Browsing by Author "Dheda K"

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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.
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.
Diagnostic yield of urine lipoarabinomannan and sputum tuberculosis tests in people living with HIV: a systematic review and meta-analysis of individual participant data.
(2023-Jun) Broger T; Koeppel L; Huerga H; Miller P; Gupta-Wright A; Blanc FX; Esmail A; Reeve BWP; Floridia M; Kerkhoff AD; Ciccacci F; Kasaro MP; Thit SS; Bastard M; Ferlazzo G; Yoon C; Van Hoving DJ; Sossen B; García JI; Cummings MJ; Wake RM; Hanson J; Cattamanchi A; Meintjes G; Maartens G; Wood R; Theron G; Dheda K; Olaru ID; Denkinger CM; Population Health Program, Tuberculosis Group, Texas Biomedical Research Institute, San Antonio, TX, USA.; National Center for Global Health, Istituto Superiore di Sanità, Rome, Italy.; Division of HIV, Infectious Diseases and Global Medicine, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Trauma Center, University of California San Francisco, San Francisco, CA, USA; Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA.; Division of Emergency Medicine, University of Cape Town, Cape Town, South Africa; Division of Emergency Medicine, Stellenbosch University, Cape Town, South Africa.; The Kirby Institute, University of New South Wales, Sydney, NSW, Australia.; Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.; Faculty of Infectious and Tropical Diseases, Department of Immunology and Infection, London School of Hygiene & Tropical Medicine, London, UK; Centre for Lung Infection and Immunity, Division of Pulmonology, Department of Medicine and UCT Lung Institute, University of Cape Town, Cape Town, South Africa; South African MRC Centre for the Study of Antimicrobial Resistance, University of Cape Town, Cape Town, South Africa.; Division of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Irving Medical Center, New York, NY, USA; Center for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY, USA.; Division of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany.; New Zealand Institute for Plant and Food Research, Auckland, New Zealand.; Department of Medicine, University of Cape Town, Cape Town, South Africa; Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.; Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA; Department of Medicine, Division of Pulmonary Diseases and Critical Care Medicine, University of California Irvine, Irvine, CA, USA.; Department of Medicine, University of Cape Town, Cape Town, South Africa; Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa.; UniCamillus, International University of Health and Medical Science, Rome, Italy; Community of Sant'Egidio, DREAM programme, Rome, Italy.; Field Epidemiology Department, Epicentre, Paris, France.; Department of Medicine, Médecins Sans Frontières, Paris, France.; Division of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany; Clinical Research Department, London School of Hygiene & Tropical Medicine, London, UK.; Centre for Lung Infection and Immunity, Division of Pulmonology, Department of Medicine and UCT Lung Institute, University of Cape Town, Cape Town, South Africa; South African MRC Centre for the Study of Antimicrobial Resistance, University of Cape Town, Cape Town, South Africa.; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia; UNC Global Projects, LLC Zambia, Lusaka, Zambia.; Institute for Global Health, University College London, London, UK; Clinical Research Department, London School of Hygiene & Tropical Medicine, London, UK.; Centre for Healthcare-Associated Infections, Antimicrobial Resistance and Mycoses, National Institute for Communicable Diseases, Johannesburg, South Africa; Institute for Infection and Immunity, St George's University of London, London, UK.; Department of Medicine, Division of Pulmonary and Critical Care Medicine, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA.; Department of Medicine, University of Medicine 2, Yangon, Myanmar.; Service de Pneumologie, l'institut du thorax, Nantes Université, CHU Nantes, Nantes, France.; Division of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany; German Center for Infection Research, partner site, Heidelberg University Hospital, Heidelberg, Germany. Electronic address: claudia.denkinger@uni-heidelberg.de.; DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; South African Medical Research Council Centre for Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
BACKGROUND: Sputum is the most widely used sample to diagnose active tuberculosis, but many people living with HIV are unable to produce sputum. Urine, in contrast, is readily available. We hypothesised that sample availability influences the diagnostic yield of various tuberculosis tests. METHODS: In this systematic review and meta-analysis of individual participant data, we compared the diagnostic yield of point-of-care urine-based lipoarabinomannan tests with that of sputum-based nucleic acid amplification tests (NAATs) and sputum smear microscopy (SSM). We used microbiologically confirmed tuberculosis based on positive culture or NAAT from any body site as the denominator and accounted for sample provision. We searched PubMed, Web of Science, Embase, African Journals Online, and clinicaltrials.gov from database inception to Feb 24, 2022 for randomised controlled trials, cross-sectional studies, and cohort studies that assessed urine lipoarabinomannan point-of-care tests and sputum NAATs for active tuberculosis detection in participants irrespective of tuberculosis symptoms, HIV status, CD4 cell count, or study setting. We excluded studies in which recruitment was not consecutive, systematic, or random; provision of sputum or urine was an inclusion criterion; less than 30 participants were diagnosed with tuberculosis; early research assays without clearly defined cutoffs were tested; and humans were not studied. We extracted study-level data, and authors of eligible studies were invited to contribute deidentified individual participant data. The main outcomes were the tuberculosis diagnostic yields of urine lipoarabinomannan tests, sputum NAATs, and SSM. Diagnostic yields were predicted using Bayesian random-effects and mixed-effects meta-analyses. This study is registered with PROSPERO, CRD42021230337. FINDINGS: We identified 844 records, from which 20 datasets and 10 202 participants (4561 [45%] male participants and 5641 [55%] female participants) were included in the meta-analysis. All studies assessed sputum Xpert (MTB/RIF or Ultra, Cepheid, Sunnyvale, CA, USA) and urine Alere Determine TB LAM (AlereLAM, Abbott, Chicago, IL, USA) in people living with HIV aged 15 years or older. Nearly all (9957 [98%] of 10 202) participants provided urine, and 82% (8360 of 10 202) provided sputum within 2 days. In studies that enrolled unselected inpatients irrespective of tuberculosis symptoms, only 54% (1084 of 1993) of participants provided sputum, whereas 99% (1966 of 1993) provided urine. Diagnostic yield was 41% (95% credible interval [CrI] 15-66) for AlereLAM, 61% (95% Crl 25-88) for Xpert, and 32% (95% Crl 10-55) for SSM. Heterogeneity existed across studies in the diagnostic yield, influenced by CD4 cell count, tuberculosis symptoms, and clinical setting. In predefined subgroup analyses, all tests had higher yields in symptomatic participants, and AlereLAM yield was higher in those with low CD4 counts and inpatients. AlereLAM and Xpert yields were similar among inpatients in studies enrolling unselected participants who were not assessed for tuberculosis symptoms (51% vs 47%). AlereLAM and Xpert together had a yield of 71% in unselected inpatients, supporting the implementation of combined testing strategies. INTERPRETATION: AlereLAM, with its rapid turnaround time and simplicity, should be prioritised to inform tuberculosis therapy among inpatients who are HIV-positive, regardless of symptoms or CD4 cell count. The yield of sputum-based tuberculosis tests is undermined by people living with HIV who cannot produce sputum, whereas nearly all participants are able to provide urine. The strengths of this meta-analysis are its large size, the carefully harmonised denominator, and the use of Bayesian random-effects and mixed-effects models to predict yields; however, data were geographically restricted, clinically diagnosed tuberculosis was not considered in the denominator, and little information exists on strategies for obtaining sputum samples. FUNDING: FIND, the Global Alliance for Diagnostics.