Development of an artificial intelligence algorithm for automated detection of cardiovascular intervention-related complications.
4 affiliations
- 1Department of Cardiology, St. Antonius Hospital, Nieuwegein, The Netherlands.
- 2Parnas, IT-Services and Consultancy, Groningen, The Netherlands.
- 3Department of Anesthesiology and Intensive Care Medicine, St. Antonius Hospital, Nieuwegein, The Netherlands.
- 4Department of Cardiology, St. Antonius Hospital, Nieuwegein, The Netherlands. [email protected].
Abstract
Background
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.Methods
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.Results
Electronic 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
We 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.
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BibTeX
@article{okken2026development,
title = {{Development of an artificial intelligence algorithm for automated detection of cardiovascular intervention-related complications.}},
author = {Arnoud Okken and Rob Wanders and Andre Klappe and Kasper F Beukema and Tom Oirbans and Peter G Noordzij and Anouk van Lent and Saskia H Haanemaaier and Jippe C Balt},
journal = {Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation},
year = {2026},
month = oct,
doi = {10.1007/s12471-026-02082-2},
url = {https://europepmc.org/article/MED/42905390},
note = {CC BY},
}
RIS
TY - JOUR
TI - Development of an artificial intelligence algorithm for automated detection of cardiovascular intervention-related complications.
AU - Arnoud Okken
AU - Rob Wanders
AU - Andre Klappe
AU - Kasper F Beukema
AU - Tom Oirbans
AU - Peter G Noordzij
AU - Anouk van Lent
AU - Saskia H Haanemaaier
AU - Jippe C Balt
PY - 2026
DA - 2026/10/21
JO - Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation
DO - 10.1007/s12471-026-02082-2
UR - https://europepmc.org/article/MED/42905390
AB - Background 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.Methods 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.Results Electronic 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 We 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.
SN - 1568-5888
SN - 1876-6250
ER -