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Facilitating safe discharge through predicting disease progression in moderate COVID-19: a prospective cohort study to develop and validate a clinical prediction model in resource-limited settings
Arjun Chandna MBBS; Raman Mahajan MPH; Priyanka Gautam MPH; Lazaro Mwandigha PhD; Karthik Gunasekaran MD; Divendu Bhusan MD; Arthur T L Cheung MSc; Nicholas Day FMedSci; Sabine Dittrich PhD; Arjen Dondorp FMedSci; Tulasi Geevar MD; Srinivasa R Ghattamaneni MSc; Samreen Hussain MBBS; Carolina Jimenez MD; Rohini Karthikeyan MSW; Sanjeev Kumar MCh; Shiril Kumar PhD; Vikash Kumar PGDPH; Debasree Kundu PhD; Ankita Lakshmanan MBBS; Abi Manesh DM; Chonticha Menggred B.Ed; Mahesh Moorthy MD; Jennifer Osborn PhD; Melissa Richard-Greenblatt PhD; Sadhana Sharma PhD; Veena K Singh MCh; Vikash K Singh BMLT; Javvad Suri MPH; Shuichi Suzuki MPH; Jaruwan Tubprasert MSc; Paul Turner PhD; Annavi M G Villanueva PhD; Naomi Waithira MSc; Pragya Kumar MD; George M Varghese FRCP; Constantinos Koshiaris Dphil; Yoel Lubell PhD; Sakib Burza PhD.
Affiliation
  • Arjun Chandna MBBS; Cambodia Oxfod Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia
  • Raman Mahajan MPH; Medecins Sans Frontieres, New Delhi, India
  • Priyanka Gautam MPH; Department of Infectious Diseases, Christian Medical College, Vellore, India
  • Lazaro Mwandigha PhD; Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
  • Karthik Gunasekaran MD; Department of Medicine, Christian Medical College, Vellore, India
  • Divendu Bhusan MD; Department of Internal Medicine, All India Institute of Medical Sciences, Patna, India
  • Arthur T L Cheung MSc; Mahidol Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
  • Nicholas Day FMedSci; Mahidol Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
  • Sabine Dittrich PhD; Foundation for Innovative Diagnostics, Geneva, Switzerland
  • Arjen Dondorp FMedSci; Mahidol Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
  • Tulasi Geevar MD; Department of Transfusion Medicine & Immunohaematology, Christian Medical College, Vellore, India
  • Srinivasa R Ghattamaneni MSc; Medecins Sans Frontieres, New Delhi, India
  • Samreen Hussain MBBS; Medecins Sans Frontieres, New Delhi, India
  • Carolina Jimenez MD; Medecins Sans Frontieres, New Delhi, India
  • Rohini Karthikeyan MSW; Department of Infectious Diseases, Christian Medical College, Vellore, India
  • Sanjeev Kumar MCh; Department of Cardiothoracic & Vascular Surgery, All India Institute of Medical Sciences, Patna, India
  • Shiril Kumar PhD; Department of Virology, Rajendra Memorial Research Institute of Medical Sciences, Patna, India
  • Vikash Kumar PGDPH; Medecins Sans Frontieres, New Delhi, India
  • Debasree Kundu PhD; Department of Infectious Diseases, Christian Medical College, Vellore, India
  • Ankita Lakshmanan MBBS; Medecins Sans Frontieres, New Delhi, India
  • Abi Manesh DM; Department of Infectious Diseases, Christian Medical College, Vellore, India
  • Chonticha Menggred B.Ed; Mahidol Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
  • Mahesh Moorthy MD; Department of Clinical Virology, Christian Medical College, Vellore, India
  • Jennifer Osborn PhD; Foundation for Innovative Diagnostics, Geneva, Switzerland
  • Melissa Richard-Greenblatt PhD; Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
  • Sadhana Sharma PhD; Department of Biochemistry, All India Institute of Medical Sciences, Patna, India
  • Veena K Singh MCh; Department of Burns & Plastic Surgery, All India Institute of Medical Sciences, Patna, India
  • Vikash K Singh BMLT; Medecins Sans Frontieres, New Delhi, India
  • Javvad Suri MPH; Medecins Sans Frontieres, New Delhi, India
  • Shuichi Suzuki MPH; School of Tropical Medicine & Global Health, Nagasaki University, Japan
  • Jaruwan Tubprasert MSc; Mahidol Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
  • Paul Turner PhD; Cambodia Oxfod Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia
  • Annavi M G Villanueva PhD; School of Tropical Medicine & Global Health, Nagasaki University, Japan
  • Naomi Waithira MSc; Mahidol Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
  • Pragya Kumar MD; Department of Community & Family Medicine, All India Institute of Medical Sciences, Patna, India
  • George M Varghese FRCP; Department of Infectious Diseases, Christian Medical College, Vellore, India
