{
  "abstract": "Background\n\nIntervention-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.Methods\n\nWe 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.Results\n\nElectronic records of 6,564 patients undergoing PVI, PTCA, and TAVI, performed at our hospital from 2017-2022, were used. The final model detected the various complications with a sensitivity between 94.4 and 100%, while specificity ranged from 99.4-100%. This resulted in positive predictive values ranging from 47.5-81%, with F‑scores varying between 0.64-0.87.Conclusion\n\nWe developed an AI-driven tool capable of automatically detecting complications following cardiac interventions from patient healthcare records, achieving a very 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Cardiology, St. Antonius Hospital, Nieuwegein, The Netherlands.",
    "Parnas, IT-Services and Consultancy, Groningen, The Netherlands.",
    "Department of Anesthesiology and Intensive Care Medicine, St. Antonius Hospital, Nieuwegein, The Netherlands.",
    "Department of Cardiology, St. Antonius Hospital, Nieuwegein, The Netherlands. j.balt@antoniusziekenhuis.nl."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Arnoud Okken"
    },
    {
      "affs": [
        1
      ],
      "name": "Rob Wanders"
    },
    {
      "affs": [
        1
      ],
      "name": "Andre Klappe"
    },
    {
      "affs": [
        0
      ],
      "name": "Kasper F Beukema"
    },
    {
      "affs": [
        0
      ],
      "name": "Tom Oirbans"
    },
    {
      "affs": [
        2
      ],
      "name": "Peter G Noordzij"
    },
    {
      "affs": [
        0
      ],
      "name": "Anouk van Lent"
    },
    {
      "affs": [
        0
      ],
      "name": "Saskia H Haanemaaier"
    },
    {
      "affs": [
        3
      ],
      "name": "Jippe C Balt",
      "orcid": "0000-0002-7880-3730"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4j1pm687hpmzm.bib",
  "cited_by": 1,
  "doi": "10.1007/s12471-026-02082-2",
  "fulltext_url": "https://europepmc.org/articles/PMC13694613",
  "id": "4j1pm687hpmzm",
  "issns": [
    "1568-5888",
    "1876-6250"
  ],
  "landing_url": "https://europepmc.org/article/MED/42905390",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7223127670",
  "pdf_url": "https://europepmc.org/articles/PMC13694613?pdf=render",
  "pmcid": "PMC13694613",
  "pmid": "42905390",
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4j1pm687hpmzm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:53Z",
      "native_id": "MED:42905390",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42905390"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:2705|Cardiac electrophysiology and arrhythmias",
      "subfield": "cardiovascular"
    }
  ],
  "title": "Development of an artificial intelligence algorithm for automated detection of cardiovascular intervention-related complications.",
  "title_text": "Development of an artificial intelligence algorithm for automated detection of cardiovascular intervention-related complications.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:53Z",
  "url": "https://research.draconitic.com/paper/4j1pm687hpmzm",
  "venue": "Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation",
  "versions": []
}