Academic Journal
Deep-learning survival analysis for patients with calcific aortic valve disease undergoing valve replacement
| Title: | Deep-learning survival analysis for patients with calcific aortic valve disease undergoing valve replacement |
|---|---|
| Authors: | Parvin Mohammadyari, Francesco Vieceli Dalla Sega, Francesca Fortini, Giada Minghini, Paola Rizzo, Paolo Cimaglia, Elisa Mikus, Elena Tremoli, Gianluca Campo, Enrico Calore, Sebastiano Fabio Schifano, Cristian Zambelli |
| Source: | Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024) |
| Publisher Information: | Nature Portfolio, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Medicine LCC:Science |
| Subject Terms: | Medicine, Science |
| More Details: | 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 $$80\%$$ 80 % . 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-61685-0 |
| Access URL: | https://doaj.org/article/7cedcbaf16354dff9a9e2c4f75fb380a |
| Accession Number: | edsdoj.7cedcbaf16354dff9a9e2c4f75fb380a |
| Database: | Directory of Open Access Journals |
| Full text is not displayed to guests. | Login for full access. |
| ISSN: | 20452322 |
|---|---|
| DOI: | 10.1038/s41598-024-61685-0 |
| Published in: | Scientific Reports |
| Language: | English |