A routine blood test-associated predictive model and application for tuberculosis diagnosis: a retrospective cohort study from northwest China

Abstract

Objectives: This study aimed to use the results of routine blood tests and relevant parameters to construct models for the prediction of active tuberculosis (ATB) and drug-resistant tuberculosis (DRTB) and to assess the diagnostic values of these models. Methods: We performed logistic regression analysis to generate models of plateletcrit-albumin scoring (PAS) and platelet distribution width-treatment-sputum scoring (PTS). Area under the curve (AUC) analysis was used to analyze the diagnostic values of these curves. Finally, we performed model validation and application assessment. Results: In the training cohort, for the PAS model, the AUC for diagnosing ATB was 0.902, sensitivity was 82.75%, specificity was 82.20%, accuracy rate was 81.00%, and optimal threshold value was 0.199. For the PTS model, the AUC for diagnosing DRTB was 0.700, sensitivity was 63.64%, specificity was 73.53%, accuracy rate was 89.00%, and optimal threshold value was −2.202. These two models showed significant differences in the AUC analysis, compared with single-factor models. Results in the validation cohort were similar. Conclusions: The PAS model had high sensitivity and specificity for the diagnosis of ATB, and the PTS model had strong predictive potential for the diagnosis of DRTB.