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Updated: Jul 11, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a multimorbidity risk prediction nomogram among Chinese middle-aged and older adults: a
Xiao Zheng1,2, Benli Xue2,3, Shujuan Xiao2,3
1Department of Health Management, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde, Foshan), Foshan, China.
Objectives:
The aim of this study is to establish a self-simple-to-use nomogram to predict the risk of multimorbidity among middle-aged and older adults.
Design:
A retrospective cohort study.
Participants:
We used data from the Chinese Longitudinal Healthy Longevity Survey, including 7735 samples.
Main Outcome Measures:
Samples' demographic characteristics, modifiable lifestyles and depression were collected. Cox proportional hazard models and nomogram model were used to estimate the risk factors of multimorbidity.
Results:
A total of 3576 (46.2%) participants have multimorbidity. The result showed that age, female (HR 0.80, 95% CI 0.72 to 0.89), chronic disease (HR 2.59, 95% CI 2.38 to 2.82), sleep time (HR 0.78, 95% CI 0.72 to 0.85), regular physical activity (HR 0.88, 95% CI 0.81 to 0.95), drinking (HR 1.27 95% CI 1.16 to 1.39), smoking (HR 1.40, 95% CI 1.26 to 1.53), body mass index (HR 1.04, 95% CI 1.03 to 1.05) and depression (HR 1.02, 95% CI 1.01 to 1.03) were associated with multimorbidity. The C-index of nomogram models for derivation and validation sets were 0.70 (95% CI 0.69 to 0.71, p=0.006) and 0.71 (95% CI 0.70 to 0.73, p=0.008), respectively.
Conclusions:
We have crafted a user-friendly nomogram model for predicting multimorbidity risk among middle-aged and older adults. This model integrates readily available and routinely assessed risk factors, enabling the early identification of high-risk individuals and offering tailored preventive and intervention strategies.
