[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124943-en":3,"doc-seo-124943-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124943,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",7,"Healthcare","Machine Learning Identification of Modifiable Predictors of Patient Outcomes After Transcatheter Aortic Valve Replacement","Machine learning models are applied to identify potentially modifiable predictors of patient-centered outcomes following transcatheter aortic valve replacement (TAVR). Using data from 8,332 TAVR cases across 21 hospitals (2016–2021), random forest models integrate 57 patient and care process characteristics to predict an excellent-outcome composite without major complications. Recursive feature elimination with cross-validation and SHAP feature importance isolates predictors with the highest relevance. Four modifiable predictors are reported: anesthesia type, early post-procedure disposition, catheterization-to-TAVR timing, and preprocedural length of stay, supporting improvement of care delivery.","UCSF  \nUC San Francisco Previously Published Works  \nTitle  \nMachine Learning Identification of Modifiable Predictors of Patient Outcomes After Transcatheter Aortic Valve Replacement.  \nPermalink  \n[https://escholarship.org/uc/item/2815h5j6](https://escholarship.org/uc/item/2815h5j6)  \nJournal  \nJACC: Advances, 3(8)  \nAuthors  \nRusso, Mark  \nElmariah, Sammy Kaneko, Tsuyoshi et al.  \nPublication Date  \n2024-08-01  \nDOI  \n10.1016/j.jacadv.2024.101116  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nJA CC: A D VA N C E S V O L . 3 , N O . 8 , 2 0 2 4  \nª 202 4 T H E A U T H O R S . P UBL I SH E D BY ELS E V I E R ON BEHA LF O F T H E A MERICA N COLLEGE O F CA RD IOLOGY FO UN DA TI ON . T H I S I S A N O P E N A CCE S S AR TICL E U N D ER TH E C C BY-NC-N D L ICE N S E ( [h ttp:// c re ative c omm o n s . o rg /lice n s es/ by-n c-n d/4 . 0 /](h ttp:// c re ative c omm o n s . o rg /lice n s es/ by-n c-n d/4 . 0 /)) .  \nORIGINAL RESEARCH  \nMachine Learning Identification of Modifiable Predictors of Patient Outcomes After Transcatheter Aortic Valve Replacement  \nMark J. Russo, MD,a Sammy Elmariah, MD, MPH,b Tsuyoshi Kaneko, MD,c David V. Daniels, MD,d Rajendra R. Makkar, MD,e Soumya G. Chikermane, PHD,f Christin Thompson, PHD,f Jose Benuzillo, MS,f Seth Clancy, MPH,f Amber Pawlikowski, MSN,g Skye Lawrence, BA,g Jeff Luck, MBA, PHDg,h  \nBACKGROUND Transcatheter aortic valve replacement (TAVR) is an important treatment option for patients with severe symptomatic aortic stenosis. It is important to identify predictors of excellent outcomes (good clinical outcomes, more time spent at home) after TAVR that are potentially amenable to improvement.  \nOBJECTIVES The purpose of the study was to use machine learning to identify potentially modifiable predictors of clinically relevant patient-centered outcomes after TAVR.  \nMETHODS We used data from 8,332 TAVR cases (January 2016-December 2021) from 21 hospitals to train random forest models with 57 patient characteristics (demographics, comorbidities, surgical risk score, lab values, health status scores) and care process parameters to predict the end point, a composite of parameters that designated an excellent outcome and included no major complications (in-hospital or at 30 days), post-TAVR length of stay of 1 day or less, discharge to home, no readmission, and alive at 30 days. We used recursive feature elimination with cross-validation and Shapley Additive Explanation feature importance to identify parameters with the highest predictive values.  \nRESULTS The final random forest model retained 29 predictors (15 patient characteristics and 14 care process components); the area under the curve, sensitivity, and specificity were 0 .77, 0 . 67, and 0 .73, respectively. Four potentially modifiable predictors with relatively high Shapley Additive Explanation values were identified: type of anesthesia, direct movement to stepdown unit post-TAVR, time between catheterization and TAVR, and preprocedural length of stay.  \nCONCLUSIONS This study identified four potentially modifiable predictors of excellent outcome after TAVR, suggesting that machine learning combined with hospital-level data can inform modifiable components of care, which could support better delivery of care for patients undergoing TAVR. (JACC Adv 2024;3:101116) © 2024 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)).  \nFrom the aDivision of Cardiac Surgery, Division of Structural Heart Disease, Rutgers-Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA; bDivision of Cardiology, Department of Medicine, University of California San Francisco, California, USA; cDivision of Cardiothoracic Surgery, Washington University, St Louis, Missouri, USA","cbCaipjjOVQSxckj","https://ap.wps.com/l/cbCaipjjOVQSxckj","pdf",1725225,1,13,"English","en",105,"# Background\n# Objectives\n# Methods\n# Results\n# Conclusions","[{\"question\":\"What clinical problem does the study address after TAVR?\",\"answer\":\"It targets the need to identify predictors of excellent patient outcomes after transcatheter aortic valve replacement that could be improved through changes in care processes.\"},{\"question\":\"How were the machine learning models built in this study?\",\"answer\":\"Random forest models were trained on 8,332 TAVR cases from 21 hospitals using 57 patient characteristics and care process parameters, with recursive feature elimination and cross-validation. SHAP values were used to estimate feature importance.\"},{\"question\":\"Which potentially modifiable predictors were identified?\",\"answer\":\"The study reports four potentially modifiable predictors: type of anesthesia, direct movement to a stepdown unit post-TAVR, time between catheterization and TAVR, and preprocedural length of stay.\"}]","Machine Learning Identification of Modifiable Predictors of Patient Outcomes After Transcatheter Aortic Valve Replacement | PDF",1785895520,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-identification-of-modifiable-predictors-of-patient-outcomes-after-transcatheter-aortic-valve-replacement","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-identification-of-modifiable-predictors-of-patient-outcomes-after-transcatheter-aortic-valve-replacement/124943/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical problem does the study address after TAVR?","Question",{"text":75,"@type":76},"It targets the need to identify predictors of excellent patient outcomes after transcatheter aortic valve replacement that could be improved through changes in care processes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the machine learning models built in this study?",{"text":80,"@type":76},"Random forest models were trained on 8,332 TAVR cases from 21 hospitals using 57 patient characteristics and care process parameters, with recursive feature elimination and cross-validation. 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