Chronic lung lesions in COVID-19 survivors: predictive clinical model
Objective This study aimed to propose a simple, accessible and low-cost predictive clinical model to detect lung lesions due to COVID-19 infection.Design This prospective cohort study included COVID-19 survivors hospitalised between 30 March 2020 and 31 August 2020 followed-up 6 months after hospita...
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BMJ Publishing Group
2022-06-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/6/e059110.full |
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| author | Paulo A Lotufo Juliana C Ferreira Eloisa Bonfa Anna S Levin Rodrigo Caruso Chate Marta Imamura Esper G Kallas Roger Chammas Thais Mauad Izabel Marcilio Nelson Gouveia Ricardo Nitrini José Eduardo Krieger Marcio Valente Yamada Sawamura Michelle Louvaes Garcia Cristiano Gomes Guilherme Fonseca Jorge Hallak Luis Yu Marcio Mancini Maria Elizabeth Rossi Thiago Avelino-Silva Edivaldo M Utiyama Aluisio C Segurado Beatriz Perondi Anna Miethke-Morais Amanda C Montal Leila Harima Marjorie F Silva Marcelo C Rocha Maria Amélia de Jesus Carolina Carmo Clarice Tanaka Julio F M Marchini Thaís Guimarães Ester Sabino Carlos Roberto Ribeiro Carvalho Celina Almeida Lamas Diego Armando Cardona Cardenas Daniel Mario Lima Paula Gobi Scudeller João Marcos Salge Cesar Higa Nomura Marco Antonio Gutierrez Adriana L Araújo Bruno F Guedes Carolina S Lázari Cassiano C Antonio Claudia C Leite Emmanuel A Burdmann Euripedes C Miguel Fabio R Pinna Fabiane Y O Kawano Geraldo F Busatto Giovanni G Cerri Heraldo P Souza Izabel C Rios Larissa S Oliveira Linamara R Batisttella Luiz Henrique M Castro Marcello M C Magri Maria Cassia J M Corrêa Maria Cristina P B Francisco Maura S Oliveira Orestes V Forlenza Ricardo F Bento Rodolfo F Damiano Rossana P Francisco Solange R G Fusco Tarcisio E P Barros-Filho Wilson J Filho |
| author_facet | Paulo A Lotufo Juliana C Ferreira Eloisa Bonfa Anna S Levin Rodrigo Caruso Chate Marta Imamura Esper G Kallas Roger Chammas Thais Mauad Izabel Marcilio Nelson Gouveia Ricardo Nitrini José Eduardo Krieger Marcio Valente Yamada Sawamura Michelle Louvaes Garcia Cristiano Gomes Guilherme Fonseca Jorge Hallak Luis Yu Marcio Mancini Maria Elizabeth Rossi Thiago Avelino-Silva Edivaldo M Utiyama Aluisio C Segurado Beatriz Perondi Anna Miethke-Morais Amanda C Montal Leila Harima Marjorie F Silva Marcelo C Rocha Maria Amélia de Jesus Carolina Carmo Clarice Tanaka Julio F M Marchini Thaís Guimarães Ester Sabino Carlos Roberto Ribeiro Carvalho Celina Almeida Lamas Diego Armando Cardona Cardenas Daniel Mario Lima Paula Gobi Scudeller João Marcos Salge Cesar Higa Nomura Marco Antonio Gutierrez Adriana L Araújo Bruno F Guedes Carolina S Lázari Cassiano C Antonio Claudia C Leite Emmanuel A Burdmann Euripedes C Miguel Fabio R Pinna Fabiane Y O Kawano Geraldo F Busatto Giovanni G Cerri Heraldo P Souza Izabel C Rios Larissa S Oliveira Linamara R Batisttella Luiz Henrique M Castro Marcello M C Magri Maria Cassia J M Corrêa Maria Cristina P B Francisco Maura S Oliveira Orestes V Forlenza Ricardo F Bento Rodolfo F Damiano Rossana P Francisco Solange R G Fusco Tarcisio E P Barros-Filho Wilson J Filho |
| collection | DOAJ |
