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.Mahidol University2023-06-202023-06-202022-07-01Clinical Infectious Diseases Vol.75 No.1 (2022) , E368-E37910584838https://repository.li.mahidol.ac.th/handle/20.500.14594/87278Background: 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.MedicineArticleSCOPUS10.1093/cid/ciac2242-s2.0-851371255701537659135323932