{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T15:50:10Z","timestamp":1791993010129,"version":"4.4.0"},"reference-count":43,"publisher":"BMJ","issue":"6","license":[{"start":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T00:00:00Z","timestamp":1654992000000},"content-version":"unspecified","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2020\/07200-9"],"award-info":[{"award-number":["2020\/07200-9"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2022,4,21]]},"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>This study aimed to propose a simple, accessible and low-cost predictive clinical model to detect lung lesions due to COVID-19 infection.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Design<\/jats:title>\n                  <jats:p>This prospective cohort study included COVID-19 survivors hospitalised between 30 March 2020 and 31 August 2020 followed-up 6\u2009months after hospital discharge. The pulmonary function was assessed using the modified Medical Research Council (mMRC) dyspnoea scale, oximetry (SpO<jats:sub>2<\/jats:sub>), spirometry (forced vital capacity (FVC)) and chest X-ray (CXR) during an in-person consultation. Patients with abnormalities in at least one of these parameters underwent chest CT. mMRC scale, SpO<jats:sub>2<\/jats:sub>, FVC and CXR findings were used to build a machine learning model for lung lesion detection on CT.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Setting<\/jats:title>\n                  <jats:p>A tertiary hospital in Sao Paulo, Brazil.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Participants<\/jats:title>\n                  <jats:p>749 eligible RT-PCR-confirmed SARS-CoV-2-infected patients aged \u226518 years.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Primary outcome measure<\/jats:title>\n                  <jats:p>A predictive clinical model for lung lesion detection on chest CT.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>There were 470 patients (63%) that had at least one sign of pulmonary involvement and were eligible for CT. Almost half of them (48%) had significant pulmonary abnormalities, including ground-glass opacities, parenchymal bands, reticulation, traction bronchiectasis and architectural distortion. The machine learning model, including the results of 257 patients with complete data on mMRC, SpO<jats:sub>2<\/jats:sub>, FVC, CXR and CT, accurately detected pulmonary lesions by the joint data of CXR, mMRC scale, SpO<jats:sub>2<\/jats:sub> and FVC (sensitivity, 0.85\u00b10.08; specificity, 0.70\u00b10.06; F1-score, 0.79\u00b10.06 and area under the curve, 0.80\u00b10.07).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>A predictive clinical model based on CXR, mMRC, oximetry and spirometry data can accurately screen patients with lung lesions after SARS-CoV-2 infection. Given that these examinations are highly accessible and low cost, this protocol can be automated and implemented in different countries for early detection of COVID-19 sequelae.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1136\/bmjopen-2021-059110","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T11:36:51Z","timestamp":1655120211000},"page":"e059110","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":9,"title":["Chronic lung lesions in COVID-19 survivors: predictive clinical model"],"prefix":"10.1136","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1618-8509","authenticated-orcid":false,"given":"Carlos Roberto Ribeiro","family":"Carvalho","sequence":"first","affiliation":[{"name":"Instituto do Cora\u00e7\u00e3o\u2014Divis\u00e3o de Pneumologia, Universidade de S\u00e3o Paulo Hospital das Cl\u00ednicas, Sao Paulo, 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