Facilitating Safe Discharge Through Predicting Disease Progression in Moderate Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study to Develop and Validate a Clinical Prediction Model in Resource-Limited Settings
Issued Date
2022-07-01
Resource Type
ISSN
10584838
eISSN
15376591
Scopus ID
2-s2.0-85137125570
Pubmed ID
35323932
Journal Title
Clinical Infectious Diseases
Volume
75
Issue
1
Start Page
E368
End Page
E379
Rights Holder(s)
SCOPUS
Bibliographic Citation
Clinical Infectious Diseases Vol.75 No.1 (2022) , E368-E379
Suggested Citation
Chandna A., Mahajan R., Gautam P., Mwandigha L., Gunasekaran K., Bhusan D., Cheung A.T.L., Day N., Dittrich S., Dondorp A., Geevar T., Ghattamaneni S.R., Hussain S., Jimenez C., Karthikeyan R., Kumar S., Kumar S., Kumar V., Kundu D., Lakshmanan A., Manesh A., Menggred C., Moorthy M., Osborn J., Richard-Greenblatt M., Sharma S., Singh V.K., Singh V.K., Suri J., Suzuki S., Tubprasert J., Turner P., Villanueva A.M.G., Waithira N., Kumar P., Varghese G.M., Koshiaris C., Lubell Y., Burza S. Facilitating Safe Discharge Through Predicting Disease Progression in Moderate Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study to Develop and Validate a Clinical Prediction Model in Resource-Limited Settings. Clinical Infectious Diseases Vol.75 No.1 (2022) , E368-E379. E379. doi:10.1093/cid/ciac224 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/87278
Title
Facilitating Safe Discharge Through Predicting Disease Progression in Moderate Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study to Develop and Validate a Clinical Prediction Model in Resource-Limited Settings
Author(s)
Chandna A.
Mahajan R.
Gautam P.
Mwandigha L.
Gunasekaran K.
Bhusan D.
Cheung A.T.L.
Day N.
Dittrich S.
Dondorp A.
Geevar T.
Ghattamaneni S.R.
Hussain S.
Jimenez C.
Karthikeyan R.
Kumar S.
Kumar S.
Kumar V.
Kundu D.
Lakshmanan A.
Manesh A.
Menggred C.
Moorthy M.
Osborn J.
Richard-Greenblatt M.
Sharma S.
Singh V.K.
Singh V.K.
Suri J.
Suzuki S.
Tubprasert J.
Turner P.
Villanueva A.M.G.
Waithira N.
Kumar P.
Varghese G.M.
Koshiaris C.
Lubell Y.
Burza S.
Mahajan R.
Gautam P.
Mwandigha L.
Gunasekaran K.
Bhusan D.
Cheung A.T.L.
Day N.
Dittrich S.
Dondorp A.
Geevar T.
Ghattamaneni S.R.
Hussain S.
Jimenez C.
Karthikeyan R.
Kumar S.
Kumar S.
Kumar V.
Kundu D.
Lakshmanan A.
Manesh A.
Menggred C.
Moorthy M.
Osborn J.
Richard-Greenblatt M.
Sharma S.
Singh V.K.
Singh V.K.
Suri J.
Suzuki S.
Tubprasert J.
Turner P.
Villanueva A.M.G.
Waithira N.
Kumar P.
Varghese G.M.
Koshiaris C.
Lubell Y.
Burza S.
Author's Affiliation
Angkor Hospital for Children
All India Institute of Medical Sciences, Patna
Rajendra Memorial Research Institute of Medical Sciences
London School of Hygiene & Tropical Medicine
Mahidol University
Nagasaki University
Nuffield Department of Medicine
Medecins Sans Frontieres
University of Pennsylvania Perelman School of Medicine
University of Oxford Medical Sciences Division
Christian Medical College, Vellore
Foundation for Innovative Diagnostics
All India Institute of Medical Sciences, Patna
Rajendra Memorial Research Institute of Medical Sciences
London School of Hygiene & Tropical Medicine
Mahidol University
Nagasaki University
Nuffield Department of Medicine
Medecins Sans Frontieres
University of Pennsylvania Perelman School of Medicine
University of Oxford Medical Sciences Division
Christian Medical College, Vellore
Foundation for Innovative Diagnostics
Other Contributor(s)
Abstract
Background: In locations where few people have received coronavirus disease 2019 (COVID-19) vaccines, health systems remain vulnerable to surges in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Tools to identify patients suitable for community-based management are urgently needed. Methods: We prospectively recruited adults presenting to 2 hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 to develop and validate a clinical prediction model to rule out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2<94%; respiratory rate>30 BPM; SpO2/FiO2<400; or death. We specified a priori that each model would contain three clinical parameters (age, sex, and SpO2) and 1 of 7 shortlisted biochemical biomarkers measurable using commercially available rapid tests (C-reactive protein [CRP], D-dimer, interleukin 6 [IL-6], neutrophil-to-lymphocyte ratio [NLR], procalcitonin [PCT], soluble triggering receptor expressed on myeloid cell-1 [sTREM-1], or soluble urokinase plasminogen activator receptor [suPAR]), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration, and clinical utility of the models in a held-out temporal external validation cohort. Results: In total, 426 participants were recruited, of whom 89 (21.0%) met the primary outcome; 257 participants comprised the development cohort, and 166 comprised the validation cohort. The 3 models containing NLR, suPAR, or IL-6 demonstrated promising discrimination (c-statistics: 0.72-0.74) and calibration (calibration slopes: 1.01-1.05) in the validation cohort and provided greater utility than a model containing the clinical parameters alone. Conclusions: We present 3 clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources.