Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review
BackgroundTranscatheter 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...
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| مؤلفون آخرون: | , , , , , |
| التنسيق: | article |
| منشور في: |
2025
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://dx.doi.org/10.1016/j.ijmedinf.2025.105840 https://www.sciencedirect.com/science/article/pii/S1386505625000577 http://hdl.handle.net/10576/64041 |
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| _version_ | 1857415085903839232 |
|---|---|
| author | Ruba, Sulaiman |
| author2 | Atick Faisal, Md.Ahasan Hasan, Maram Chowdhury, Muhammad E.H. Bensaali, Faycal Alnabti, Abdulrahman Yalcin, Huseyin C. |
| author2_role | author author author author author author |
| author_facet | Ruba, Sulaiman Atick Faisal, Md.Ahasan Hasan, Maram Chowdhury, Muhammad E.H. Bensaali, Faycal Alnabti, Abdulrahman Yalcin, Huseyin C. |
| author_role | author |
| dc.creator.none.fl_str_mv | Ruba, Sulaiman Atick Faisal, Md.Ahasan Hasan, Maram Chowdhury, Muhammad E.H. Bensaali, Faycal Alnabti, Abdulrahman Yalcin, Huseyin C. |
| dc.date.none.fl_str_mv | 2025-03-30T08:00:23Z 2025-02-16 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://dx.doi.org/10.1016/j.ijmedinf.2025.105840 Sulaiman, R., Faisal, M. A. A., Hasan, M., Chowdhury, M. E., Bensaali, F., Alnabti, A., & Yalcin, H. C. (2025). Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review. International Journal of Medical Informatics, 105840. 1386-5056 https://www.sciencedirect.com/science/article/pii/S1386505625000577 http://hdl.handle.net/10576/64041 197 1872-8243 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Transcatheter aortic valve implantation TAVI TAVR 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 | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | BackgroundTranscatheter 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. MethodsPreferred 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. ResultsFollowing 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. ConclusionML 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | qu_13eef3bc91505158becd30041e987f1c |
| identifier_str_mv | Sulaiman, R., Faisal, M. A. A., Hasan, M., Chowdhury, M. E., Bensaali, F., Alnabti, A., & Yalcin, H. C. (2025). Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review. International Journal of Medical Informatics, 105840. 1386-5056 197 1872-8243 |
| language_invalid_str_mv | en |
| network_acronym_str | qu |
| network_name_str | Qatar University repository |
| oai_identifier_str | oai:qspace.qu.edu.qa:10576/64041 |
| publishDate | 2025 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| spelling | Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic reviewRuba, SulaimanAtick Faisal, Md.AhasanHasan, MaramChowdhury, Muhammad E.H.Bensaali, FaycalAlnabti, AbdulrahmanYalcin, Huseyin C.Transcatheter aortic valve implantationTAVITAVRMachine learningDeep learningOutcome predictionAortic stenosisBackgroundTranscatheter 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. MethodsPreferred 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. ResultsFollowing 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. ConclusionML 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.This study is supported by Qatar National Research Fund (QNRF)-National Priorities Research Program (NPRP), NPRP13S-0108\u2013200024. Open-access funding was provided by the Qatar National Library.Elsevier2025-03-30T08:00:23Z2025-02-16Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1016/j.ijmedinf.2025.105840Sulaiman, R., Faisal, M. A. A., Hasan, M., Chowdhury, M. E., Bensaali, F., Alnabti, A., & Yalcin, H. C. (2025). Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review. International Journal of Medical Informatics, 105840.1386-5056https://www.sciencedirect.com/science/article/pii/S1386505625000577http://hdl.handle.net/10576/640411971872-8243enhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:qspace.qu.edu.qa:10576/640412025-03-30T19:06:26Z |
| spellingShingle | Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review Ruba, Sulaiman Transcatheter aortic valve implantation TAVI TAVR 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 | Transcatheter aortic valve implantation TAVI TAVR Machine learning Deep learning Outcome prediction Aortic stenosis |
| url | http://dx.doi.org/10.1016/j.ijmedinf.2025.105840 https://www.sciencedirect.com/science/article/pii/S1386505625000577 http://hdl.handle.net/10576/64041 |