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Published on: December 11, 2017
Deep-learning survival analysis for patients with calcific aortic valve disease undergoing valve replacement
Parvin Mohammadyari1, Francesco Vieceli Dalla Sega2, Francesca Fortini2
1Istituto Nazionale di Fisica Nucleare (INFN), Ferrara, Italy.
Insights
Machine learning models effectively predict one-year mortality risk after aortic valve intervention (SAVR or TAVI). Six key variables, including albumin and age, improve risk assessment for patients with calcification of the aortic valve (CAVDS).
Area of Science:
- Cardiology
- Medical Informatics
- Biostatistics
Background:
- Calcification of the aortic valve (CAVDS) causes aortic stenosis (AS), necessitating valve replacement via surgical aortic valve replacement (SAVR) or transcatheter aortic valve intervention (TAVI).
- High post-intervention mortality rates underscore the clinical importance of accurate risk assessment for SAVR and TAVI procedures.
Purpose of the Study:
- To compare traditional Cox Proportional Hazard (CPH) models with Machine Learning (ML) approaches (DeepSurv, RSF) for predicting one-year mortality risk post-SAVR or TAVI.
- To identify key variables for estimating mortality risk in patients undergoing aortic valve interventions.
Main Methods:
- Comparative analysis of Cox Proportional Hazard (CPH), Deep Learning Survival (DeepSurv), and Random Survival Forest (RSF) models.
- Utilized six variables: albumin, age, BMI, glucose, hypertension, and clonal hemopoiesis of indeterminate potential (CHIP) for risk prediction.
- Evaluated prediction capability using the c-index metric.
Main Results:
- A combination of six variables (albumin, age, BMI, glucose, hypertension, CHIP) accurately predicted one-year mortality across all three methods.
- Machine learning models (DeepSurv, RSF) demonstrated superior prediction capabilities compared to the traditional CPH model.
- ML models effectively capture non-linear relationships, enhancing their utility in medical statistical analysis.
Conclusions:
- Machine learning models offer improved prediction accuracy for post-aortic valve intervention mortality.
- The identified six-variable set provides a robust tool for risk stratification.
- Enhanced early identification of high-risk patients can guide therapeutic interventions and improve outcomes.
Abstract:
Calcification of the aortic valve (CAVDS) is a major cause of aortic stenosis (AS) leading to loss of valve function which requires the substitution by surgical aortic valve replacement (SAVR) or transcatheter aortic valve intervention (TAVI). These procedures are associated with high post-intervention mortality, then the corresponding risk assessment is relevant from a clinical standpoint. This study compares the traditional Cox Proportional Hazard (CPH) against Machine Learning (ML) based methods, such as Deep Learning Survival (DeepSurv) and Random Survival Forest (RSF), to identify variables able to estimate the risk of death one year after the intervention, in patients undergoing either to SAVR or TAVI. We found that with all three approaches the combination of six variables, named albumin, age, BMI, glucose, hypertension, and clonal hemopoiesis of indeterminate potential (CHIP), allows for predicting mortality with a c-index of approximately . Importantly, we found that the ML models have a better prediction capability, making them as effective for statistical analysis in medicine as most state-of-the-art approaches, with the additional advantage that they may expose non-linear relationships. This study aims to improve the early identification of patients at higher risk of death, who could then benefit from a more appropriate therapeutic intervention.
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