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Absolute Mortality Risk Assessment of Covid-19 Patients: The Khorshid Covid Cohort (Kcc) Study Publisher Pubmed

Summary: Research on 630 COVID-19 patients in Iran developed a risk chart using age, sex, and health factors to predict mortality, aiding doctors in tough choices. High accuracy shown. #COVID19 #HospitalCare

Marateb HR1, 2 ; Von Cube M3 ; Sami R4 ; Haghjooy Javanmard S5 ; Mansourian M2, 6 ; Amra B7 ; Soltaninejad F8 ; Mortazavi M9 ; Adibi P10 ; Khademi N11 ; Sadat Hosseini N11 ; Toghyani A11 ; Hassannejad R12 ; Mananas MA2, 13 Show All Authors
Authors
  1. Marateb HR1, 2
  2. Von Cube M3
  3. Sami R4
  4. Haghjooy Javanmard S5
  5. Mansourian M2, 6
  6. Amra B7
  7. Soltaninejad F8
  8. Mortazavi M9
  9. Adibi P10
  10. Khademi N11
  11. Sadat Hosseini N11
  12. Toghyani A11
  13. Hassannejad R12
  14. Mananas MA2, 13
  15. Binder H3
  16. Wolkewitz M3

Source: BMC Medical Research Methodology Published:2021


Abstract

Background: Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. Methods: We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. Results: Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835–0.910]). Conclusions: This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions. © 2021, The Author(s).
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