AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study
Enrico de Koning1, Yvette van der Haas2, Saguna Saguna2
1Cardiology Department, Leiden University Medical Center, Leiden, Netherlands.
JMIR Cardio
|October 31, 2023
Summary
An artificial intelligence (AI) model can predict acute coronary syndrome (ACS) before hospital arrival, improving diagnostic accuracy. This AI tool enhances specificity and negative predictive value in prehospital care.
Area of Science:
- Emergency Medicine
- Artificial Intelligence in Healthcare
- Cardiology
Background:
- Hospital overcrowding and emergency department (ED) overload are significant issues.
- A substantial proportion of ED chest pain presentations do not involve acute coronary syndrome (ACS).
- Artificial intelligence (AI) offers potential for prehospital clinical decision support.
Purpose of the Study:
- To develop an AI model for predicting ACS prior to ED presentation.
- To analyze retrospective prehospital data collected by emergency medical services (EMS).
Main Methods:
- A supervised text classification algorithm was employed to develop the AI model.
- Data from 7458 patients with ACS symptoms (Sept 2018-Sept 2020) were analyzed.
- Model performance was evaluated using specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).
Main Results:
- The AI model achieved 11% specificity and 99.5% sensitivity.
- AI model demonstrated a PPV of 15% and an NPV of 99%.
- Compared to usual care (1% specificity, 99.5% sensitivity, 13% PPV, 94% NPV), the AI model improved specificity and NPV.
Conclusions:
- The AI model successfully predicted ACS from prehospital data.
- The AI model significantly increased specificity and NPV compared to usual care, maintaining high sensitivity.
- This study serves as a proof-of-concept, requiring prospective validation for broader implementation.
Keywords:
Hollands Midden Acute Regional Triage–cardiologyacute coronary syndromealgorithmanginaartificial intelligencecardiologyclinical decision-makingemergency departmentemergency medical servicenatural language processingovercrowdingparamedicsprehospitaltriageMore Related Videos
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