Machine Learning Algorithms for Prediction of Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement

Tsushima, Takahiro; Al‐Kindi, Sadeer; Nadeem, Fahd; Attizzani, Guilherme F.; Elgudin, Yakov; Markowitz, Alan H.; Costa, Marco A.; Simon, Daniel I.; Arruda, Maurício; Mackall, Judith A.; Thal, Sergio

doi:10.1161/circep.120.008941

Cited by 11 publications

(3 citation statements)

References 5 publications

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“…[80] The results showed that the random forest (RF) model incorporating post-TAVR electrocardiogram data (AUC 0.81) more accurately predicted PPI risk compared to the RF model without TAVR ECG data (AUC 0.72). Similarly, Takahiro Tsushima et al [81] study with a larger patient sample also demonstrated that ML algorithms could accurately predict the risk of PPI implantation after TAVR. According to the research conducted by these scholars, ML has the potential to improve patient selection and risk management in interventional cardiovascular procedures and compared to traditional logistic regression analysis, it makes better predictions.…”

Section: Valvular Heart Diseasementioning

confidence: 80%

How AI and Robotics Will Advance Interventional Radiology: A Narrative Review and Future Perspectives

Zhang,

Fang,

et al. 2024

Preprint

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The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields, including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure comprehensiveness and reliability of the results. We discuss the role of AI techniques used before, during, and after intervention treatments and the advantages of robotics in achieving precise operations and reducing intra-operative risks. Challenges faced in the implementation of these techniques are also addressed. Finally, we explore the future trends and potential applications of AI and robotics in interventional therapy, highlighting their potential to improve patient treatment outcomes and quality of life.

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Section: Valvular Heart Diseasementioning

confidence: 80%

How AI and Robotics Will Advance Interventional Radiology: A Narrative Review and Future Perspectives

Zhang,

Fang,

et al. 2024

Preprint

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“…Artificial intelligence is growing rapidly in recent years. The previously reported machine learning-based prediction models demonstrated significantly high predictive accuracy [ 36 , 37 ]. Due to the restricted data volume, machine learning is not applicable to our study yet.…”

Section: Discussionmentioning

confidence: 99%

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

Qi,

Lin,

Pan

et al. 2023

Eur J Med Res

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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.

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“…12,[16][17][18][19] Risk factors for the development of high-grade AV block have been previously defined 14,20,21 and incorporated into models for determining the risk of PPM after TAVR. Unfortunately, many of those risk prediction tools were developed using analysis of early generation prostheses, 20,21 complex modeling systems, 22 or small patient populations. 23,24 Furthermore, these tools often incorporate procedural data from the time of TAVR implantation, 21,25 which limits their utility for preprocedure planning in an outpatient clinic setting.…”

Section: Introductionmentioning

confidence: 99%

PRIME score for prediction of permanent pacemaker implantation after transcatheter aortic valve replacement

Barrett,

Nickel,

Rosenberg

et al. 2023

Cathet Cardio Intervent

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ObjectivesWe sought to produce a simple scoring system that can be applied at clinical visits before transcatheter aortic valve replacement (TAVR) to stratify the risk of permanent pacemaker (PPM) after the procedure.BackgroundAtrioventricular block is a known complication of TAVR. Current models for predicting the risk of PPM after TAVR are not designed to be applied clinically to assist with preprocedural planning.MethodsPatients undergoing TAVR at the University of Colorado were split into a training cohort for the development of a predictive model, and a testing cohort for model validation. Stepwise and binary logistic regressions were performed on the training cohort to produce a predictive model. Beta coefficients from the binary logistic regression were used to create a simple scoring system for predicting the need for PPM implantation. Scores were then applied to the validation cohort to assess predictive accuracy.ResultsPatients undergoing TAVR from 2013 to 2019 were analyzed: with 483 included in the training cohort and 123 included in the validation cohort. The need for a pacemaker was associated with five preprocedure variables in the training cohort: PR interval > 200 ms, Right bundle branch block, valve‐In‐valve procedure, prior Myocardial infarction, and self‐Expandable valve. The PRIME score was developed using these clinical features, and was highly accurate for predicting PPM in both the training and model validation cohorts (area under the curve 0.804 and 0.830 in the model training and validation cohorts, respectively).ConclusionsThe PRIME score is a simple and accurate preprocedural tool for predicting the need for PPM implantation after TAVR.

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Machine Learning Algorithms for Prediction of Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement

Cited by 11 publications

References 5 publications

How AI and Robotics Will Advance Interventional Radiology: A Narrative Review and Future Perspectives

How AI and Robotics Will Advance Interventional Radiology: A Narrative Review and Future Perspectives

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

PRIME score for prediction of permanent pacemaker implantation after transcatheter aortic valve replacement

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