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
| Autor: | Arjun Chandna, Raman Mahajan, Priyanka Gautam, Lazaro Mwandigha, Karthik Gunasekaran, Divendu Bhusan, Arthur T L Cheung, Nicholas Day, Sabine Dittrich, Arjen Dondorp, Tulasi Geevar, Srinivasa R Ghattamaneni, Samreen Hussain, Carolina Jimenez, Rohini Karthikeyan, Sanjeev Kumar, Shiril Kumar, Vikash Kumar, Debasree Kundu, Ankita Lakshmanan, Abi Manesh, Chonticha Menggred, Mahesh Moorthy, Jennifer Osborn, Melissa Richard-Greenblatt, Sadhana Sharma, Veena K Singh, Vikash K Singh, Javvad Suri, Shuichi Suzuki, Jaruwan Tubprasert, Paul Turner, Annavi M G Villanueva, Naomi Waithira, Pragya Kumar, George M Varghese, Constantinos Koshiaris, Yoel Lubell, Sakib Burza |
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| Přispěvatelé: | AII - Infectious diseases, Intensive Care Medicine |
| Rok vydání: | 2022 |
| Předmět: |
Microbiology (medical)
Adult Models Statistical Interleukin-6 SARS-CoV-2 COVID-19 Reproducibility of Results low- and middle-income country Prognosis Patient Discharge Receptors Urokinase Plasminogen Activator LMIC Infectious Diseases Disease Progression prognostic model Humans Patient Safety Prospective Studies triage |
| Zdroj: | Clinical infectious diseases, 75(1), e368-e379. Oxford University Press |
| ISSN: | 1537-6591 1058-4838 |
| Popis: | 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 30 BPM; SpO2/FiO2 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. |
| Databáze: | OpenAIRE |
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