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Updated: Jul 15, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
A nomogram model for predicting frailty in middle-aged and elderly patients with colorectal cancer:A cross-sectional
Mengjiao Zhong1, Xiaodan Wu1, Xinxin Li2
1Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, PR China.
Purpose:
Frailty is a syndrome caused by multiple factors and can lead to serious consequences for middle-aged and elderly patients with colorectal cancer. However, few studies have comprehensively explored predictors of frailty and built predictive models. Therefore, our aim is to develop and evaluate a predictive model for frailty in middle-aged and elderly patients with colorectal cancer.
Methods:
From July 2023 to February 2024, a total of 502 middle-aged and elderly patients with colorectal cancer participated in this survey. Patients were randomly divided into training and validation groups in a 7:3 ratio. Univariate and multiple logistic regression analysis were employed to identify potential predictors of frailty in these patients. A nomogram was constructed based on the predictive factors, and the model underwent internal validation.
Results:
In the training cohort, logistic regression revealed that self-perceived health, chronic pain, loneliness, depression, and health-promoting lifestyle were independent predictors of frailty. The Areas Under the Curve (AUC) of the training and validation groups were 0.845 and 0.851, respectively. The calibration curve of the nomogram demonstrated good consistency between predicted and actual probabilities. Decision curve analysis revealed good clinical benefit.
Conclusions:
This study established a predictive model with satisfactory predictive ability, providing empirical evidence for the early detection and intervention of frailty in middle-aged and elderly patients with colorectal cancer. The nomogram model has significant potential for clinical application, as it can be integrated into routine oncology practice to identify high-risk patients early, allowing for timely and individualized interventions to improve patient outcomes.
