ArticleJournal of the Society for Cardiovascular Angiography & Interventions2025
Effects of Artificial Intelligence Clinical Decision Support Tools on Complications Following Percutaneous Coronary Intervention.
Article in Journal of the Society for Cardiovascular Angiography & Interventions, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Precision Medicine for Anticoagulation Strategies in the Cath Lab: Part 2.Current cardiology reports · 2026Review
- Percutaneous Coronary Intervention and vascular access complications: A contemporary review.NPJ cardiovascular health · 2026Review
- Artificial intelligence in the cath lab: Bridging predictive models and real-time procedural decision support.EXCLI journal · 2026Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) models have been created that incorporate unique patient characteristics to risk stratify patients undergoing cardiac catheterization with percutaneous coronary intervention (PCI). The most frequent complications following PCI are contrast-induced acute kidney injury (CI-AKI) and postprocedural bleeding, resulting in increased adverse outcomes, length of stay (LOS), and health care costs. Our study investigates the impact of AI clinical decision support tools on these events. Methods: A retrospective review of patients undergoing PCI at our institution from April 2023 to March 2024 was performed. All patients had an ePRISM (Terumo Health Outcomes AI clinical decision support tool) generated risk assessment and maximum contrast volume recommendation reported during procedure time-out. Statistical analysis was performed to determine the incidence of post-PCI CI-AKI, bleeding events, and LOS. Results: A total of 642 patients were analyzed. The incidence of CI-AKI significantly declined from a baseline of 10% to an average of 2.18% ( Conclusions: AI clinical decision support tools can be effectively incorporated into clinical practice. ePRISM successfully risk-stratified patients undergoing PCI for CI-AKI and bleeding events and gave meaningful recommendations which resulted in a significant reduction in adverse events and LOS.
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Registered trials
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