Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review
<h3>Background</h3><p dir="ltr">Transcatheter aortic valve implantation (TAVI) therapy has demonstrated its clear benefits such as low invasiveness, to treat aortic stenosis. Despite associated benefits, still post-procedural complications might occur. The severity of the...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2025
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513551198060544 |
|---|---|
| author | Ruba Sulaiman (17734065) |
| author2 | Md.Ahasan Atick Faisal (20837645) Maram Hasan (6672440) Muhammad E.H. Chowdhury (17151154) Faycal Bensaali (12427401) Abdulrahman Alnabti (20667683) Huseyin C. Yalcin (6695099) |
| author2_role | author author author author author author |
| author_facet | Ruba Sulaiman (17734065) Md.Ahasan Atick Faisal (20837645) Maram Hasan (6672440) Muhammad E.H. Chowdhury (17151154) Faycal Bensaali (12427401) Abdulrahman Alnabti (20667683) Huseyin C. Yalcin (6695099) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ruba Sulaiman (17734065) Md.Ahasan Atick Faisal (20837645) Maram Hasan (6672440) Muhammad E.H. Chowdhury (17151154) Faycal Bensaali (12427401) Abdulrahman Alnabti (20667683) Huseyin C. Yalcin (6695099) |
| dc.date.none.fl_str_mv | 2025-02-17T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.ijmedinf.2025.105840 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_learning_for_predicting_outcomes_of_transcatheter_aortic_valve_implantation_A_systematic_review/28546466 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Cardiovascular medicine and haematology Health sciences Health services and systems Information and computing sciences Artificial intelligence Transcatheter aortic valve implantation TAVITAVR Machine learning Deep learning Outcome prediction Aortic stenosis |
| dc.title.none.fl_str_mv | Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Transcatheter aortic valve implantation (TAVI) therapy has demonstrated its clear benefits such as low invasiveness, to treat aortic stenosis. Despite associated benefits, still post-procedural complications might occur. The severity of these complications depends on pre-existing clinical conditions and patient specific complex anatomical features. Accurate prediction of TAVI outcomes will assist in the precise risk assessment for patients undergoing TAVI. Throughout the past decade, different machine learning (ML) approaches have been utilized to predict outcomes of TAVI. This systematic review aims to assess the application of ML in TAVI for the purpose of outcome prediction. </p><h3>Methods</h3><p dir="ltr">Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was adapted for searching the PubMed and Scopus databases on ML use in TAVI outcomes prediction. Once the studies that meet the inclusion criteria were identified, data from these studies were retrieved and were further examined. 17 parameters relevant to TAVI outcomes were carefully identified for assessing the quality of the included studies. </p><h3>Results</h3><p dir="ltr">Following the search of the mentioned databases, 78 studies were initially retrieved, and 17 of these studies were included for further assessment. Most of the included studies focused on mortality prediction, utilizing datasets of varying sizes and diverse ML algorithms. The most employed ML algorithms were random forest, logistics regression, and gradient boosting. Among the studied parameters, serum creatinine, age, BMI, hemoglobin, and aortic valve mean gradient were identified as key predictors for TAVI outcomes. These predictors were found to be well aligned with established associations in current literature. </p><h3>Conclusion</h3><p dir="ltr">ML presents a promising opportunity for improving the success and safety of TAVI and enhancing patient-centered care. While currently retrospective studies with low generalizability and heterogeneity form the basis of ML TAVI research, future prospective investigations with highly heterogeneous patient TAVI cohorts will be critically important for firmly establishing the applicability of ML in predicting TAVI outcomes.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Medical Informatics<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.ijmedinf.2025.105840" target="_blank">https://dx.doi.org/10.1016/j.ijmedinf.2025.105840</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_fca041645ec8d2a4fcb37b1789b6452e |
| identifier_str_mv | 10.1016/j.ijmedinf.2025.105840 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28546466 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic reviewRuba Sulaiman (17734065)Md.Ahasan Atick Faisal (20837645)Maram Hasan (6672440)Muhammad E.H. Chowdhury (17151154)Faycal Bensaali (12427401)Abdulrahman Alnabti (20667683)Huseyin C. Yalcin (6695099)Biomedical and clinical sciencesCardiovascular medicine and haematologyHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceTranscatheter aortic valve implantationTAVITAVRMachine learningDeep learningOutcome predictionAortic stenosis<h3>Background</h3><p dir="ltr">Transcatheter aortic valve implantation (TAVI) therapy has demonstrated its clear benefits such as low invasiveness, to treat aortic stenosis. Despite associated benefits, still post-procedural complications might occur. The severity of these complications depends on pre-existing clinical conditions and patient specific complex anatomical features. Accurate prediction of TAVI outcomes will assist in the precise risk assessment for patients undergoing TAVI. Throughout the past decade, different machine learning (ML) approaches have been utilized to predict outcomes of TAVI. This systematic review aims to assess the application of ML in TAVI for the purpose of outcome prediction. </p><h3>Methods</h3><p dir="ltr">Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was adapted for searching the PubMed and Scopus databases on ML use in TAVI outcomes prediction. Once the studies that meet the inclusion criteria were identified, data from these studies were retrieved and were further examined. 17 parameters relevant to TAVI outcomes were carefully identified for assessing the quality of the included studies. </p><h3>Results</h3><p dir="ltr">Following the search of the mentioned databases, 78 studies were initially retrieved, and 17 of these studies were included for further assessment. Most of the included studies focused on mortality prediction, utilizing datasets of varying sizes and diverse ML algorithms. The most employed ML algorithms were random forest, logistics regression, and gradient boosting. Among the studied parameters, serum creatinine, age, BMI, hemoglobin, and aortic valve mean gradient were identified as key predictors for TAVI outcomes. These predictors were found to be well aligned with established associations in current literature. </p><h3>Conclusion</h3><p dir="ltr">ML presents a promising opportunity for improving the success and safety of TAVI and enhancing patient-centered care. While currently retrospective studies with low generalizability and heterogeneity form the basis of ML TAVI research, future prospective investigations with highly heterogeneous patient TAVI cohorts will be critically important for firmly establishing the applicability of ML in predicting TAVI outcomes.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Medical Informatics<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.ijmedinf.2025.105840" target="_blank">https://dx.doi.org/10.1016/j.ijmedinf.2025.105840</a></p>2025-02-17T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.ijmedinf.2025.105840https://figshare.com/articles/journal_contribution/Machine_learning_for_predicting_outcomes_of_transcatheter_aortic_valve_implantation_A_systematic_review/28546466CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/285464662025-02-17T12:00:00Z |
| spellingShingle | Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review Ruba Sulaiman (17734065) Biomedical and clinical sciences Cardiovascular medicine and haematology Health sciences Health services and systems Information and computing sciences Artificial intelligence Transcatheter aortic valve implantation TAVITAVR Machine learning Deep learning Outcome prediction Aortic stenosis |
| status_str | publishedVersion |
| title | Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review |
| title_full | Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review |
| title_fullStr | Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review |
| title_full_unstemmed | Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review |
| title_short | Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review |
| title_sort | Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Health sciences Health services and systems Information and computing sciences Artificial intelligence Transcatheter aortic valve implantation TAVITAVR Machine learning Deep learning Outcome prediction Aortic stenosis |