COVID-19 Severity Index: predictive score for hospitalized patients

Huespe, I.; Carboni Bisso, I.; Gemelli, N. A.; Terrasa, S. A.; Di Stefano, S.; Burgos, V.; Sinner, J. F.; Risk, M. R.; San Roman, E.; Las Heras, M. J.

Introduction: Pandemics pose a major challenge for public health preparedness, requiring a coordinated international response and the development of solid containment plans. An early and accurate identification of high-risk patients in the course of the actual COVID-19 pandemic is vital for planning and for making proper use of available resources. Objective: The purpose of this study was to identify the key variables to create a predictive model that could be used effectively for triage. Method: A narrative literature review of 651 articles was conducted to assess clinical, laboratory and imaging findings of COVID-19 confirmed cases. After screening, 10 articles met the inclusion criteria and a list of suggested variables was gathered. A modified Delphi process analysis was performed to consult experts in order to generate a final list of variables for the creation of the predictive model. Results: The modified Delphi process analysis identified 44 predictive variables that were used for building a severity prediction score, the COVID-19 Severity Index. Conclusion: Specifically designed for current COVID-19 pandemic, COVID-19 Severity Index could be used as a reliable tool for strategic planning, organization and administration of resources by easily identifying hospitalized patients with higher risk of transfer to Intensive Care Unit (ICU).