Breaking the threshold: Developing multivariable models using computer-aided chest X-ray analysis for tuberculosis triage

Autor: Coralie Geric, Gamuchirai Tavaziva, Marianne Breuninger, Keertan Dheda, Ali Esmail, Alex Scott, Mary Kagujje, Monde Muyoyeta, Klaus Reither, Aamir J. Khan, Andrea Benedetti, Faiz Ahmad Khan
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: International Journal of Infectious Diseases, Vol 147, Iss , Pp 107221- (2024)
Druh dokumentu: article
ISSN: 1201-9712
DOI: 10.1016/j.ijid.2024.107221
Popis: Objectives: Computer-aided detection (CAD) software packages quantify tuberculosis (TB)-compatible chest X-ray (CXR) abnormality as continuous scores. In practice, a threshold value is selected for binary CXR classification. We assessed the diagnostic accuracy of an alternative approach to applying CAD for TB triage: incorporating CAD scores in multivariable modeling. Methods: We pooled individual patient data from four studies. Separately, for two commercial CAD, we used logistic regression to model microbiologically confirmed TB. Models included CAD score, study site, age, sex, human immunodeficiency virus status, and prior TB. We compared specificity at target sensitivities ≥90% between the multivariable model and the current threshold-based approach for CAD use. Results: We included 4,733/5,640 (84%) participants with complete covariate data (median age 36 years; 45% female; 22% with prior TB; 22% people living with human immunodeficiency virus). A total of 805 (17%) had TB. Multivariable models demonstrated excellent performance (areas under the receiver operating characteristic curve [95% confidence interval]: software A, 0.91 [0.90-0.93]; software B, 0.92 [0.91-0.93]). Compared with threshold scores, multivariable models increased specificity (e.g., at 90% sensitivity, threshold vs model specificity [95% confidence interval]: software A, 71% [68-74%] vs 75% [74-77%]; software B, 69% [63-75%] vs 75% [74-77%]). Conclusion: Using CAD scores in multivariable models outperformed the current practice of CAD-threshold-based CXR classification for TB diagnosis.
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