Towards patient-specific prediction of conduction abnormalities induced by transcatheter aortic valve implantation: a combined mechanistic modelling and machine learning approach. Issue 4 (20th August 2021)

• Record Type: Journal Article
• Authors: Galli, Valeria; Loncaric, Filip; Rocatello, Giorgia; Astudillo, Patricio; Sanchis, Laura; Regueiro, Ander; De Backer, Ole; Swaans, Martin; Bosmans, Johan; Ribeiro, Joana Maria; Lamata, Pablo; Sitges, Marta; de Jaegere, Peter; Mortier, Peter
• Is Part Of: European heart journal. Volume 2:Issue 4(2021)
• Page Start: 606
• Page End: 615
• Publication Date: 2021-08-20
• Subjects: TAVI -- Conduction abnormalities -- Machine learning -- Mechanistic modelling -- Digital twin; Medical informatics -- Periodicals; Medical technology -- Periodicals; Cardiovascular system -- Periodicals; 616.100284
• DOI: 10.1093/ehjdh/ztab063
• Languages: English
• ISSNs: 2634-3916

Abstract

Abstract: Aims: Post-procedure conduction abnormalities (CA) remain a common complication of transcatheter aortic valve implantation (TAVI), highlighting the need for personalized prediction models. We used machine learning (ML), integrating statistical and mechanistic modelling to provide a patient-specific estimation of the probability of developing CA after TAVI. Methods and results: The cohort consisted of 151 patients with normal conduction and no pacemaker at baseline who underwent TAVI in nine European centres. Devices included CoreValve, Evolut R, Evolut PRO, and Lotus. Preoperative multi-slice computed tomography was performed. Virtual valve implantation with patient-specific computer modelling and simulation (CM&S) allowed calculation of valve-induced contact pressure on the anatomy. The primary composite outcome was new onset left or right bundle branch block or permanent pacemaker implantation (PPI) before discharge. A supervised ML approach was applied with eight models predicting CA based on anatomical, procedural and mechanistic data. CA occurred in 59% of patients ( n = 89), more often after mechanical than first or second generation self-expanding valves (68% vs. 60% vs. 41%). CM&S revealed significantly higher contact pressure and contact pressure index in patients with CA. The best model achieved 83% accuracy (area under the curve 0.84) and sensitivity, specificity, positive predictive value, negative predictive value, and F1-score of 100%, 62%, 76%, 100%, and 82%. Conclusion: ML, integrating statistical and mechanistic modelling, achieved an accurate prediction of CA after TAVI. This study demonstrates the potential of a synergetic approach for personalizing procedure planning, allowing selection of the optimal device and implantation strategy, avoiding new CA and/or PPI. Graphical Abstract: