Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study.
Sci Rep
; 12(1): 14593, 2022 08 26.
Article
in En
| MEDLINE
| ID: mdl-36028534
ABSTRACT
Rapid and precise prehospital recognition of acute coronary syndrome (ACS) is key to improving clinical outcomes. The aim of this study was to investigate a predictive power for predicting ACS using the machine learning-based prehospital algorithm. 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. In this study, we used nested cross-validation to evaluate the predictive performance of the model. The primary outcomes were binary classification models for ACS prediction based on the nine machine learning algorithms. The voting classifier model for ACS using 43 features had the highest area under the receiver operating curve (AUC) (0.861 [95% CI 0.775-0.832]) in the test score. After validating the accuracy of the model using the external cohort, we repeated the analysis with a limited number of selected features. The performance of the algorithms using 17 features remained high AUC (voting classifier, 0.864 [95% CI 0.830-0.898], support vector machine (radial basis function), 0.864 [95% CI 0.829-0.887]) in the test score. We found that the machine learning-based prehospital algorithms showed a high predictive power for predicting ACS.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Emergency Medical Services
/
Acute Coronary Syndrome
Type of study:
Clinical_trials
/
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Adult
/
Humans
Language:
En
Journal:
Sci Rep
Year:
2022
Document type:
Article
Affiliation country:
Japan