Browsing by Author "Breuninger M"

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    Breaking the threshold: Developing multivariable models using computer-aided chest X-ray analysis for tuberculosis triage.
    (2024-Oct) Geric C; Tavaziva G; Breuninger M; Dheda K; Esmail A; Scott A; Kagujje M; Muyoyeta M; Reither K; Khan AJ; Benedetti A; Ahmad Khan F; Tuberculosis Department, Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; IRD Global, Singapore.; Tuberculosis Department, Centre for Infectious Disease Research in Zambia, Lusaka, Zambia; Zambart, Lusaka, Zambia.; McGill International TB Centre, Research Institute of the McGill University Health Centre, Montreal, Canada; Department of Medicine, McGill University, Montreal, Canada; Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada. Electronic address: faiz.ahmadkhan@mcgill.ca.; McGill International TB Centre, Research Institute of the McGill University Health Centre, Montreal, Canada; Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.; Centre for Lung Infection and Immunity Unit, Division of Pulmonology and UCT Lung Institute, University of Cape Town, Cape Town, South Africa.; Centre for Lung Infection and Immunity Unit, Division of Pulmonology and UCT Lung Institute, University of Cape Town, Cape Town, South Africa; Faculty of Infectious and Tropical Diseases, Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.; Division of Infectious Diseases, Department I of Internal Medicine, University of Cologne, Cologne, Germany.; McGill International TB Centre, Research Institute of the McGill University Health Centre, Montreal, Canada; Department of Medicine, McGill University, Montreal, Canada; Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada.; Swiss Tropical and Public Health Institute, Allschwill, Switzerland; University of Basel, Basel, Switzerland.; McGill International TB Centre, Research Institute of the McGill University Health Centre, Montreal, Canada; Department of Medicine, McGill University, Montreal, Canada; Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
    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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    Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.
    (2022-Apr-28) Tavaziva G; Harris M; Abidi SK; Geric C; Breuninger M; Dheda K; Esmail A; Muyoyeta M; Reither K; Majidulla A; Khan AJ; Campbell JR; David PM; Denkinger C; Miller C; Nathavitharana R; Pai M; Benedetti A; Ahmad Khan F; Swiss Tropical and Public Health Institute, Basel, Switzerland.; IRD Global, Singapore.; World Health Organization, Geneva, Switzerland.; Département des Médicaments et Santé des Populations, Faculty of Pharmacy, Université de Montréal, Montreal, Canada.; Centre for Lung Infection and Immunity Unit, Division of Pulmonology and UCT Lung Institute, University of Cape Town, Cape Town, South Africa.; Clinical Addiction Research and Education Unit, Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts, USA.; McGill International TB Centre, Research Institute of the McGill University Health Centre, Montreal, Canada.; Division of Infectious Diseases, Department I of Internal Medicine, University of Cologne, Cologne, Germany.; Interactive Research & Development (IRD) Pakistan, Karachi, Pakistan.; Division of Tropical Medicine, Center of Infectious Diseases, University Hospital Heidelberg, Heidelberg, Germany.; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; University of Basel, Basel, Switzerland.; Division of Infectious Diseases, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.; Faculty of Infectious and Tropical Diseases, Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.; Departments of Medicine & Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada.; Zambart, Lusaka, Zambia.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
    BACKGROUND: Automated radiologic analysis using computer-aided detection software (CAD) could facilitate chest X-ray (CXR) use in tuberculosis diagnosis. There is little to no evidence on the accuracy of commercially available deep learning-based CAD in different populations, including patients with smear-negative tuberculosis and people living with human immunodeficiency virus (HIV, PLWH). METHODS: We collected CXRs and individual patient data (IPD) from studies evaluating CAD in patients self-referring for tuberculosis symptoms with culture or nucleic acid amplification testing as the reference. We reanalyzed CXRs with three CAD programs (CAD4TB version (v) 6, Lunit v3.1.0.0, and qXR v2). We estimated sensitivity and specificity within each study and pooled using IPD meta-analysis. We used multivariable meta-regression to identify characteristics modifying accuracy. RESULTS: We included CXRs and IPD of 3727/3967 participants from 4/7 eligible studies. 17% (621/3727) were PLWH. 17% (645/3727) had microbiologically confirmed tuberculosis. Despite using the same threshold score for classifying CXR in every study, sensitivity and specificity varied from study to study. The software had similar unadjusted accuracy (at 90% pooled sensitivity, pooled specificities were: CAD4TBv6, 56.9% [95% confidence interval {CI}: 51.7-61.9]; Lunit, 54.1% [95% CI: 44.6-63.3]; qXRv2, 60.5% [95% CI: 51.7-68.6]). Adjusted absolute differences in pooled sensitivity between PLWH and HIV-uninfected participants were: CAD4TBv6, -13.4% [-21.1, -6.9]; Lunit, +2.2% [-3.6, +6.3]; qXRv2: -13.4% [-21.5, -6.6]; between smear-negative and smear-positive tuberculosis was: were CAD4TBv6, -12.3% [-19.5, -6.1]; Lunit, -17.2% [-24.6, -10.5]; qXRv2, -16.6% [-24.4, -9.9]. Accuracy was similar to human readers. CONCLUSIONS: For CAD CXR analysis to be implemented as a high-sensitivity tuberculosis rule-out test, users will need threshold scores identified from their own patient populations and stratified by HIV and smear status.