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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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[Development and validation of nomograms for predicting stroke recurrence after firstepisode ischemic stroke]
1Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China.
Summary
Older age, higher modified Rankin Scale (mRS) scores, and a history of coronary heart disease are key risk factors for recurrent ischemic stroke. A predictive nomogram demonstrates good accuracy in identifying patients at higher risk of stroke recurrence.
Area of Science:
- Neurology
- Cardiovascular Medicine
- Biostatistics
Background:
- First-episode ischemic stroke survivors face a significant risk of recurrence.
- Identifying predictive factors for stroke recurrence is crucial for timely intervention and improved patient outcomes.
Purpose of the Study:
- To identify risk factors associated with stroke recurrence in first-episode ischemic stroke survivors.
- To develop and validate a predictive model (nomogram) for stroke recurrence.
Main Methods:
- A cohort of 821 first-episode ischemic stroke survivors was analyzed.
- Cox proportional risk regression was used to identify risk factors.
- A nomogram was constructed and validated using R software for visual prediction.
Main Results:
- The 3- and 5-year recurrence rates were 16.81% and 19.98%, respectively.
- Significant risk factors identified include age over 65 (HR=2.596), age 45-64 (HR=2.510), mRS score >3 (HR=2.284), and coronary heart disease history (HR=1.353).
- The nomogram achieved C-indexes of 0.640 for 3-year and 0.671 for 5-year prediction.
Conclusions:
- Age, mRS score, and coronary heart disease history are significant predictors of stroke recurrence.
- The developed nomogram demonstrates good discriminative and predictive power for ischemic stroke recurrence.
