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Effects of Artificial Intelligence Clinical Decision Support Tools on Complications Following Percutaneous Coronary
Karley B Fischer1, Damian N Valencia2, Ananya Reddy3
1Department of Internal Medicine, Kettering Health Main Campus, Kettering, Ohio.
Insights
Artificial intelligence tools significantly reduced contrast-induced acute kidney injury and bleeding after cardiac procedures. AI also decreased patient length of stay, improving outcomes and lowering costs.
Area of Science:
- Cardiology
- Nephrology
- Artificial Intelligence
Background:
- Percutaneous coronary intervention (PCI) carries risks of contrast-induced acute kidney injury (CI-AKI) and bleeding.
- These complications increase adverse outcomes, length of stay (LOS), and healthcare costs.
- AI models can stratify patient risk for PCI complications.
Purpose of the Study:
- To evaluate the impact of AI clinical decision support tools on CI-AKI and bleeding events post-PCI.
- To assess the effect of AI on patient length of stay (LOS) following PCI.
Main Methods:
- Retrospective review of 642 patients undergoing PCI from April 2023 to March 2024.
- Utilized ePRISM AI tool for patient risk assessment and contrast volume recommendations.
- Analyzed incidence of CI-AKI, bleeding events, and LOS.
Main Results:
- CI-AKI incidence decreased from 10% to 2.18% (P < .0001).
- Bleeding complications reduced from 2.15 to 1.54 events per month.
- Average LOS decreased from 3.44 to 1.79 days.
Conclusions:
- AI clinical decision support tools are effective in clinical practice.
- ePRISM successfully reduced CI-AKI, bleeding, and LOS in PCI patients.
- AI integration leads to significant improvements in patient outcomes and resource utilization.
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% (P < .0001). Of the remaining CI-AKI, 92.9% occurred in hospitalized patients. The incidence of bleeding complications declined from a baseline incidence of 2.15 per month to an average of 1.54 per month. Our institution's average LOS declined from a baseline of 3.44 to 1.79 days.
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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