TY - JOUR
T1 - Facilitating Safe Discharge Through Predicting Disease Progression in Moderate Coronavirus Disease 2019 (COVID-19)
T2 - A Prospective Cohort Study to Develop and Validate a Clinical Prediction Model in Resource-Limited Settings
AU - Chandna, Arjun
AU - Mahajan, Raman
AU - Gautam, Priyanka
AU - Mwandigha, Lazaro
AU - Gunasekaran, Karthik
AU - Bhusan, Divendu
AU - Cheung, Arthur T. L.
AU - Day, Nicholas
AU - Dittrich, Sabine
AU - Dondorp, Arjen
AU - Geevar, Tulasi
AU - Ghattamaneni, Srinivasa R.
AU - Hussain, Samreen
AU - Jimenez, Carolina
AU - Karthikeyan, Rohini
AU - Kumar, Sanjeev
AU - Kumar, Shiril
AU - Kumar, Vikash
AU - Kundu, Debasree
AU - Lakshmanan, Ankita
AU - Manesh, Abi
AU - Menggred, Chonticha
AU - Moorthy, Mahesh
AU - Osborn, Jennifer
AU - Richard-Greenblatt, Melissa
AU - Sharma, Sadhana
AU - Singh, Veena K.
AU - Singh, Vikash K.
AU - Suri, Javvad
AU - Suzuki, Shuichi
AU - Tubprasert, Jaruwan
AU - Turner, Paul
AU - Villanueva, Annavi M. G.
AU - Waithira, Naomi
AU - Kumar, Pragya
AU - Varghese, George M.
AU - Koshiaris, Constantinos
AU - Lubell, Yoel
AU - Burza, Sakib
N1 - Publisher Copyright: © 2022 The Author(s). Published by Oxford University Press for the Infectious Diseases Society of America.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
AB - 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.
KW - COVID-19
KW - LMIC
KW - low- and middle-income country
KW - prognostic model
KW - triage
UR - http://www.scopus.com/inward/record.url?scp=85137125570&partnerID=8YFLogxK
U2 - https://doi.org/10.1093/cid/ciac224
DO - https://doi.org/10.1093/cid/ciac224
M3 - Article
C2 - 35323932
SN - 1058-4838
VL - 75
SP - e368-e379
JO - Clinical Infectious Diseases
JF - Clinical Infectious Diseases
IS - 1
ER -