Daniel Lorenzatti, Annalisa Filtz, Jolien Geers, Kajetan Grodecki, Vita Jaspan, Colin Pierce, Matthew J Miller, Christine Park, Alexandrina Danilov, Ron Blankstein, Thomas A Treibel, João L Cavalcante, Leslee J Shaw, Marc R Dweck, Piotr J Slomka, Damini Dey, Leandro Slipczuk
{"title":"Novel CT-derived markers for enhanced long-term risk stratification in the planning of TAVR for aortic stenosis.","authors":"Daniel Lorenzatti, Annalisa Filtz, Jolien Geers, Kajetan Grodecki, Vita Jaspan, Colin Pierce, Matthew J Miller, Christine Park, Alexandrina Danilov, Ron Blankstein, Thomas A Treibel, João L Cavalcante, Leslee J Shaw, Marc R Dweck, Piotr J Slomka, Damini Dey, Leandro Slipczuk","doi":"10.1016/j.jcct.2025.01.008","DOIUrl":null,"url":null,"abstract":"<p><p>In an era of rapidly expanding use of transcatheter aortic valve replacement (TAVR), cardiovascular computed tomography (CCT) has become an essential component in the evaluation process for the growing number of patients. Because of the nature of the guideline-recommended protocol -involving several different CCT acquisitions-it represents a unique dataset for comprehensive phenotyping of the patient with significant aortic stenosis. A substantial body of data has established CCT as a central tool in pre-procedural implantation planning. However, emerging evidence suggests a potential new role for CCT in phenotyping patient risk beyond the index procedure. This new role could represent a unique opportunity in patient selection, medication optimization and follow up post TAVR aiming to improve long-term prognosis. This review highlights emerging data on CCT imaging features for risk stratification in patients during long-term follow-up after TAVR. We summarize the existing literature on this topic and explore whether comprehensive CCT-derived information could be integrated into clinical practice, potentially enhancing TAVR patient selection and post-procedural care.</p>","PeriodicalId":94071,"journal":{"name":"Journal of cardiovascular computed tomography","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of cardiovascular computed tomography","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.jcct.2025.01.008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In an era of rapidly expanding use of transcatheter aortic valve replacement (TAVR), cardiovascular computed tomography (CCT) has become an essential component in the evaluation process for the growing number of patients. Because of the nature of the guideline-recommended protocol -involving several different CCT acquisitions-it represents a unique dataset for comprehensive phenotyping of the patient with significant aortic stenosis. A substantial body of data has established CCT as a central tool in pre-procedural implantation planning. However, emerging evidence suggests a potential new role for CCT in phenotyping patient risk beyond the index procedure. This new role could represent a unique opportunity in patient selection, medication optimization and follow up post TAVR aiming to improve long-term prognosis. This review highlights emerging data on CCT imaging features for risk stratification in patients during long-term follow-up after TAVR. We summarize the existing literature on this topic and explore whether comprehensive CCT-derived information could be integrated into clinical practice, potentially enhancing TAVR patient selection and post-procedural care.