Please use this identifier to cite or link to this item: http://hdl.handle.net/11054/2952
Title: Improving risk prediction after transcatheter aortic valve implantation: A comparison of machine learning with traditional methods.
Author: Zaka, A.
Mutahar, D.
Mustafiz, C.
Sinhal, S.
Gorcilov, J.
Evans, S.
Kovoor, Joshua
Bacchi, S.
Issue Date: 2024
Conference Name: 72nd Annual Scientific Meeting of the Cardiac Society of Australia and New Zealand
Conference Date: August 1-4
Conference Place: Perth, Australia
Abstract: Background: Accurate mortality prediction following transcatheter aortic valve implantation (TAVI) is essential for mitigating risk, shared decision-making, and periprocedural planning. Surgical risk models have demonstrated modest discriminative value for patients undergoing TAVI and are typically poorly calibrated. Machine learning (ML) models offer an alternative risk stratification that may offer improved predictive accuracy. We performed a systematic review and meta-analysis comparing ML models with traditional risk scores for prediction of all-cause mortality after TAVI. Methods: PubMed, EMBASE, Web of Science and Cochrane databases were searched until 16 December 2023 for studies comparing ML models with traditional statistical methods for event prediction after TAVI. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals between ML models and traditional methods in estimating the risk of all-cause mortality at 30-days and 1-year. Results: Sixteen models were assessed across 9 observational studies (29608 patients). The summary C-statistic of the top-performing ML models for all-cause mortality was 0.82 (95% CI, 0.77–0.87), compared to traditional methods 0.65 (95% CI, 0.62–0.68). The difference in C-statistic between ML models and traditional methods was 0.17 (95% CI 0.15–0.19, p<0.00001). Of all included studies, 2 models were externally validated. Calibration was inconsistently reported. Conclusion: ML models outperformed traditional risk scores in the discrimination of all-cause mortality following TAVI. While integration of ML algorithms into electronic healthcare systems may improve periprocedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations.
URI: http://hdl.handle.net/11054/2952
Internal ID Number: 02775
Health Subject: CARDIOLOGY
LARGE LANGUAGE MODELS
Type: Conference
Presentation
Appears in Collections:Research Output

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.