Pattern of abnormalities amongst chest X-rays of adults undergoing computer-assisted digital chest X-ray screening for tuberculosis in Peri-Urban Blantyre, Malawi: A cross-sectional study.
digital health
epidemiology
radiography
screening
tuberculosis
Journal
Tropical medicine & international health : TM & IH
ISSN: 1365-3156
Titre abrégé: Trop Med Int Health
Pays: England
ID NLM: 9610576
Informations de publication
Date de publication:
11 2021
11 2021
Historique:
pubmed:
24
7
2021
medline:
1
1
2022
entrez:
23
7
2021
Statut:
ppublish
Résumé
The prevalence of diseases other than tuberculosis (TB) detected during chest X-ray screening is poorly described in sub-Saharan Africa. Computer-assisted digital chest X-ray technology is available for TB screening and has the potential to be a screening tool for non-communicable diseases as well. Low- and middle-income countries are in a transition period where the burden of non-communicable diseases is increasing, but health systems are mainly focused on addressing infectious diseases. Participants were adults undergoing computer-assisted chest X-ray screening for tuberculosis in a community-wide tuberculosis prevalence survey in Blantyre, Malawi. Adults with abnormal radiographs by field radiographer interpretation were evaluated by a physician in a community-based clinic. X-ray classifications were compared to classifications of a random sample of normal chest X-rays by radiographer interpretation. Radiographic features were classified using WHO Integrated Management for Adult Illnesses (IMAI) guidelines. All radiographs taken at the screening tent were analysed by the Qure.ai qXR v2.0 software. 5% (648/13,490) of adults who underwent chest radiography were identified to have an abnormal chest X-ray by the radiographer. 387 (59.7%) of the participants attended the X-ray clinic, and another 387 randomly sampled normal X-rays were available for comparison. Participants who were referred to the community clinic had a significantly higher HIV prevalence than those who had been identified to have a normal CXR by the field radiographer (90 [23.3%] vs. 43 [11.1%] p-value < 0.001). The commonest radiographic finding was cardiomegaly (20.7%, 95% CI 18.0-23.7). One in five (81/387) chest X-rays were misclassified by the radiographer. The overall mean Qure.ai qXR v2.0 score for all reviewed X-rays was 0.23 (SD 0.20). There was a high concordance of cardiomegaly classification between the physician and the computer-assisted software (109/118, 92.4%). There is a high burden of cardiomegaly on a chest X-ray at a community level, much of which is in patients with diabetes, heart disease and high blood pressure. Cardiomegaly on chest X-ray may be a potential tool for screening for cardiovascular NCDs at the primary care level as well as in the community.
Sections du résumé
BACKGROUND
The prevalence of diseases other than tuberculosis (TB) detected during chest X-ray screening is poorly described in sub-Saharan Africa. Computer-assisted digital chest X-ray technology is available for TB screening and has the potential to be a screening tool for non-communicable diseases as well. Low- and middle-income countries are in a transition period where the burden of non-communicable diseases is increasing, but health systems are mainly focused on addressing infectious diseases.
METHODS
Participants were adults undergoing computer-assisted chest X-ray screening for tuberculosis in a community-wide tuberculosis prevalence survey in Blantyre, Malawi. Adults with abnormal radiographs by field radiographer interpretation were evaluated by a physician in a community-based clinic. X-ray classifications were compared to classifications of a random sample of normal chest X-rays by radiographer interpretation. Radiographic features were classified using WHO Integrated Management for Adult Illnesses (IMAI) guidelines. All radiographs taken at the screening tent were analysed by the Qure.ai qXR v2.0 software.
RESULTS
5% (648/13,490) of adults who underwent chest radiography were identified to have an abnormal chest X-ray by the radiographer. 387 (59.7%) of the participants attended the X-ray clinic, and another 387 randomly sampled normal X-rays were available for comparison. Participants who were referred to the community clinic had a significantly higher HIV prevalence than those who had been identified to have a normal CXR by the field radiographer (90 [23.3%] vs. 43 [11.1%] p-value < 0.001). The commonest radiographic finding was cardiomegaly (20.7%, 95% CI 18.0-23.7). One in five (81/387) chest X-rays were misclassified by the radiographer. The overall mean Qure.ai qXR v2.0 score for all reviewed X-rays was 0.23 (SD 0.20). There was a high concordance of cardiomegaly classification between the physician and the computer-assisted software (109/118, 92.4%).
CONCLUSION
There is a high burden of cardiomegaly on a chest X-ray at a community level, much of which is in patients with diabetes, heart disease and high blood pressure. Cardiomegaly on chest X-ray may be a potential tool for screening for cardiovascular NCDs at the primary care level as well as in the community.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1427-1437Subventions
Organisme : Wellcome Trust
ID : 200901/Z/16/Z
Pays : United Kingdom
Informations de copyright
© 2021 The Authors Tropical Medicine & International Health Published by John Wiley & Sons Ltd.
