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.
Insights
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.
Background:
Overcrowding of hospitals and emergency departments (EDs) is a growing problem. However, not all ED consultations are necessary. For example, 80% of patients in the ED with chest pain do not have an acute coronary syndrome (ACS). Artificial intelligence (AI) is useful in analyzing (medical) data, and might aid health care workers in prehospital clinical decision-making before patients are presented to the hospital.
Objective:
The aim of this study was to develop an AI model which would be able to predict ACS before patients visit the ED. The model retrospectively analyzed prehospital data acquired by emergency medical services' nurse paramedics.
Methods:
Patients presenting to the emergency medical services with symptoms suggestive of ACS between September 2018 and September 2020 were included. An AI model using a supervised text classification algorithm was developed to analyze data. Data were analyzed for all 7458 patients (mean 68, SD 15 years, 54% men). Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for control and intervention groups. At first, a machine learning (ML) algorithm (or model) was chosen; afterward, the features needed were selected and then the model was tested and improved using iterative evaluation and in a further step through hyperparameter tuning. Finally, a method was selected to explain the final AI model.
Results:
The AI model had a specificity of 11% and a sensitivity of 99.5% whereas usual care had a specificity of 1% and a sensitivity of 99.5%. The PPV of the AI model was 15% and the NPV was 99%. The PPV of usual care was 13% and the NPV was 94%.
Conclusions:
The AI model was able to predict ACS based on retrospective data from the prehospital setting. It led to an increase in specificity (from 1% to 11%) and NPV (from 94% to 99%) when compared to usual care, with a similar sensitivity. Due to the retrospective nature of this study and the singular focus on ACS it should be seen as a proof-of-concept. Other (possibly life-threatening) diagnoses were not analyzed. Future prospective validation is necessary before implementation.
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