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Updated: Nov 28, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19
Xiaojun Ma1, Huifang Wang2, Junwei Huang3
1Department of Infectious Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong Province, China.
Insights
A new nomogram accurately predicts COVID-19 patient survival using clinical and lab data. This tool aids clinicians in early intervention for better patient outcomes.
Area of Science:
- Medical Informatics
- Clinical Medicine
- Epidemiology
Background:
- COVID-19 poses a significant global health threat.
- Accurate prognosis prediction is crucial for patient management.
Purpose of the Study:
- To develop a nomogram model for predicting COVID-19 patient survival.
- Utilize clinical and laboratory data available at admission.
Main Methods:
- Retrospective review of 262 COVID-19 patients from Wuhan hospitals.
- Statistical analysis including Pearson's χ2-test, Fisher's exact test, Student's t-test, and Mann Whitney U-test.
- Development of a nomogram using log-binomial regression for independent risk factors.
Main Results:
- Seven independent risk factors identified: age, chronic heart disease (CHD), lymphocyte percentage (Lym%), platelets, C-reactive protein, lactate dehydrogenase (LDH), and D-dimer.
- The nomogram demonstrated high predictive accuracy with an Area Under the Curve (AUC) of 0.948.
- Internal validation using the Bootstrap method confirmed the model's reliability.
Conclusions:
- A nomogram incorporating age, CHD, Lym%, platelets, C-reactive protein, LDH, and D-dimer accurately predicts COVID-19 patient prognosis.
- This nomogram serves as a valuable tool for early clinical intervention and improved patient management.
Background:
COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission.
Methods:
COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson's χ2-test or Fisher's exact test, and continuous variables were analyzed using Student's t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method.
Results:
A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P < 0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P < 0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P = 0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P = 0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P < 0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P < 0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P < 0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973).
Conclusions:
A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.
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