{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T07:37:54Z","timestamp":1792827474810,"version":"4.5.0"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neth Heart J"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Intervention-related complications negatively affect healthcare quality and costs. Complication registration and reporting play an important role in the improvement of healthcare outcomes but are time-consuming and hampered by under-reporting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed an artificial intelligence (AI)-aided tool that extracts specific complications, including bleeding, tamponade, CVA and phrenic paralysis, from electronic healthcare records of patients undergoing pulmonary vein isolation (PVI), percutaneous transluminal coronary angioplasty (PTCA) and transcatheter aortic valve implantation (TAVI). Both structured and unstructured data were used, along with additional signals identified through machine learning. The model was optimized to achieve high sensitivity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Electronic records of 6,564 patients undergoing PVI, PTCA, and TAVI, performed at our hospital from 2017\u20132022, were used. The final model detected the various complications with a\u00a0sensitivity between 94.4 and 100%, while specificity ranged from 99.4\u2013100%. This resulted in positive predictive values ranging from 47.5\u201381%, with F\u2011scores varying between 0.64\u20130.87.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed an AI-driven tool capable of automatically detecting complications following cardiac interventions from patient healthcare records, achieving a\u00a0very high sensitivity. Our tool enables registration of complications while significantly reducing administrative burden on the healthcare providers and data managers. It provides valuable and high-quality input for improvement cycles, thereby enhancing patient safety and healthcare quality.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s12471-026-02082-2","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T14:40:41Z","timestamp":1792593641000},"page":"407-413","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Development of an artificial intelligence algorithm for automated detection of cardiovascular intervention-related complications"],"prefix":"10.1007","volume":"34","author":[{"given":"Arnoud","family":"Okken","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rob","family":"Wanders","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andre","family":"Klappe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kasper F.","family":"Beukema","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom","family":"Oirbans","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter G.","family":"Noordzij","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anouk","family":"van Lent","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saskia H.","family":"Haanemaaier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7880-3730","authenticated-orcid":false,"given":"Jippe C.","family":"Balt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,21]]},"reference":[{"issue":"6","key":"2082_CR1","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1056\/NEJM199102073240604","volume":"324","author":"TA Brennan","year":"1991","unstructured":"Brennan\u00a0TA, et\u00a0al. Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard Medical Practice Study I. N Engl J Med. 1991;324(6):370\u20136.","journal-title":"N Engl J Med"},{"issue":"20","key":"2082_CR2","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1056\/NEJMNEJMhpr011493","volume":"347","author":"LL Leape","year":"2002","unstructured":"Leape\u00a0LL. Reporting of adverse events. N Engl J Med. 2002;347(20):1633\u20138.","journal-title":"N Engl J Med"},{"issue":"5","key":"2082_CR3","doi-asserted-by":"publisher","first-page":"370","DOI":"10.7326\/0003-4819-119-5-199309010-00004","volume":"119","author":"AC O\u2019Neil","year":"1993","unstructured":"O\u2019Neil\u00a0AC, Petersen\u00a0LA, Cook\u00a0EF, et\u00a0al. Physician reporting compared with medical-record review to identify adverse medical events. Ann Intern Med. 1993;119(5):370\u20136.","journal-title":"Ann Intern Med"},{"issue":"4","key":"2082_CR4","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1097\/PTS.0000000000000153","volume":"13","author":"SK Bell","year":"2017","unstructured":"Bell\u00a0SK, et\u00a0al. Transparency When Things Go Wrong: Physician Attitudes About Reporting Medical Errors to Patients, Peers, and Institutions. J\u00a0Patient Saf. 2017;13:243\u20138.","journal-title":"J Patient Saf"},{"key":"2082_CR5","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082505","volume":"388","author":"KGM Moons","year":"2025","unstructured":"Moons\u00a0KGM, Damen\u00a0JAA, Kaul\u00a0T, et\u00a0al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ. 2025;388:e082505.","journal-title":"BMJ"},{"issue":"1 Pt B","key":"2082_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpcardiol.2023.102097","volume":"49","author":"G Raileanu","year":"2024","unstructured":"Raileanu\u00a0G, de Jong\u00a0JSSG. Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached? Curr Probl Cardiol. 2024;49(1 Pt B)102097.","journal-title":"Curr Probl Cardiol"},{"issue":"7","key":"2082_CR7","first-page":"537","volume":"17","author":"R Beyar","year":"2021","unstructured":"Beyar\u00a0R, Davies\u00a0JE, Cook\u00a0C, et\u00a0al. Robotics, imaging, and artificial intelligence in the catheterisation laboratory. EuroIntervention J Eur Collab with Work Gr Interv Cardiol Eur Soc Cardiol. 