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Original research
Clinical risk score to predict in-hospital mortality in COVID-19 patients: a retrospective cohort study
  1. Carlo Fumagalli1,
  2. Renzo Rozzini2,
  3. Matteo Vannini3,
  4. Flaminia Coccia2,
  5. Giulia Cesaroni2,
  6. Francesca Mazzeo2,
  7. Maria Cola4,
  8. Alessandro Bartoloni1,4,
  9. Paolo Fontanari3,
  10. Federico Lavorini1,3,
  11. Rossella Marcucci1,3,
  12. Alessandro Morettini4,
  13. Carlo Nozzoli4,
  14. Adriano Peris5,
  15. Filippo Pieralli4,
  16. Riccardo Pini1,4,
  17. Loredana Poggesi1,4,
  18. Andrea Ungar1,
  19. Stefano Fumagalli1,
  20. Niccolò Marchionni1,3
  1. 1Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
  2. 2Department of Internal Medicine and Geriatrics, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Lombardy, Italy
  3. 3Department of Cardiothoracovascular Medicine, Careggi Hospital, Florence, Italy
  4. 4Department of Internal and Emergency Medicine, Careggi Hospital, Florence, Italy
  5. 5Intensive Care Unit and Regional ECMO Referral Center, Careggi Hospital, Florence, Italy
  1. Correspondence to Dr Niccolò Marchionni; niccolo.marchionni{at}unifi.it

Abstract

Objectives Several physiological abnormalities that develop during COVID-19 are associated with increased mortality. In the present study, we aimed to develop a clinical risk score to predict the in-hospital mortality in COVID-19 patients, based on a set of variables available soon after the hospitalisation triage.

Setting Retrospective cohort study of 516 patients consecutively admitted for COVID-19 to two Italian tertiary hospitals located in Northern and Central Italy were collected from 22 February 2020 (date of first admission) to 10 April 2020.

Participants Consecutive patients≥18 years admitted for COVID-19.

Main outcome measures Simple clinical and laboratory findings readily available after triage were compared by patients’ survival status (‘dead’ vs ‘alive’), with the objective of identifying baseline variables associated with mortality. These were used to build a COVID-19 in-hospital mortality risk score (COVID-19MRS).

Results Mean age was 67±13 years (mean±SD), and 66.9% were male. Using Cox regression analysis, tertiles of increasing age (≥75, upper vs <62 years, lower: HR 7.92; p<0.001) and number of chronic diseases (≥4 vs 0–1: HR 2.09; p=0.007), respiratory rate (HR 1.04 per unit increase; p=0.001), PaO2/FiO2 (HR 0.995 per unit increase; p<0.001), serum creatinine (HR 1.34 per unit increase; p<0.001) and platelet count (HR 0.995 per unit increase; p=0.001) were predictors of mortality. All six predictors were used to build the COVID-19MRS (Area Under the Curve 0.90, 95% CI 0.87 to 0.93), which proved to be highly accurate in stratifying patients at low, intermediate and high risk of in-hospital death (p<0.001).

Conclusions The COVID-19MRS is a rapid, operator-independent and inexpensive clinical tool that objectively predicts mortality in patients with COVID-19. The score could be helpful from triage to guide earlier assignment of COVID-19 patients to the most appropriate level of care.

  • infectious diseases
  • public health
  • adult intensive & critical care
  • internal medicine
  • COVID-19
http://creativecommons.org/licenses/by-nc/4.0/

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Footnotes

  • Contributors CF and NM conceived the study, conducted data acquisition, management and

    analysis, drafted and critically revised the manuscript and are accountable for all aspects of the work. RR, FL and RM contributed to conceive the study and critically revised the manuscript. MV conducted data acquisition, drafted and critically revised the manuscript, with the contribution of FC, GC, FM, and MC. AB, PF, AM, CN, AP, FP, RP, LP and AU critically revised the manuscript for important intellectual content. SF contributed substantially to data analysis and to critical revision of the manuscript. All Authors read and approved the final manuscript.

  • 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 None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Patient consent for publication Not required.

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

  • Data availability statement Deidentified participant data are stored in a University of Florence password protected in-house server

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