Arjun Chandna1,2, Raman Mahajan3, Priyanka Gautam4, Lazaro Mwandigha5, Karthik Gunasekaran6, Divendu Bhusan7, Arthur T L Cheung2,8, Nicholas Day2,8, Sabine Dittrich2,9, Arjen Dondorp2,8, Tulasi Geevar10, Srinivasa R Ghattamaneni3, Samreen Hussain3, Carolina Jimenez3, Rohini Karthikeyan4, Sanjeev Kumar11, Shiril Kumar12, Vikash Kumar3, Debasree Kundu4, Ankita Lakshmanan3, Abi Manesh4, Chonticha Menggred8, Mahesh Moorthy13, Jennifer Osborn9, Melissa Richard-Greenblatt14, Sadhana Sharma15, Veena K Singh16, Vikash K Singh3, Javvad Suri3, Shuichi Suzuki17, Jaruwan Tubprasert8, Paul Turner1,2, Annavi M G Villanueva17, Naomi Waithira2,8, Pragya Kumar18, George M Varghese4, Constantinos Koshiaris5, Yoel Lubell2,8, Sakib Burza3,19. 1. Cambodia Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia. 2. Centre for Tropical Medicine & Global Health, University of Oxford, Oxford, United Kingdom. 3. Médecins Sans Frontières, New Delhi, India. 4. Department of Infectious Diseases, Christian Medical College, Vellore, India. 5. Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom. 6. Department of Medicine, Christian Medical College, Vellore, India. 7. Department of Internal Medicine, All India Institute of Medical Sciences, Patna, India. 8. Mahidol Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand. 9. Foundation for Innovative Diagnostics, Geneva, Switzerland. 10. Department of Transfusion Medicine & Immunohaematology, Christian Medical College, Vellore, India. 11. Department of Cardiothoracic & Vascular Surgery, All India Institute of Medical Sciences, Patna, India. 12. Department of Virology, Rajendra Memorial Research Institute of Medical Sciences, Patna, India. 13. Department of Clinical Virology, Christian Medical College, Vellore, India. 14. Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. 15. Department of Biochemistry, All India Institute of Medical Sciences, Patna, India. 16. Department of Burns & Plastic Surgery, All India Institute of Medical Sciences, Patna, India. 17. School of Tropical Medicine & Global Health, Nagasaki University, Nagasaki, Japan. 18. Department of Community & Family Medicine, All India Institute of Medical Sciences, Patna, Indiaand. 19. Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, United Kingdom.
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.
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.
Authors: Richard D Riley; Joie Ensor; Kym I E Snell; Frank E Harrell; Glen P Martin; Johannes B Reitsma; Karel G M Moons; Gary Collins; Maarten van Smeden Journal: BMJ Date: 2020-03-18
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Authors: Laure Wynants; Ben Van Calster; Gary S Collins; Richard D Riley; Georg Heinze; Ewoud Schuit; Marc M J Bonten; Darren L Dahly; Johanna A A Damen; Thomas P A Debray; Valentijn M T de Jong; Maarten De Vos; Paul Dhiman; Maria C Haller; Michael O Harhay; Liesbet Henckaerts; Pauline Heus; Michael Kammer; Nina Kreuzberger; Anna Lohmann; Kim Luijken; Jie Ma; Glen P Martin; David J McLernon; Constanza L Andaur Navarro; Johannes B Reitsma; Jamie C Sergeant; Chunhu Shi; Nicole Skoetz; Luc J M Smits; Kym I E Snell; Matthew Sperrin; René Spijker; Ewout W Steyerberg; Toshihiko Takada; Ioanna Tzoulaki; Sander M J van Kuijk; Bas van Bussel; Iwan C C van der Horst; Florien S van Royen; Jan Y Verbakel; Christine Wallisch; Jack Wilkinson; Robert Wolff; Lotty Hooft; Karel G M Moons; Maarten van Smeden Journal: BMJ Date: 2020-04-07