2021;17(7):537\u201349.","journal-title":"EuroIntervention J Eur Collab with Work Gr Interv Cardiol Eur Soc Cardiol"},{"issue":"4","key":"2082_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacadv.2022.100080","volume":"1","author":"Y Katagiri","year":"2022","unstructured":"Katagiri\u00a0Y, et\u00a0al. Artificial Intelligence vs Visual Assessment of Calcified Plaque in Coronary Artery Using Optical Coherence Tomography. JACC Adv. 2022;1100080.","journal-title":"JACC Adv"},{"issue":"3","key":"2082_CR9","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/j.jcmg.2020.08.024","volume":"14","author":"R Nakanishi","year":"2021","unstructured":"Nakanishi\u00a0R, et\u00a0al. Machine Learning Adds to Clinical and CAC Assessments in Predicting 10-Year CHD and CVD Deaths. JACC Cardiovasc Imaging. 2021;14:615\u201325.","journal-title":"JACC Cardiovasc Imaging"},{"issue":"8","key":"2082_CR10","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1001\/jama.2011.1204","volume":"306","author":"HJ Murff","year":"2011","unstructured":"Murff\u00a0HJ, FitzHenry\u00a0F, Matheny\u00a0ME, et\u00a0al. Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing. JAMA. 2011;306(8):848\u201355.","journal-title":"JAMA"},{"key":"2082_CR11","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.surg.2022.10.026","volume":"173","author":"L Colborn","year":"2023","unstructured":"Colborn\u00a0L, Zhuang\u00a0Y, Dyas\u00a0AR, et\u00a0al. Development and validation of models for detection of postoperative infections using structured electronic health records data and machine learning. Surgery. 2023;173:464\u201371.","journal-title":"Surgery"},{"issue":"4","key":"2082_CR12","first-page":"438","volume":"165","author":"WW Weston","year":"2001","unstructured":"Weston\u00a0WW. Informed and shared decision-making: the crux of patient-centered care. C Can Med Assoc J. l\u2019Association Medicale Can. 2001;165(4):438\u20139.","journal-title":"C Can Med Assoc J = J l\u2019Association medicale Can"},{"key":"2082_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.amsu.2022.103520","volume":"77","author":"T Fukami","year":"2022","unstructured":"Fukami\u00a0T, Nagao\u00a0Y. The comprehensive double loop activities for patient safety management. Ann Med Surg. 2022;77:103520.","journal-title":"Ann Med Surg"},{"issue":"6","key":"2082_CR14","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCINTERVENTIONS.120.008962","volume":"13","author":"JA Doll","year":"2020","unstructured":"Doll\u00a0JA, et\u00a0al. Management of Percutaneous Coronary Intervention Complications: Circ Cardiovasc Interv. 2020;13(6)e008962.","journal-title":"Circ Cardiovasc Interv"},{"key":"2082_CR15","unstructured":"NHR website. 29-3-2024. https:\/\/nhr.nl\/over-nhr\/."},{"issue":"1","key":"2082_CR16","first-page":"49","volume":"6","author":"D van Veghel","year":"2020","unstructured":"van Veghel\u00a0D, Daeter\u00a0EJ, Bax\u00a0M, et\u00a0al. Organization of outcome-based quality improvement in Dutch heart centres. Eur Hear journal Qual care Clin outcomes. 2020;6(1):49\u201354.","journal-title":"Eur Hear journal Qual care Clin outcomes"},{"issue":"5","key":"2082_CR17","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1161\/CIRCULATIONAHA.115.016137","volume":"132","author":"JM Lee","year":"2015","unstructured":"Lee\u00a0JM, et\u00a0al. Percutaneous Coronary Intervention at Centers With and Without On-Site Surgical Backup. Circulation. 2015;132:388\u2013401.","journal-title":"Circulation"},{"issue":"21","key":"2082_CR18","doi-asserted-by":"publisher","first-page":"2089","DOI":"10.1016\/j.jacc.2023.03.418","volume":"81","author":"K Benali","year":"2023","unstructured":"Benali\u00a0K, Khairy\u00a0P, Hammache\u00a0N, et\u00a0al. Procedure-Related Complications of Catheter Ablation for Atrial Fibrillation. J\u00a0Am Coll Cardiol. 2023;81(21):2089\u201399.","journal-title":"J Am Coll Cardiol"},{"issue":"1","key":"2082_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-72453-1","volume":"10","author":"MP Winter","year":"2020","unstructured":"Winter\u00a0MP, Bartko\u00a0P, Hofer\u00a0F, et\u00a0al. Evolution of outcome and complications in TAVR: a\u00a0meta-analysis of observational and randomized studies. Sci Rep. 2020;10(1)15568.","journal-title":"Sci Rep"},{"key":"2082_CR20","unstructured":"van Otten N. F1 Score The Ultimate Guide: Formulas, Explanations, Examples, Advantages, Disadvantages, Alternatives & Python Code. https:\/\/spotintelligence.com\/."}],"container-title":["Netherlands Heart Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12471-026-02082-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12471-026-02082-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12471-026-02082-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T07:01:51Z","timestamp":1792825311000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12471-026-02082-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,21]]},"references-count":20,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["2082"],"URL":"https:\/\/doi.org\/10.1007\/s12471-026-02082-2","relation":{},"ISSN":["1568-5888","1876-6250"],"issn-type":[{"value":"1568-5888","type":"print"},{"value":"1876-6250","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,21]]},"assertion":[{"value":"17 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A.\u00a0Klappe and R.\u00a0Wanders are founders and shareholders of Parnas B.V, which has a\u00a0commercial interest in the AI model examined in this study. This affiliation may be perceived as a\u00a0potential conflict of interest. The authors declare that the research was conducted with independence and objectivity, and that the presentation of results has not been influenced by any commercial or financial considerations. A.\u00a0Okken, K.F.\u00a0Beukema, T.\u00a0Oirbans, P.G.\u00a0Noordzij, A.\u00a0van Lent, S.H.\u00a0Haanemaaier and J.C.\u00a0Balt declare that they have no competing interests.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}