Références
World Health Organization. Global Tuberculosis Report. Blood. Geneva, Switzerland: World Health Organization; 2015. Available from: http://www.who.int/tb/publications/global_report/en/index.html
MacPherson P, Khundi McEwen, Nliwasa M, Choko AT, Phiri VK, Webb EL, et al. Disparities in access to diagnosis and care in Blantyre, Malawi, identified through enhanced tuberculosis surveillance and spatial analysis. BMC Med. 2019;17(1):1-11.
Ho J, Fox GJ, Marais BJ. Passive case finding for tuberculosis is not enough. Int J Mycobacteriol. 2016;5(4):374-8.
Mungai BN, Joekes E, Masini E, Obasi A, Manduku V, Mugi B, et al. “If not TB, what could it be?” Chest X-ray findings from the 2016 Kenya Tuberculosis Prevalence Survey. Thorax. 2021;76(6):607-14.
Murphy K, Habib SS, Zaidi SMA, Khowaja S, Khan A, Melendez J, et al. Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system. Sci Rep. 2020;10(1):1-11.
Elsey H, Agyepong I, Huque R, Quayyem Z, Baral S, Ebenso B, et al. Rethinking health systems in the context of urbanisation: challenges from four rapidly urbanising low-income and middle-income countries. BMJ Glob Heal. 2019;4(3):1-6.
Boutayeb A. The double burden of communicable and non-communicable diseases in developing countries. Trans R Soc Trop Med Hyg. 2006;100(3):191-9.
Peer N. The converging burdens of infectious and non-communicable diseases in rural-to-urban migrant Sub-Saharan African populations: A focus on HIV/AIDS, tuberculosis and cardio-metabolic diseases. Trop Dis Travel Med Vaccines. 2015;1(1):1-8.
de-Graft Aikins A. Ghana’s neglected chronic disease epidemic: a developmental challenge. Ghana Med J. 2007;41(4):154-9.
Cundale K, Wroe E, Matanje-Mwagomba BL, Muula AS, Gupta N, Berman J, et al. Reframing noncommunicable diseases and injuries for the poorest Malawians: The Malawi national NCDI poverty commission. Malawi Med J. 2017;29(2):194-7.
Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Medicine. 2006;3(11):2011-30.
Allain TJ, Aston S, Mapurisa G, Ganiza TN, Banda NP, Sakala S, et al. Age related patterns of disease and mortality in hospitalised adults in Malawi. PLoS One. 2017;12(1):1-13.
van Zyl Smit RN, Pai M, Yew WW, Leung CC, Zumla A, Bateman ED, et al. Global lung health: The colliding epidemics of tuberculosis, tobacco smoking, HIV and COPD. Eur Respiratory J. 2010;35(1):27-33.
Traoré Y, Bensghir R, Ihbibane F, OuladLashen A, Sodqi M, Marih L, et al. Diabetes and human immunodeficiency virus infection: Epidemiological, therapeutic aspects and patient experience. Press Medicale. 2016;45(6):e139-43.
Salindri AD, Wang J-Y, Lin H-H, Magee MJ. Post-tuberculosis incidence of diabetes, myocardial infarction, and stroke: Retrospective cohort analysis of patients formerly treated for tuberculosis in Taiwan, 2002-2013. Int J Infect Dis. 2019;84:127-30.
Huaman MA, Henson D, Ticona E, Sterling TR, Garvy BA. Tuberculosis and cardiovascular disease: linking the epidemics. Trop Dis Travel Med Vaccines. 2015;1(1):1-7.
Stevenson CR, Forouhi NG, Roglic G, Williams BG, Lauer JA, Dye C, et al. Diabetes and tuberculosis: The impact of the diabetes epidemic on tuberculosis incidence. BMC Public Health. 2007;7:1-8.
Mathers CD, Stevens GA, Boerma T, White RA, Tobias MI. Causes of international increases in older age life expectancy. Lancet. 2015;385(9967):540-8.
Hirschhorn LR, Kaaya SF, Garrity PS, Chopyak E, Fawzi MCS. Cancer and the “other” noncommunicable chronic diseases in older people living with HIV/AIDS in resource-limited settings: a challenge to success. AIDS. 2012;26(Suppl):S65-75.
Petersen M, Yiannoutsos CT, Justice A, Egger M. Observational research on NCDs in HIV-positive populations: conceptual and methodological considerations. J Acquir Immune Defic Syndr. 2014;67(Suppl 1):S8-S16.
