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. (21st March 2022) - Chandna, Arjun; Mahajan, Raman; Gautam, Priyanka; Mwandigha, Lazaro; Gunasekaran, Karthik; Bhusan, Divendu; Cheung, Arthur T L; Day, Nicholas; Dittrich, Sabine; Dondorp, Arjen; Geevar, Tulasi; Ghattamaneni, Srinivasa R; et al.
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. Abstract : We report 3 clinical prediction models to help identify which patients with moderate COVID-19 can be safely managed in the community. Each model contains 3 easily ascertained clinical parameters and 1 biochemical biomarker, measurable with a commercially available rapid test.