Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients
85
Zitationen
10
Autoren
2019
Jahr
Abstract
BACKGROUND: Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is not fully established. Electrocardiogram is an easy and rapid procedure, but may contain crucial information not recognized even by well-trained physicians. OBJECTIVE: To make a prediction model for the needs for urgent revascularization from 12-lead electrocardiogram recorded in the emergency room. METHOD: We developed an artificial intelligence model enabling the detection of hidden information from a 12-lead electrocardiogram recorded in the emergency room. Electrocardiograms obtained from consecutive patients visiting the emergency room at Keio University Hospital from January 2012 to April 2018 with chest discomfort was collected. These data were splitted into validation and derivation dataset with no duplication in each dataset. The artificial intelligence model was constructed to select patients who require urgent revascularization within 48 hours. The model was trained with the derivation dataset and tested using the validation dataset. RESULTS: Of the consecutive 39,619 patients visiting the emergency room with chest discomfort, 362 underwent urgent revascularization. Of them, 249 were included in the derivation dataset and the remaining 113 were included in validation dataset. For the control, 300 were randomly selected as derivation dataset and another 130 patients were randomly selected for validation dataset from the 39,317 who did not undergo urgent revascularization. On validation, our artificial intelligence model had predictive value of the c-statistics 0.88 (95% CI 0.84-0.93) for detecting patients who required urgent revascularization. CONCLUSIONS: Our artificial intelligence model provides information to select patients who need urgent revascularization from only 12-leads electrocardiogram in those visiting the emergency room with chest discomfort.
Ähnliche Arbeiten
A Real-Time QRS Detection Algorithm
1985 · 7.656 Zit.
An Overview of Heart Rate Variability Metrics and Norms
2017 · 6.560 Zit.
Power Spectrum Analysis of Heart Rate Fluctuation: A Quantitative Probe of Beat-to-Beat Cardiovascular Control
1981 · 5.070 Zit.
The impact of the MIT-BIH Arrhythmia Database
2001 · 4.549 Zit.
Decreased heart rate variability and its association with increased mortality after acute myocardial infarction
1987 · 3.997 Zit.