Development and validation of a risk prediction model for pulmonary tuberculosis in presumptive tuberculosis patients in Tigray, northern Ethiopia.

The incidence of tuberculosis (TB) has increased in Tigray, Ethiopia due to war and a crippled healthcare system. Although early detection and treatment are critical for TB control, over 30% of TB cases are missed using current diagnostic techniques. Thus, we developed and validated a risk prediction model for pulmonary TB in presumptive cases. In this multicenter cross-sectional study, we consecutively enrolled 907 respondents from primary healthcare facilities in Tigray, northern Ethiopia. We used least absolute shrinkage and selection operator regression to identify variables for the model. Risk scores were generated from the coefficients of multivariable logistic regression. We evaluated the model performance using the area under the curve and calibration plots, and clinical utility using decision curves. Among all respondents, 155 (17%) had GeneXpert-confirmed pulmonary TB. At an optimal cutoff value of 8.5, the model demonstrated a discrimination accuracy of 0.82 (95% CI: 0.78-0.85), a sensitivity of 82.6%, and a specificity of 68.9%. The model had a calibration slope of 0.98 and an intercept of 0.001. The model exhibits acceptable discrimination and calibration performance. Thus, it can be used for screening patients for pulmonary TB in primary healthcare settings where accurate diagnostic resources are limited.