World Health Organization. Acute Care - Integrated Management of Adolescent and Adult Illness (IMAI) modules [Internet]. WHO/CDS/IMAI/2004.1 Rev. 2; 2005. 16-17 p. Available from: http://www.who.int/3by5/publications/documents/imai/en/
Engle E, Gabrielian A, Long A, Hurt DE, Rosenthal A. Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis. PLoS One. 2020;15(1):1-19.
Wilkinson IB, Raine T, Wiles K, Goodhart A, Hall C, O’Neill H. Oxford Handbook of Clinical Medicine, 10th edn. Oxford University Press; 2017.
Malawi Standard Treatment Guidelines (MSTG). Ministry of Health Malawi, 5th edn; 2015.
Esmail H, Oni T, Thienemann F, Omar-Davies N, Wilkinson RJ, Ntsekhe M. Cardio-thoracic ratio is stable, reproducible and has potential as a screening tool for HIV-1 related cardiac disorders in resource poor settings. PLoS One. 2016;11(10):1-10.
Schwartz T, Magdi G, Steen TW, Sjaastad I. HIV as a risk factor for cardiac disease in Botswana: A cross-sectional study. Int Health. 2012;4(1):30-7.
Gwaba N, Isaacs F, Harneck M. Serosurvey and factors associated with Leishmania donovani infection in febrile HIV infected individuals attending Abuja Teaching Hospital, Nigeria. Med J Zambia. 2018;45(4):216-25.
Loomba RS, Shah PH, Nijhawan K, Aggarwal S, Arora R. Cardiothoracic ratio for prediction of left ventricular dilation: a systematic review and pooled analysis. Future Cardiol. 2015;11(2):171-5.
Mensah GA, Roth GA, Sampson UKA, Moran AE, Feigin VL, Forouzanfar MH, et al. Mortality from cardiovascular diseases in sub-Saharan Africa, 1990-2013: A systematic analysis of data from the Global Burden of Disease Study 2013. Cardiovasc J Afr. 2015;26(2):S6-10.
Miniati M, Monti S, Stolk J, Mirarchi G, Falaschi F, Rabinovich R, et al. Value of chest radiography in phenotyping chronic obstructive pulmonary disease. Eur Respir J. 2008;31(3):509-14.
Washko GR. Diagnostic imaging in COPD. Semin Respir Crit Care Med. 2010;31(3):276-85.
Mkoko P, Naidoo S, Mbanga LC, Nomvete F, Muloiwa R, Dlamini S. Chronic lung disease and a history of tuberculosis (Post-tuberculosis lung disease): Clinical features and in-hospital outcomes in a resource-limited setting with a high HIV burden. South African Med J. 2019;109(3):169-73.
Chin AT, Rylance J, Makumbirofa S, Meffert S, Vu T, Clayton J, et al. Chronic lung disease in adult recurrent tuberculosis survivors in Zimbabwe: a cohort study. Int J Tuberc Lung Dis. 2019;23(2):203-11.
Meghji J, Simpson H, Squire SB, Mortimer K. A systematic review of the prevalence and pattern of imaging defined post-TB lung disease. PLoS One. 2016;11(8):1-17.
WHO. Noncommunicable Diseases Country Profiles 2018 [Internet], vol. 369. World Health Organization; 2018:1336-43 p. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24088093
World Health Organisation. Malawi National STEPS Survey for Chronic Non-Communicable Diseases and their Risk Factors Final Report. 2009;(June):1-131. Available from: http://www.who.int/chp/steps/Malawi_2009_STEPS_Report.pdf
Ministry of Health Malawi. Malawi Population-based HIV Impact Assessment (MPHIA) 2015-16; 2018.
Qin ZZ, Sander MS, Rai B, Titahong CN, Sudrungrot S, Laah SN, et al. Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems. Sci Rep. 2019;9(1):1-10.
McCarthy K, Fielding K, Churchyard GJ, Grant AD. Empiric tuberculosis treatment in South African primary health care facilities - For whom, where, when and why: Implications for the development of tuberculosis diagnostic tests. PLoS One. 2018;13(1):1-14.
Imperial MZ, Nahid P, Phillips PPJ, Davies GR, Fielding K, Hanna D, et al. A patient-level pooled analysis of treatment-shortening regimens for drug-susceptible pulmonary tuberculosis. Nat Med. 2018;24(11):1708-15.
Lieberman R, Kwong H, Liu B, Huang HK. Computer-assisted detection (CAD) methodology for early detection of response to pharmaceutical therapy in tuberculosis patients. Med Imaging 2009 Comput Diagnosis. 2009;7260:726030.
Kachimanga C, Cundale K, Wroe E, Nazimera L, Jumbe A, Dunbar E, et al. Novel approaches to screening for noncommunicable diseases: Lessons from Neno, Malawi. Malawi Med J. 2017;29(2):78-83.