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Updated: Oct 19, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized
Rahmet Guner1, Bircan Kayaaslan2, Imran Hasanoglu1
1Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey.
Insights
A new nomogram accurately predicts severe COVID-19 patients needing intensive care unit (ICU) admission using accessible clinical and lab values. This tool aids early intervention and resource allocation for better patient outcomes.
Area of Science:
- Medical Informatics
- Critical Care Medicine
- Infectious Diseases
Background:
- Early identification of severe COVID-19 cases is crucial for timely intensive care unit (ICU) admission and resource management.
- Predictive models can aid clinicians in stratifying patients and optimizing care pathways.
Purpose of the Study:
- To develop and validate a nomogram for predicting severe COVID-19 requiring ICU follow-up.
- To utilize readily available patient data for accurate risk stratification.
Main Methods:
- Retrospective analysis of 1022 hospitalized COVID-19 patients.
- Development of a nomogram using logistic regression and bootstrap sampling on 686 patients.
- Validation of the nomogram using Area Under the Curve (AUC) and Harrell's C-index.
Main Results:
- The final nomogram incorporated oxygen saturation, C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), and troponin.
- The model demonstrated strong predictive power with an AUC of 0.93 and a C-index of 0.91.
- The nomogram was well-calibrated, indicating reliable predictions.
Conclusions:
- A simple, accessible nomogram was developed to predict severe COVID-19 needing ICU care.
- The tool empowers clinicians to make informed decisions regarding patient management and follow-up.
- This nomogram facilitates early intervention and efficient allocation of critical care resources.
Background:
Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values.
Methods:
Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer-Lemeshow Goodness-of-fit test, and calibration curve analysis.
Results:
Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902-0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899-0.947). Hosmer-Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703).
Conclusion:
We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.
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