| description | Objective This study aimed to propose a simple, accessible and low-cost predictive clinical model to detect lung lesions due to COVID-19 infection.Design This prospective cohort study included COVID-19 survivors hospitalised between 30 March 2020 and 31 August 2020 followed-up 6 months after hospital discharge. The pulmonary function was assessed using the modified Medical Research Council (mMRC) dyspnoea scale, oximetry (SpO2), 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, SpO2, FVC and CXR findings were used to build a machine learning model for lung lesion detection on CT.Setting A tertiary hospital in Sao Paulo, Brazil.Participants 749 eligible RT-PCR-confirmed SARS-CoV-2-infected patients aged ≥18 years.Primary outcome measure A predictive clinical model for lung lesion detection on chest CT.Results 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, SpO2, FVC, CXR and CT, accurately detected pulmonary lesions by the joint data of CXR, mMRC scale, SpO2 and FVC (sensitivity, 0.85±0.08; specificity, 0.70±0.06; F1-score, 0.79±0.06 and area under the curve, 0.80±0.07).Conclusion 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. |
| format | Article |
| id | doaj-art-d6a2820edfa9450580e08d6915561038 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-d6a2820edfa9450580e08d69155610382025-01-24T02:35:19ZengBMJ Publishing GroupBMJ Open2044-60552022-06-0112610.1136/bmjopen-2021-059110Chronic lung lesions in COVID-19 survivors: predictive clinical model Paulo A Lotufo0Juliana C FerreiraEloisa Bonfa1Anna S Levin2Rodrigo Caruso Chate3Marta Imamura4Esper G Kallas5Roger ChammasThais MauadIzabel Marcilio6Nelson Gouveia7Ricardo Nitrini8José Eduardo KriegerMarcio Valente Yamada Sawamura9Michelle Louvaes Garcia10Cristiano GomesGuilherme FonsecaJorge HallakLuis YuMarcio ManciniMaria Elizabeth RossiThiago Avelino-SilvaEdivaldo M UtiyamaAluisio C SeguradoBeatriz PerondiAnna Miethke-MoraisAmanda C MontalLeila HarimaMarjorie F SilvaMarcelo C RochaMaria Amélia de JesusCarolina CarmoClarice TanakaJulio F M MarchiniThaís GuimarãesEster Sabino11Carlos Roberto Ribeiro Carvalho12Celina Almeida Lamas13Diego Armando Cardona Cardenas14Daniel Mario Lima15Paula Gobi Scudeller16João Marcos Salge17Cesar Higa Nomura18Marco Antonio Gutierrez19Adriana L AraújoBruno F GuedesCarolina S LázariCassiano C AntonioClaudia C LeiteEmmanuel A BurdmannEuripedes C MiguelFabio R PinnaFabiane Y O KawanoGeraldo F BusattoGiovanni G CerriHeraldo P SouzaIzabel C RiosLarissa S OliveiraLinamara R BatisttellaLuiz Henrique M CastroMarcello M C MagriMaria Cassia J M CorrêaMaria Cristina P B FranciscoMaura S OliveiraOrestes V ForlenzaRicardo F BentoRodolfo F DamianoRossana P FranciscoSolange R G FuscoTarcisio E P Barros-FilhoWilson J Filho6 Center for Clinical and Epidemiologic Research, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, BrazilHospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo, Rheumatology Division, Sao Paulo, BrazilDepartment of Infectious and Parasitic Diseases, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, SP, BrazilDepartment of Radiology, Hospital Israelita Albert Einstein, São Paulo, Brazil5 Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, BrazilDepartment of Infectious and Parasitic Diseases, University of Sao Paulo, São Paulo, São Paulo, BrazilHospital das Clinicas da Faculdade de Medicina da Universidade de São Paulo, University of São Paulo, São Paulo, Brazil1 Departamento de Medicina Preventiva, Universidade de Sao Paulo Faculdade de Medicina, Sao Paulo, BrazilDepartamento de Neurologia, Faculdade de Medicina da Universidade de São Paulo, Sao Paulo, BrazilInstituto de Radiologia, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Sao Paulo, BrazilDepartamento de Cardio-Pneumologia, Faculdade de Medicina da Universidade de São Paulo, Sao Paulo, Brazil7Department of Infectious Diseases, School of Medicine and Institute of Tropical Medicine, University of São Paulo, São Paulo, BrazilDiscipline of Pulmonology, Heart Institute (InCor), Hospital das Clínicas, Faculty of Medicine, University of São Paulo, São Paulo, BrazilInstituto do Coração—Divisão de Pneumologia, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, BrazilInstituto do Coração—Divisão de Informática, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, BrazilInstituto