Background:

Consultations for chest pain are common in emergency medical services (EMS). A diagnostic strategy using both objective and subjective pain has not been identified yet.

Objective:

To evaluate a machine learning classifier as a tool for prediction of the risk of presenting a non-ST segment elevation acute coronary syndrome (ACS) in patients consulting an SEM with chest pain.

Methods:

161 patients consulting SEM with chest pain were analyzed. Objective variables of the patient and subjective variables of pain characterization were recorded during the triage stage by means of a machine learning classifier.

Results:

The mean age was 57.43±12 years, 75% male and 16% had prior cardiovascular disease. 57.8% presented an ACS with an incidence of 29.8%, which 35% required PCI and 9.9% CRM in a 30-day follow-up period. A Random Forest Classifier was used as a classification model. The Random Forest Classifier presented an area under the ROC curve of 0.8991, sensitivity of 0.8552, specificity of 0.8588 and precision of 0.8441. The most strongest predictor variables were weight (p=0.002), age (p=5.011e-07), pain intensity (p=3.0679e-05), systolic blood pressure (p = 0.6068) and subjective pain characteristics (p=1.590e-04).

Conclusions:

Machine learning classifiers are a useful tool for predicting the risk of acute coronary syndrome at 30 days follow-up period.

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