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Validation of clinical risk models for predicting COVID-19 severity
  1. Rahul Aggarwal1,
  2. Timothy S Anderson1,2,
  3. Aditya Mohanty1,2,
  4. Adlin Pinheiro2,
  5. Long Ngo1,2,
  6. Andrew Ahn2,
  7. Neal Peterson2,
  8. Mark Dunlop2,
  9. Thomas Mawson2,
  10. Taliya Lantsman2,
  11. Natalia Forbath2,
  12. Jennifer P Stevens1,2,
  13. Shoshana J Herzig1,2
  1. 1 Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
  2. 2 Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
  1. Correspondence to Dr Shoshana J Herzig, Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA; sherzig{at}

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Liang and colleagues developed a risk prediction score, COVID-GRAM, to identify adults with COVID-19 at higher risk of intensive care stay, mechanical ventilation or death.1 This score had strong performance in Chinese cohorts and has been validated in multiple non-US cohorts, although with variation in its performance (C-statistic ranging from 0.64 to 0.91).1 2 It has yet to been studied in US populations.1 2 Differences in the US hospital practices and patient population may affect the applicability of COVID-GRAM to this population. Additionally, clinical rationale and prior studies suggest that CURB-65 may predict severe disease in COVID-19.3 We compare the performances of COVID-GRAM with CURB-65 for predicting critical illness in patients with COVID-19 in a US population.

This retrospective study included adult patients admitted to an academic medical centre in Boston Massachusetts with a diagnosis of COVID-19 between 1 January 2020 and 29 June 2020. Individuals with prior COVID-19 hospitalisations were excluded. Patients were followed until outcome occurrence or the end of hospitalisation (whichever came first). Demographic and clinical data, patient outcomes and variables used in COVID-GRAM and CURB-65 were obtained from the electronic health record. The primary outcome was critical illness—defined as a …

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  • Handling editor Richard Body

  • Twitter @RahulAggarwalMD

  • Contributors RA, TSA, JPS, SJH conceptualised the idea. AM, AP, AA, NP, MD, TM, TL, NF were involved in data collection. RA was involved in data analytics. LN was involved in analytical planning and statistical guidance. JPS and SJH supervised the study. All authors were involved in drafting the manuscript, intellectual design and critical revision of the manuscript for intellectual content.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests TSA discloses consulting fees from Alosa Health.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.