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Updated: Oct 28, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Absolute mortality risk assessment of COVID-19 patients: the Khorshid COVID Cohort (KCC) study
Hamid Reza Marateb1,2, Maja von Cube3, Ramin Sami4
1Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan, Iran. h.marateb@eng.ui.ac.ir.
Insights
Clinicians need tools to identify high-risk COVID-19 patients. This study developed a validated mortality risk calculator using simple admission variables, achieving high accuracy for better patient management.
Area of Science:
- Epidemiology
- Biostatistics
- Clinical Medicine
Background:
- Hospitalized COVID-19 patients require tools for mortality risk assessment.
- Effective risk stratification improves resource allocation and patient management.
- Statistical models accounting for competing risks and censoring are crucial for analyzing active cases.
Purpose of the Study:
- To develop and validate a personalized mortality risk calculator for hospitalized COVID-19 patients.
- To provide physicians with enhanced guidance for critical decision-making.
- To improve risk assessment for patient mortality.
Main Methods:
- Utilized the Khorshid COVID Cohort (KCC) study data from 630 patients.
- Employed competing risk methods to create a death risk chart.
- Included baseline variables: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index.
- Assessed accuracy using the area under the receiver operator curve (AUC).
Main Results:
- Cause-specific hazard regression models identified associations between baseline variables and mortality/discharge.
- The developed risk chart integrated results from two cause-specific hazard models.
- The risk assessment method demonstrated high accuracy with an AUC of 0.872 (95% CI: 0.835-0.910).
Conclusions:
- The validated mortality risk calculator offers a personalized approach to assessing patient risk.
- Physicians can utilize this tool for improved clinical decision support.
- Accurate risk stratification aids in managing hospitalized COVID-19 patients effectively.
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.
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