Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement

Artificial Intelligence in Transcatheter Aortic Valve Replacement: Its Current Role and Ongoing Challenges

Transcatheter aortic valve replacement (TAVR) has emerged as a viable alternative to surgical aortic valve replacement, as accumulating clinical evidence has demonstrated its safety and efficacy. TAVR indications have expanded beyond high-risk or inoperable patients to include intermediate and low-risk patients with severe aortic stenosis. Artificial intelligence (AI) is revolutionizing the field of cardiology, aiding in the interpretation of medical imaging and developing risk models for at-risk individuals and those with cardiac disease. This article explores the growing role of AI in TAVR procedures and assesses its potential impact, with particular focus on its ability to improve patient selection, procedural planning, post-implantation monitoring and contribute to optimized patient outcomes. In addition, current challenges and future directions in AI implementation are highlighted.

Cardiac pacing and lead devices management: 25 years of research at EP Europace journal

AIMS

Cardiac pacing represents a key element in the field of electrophysiology and the treatment of conduction diseases. Since the first issue published in 1999, EP Europace has significantly contributed to the development and dissemination of the research in this area.

METHODS

In the last 25 years, there has been a continuous improvement of technologies and a great expansion of clinical indications making the field of cardiac pacing a fertile ground for research still today. Pacemaker technology has rapidly evolved, from the first external devices with limited longevity, passing through conventional transvenous pacemakers to leadless devices. Constant innovations in pacemaker size, longevity, pacing mode, algorithms, and remote monitoring highlight that the fascinating and exciting journey of cardiac pacing is not over yet.

CONCLUSION

The aim of the present review is to provide the current 'state of the art' on cardiac pacing highlighting the most important contributions from the Journal in the field.

A prediction model for permanent pacemaker implantation after transcatheter aortic valve replacement

BACKGROUND

This study aims to develop a post-procedural risk prediction model for permanent pacemaker implantation (PPMI) in patients treated with transcatheter aortic valve replacement (TAVR).

METHODS

336 patients undergoing TAVR at a single institution were included for model derivation. For primary analysis, multivariate logistic regression model was used to evaluate predictors and a risk score system was devised based on the prediction model. For secondary analysis, a Cox proportion hazard model was performed to assess characteristics associated with the time from TAVR to PPMI. The model was validated internally via bootstrap and externally using an independent cohort.

RESULTS

48 (14.3%) patients in the derivation set had PPMI after TAVR. Prior right bundle branch block (RBBB, OR: 10.46; p < 0.001), pre-procedural aortic valve area (AVA, OR: 1.41; p = 0.004) and post- to pre-procedural AVA ratio (OR: 1.72; p = 0.043) were identified as independent predictors for PPMI. AUC was 0.7 and 0.71 in the derivation and external validation set. Prior RBBB (HR: 5.07; p < 0.001), pre-procedural AVA (HR: 1.33; p = 0.001), post-procedural AVA to prosthetic nominal area ratio (HR: 0.02; p = 0.039) and post- to pre-procedural troponin-T difference (HR: 1.72; p = 0.017) are independently associated with time to PPMI.

CONCLUSIONS

The post-procedural prediction model achieved high discriminative power and accuracy for PPMI. The risk score system was constructed and validated, providing an accessible tool in clinical setting regarding the Chinese population.

Random forest vs. logistic regression: Predicting angiographic in-stent restenosis after second-generation drug-eluting stent implantation

As the rate of percutaneous coronary intervention increases, in-stent restenosis (ISR) has become a burden. Random forest (RF) could be superior to logistic regression (LR) for predicting ISR due to its robustness. We developed an RF model and compared its performance with the LR one for predicting ISR. We retrospectively included 1501 patients (age: 64.0 ± 10.3; male: 76.7%; ISR events: 279) who underwent coronary angiography at 9 to 18 months after implantation of 2nd generation drug-eluting stents. The data were randomly split into a pair of train and test datasets for model development and validation with 50 repeats. The predictive performance was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC). The RF models predicted ISR with larger AUC-ROCs of 0.829 ± 0.025 compared to 0.784 ± 0.027 of the LR models. The difference was statistically significant in 29 of the 50 repeats. The RF and LR models had similar sensitivity using the same cutoff threshold, but the specificity was significantly higher in the RF models, reducing 25% of the false positives. By removing the high leverage outliers, the LR models had comparable AUC-ROC to the RF models. Compared to the LR, the RF was more robust and significantly improved the performance for predicting ISR. It could cost-effectively identify patients with high ISR risk and help the clinical decision of coronary stenting.

Predictors for the risk of permanent pacemaker implantation after transcatheter aortic valve replacement: A systematic review and meta-analysis

BACKGROUND

Transcatheter aortic valve replacement (TAVR) is a less invasive treatment than surgery for severe aortic stenosis. However, its use is restricted by the fact that many patients eventually require permanent pacemaker implantation (PPMI). This meta-analysis was performed to identify predictors of post-TAVR PPMI.

METHODS

The PubMed, Embase, Web of Science, and Cochrane Library databases were systematically searched. Relevant studies that met the inclusion criteria were included in the pooling analysis after quality assessment.

RESULTS

After pooling 67 studies on post-TAVR PPMI risk in 97,294 patients, balloon-expandable valve use was negatively correlated with PPMI risk compared with self-expandable valve (SEV) use (odds ratio [OR]: 0.44, 95% confidence interval [CI]: 0.37-0.53). Meta-regression analysis revealed that history of coronary artery bypass grafting and higher Society of Thoracic Surgeons (STS) risk score increased the risk of PPMI with SEV utilization. Patients with pre-existing cardiac conduction abnormalities in 28 pooled studies also had a higher risk of PPMI (OR: 2.33, 95% CI: 1.90-2.86). Right bundle branch block (OR: 5.2, 95% CI: 4.37-6.18) and first-degree atrioventricular block (OR: 1.97, 95% CI: 1.38-2.79) also increased PPMI risk. Although the trans-femoral approach was positively correlated with PPMI risk, the trans-apical pathway showed no statistical difference to the trans-femoral pathway. The approach did not increase PPMI risk in patients with STS scores >8. Patient-prosthesis mismatch did not influence post-TAVR PPMI risk (OR: 0.88, 95% CI: 0.67-1.16). We also analyzed implantation depth and found no difference between patients with PPMI after TAVR and those without.

CONCLUSIONS

SEV selection, pre-existing cardiac conduction abnormality, and trans-femoral pathway selection are positively correlated with PPMI after TAVR. Pre-existing left bundle branch block, patient-prosthesis mismatch, and implantation depth did not affect the risk of PPMI after TAVR.