Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study

Rapid and precise prehospital recognition of acute coronary syndrome (ACS) is key to improving clinical outcomes. We tested the hypothesis that the machine learning-based prehospital algorithm shows a high predictive power for predicting ACS. We conducted a multicenter observational prospective study that included 10 participating facilities in an urban area of Japan. The data from consecutive adult patients, identified by emergency medical service personnel with suspected ACS, were analyzed. The primary outcomes were binary classification models for ACS prediction based on eXtreme Gradient Boosting. Of the 555 enrolled patients, 388 (70%) were randomly assigned to a training cohort, and 167 (30%) were placed in a test cohort. The eXtreme Gradient Boosting model for ACS using 43 features performed well (area under the receiver operating curve 0.879 [95% CI 0.815–0.935]) in the test cohort. We repeated the analysis with a limited number of selected features, and the performance of the eXtreme Gradient Boosting model using 17 features remained high (area under the receiver operating curve 0.883 [95% CI 0.820–0.939]) in the test cohort. We found that the machine learning-based prehospital algorithms showed a high predictive power for predicting ACS.