Background
Heart rate variability (HRV) analysis powered by artificial intelligence (AI) offers a rapid, non-invasive, and objective approach for acute coronary syndrome (ACS) risk stratification in the emergency department (ED). The objective of this study was to evaluate the feasibility and impact of aiTriage™, an AI HRV-guided tool for chest pain triage, compared with standard care.
Methods
In this single-blinded randomized controlled trial, 560 ED patients with suspected ACS underwent 5- minute ECG monitoring for HRV analysis, which generated a 0–100 risk score and triage recommendations (high, medium, or low risk). Patients were randomized to standard care (control) or an HRV-guided protocol (intervention). Physicians in the control group were blinded to HRV results.
Results
Of 426 analysed patients (mean age 54 ± 13 years, 35% female, 16.2% prior MI), the HRV-guided protocol reduced hospital admissions (50.2% vs 61.1%; risk difference -10.9 percentage points, 95% CI: -20.1 to -1.8) and serial cardiac enzyme testing (32.1% vs 41.7%; risk difference -9.6 percentage points, 95% CI: -18.4 to -0.9) compared with standard care. Among discharged patients, the median ED length of stay was 20 minutes shorter in the intervention group (95% CI: -45 minutes to 3 minutes). The overall 30-day MACE rate was 9.5%, with no events among discharged patients.
Conclusion
A rapid AI HRV-guided risk stratification tool was feasible to deploy, and has potential to reduce serial cardiac enzyme testing, ED LOS and hospital admissions. An adequately powered RCT is needed to confirm these findings and assess clinical safety. This trial is registered at ClinicalTrials.gov (NCT07074808).
Objective We aimed to determine whether there are similar rates of regional wall motion abnormalities (RWMAs) in patients with acute coronary occlusion myocardial infarction (OMI) with and without ST-elevation myocardial infarction (STEMI) on electrocardiogram (ECG).
Methods We performed a retrospective review of a database of patients at high risk for acute coronary syndrome with previously established outcomes for the presence of OMI in order to compare rates of RWMA in patients presenting with STEMI(+) OMI versus STEMI(–) OMI. Furthermore, we compared how often the RWMA aligned with the anatomical territory observed on ECG.
Results Among 808 patients, 551 underwent formal echocardiography, including 256 of 265 OMI patients and 295 of 543 patients with no occlusion. Of the 256 OMI patients that underwent formal echocardiography, only 105 (41.0%) met STEMI criteria. Among them, 94 of 105 (89.5%) STEMI(+) OMI patients had RWMAs compared to 124 of 151 (82.1%) STEMI(–) OMI patients (P=0.10; 95 confidence interval, –1.63% to 15.6%). Both groups had a greater prevalence of RWMA than the non-OMI group (45%). RWMA matched the anatomic territory predicted by ECG in 92.5% of STEMI(+) OMI, 82.3% of STEMI(–) OMI, and 2.9% of the no-occlusion cohort.
Conclusion Location of RWMAs was well-correlated with ECG findings regardless of the presence or absence of STEMI criteria. A prospective study is warranted to determine the utility of echocardiography in the detection of STEMI(–) OMI.
Citations
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Objective We previously developed and validated an artificial intelligence-based electrocardiogram (ECG) analysis tool (ECG Buddy) in a Korean population. This study investigated the performance of this tool in a US population, specifically assessing the left ventricular (LV) dysfunction score and LV ejection fraction (LVEF)-ECG feature for predicting LVEF <40%. The study used N-terminal pro-B-type natriuretic peptide (NT-ProBNP) as a comparator.
Methods We identified emergency department (ED) visits from the MIMIC-IV dataset with information on LVEF <40% or ≥40% and matched 12-lead ECG data recorded within 48 hours of the ED visit. The performance of ECG Buddy’s LV dysfunction score and the LVEF-ECG feature was compared with those of NT-ProBNP using area under the receiver operating characteristic curve (AUC) analysis.
Results A total of 22,599 ED visits was analyzed. The LV dysfunction score had an AUC of 0.905 (95% confidence interval [CI], 0.899–0.910), with a sensitivity of 85.4% and specificity of 80.8%. The LVEF-ECG feature had an AUC of 0.908 (95% CI, 0.902–0.913), sensitivity of 83.5%, and specificity of 83.0%. NT-ProBNP had an AUC of 0.740 (95% CI, 0.727–0.752), with a sensitivity of 74.8% and specificity of 62.0%. The ECG-based predictors demonstrated superior diagnostic performance compared to NT-ProBNP (all P<0.001). In the sinus rhythm subgroup, the LV dysfunction score achieved an AUC of 0.913 and LVEF-ECG had an AUC of 0.917, both outperforming NT-ProBNP (AUC, 0.748; 95% CI, 0.732–0.763; all P<0.001).
Conclusion ECG Buddy demonstrated superior accuracy compared with NT-ProBNP in predicting LV systolic dysfunction, validating its utility in a US ED population.
