{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T21:53:09Z","timestamp":1791237189195,"version":"4.1.0"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:00:00Z","timestamp":1740096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background: Early safety outcomes following transcatheter aortic valve implantation (TAVI) for severe aortic stenosis are critical for patient prognosis. Accurate prediction of adverse events can enhance patient management and improve outcomes. Aim: This study aimed to develop a machine learning model to predict early safety outcomes in patients with severe aortic stenosis undergoing TAVI. Methods: We conducted a retrospective single-centre study involving 224 patients with severe aortic stenosis who underwent TAVI. Seventy-seven clinical and biochemical variables were collected for analysis. To handle unbalanced classification problems, an adaptive synthetic (ADASYN) sampling approach was used. A fined-tuned random forest (RF) machine learning model was developed to predict early safety outcomes, defined as all-cause mortality, stroke, life-threatening bleeding, acute kidney injury (stage 2 or 3), coronary artery obstruction requiring intervention, major vascular complications, and valve-related dysfunction requiring repeat procedures. Shapley Additive Explanations (SHAPs) were used to explain the output of the machine learning model by attributing each variable\u2019s contribution to the final prediction of early safety outcomes. Results: The random forest model identified left femoral artery diameter and aortic valve calcification volume as the most influential predictors of early safety outcomes. SHAPs analysis demonstrated that smaller left femoral artery diameter and higher aortic valve calcification volume were associated with poorer early safety prognoses. Conclusions: The machine learning model highlights of early safety outcomes after TAVI. These findings suggest that incorporating these variables into pre-procedural assessments may improve risk stratification and inform clinical decision-making to enhance patient care.<\/jats:p>","DOI":"10.3390\/medicina61030374","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:39:07Z","timestamp":1740119947000},"page":"374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An Integrative Machine Learning Model for Predicting Early Safety Outcomes in Patients Undergoing Transcatheter Aortic Valve Implantation"],"prefix":"10.3390","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4803-0181","authenticated-orcid":false,"given":"Abilkhair","family":"Kurmanaliyev","sequence":"first","affiliation":[{"name":"Department of Cardiology, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, Lithuanian University of Health Sciences, 2 Eiveni\u0173 Str., LT-50009 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5412-3194","authenticated-orcid":false,"given":"Kristina","family":"Sutiene","sequence":"additional","affiliation":[{"name":"Department of Mathematical Modeling, Kaunas University of Technology, Student\u0173 Str. 50\u2013143, LT-50009 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rima","family":"Braukylien\u0117","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, Lithuanian University of Health Sciences, 2 Eiveni\u0173 Str., LT-50009 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Aldujeli","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, Lithuanian University of Health Sciences, 2 Eiveni\u0173 Str., LT-50009 Kaunas, Lithuania"},{"name":"CORRIB Research Centre for Advanced Imaging and Core Laboratory, University of Galway, 1 University Road Str., H91 TK33 Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0598-2666","authenticated-orcid":false,"given":"Martynas","family":"Jurenas","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, Lithuanian University of Health Sciences, 2 Eiveni\u0173 Str., LT-50009 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rugile","family":"Kregzdyte","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, Lithuanian University of Health Sciences, 2 Eiveni\u0173 Str., LT-50009 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurynas","family":"Braukyla","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, Lithuanian University of Health Sciences, 2 Eiveni\u0173 Str., LT-50009 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rassul","family":"Zhumagaliyev","sequence":"additional","affiliation":[{"name":"Department of Cardiac, Thoracic and Vascular Surgery, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, Lithuanian University of Health Sciences, 2 Eiveni\u0173 Str., LT-50009 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3014-9100","authenticated-orcid":false,"given":"Serik","family":"Aitaliyev","sequence":"additional","affiliation":[{"name":"Department of Cardiac, Thoracic and Vascular Surgery, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, Lithuanian University of Health Sciences, 2 Eiveni\u0173 Str., LT-50009 Kaunas, Lithuania"},{"name":"Faculty of Medicine and Health Care, Al-Farabi Kazakh National University, 71 Al-Farabi Ave., Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nurlan","family":"Zhanabayev","sequence":"additional","affiliation":[{"name":"South Kazakhstan Medical Academy, 1 Al-Farabi Square, Shymkent 160019, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rauan","family":"Botabayeva","sequence":"additional","affiliation":[{"name":"South Kazakhstan Medical Academy, 1 Al-Farabi Square, Shymkent 160019, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0844-7268","authenticated-orcid":false,"given":"Yerlan","family":"Orazymbetov","sequence":"additional","affiliation":[{"name":"Department of Cardiac, Thoracic and Vascular Surgery, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, Lithuanian University of Health Sciences, 2 Eiveni\u0173 Str., LT-50009 Kaunas, Lithuania"},{"name":"National Scientific Medical Center, 42 Abylaikhan Avenue, Astana 010009, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramunas","family":"Unikas","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Hospital of Lithuanian University of Health Sciences Kauno Klinikos, Lithuanian University of Health Sciences, 2 Eiveni\u0173 Str., LT-50009 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1007\/s13670-015-0148-3","article-title":"A Decision-Making Framework for Objective Risk Assessment in Older Adults With Severe Symptomatic Aortic 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