Abstract
(1) Background: While an artificial intelligence (AI)-based, cardiologist-level, deep-learning model for detecting acute myocardial infarction (AMI), based on a 12-lead electrocardiogram (ECG), has been established to have extraordinary capabilities, its real-world performance and clinical applications are currently unknown. (2) Methods and Results: To set up an artificial intelligence-based alarm strategy (AI-S) for detecting AMI, we assembled a strategy development cohort including 25,002 visits from August 2019 to April 2020 and a prospective validation cohort including 14,296 visits from May to August 2020 at an emergency department. The components of AI-S consisted of chest pain symptoms, a 12-lead ECG, and high-sensitivity troponin I. The primary endpoint was to assess the performance of AI-S in the prospective validation cohort by evaluating F-measure, precision, and recall. The secondary endpoint was to evaluate the impact on door-to-balloon (DtoB) time before and after AI-S implementation in STEMI patients treated with primary percutaneous coronary intervention (PPCI). Patients with STEMI were alerted precisely by AI-S (F-measure = 0.932, precision of 93.2%, recall of 93.2%). Strikingly, in comparison with pre-AI-S (N = 57) and post-AI-S (N = 32) implantation in STEMI protocol, the median ECG-to-cardiac catheterization laboratory activation (EtoCCLA) time was significantly reduced from 6.0 (IQR, 5.0–8.0 min) to 4.0 min (IQR, 3.0–5.0 min) (p < 0.01). The median DtoB time was shortened from 69 (IQR, 61.0–82.0 min) to 61 min (IQR, 56.8–73.2 min) (p = 0.037). (3) Conclusions: AI-S offers front-line physicians a timely and reliable diagnostic decision-support system, thereby significantly reducing EtoCCLA and DtoB time, and facilitating the PPCI process. Nevertheless, large-scale, multi-institute, prospective, or randomized control studies are necessary to further confirm its real-world performance.
Highlights
Acute coronary syndrome, consisting of ST elevation myocardial infarction (STEMI)and non-ST elevation myocardial infarction (NSTEMI) based on electrocardiogram (ECG)presentations, refers to a spectrum of conditions that abruptly cause an unmet need for coronary blood supply to the myocardium [1,2,3]
At triage, patients with a chief complaint of chest pain were included in strategy 1 for 3320 patients, whereas patients without a chief complaint of chest pain were included in strategy 2 for 21,682 patients
Both strategies were only based on the STEMI score predicted by artificial intelligence (AI) via ECG
Summary
Acute coronary syndrome, consisting of ST elevation myocardial infarction (STEMI). and non-ST elevation myocardial infarction (NSTEMI) based on electrocardiogram (ECG). To shorten DtoB time, intensive strategies, including: simplified cardiac catheterization laboratory activation (CCLA) by emergency physicians with a single call to a central page operator; recommending a time of less than 20 min from arrival to the CCL to staff being paged; establishing an on-site cardiologist; and providing real-time data feedback have been proposed [13]. Through these strategies, the DtoB time was reduced from 96 to 71 min with a corresponding increase in the rate of achieving DtoB time within. We hypothesize that the proposed strategy may minimize EtoCCLA time and further reduce DtoB time for STEMI patients
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