corona virus-Covid19

COVID-19: SARS2 Risk Equations Estimate Risk of Hospitalization and Death Interview with:

Hesam Dashti, PhD Brigham and Women's Hospital, Harvard Medical School Senior Computational Scientist The Broad Institute of MIT and Harvard

Dr. Dashti

Hesam Dashti, PhD
Brigham and Women’s Hospital, Harvard Medical School
Senior Computational Scientist
The Broad Institute of MIT and Harvard What is the background for this study? What parameters does the SARS2 score take into consideration?

Response: While complex models have been developed for predicting the severity of COVID-19 from the medical history, laboratory, and imaging results of patients, simplified models with similar accuracy would be more practical for individualizing the decision making, especially when detailed medical history of patients is not readily available. In this study, we developed the SARS2 risk equations for estimating risk of hospitalization of patients with COVID-19 and also the risk of mortality among hospitalized patients. The “SARS2” risk equations are named for their input variables: Sex, Age, Race, Socioeconomic and Smoking status.

To develop and validate the models, we used the electronic records from 12,347 patients who tested positive for COVID-19 at the Mass General Brigham medical centers in Massachusetts between 02/26/2020 and 07/14/2020 to construct derivation and validation cohorts for estimating 1) risk of hospitalization within 30 days of COVID-19 positive PCR test, and 2) for the hospitalized patients, risk of mortality within approximately 3 months. What are the main findings? 

Response: The SARS2 risk equations resulted in high model discrimination (c-statistics of 0.77 [95% CI 0.73–0.80] for hospitalization, and 0.84 [95% CI 0.74–0.94] for mortality among hospitalized patients) and were well calibrated. The excellent model performance of the SARS2 risk equations was on par with more complex published risk models. Higher risk was associated with older age, male sex, Black ethnicity, lower median household income, and current/past smoking status. What should readers take away from your report? Where can providers find the score online? 

Response: Simplicity and accuracy of the SARS2 model allows medical professionals to use it in situations when rapid or home-based decisions are made about the risk of severe outcome due to COVID-19. The model is free and publicly available ( What recommendations do you have for future research as a result of this work?

Response: Using electronic health records is a challenging process that uses advanced text processing algorithms and manual validations. There are advantages to the electronic health records. In this case, the ease of incorporating such risk equations as the SARS2 scores – since they are based on demographic variables and smoking – which could provide rapid insight and risk stratification for time-sensitive decisions.

While we intentionally did not use test results such as laboratory values and imaging data such as X-rays and history of comorbid conditions, when available these results should be taken into account as well as the severity of symptoms at presentation for clinical management. Is there anything else you would like to add? Any disclosures?

Response: We are grateful for the support from the Enterprise Data Warehouse, Research Patient

Data Repository, and COVID-19 Data Mart personnel at Mass General Brigham, in particular Stacey A. Duey and Julie M. Fiskio. This work was supported in part by the National Heart Lung and Blood Institute (T32 HL007575, K24 HL136852, and 5K01HL135342), by 17IGMV33860009

from the American Heart Association, by the BWH Lerner Junior Faculty Research Award, and by philanthropic support from the Brigham and Women’s Hospital COVID fund. The authors have no disclosures pertaining to this study.


Dashti H, Roche EC, Bates DW, Mora S, Demler O. SARS2 simplified scores to estimate risk of hospitalization and death among patients with COVID-19. medRxiv [Preprint]. 2020 Sep 13:2020.09.11.20190520. doi: 10.1101/2020.09.11.20190520. Update in: Sci Rep. 2021 Mar 2;11(1):4945. PMID: 32935112; PMCID: PMC7491527.



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Last Updated on March 25, 2021 by Marie Benz MD FAAD