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Updated: Jun 9, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Risk score model for predicting mortality among patients with lung cancer
Youn Huh1, Hae-Rim Kim2, Hwa Jung Kim3
1Department of Family Medicine, Uijeongbu Eulji Medical Center, Eulji University, Gyeonggi-do, Republic of Korea.
Background:
To develop an accurate mortality risk predictive model among patients with lung cancer.
Methods:
The development cohort included 96,255 patients with lung cancer aged ≥19 years, who underwent a Korean National Health Insurance Service health check-up from 2005 to 2015. The validation cohort consisted of 18,432 patients (≥19 years) with lung cancer from another region. The outcome was all-cause mortality between January 1, 2005, and December 31, 2020.
Results:
Approximately 60.5% of the development cohort died within a median follow-up period of 2.32 (0.72-5.00) years. Risk score was highest in participants aged ≥65 years, followed by those who underwent treatment, had a history of emergency room visits, and were current smokers. Participants treated by surgery had the lowest risk score, followed by combined surgery and chemotherapy, combined surgery and radiation therapy, women, and regular exercisers. The C statistic in the development and validation cohorts was 0.78 (95% confidence interval, 0.77-0.78) and 0.81 (95% confidence interval, 0.78-0.84), respectively.
Conclusion:
Advanced age, lung cancer stage, and treatment type were strong risk factors of mortality in lung cancer patients, while being a woman and exercise were preventive factors. These will aid in the prediction of mortality and management of lung cancer patients.
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