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Published on: October 23, 2020
Risk stratification for mortality in cardiovascular disease survivors: A survival conditional inference tree analysis
Zhijun Wu1, Zhe Huang2, Yuntao Wu2
1Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Insights
A new machine-learning model, Survival Conditional Inference Tree (SCTREE), identifies older age, hyperglycemia, and proteinuria as key predictors of cardiovascular disease mortality. This aids in better risk stratification for cardiovascular disease survivors.
Area of Science:
- Cardiology
- Biostatistics
- Machine Learning
Background:
- Efficient risk stratification models for cardiovascular disease (CVD) are lacking.
- Machine learning offers potential for improved CVD prognosis analysis.
Purpose of the Study:
- To identify an optimized model for studying CVD prognosis.
- To utilize Survival Conditional Inference Tree (SCTREE) for CVD risk stratification.
Main Methods:
- A large cohort (Kailuan I, n=101,510) was used for training, with a validation cohort (Kailuan II, n=57,511).
- SCTREE, a machine-learning method, was applied to identify predictors of post-CVD mortality.
- Data included demographics, risk factors, and clinical measurements.
Main Results:
- Older age, hyperglycemia, and proteinuria were identified as the primary predictors of post-CVD mortality.
- High-risk individuals (2-3 risk factors) showed significantly higher mortality (HR: 5.41-6.04) compared to low-risk individuals (0-1 risk factor).
Conclusions:
- SCTREE effectively identifies key predictors for post-CVD mortality.
- The identified risk factors enable improved risk stratification for cardiovascular disease survivors.
Background And Aims:
Efficient analysis strategies for complex network with cardiovascular disease (CVD) risk stratification remain lacking. We sought to identify an optimized model to study CVD prognosis using survival conditional inference tree (SCTREE), a machine-learning method.
Methods And Results:
We identified 5379 new onset CVD from 2006 (baseline) to May, 2017 in the Kailuan I study including 101,510 participants (the training dataset). The second cohort composing 1,287 CVD survivors was used to validate the algorithm (the Kailuan II study, n = 57,511). All variables (e.g., age, sex, family history of CVD, metabolic risk factors, renal function indexes, heart rate, atrial fibrillation, and high sensitivity C-reactive protein) were measured at baseline and biennially during the follow-up period. Up to December 2017, we documented 1,104 deaths after CVD in the Kailuan I study and 170 deaths in the Kailuan II study. Older age, hyperglycemia and proteinuria were identified by the SCTREE as main predictors of post-CVD mortality. CVD survivors in the high risk group (presence of 2-3 of these top risk factors), had higher mortality risk in the training dataset (hazard ratio (HR): 5.41; 95% confidence Interval (CI): 4.49-6.52) and in the validation dataset (HR: 6.04; 95%CI: 3.59-10.2), than those in the lowest risk group (presence of 0-1 of these factors).
Conclusion:
Older age, hyperglycemia and proteinuria were the main predictors of post-CVD mortality.
Trial Registration:
ChiCTR-TNRC-11001489.
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