do Coração—Divisão de Informática, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, BrazilInstituto do Coração—Divisão de Pneumologia, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, BrazilInstituto do Coração—Divisão de Pneumologia, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, BrazilInstituto de Radiologia, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, BrazilInstituto do Coração—Divisão de Pneumologia, Universidade de São Paulo Hospital das Clínicas, Sao Paulo, BrazilObjective This study aimed to propose a simple, accessible and low-cost predictive clinical model to detect lung lesions due to COVID-19 infection.Design This prospective cohort study included COVID-19 survivors hospitalised between 30 March 2020 and 31 August 2020 followed-up 6 months after hospital discharge. The pulmonary function was assessed using the modified Medical Research Council (mMRC) dyspnoea scale, oximetry (SpO2), 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, SpO2, FVC and CXR findings were used to build a machine learning model for lung lesion detection on CT.Setting A tertiary hospital in Sao Paulo, Brazil.Participants 749 eligible RT-PCR-confirmed SARS-CoV-2-infected patients aged ≥18 years.Primary outcome measure A predictive clinical model for lung lesion detection on chest CT.Results 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, SpO2, FVC, CXR and CT, accurately detected pulmonary lesions by the joint data of CXR, mMRC scale, SpO2 and FVC (sensitivity, 0.85±0.08; specificity, 0.70±0.06; F1-score, 0.79±0.06 and area under the curve, 0.80±0.07).Conclusion 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.https://bmjopen.bmj.com/content/12/6/e059110.full |
| spellingShingle | Paulo A Lotufo Juliana C Ferreira Eloisa Bonfa Anna S Levin Rodrigo Caruso Chate Marta Imamura Esper G Kallas Roger Chammas Thais Mauad Izabel Marcilio Nelson Gouveia Ricardo Nitrini José Eduardo Krieger Marcio Valente Yamada Sawamura Michelle Louvaes Garcia Cristiano Gomes Guilherme Fonseca Jorge Hallak Luis Yu Marcio Mancini Maria Elizabeth Rossi Thiago Avelino-Silva Edivaldo M Utiyama Aluisio C Segurado Beatriz Perondi Anna Miethke-Morais Amanda C Montal Leila Harima Marjorie F Silva Marcelo C Rocha Maria Amélia de Jesus Carolina Carmo Clarice Tanaka Julio F M Marchini Thaís Guimarães Ester Sabino Carlos Roberto Ribeiro Carvalho Celina Almeida Lamas Diego Armando Cardona Cardenas Daniel Mario Lima Paula Gobi Scudeller João Marcos Salge Cesar Higa Nomura Marco Antonio Gutierrez Adriana L Araújo Bruno F Guedes Carolina S Lázari Cassiano C Antonio Claudia C Leite Emmanuel A Burdmann Euripedes C Miguel Fabio R Pinna Fabiane Y O Kawano Geraldo F Busatto Giovanni G Cerri Heraldo P Souza Izabel C Rios Larissa S Oliveira Linamara R Batisttella Luiz Henrique M Castro Marcello M C Magri Maria Cassia J M Corrêa Maria Cristina P B Francisco Maura S Oliveira Orestes V Forlenza Ricardo F Bento Rodolfo F Damiano Rossana P Francisco Solange R G Fusco Tarcisio E P Barros-Filho Wilson J Filho Chronic lung lesions in COVID-19 survivors: predictive clinical model BMJ Open |
| title | Chronic lung lesions in COVID-19 survivors: predictive clinical model |
| title_full | Chronic lung lesions in COVID-19 survivors: predictive clinical model |
| title_fullStr | Chronic lung lesions in COVID-19 survivors: predictive clinical model |
| title_full_unstemmed | Chronic lung lesions in COVID-19 survivors: predictive clinical model |
| title_short | Chronic lung lesions in COVID-19 survivors: predictive clinical model |
| title_sort | chronic lung lesions in covid 19 survivors predictive clinical model |
| url | https://bmjopen.bmj.com/content/12/6/e059110.full |
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