Performance and limitations of automated ECG interpretation statements in patients with suspected acute coronary syndrome

Faramand, Ziad; Helman, Stephanie; Ahmad, Abdullah; Martin‐Gill, Christian; Callaway, Clifton W.; Saba, Samir; Gregg, Richard E.; Wang, John; Al‐Zaiti, Salah S.

doi:10.1016/j.jelectrocard.2021.08.014

Cited by 11 publications

(9 citation statements)

References 28 publications

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“…The recently completed DIagnostic accuracy oF electrocardiogram for acute coronary OCClUsion resuLTing in MI (DIFOCCULT Study) reported that the true sensitivity of STEMI criteria to be lower at only 54.4% (Aslanger et al 2020). The degree of STsegment elevation relates to the degree of coronary occlusion, and the true sensitivity varies between 23% and 100% (Faramand et al 2021). Since ST-segment elevation lacks the adequate sensitivity to detect an occluded culprit artery, a considerable amount of NSTEMI are found to have a occluded culprit artery similar to STEMI during delayed angiography (Khan et al 2017, Hung et al 2018, Meyers et al 2021a.…”

Section: Shortcomings Of the Stemi/nstemi Stratificationmentioning

confidence: 99%

“…Newer computerized algorithms may be superior in identifying transient ischemia which may prompt a transition toward use in hospital settings (Pelter et al 2018. While the hypothesis of spontaneous reperfusion is feasible for the continuum of arterial occlusion (Faramand et al 2021), it is more likely to affect NSTEMI with a sub-occluded artery (TIMI flow 2-3). As mentioned above, those with an occluded culprit artery present critically ill with greater myocardial damage, lower left ventricular ejection fraction, and larger infarct zone so it may be less likely that their coronary artery will spontaneous re-perfuse…”

Section: Transient Occlusionmentioning

confidence: 99%

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Secondary electrocardiographic stratification of NSTEMI to identify an acutely occluded culprit artery

Dzikowicz

Carey

2023

Physiol. Meas.

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In the United States, approximately 720,000 adults will experience a myocardial infarction (MI) every year. The 12-lead electrocardiogram (ECG) is quintessential for the classification of a MI. About 30% of all MIs exhibit ST-segment elevation on the 12-lead ECG and is therefore classified as an ST-Elevation Myocardial Infarction (STEMI), which is treated emergently with percutaneous coronary intervention to restore blood flow. However, in the remaining 70% of MIs, the 12-lead ECG lacks ST-segment elevation and instead exhibits a motley of changes, including ST-segment depression, T-wave inversion, or, in up to 20% of patients, have no changes whatsoever; as such, these MIs are classified as a Non-ST Elevation Myocardial Infarction (NSTEMI). Of this large cluster of MIs, 33% of NSTEMI actually have an occlusion of the culprit artery consistent with a Type I MI per the 4th Universal Definition of MI. This is a serious clinical problem because NSTEMI with an occluded culprit artery have similar myocardial damage like STEMI and are more likely to suffer from adverse outcomes compared to NSTEMI without an occluded culprit artery. In this review article, we review the extant literature on NSTEMI with an occluded culprit artery. Afterward, we generate and discuss hypotheses for the absence of ST-segment elevation on the 12-lead ECG: (1) Transient Occlusion (2) Collateral Blood Flow and Chronically Occluded Artery and (3) ECG-Silent Myocardial Regions. Lastly, we describe and define novel ECG features that are associated with an occluded culprit artery in NSTEMI which include T-wave morphology abnormalities and novel markers of ventricular repolarization heterogeneity.

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Section: Shortcomings Of the Stemi/nstemi Stratificationmentioning

confidence: 99%

Section: Transient Occlusionmentioning

confidence: 99%

Secondary electrocardiographic stratification of NSTEMI to identify an acutely occluded culprit artery

Dzikowicz

Carey

2023

Physiol. Meas.

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“…The automatic interpretation statement for ACS on a prehospital ECG is very specific but not sensitive enough to exclude symptomatic coronary artery disease. 10 If there is a system in the future using artificial intelligence that can infer ACS based on ECG findings, symptoms, vitals, etc. and determine the need for primary PCI correctly when the EMS team arrives at the patient, it will increase the probability of correct diagnosis, which will further assist in transporting ACS-suspicious patients to the optimal institute.…”

Section: Disclosuresmentioning

confidence: 99%

Prehospital 12-Lead Electrocardiography System in Oita Assisted Transport of “True” Acute Coronary Syndrome Patients to Optimal Institutes

Kawano

Yufu

Shimomura

et al. 2022

Circ J

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Background: Mobile cloud electrocardiography (C-ECG) can reduce the door-to-balloon time of acute coronary syndrome (ACS) patients, so we hypothesized it would also assist in transporting ACS-suspected patients to the optimal institutes. Methods and Results: Initially, 10 fire departments in Oita had 10 ambulances equipped with C-ECG. Ambulance crews recorded a 12-lead ECG from the patient at the first point of contact and transmitted them to 18 hospitals (13 institutions (PCII) with 24-h availability for percutaneous coronary intervention (PCI) and 5 regional core hospitals (RCH) without 24-h PCI) for analysis by a cardiologist. During 41 months, 476 ECGs suspected to be ACS were transmitted and analyzed. Of these, 24 ECGs transmitted to PCII were judged as not requiring PCI, and the patients were directly transported to a RCH (PCII-RCH); 35 ECGs sent to a RCH were judged as requiring PCI, and the patients were directly transported to a PCII (RCH-PCII). The prevalence of cardiovascular disease was significantly higher in the RCH-PCII group than in the PCII-RCH group (P<0.01). There was no significant difference in the doorto-balloon time between the RCH-PCII and the group in which the C-ECG was sent to a PCII and the patients were transported directly to PCII (PCII-PCII) (49±14 vs. 59±20 min, P=0.14). Conclusions:Prehospital 12-lead ECG can assist in transporting ACS-suspect patients to the optimal treatment facility.

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“…One being the influence of the machine diagnosis on the human interpreter. This is important given that a recent study by Faramand et al (2021) shows that machine diagnosis only has a sensitivity of 63% for detecting STEMI in patients with chest pain. If the human reader initially considers the machine's interpretation before undertaking their own unbiased interpretation, then they could be prone to 'automation bias' which is where they could complacently accept the automated suggestion (Bond et al, 2018).…”

Section: The Algorithmsmentioning

confidence: 99%

Exploring decision making ‘noise’ when interpreting the electrocardiogram in the context of cardiac cath lab activation

Peace¹,

Al‐Zaiti

Finlay

et al. 2022

Journal of Electrocardiology

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Performance and limitations of automated ECG interpretation statements in patients with suspected acute coronary syndrome

Cited by 11 publications

References 28 publications

Secondary electrocardiographic stratification of NSTEMI to identify an acutely occluded culprit artery

Secondary electrocardiographic stratification of NSTEMI to identify an acutely occluded culprit artery

Prehospital 12-Lead Electrocardiography System in Oita Assisted Transport of “True” Acute Coronary Syndrome Patients to Optimal Institutes

Exploring decision making ‘noise’ when interpreting the electrocardiogram in the context of cardiac cath lab activation

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