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Related Experiment Video

Updated: Dec 9, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Developing a COVID-19 mortality risk prediction model when individual-level data are not available.

Noam Barda^1,2,3, Dan Riesel¹, Amichay Akriv¹

¹Clalit Research Institute, Innovation Division, Clalit Health Services, Toval 40, Ramat-Gan, Israel.

Nature Communications

|September 8, 2020

Summary

Early in the COVID-19 pandemic, a hybrid risk predictor was developed using a baseline model and calibration. This validated predictor accurately identifies high-risk patients for better decision-making.

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Area of Science:

Epidemiology
Infectious Disease Modeling
Clinical Decision Support

Background:

The COVID-19 pandemic necessitated immediate risk prediction tools despite limited initial patient data.
Existing models lacked specificity for severe respiratory infections during the pandemic's early stages.

Purpose of the Study:

To develop and validate a hybrid risk prediction model for severe respiratory infections, specifically for COVID-19.
To calibrate predictions using early case-fatality rates to improve accuracy.

Main Methods:

A hybrid strategy combining a baseline severe respiratory infection risk predictor with a post-processing calibration method.
Validation using an accumulating COVID-19 patient cohort, assessing discrimination and calibration.

Main Results:

The validated predictor demonstrated strong discrimination (AUC 0.943) and improved calibration.
At a 5% risk threshold, 15% of patients were identified as high-risk with 88% sensitivity.

Conclusions:

A useful risk predictor for COVID-19 can be developed even at the pandemic's onset.
The hybrid model provides effective risk stratification, now implemented in a major healthcare organization.