity/specificity were 80.0%/80.3% internally and 67.2%/83.7% externally; 42 of 128 external NTM cases were classified below threshold. Because feature selection preceded the original OOF evaluation, a post-hoc fully nested analysis was performed (AUC 0.875; AUPRC 0.525), indicating modest internal optimism. A post-hoc benchmark did not show an overall advantage for Extra Trees over other algorithms. Routine variables may help prioritize confirmatory testing, but the frozen threshold cannot support stand-alone diagnosis or exclusion of NTM. Prospective evaluation must verify patient-level measurement timing, reference diagnoses and clinical impact.