tetano
Editor, Senior Moderator
Mayo Clin Proc
. 2021 Dec;96(12):3030-3041.
doi: 10.1016/j.mayocp.2021.09.002. Epub 2021 Sep 17.
Development and Validation of a Multivariable Risk Prediction Model for COVID-19 Mortality in the Southern United States
Aashish Gupta[SUP] 1 [/SUP], Sergey M Kachur[SUP] 2 [/SUP], Jose D Tafur[SUP] 3 [/SUP], Harsh K Patel[SUP] 4 [/SUP], Divina O Timme[SUP] 4 [/SUP], Farnoosh Shariati[SUP] 4 [/SUP], Kristen D Rogers[SUP] 4 [/SUP], Daniel P Morin[SUP] 3 [/SUP], Carl J Lavie[SUP] 3 [/SUP]
Affiliations
Abstract
Objective: To evaluate clinical characteristics of patients admitted to the hospital with coronavirus disease 2019 (COVID-19) in Southern United States and development as well as validation of a mortality risk prediction model.
Patients and methods: Southern Louisiana was an early hotspot during the pandemic, which provided a large collection of clinical data on inpatients with COVID-19. We designed a risk stratification model to assess the mortality risk for patients admitted to the hospital with COVID-19. Data from 1673 consecutive patients diagnosed with COVID-19 infection and hospitalized between March 1, 2020, and April 30, 2020, was used to create an 11-factor mortality risk model based on baseline comorbidity, organ injury, and laboratory results. The risk model was validated using a subsequent cohort of 2067 consecutive hospitalized patients admitted between June 1, 2020, and December 31, 2020.
Results: The resultant model has an area under the curve of 0.783 (95% CI, 0.76 to 0.81), with an optimal sensitivity of 0.74 and specificity of 0.69 for predicting mortality. Validation of this model in a subsequent cohort of 2067 consecutively hospitalized patients yielded comparable prognostic performance.
Conclusion: We have developed an easy-to-use, robust model for systematically evaluating patients presenting to acute care settings with COVID-19 infection.
. 2021 Dec;96(12):3030-3041.
doi: 10.1016/j.mayocp.2021.09.002. Epub 2021 Sep 17.
Development and Validation of a Multivariable Risk Prediction Model for COVID-19 Mortality in the Southern United States
Aashish Gupta[SUP] 1 [/SUP], Sergey M Kachur[SUP] 2 [/SUP], Jose D Tafur[SUP] 3 [/SUP], Harsh K Patel[SUP] 4 [/SUP], Divina O Timme[SUP] 4 [/SUP], Farnoosh Shariati[SUP] 4 [/SUP], Kristen D Rogers[SUP] 4 [/SUP], Daniel P Morin[SUP] 3 [/SUP], Carl J Lavie[SUP] 3 [/SUP]
Affiliations
- PMID: 34863394
- DOI: 10.1016/j.mayocp.2021.09.002
Abstract
Objective: To evaluate clinical characteristics of patients admitted to the hospital with coronavirus disease 2019 (COVID-19) in Southern United States and development as well as validation of a mortality risk prediction model.
Patients and methods: Southern Louisiana was an early hotspot during the pandemic, which provided a large collection of clinical data on inpatients with COVID-19. We designed a risk stratification model to assess the mortality risk for patients admitted to the hospital with COVID-19. Data from 1673 consecutive patients diagnosed with COVID-19 infection and hospitalized between March 1, 2020, and April 30, 2020, was used to create an 11-factor mortality risk model based on baseline comorbidity, organ injury, and laboratory results. The risk model was validated using a subsequent cohort of 2067 consecutive hospitalized patients admitted between June 1, 2020, and December 31, 2020.
Results: The resultant model has an area under the curve of 0.783 (95% CI, 0.76 to 0.81), with an optimal sensitivity of 0.74 and specificity of 0.69 for predicting mortality. Validation of this model in a subsequent cohort of 2067 consecutively hospitalized patients yielded comparable prognostic performance.
Conclusion: We have developed an easy-to-use, robust model for systematically evaluating patients presenting to acute care settings with COVID-19 infection.