  • Constantinos Koshiaris Dphil; Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
  • Yoel Lubell PhD; Mahidol Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
  • Sakib Burza PhD; Medecins Sans Frontieres, New Delhi, India
Preprint in English | medRxiv | ID: ppmedrxiv-21267170
ABSTRACT
BackgroundIn locations where few people have received COVID-19 vaccines, health systems remain vulnerable to surges in SARS-CoV-2 infections. Tools to identify patients suitable for community-based management are urgently needed. MethodsWe prospectively recruited adults presenting to two hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 in order 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 one of seven shortlisted biochemical biomarkers measurable using near-patient tests (CRP, D-dimer, IL-6, NLR, PCT, sTREM-1 or suPAR), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration and clinical utility of the models in a temporal external validation cohort. Findings426 participants were recruited, of whom 89 (21{middle dot}0%) met the primary outcome. 257 participants comprised the development cohort and 166 comprised the validation cohort. The three models containing NLR, suPAR or IL-6 demonstrated promising discrimination (c-statistics 0{middle dot}72 to 0{middle dot}74) and calibration (calibration slopes 1{middle dot}01 to 1{middle dot}05) in the validation cohort, and provided greater utility than a model containing the clinical parameters alone. InterpretationWe present three 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. FundingMedecins Sans Frontieres, India. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSA living systematic review by Wynants et al. identified 137 COVID-19 prediction models, 47 of which were derived to predict whether patients with COVID-19 will have an adverse outcome. Most lacked external validation, relied on retrospective data, did not focus on patients with moderate disease, were at high risk of bias, and were not practical for use in resource-limited settings. To identify promising biochemical biomarkers which may have been evaluated independently of a prediction model and therefore not captured by this review, we searched PubMed on 1 June 2020 using synonyms of "SARS-CoV-2" AND ["biomarker" OR "prognosis"]. We identified 1,214 studies evaluating biochemical biomarkers of potential value in the prognostication of COVID-19 illness. In consultation with FIND (Geneva, Switzerland) we shortlisted seven candidates for evaluation in this study, all of which are measurable using near-patient tests which are either currently available or in late-stage development. Added value of this studyWe followed the TRIPOD guidelines to develop and validate three promising clinical prediction models to help clinicians identify which patients presenting with moderate COVID-19 can be safely managed in the community. Each model contains three easily ascertained clinical parameters (age, sex, and SpO2) and one biochemical biomarker (NLR, suPAR or IL-6), and would be practical for implementation in high-patient-throughput low resource settings. The models showed promising discrimination and calibration in the validation cohort. The inclusion of a biomarker test improved prognostication compared to a model containing the clinical parameters alone, and extended the range of contexts in which such a tool might provide utility to include situations when bed pressures are less critical, for example at earlier points in a COVID-19 surge. Implications of all the available evidencePrognostic models should be developed for clearly-defined clinical use-cases. We report the development and temporal validation of three clinical prediction models to rule-out progression to supplemental oxygen requirement amongst patients presenting with moderate COVID-19. The models are readily implementable and should prove useful in triage and resource allocation. We provide our full models to enable independent validation.
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Cohort_studies / Experimental_studies / Observational study / Prognostic study / Review / Systematic review Language: English Year: 2021 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Cohort_studies / Experimental_studies / Observational study / Prognostic study / Review / Systematic review Language: English Year: 2021 Document type: Preprint
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