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Objective Based on the development of artificial intelligence (AI), an emerging number of methods have achieved outstanding performances in the diagnosis of acute myocardial infarction (AMI) using an electrocardiogram (ECG). However, AI-ECG analysis using a multicenter prospective design for detecting AMI has yet to be conducted. This prospective multicenter observational study aims to validate an AI-ECG model for detecting AMI in patients visiting the emergency department.
Methods Approximately 9,000 adult patients with chest pain and/or equivalent symptoms of AMI will be enrolled in 18 emergency medical centers in Korea. The AI-ECG analysis algorithm we developed and validated will be used in this study. The primary endpoint is the diagnosis of AMI on the day of visiting the emergency center, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration has begun at centers approved by the institutional review board.
Discussion This is the first prospective study designed to identify the efficacy of an AI-based 12-lead ECG analysis algorithm for diagnosing AMI in emergency departments across multiple centers. This study may provide insights into the utility of deep learning in detecting AMI on electrocardiograms in emergency departments.
Trial registration ClinicalTrials.gov identifier: NCT05435391. Registered on June 28, 2022.
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Objective This study aimed to analyze the association between the culprit artery and the diagnostic accuracy of automatic electrocardiogram (ECG) interpretation in patients with ST-segment elevation myocardial infarction (STEMI).
Methods This single-centered, retrospective cohort study included adult patients with STEMI who visited the emergency department between January 2017 and December 2020. The primary endpoint was the association between the culprit artery occlusion and the misinterpretation of ECG, evaluated by the chi-square test or Fisher exact test.
Results The rate of misinterpretation of the automated ECG for patients with STEMI was 26.5% (31/117 patients). There was no significant correlation between the ST segment change in the four involved leads (anteroseptal, lateral, inferior, and aVR) and the misinterpretation of ECG (all P > 0.05). Single culprit artery occlusion significantly affected the misinterpretation of ECG compared with multiple culprit artery occlusion (single vs. multiple, 27/86 [31.3%] vs. 4/31 [12.9%], P = 0.045). There was no association between culprit artery and the misinterpretation of ECG (P = 0.132).
Conclusion Single culprit artery occlusion might increase misinterpretation of ECG compared with multiple culprit artery occlusions in the automatic interpretation of STEMI.
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Objective Electrocardiogram (ECG) interpretation skills are of critical importance for diagnostic accuracy and patient safety. In our emergency department (ED), senior third-year emergency medicine residents (EM3s) are the initial interpreters of all ED ECGs. While this is an integral part of emergency medicine education, the accuracy of ECG interpretation is unknown. We aimed to review the adverse quality assurance (QA) events associated with ECG interpretation by EM3s.
Methods We conducted a retrospective study of all ED ECGs performed between October 2015 and October 2018, which were read primarily by EM3s, at an urban tertiary care medical center treating 56,000 patients per year. All cases referred to the ED QA committee during this time were reviewed. Cases involving a perceived error were referred to a 20-member committee of ED leadership staff, attendings, residents, and nurses for further consensus review. Ninety-five percent confidence intervals (CIs) were calculated.
Results EM3s read 92,928 ECGs during the study period. Of the 3,983 total ED QA cases reviewed, errors were identified in 268 (6.7%; 95% CI, 6.0%–7.6%). Four of the 268 errors involved ECG misinterpretation or failure to act on an ECG abnormality by a resident (1.5%; 95% CI, 0.0%–2.9%).
Conclusion A small percentage of the cases referred to the QA committee were a result of EM3 misinterpretation of ECGs. The majority of emergency medicine residencies do not include the senior resident as a primary interpreter of ECGs. These findings support the use of EM3s as initial ED ECG interpreters to increase their clinical exposure.
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Objective Cardiogenic syncope can present as a seizure. The distinction between seizure disorder and cardiogenic syncope can only be made if one considers the diagnosis. Our main objective was to identify whether patients presenting with a chief complaint (reason for visit) as seizure or syncope received an electrocardiogram in the emergency department across all age groups.
Methods We conducted a secondary analysis of data collected in the 2010 to 2014 National Hospital Ambulatory Medical Care Survey comparing patients presenting with a chief complaint of syncope versus seizure to determine likelihood of getting an evaluation for possible life threatening cardiovascular disease. The primary endpoint was receiving an electrocardiogram in the emergency department; secondary endpoint was receiving cardiac biomarkers.
Results There was a total of 144,094 patient encounters. Of these visits, 1,553 had syncope and 1,470 had seizure (60.3% vs. 44.2% female, 19.9% vs. 29.0% non-white). After adjusting for age, sex, mode of arrival and insurance, patients with syncope were more likely to receive an electrocardiogram compared to patients with seizure (odds ratio, 10.86; 95% confidence interval [CI], 8.52 to 13.84). This was true across all age groups (0 to 18 years, 56% vs. 7.5%; 18 to 44 years, 60% vs. 27%; 45 to 64 years, 82% vs. 41%; ≥65 years, 85% vs. 68%; P<0.01 for all). Car- diac biomarkers were also obtained more frequently in adult patients with syncope patients (18 to 44 years, 17.5% vs. 10.5%; 45 to 64 years, 33.8% vs. 21.4%; ≥65 years, 47.1% vs. 32.3%; P<0.01 for all).
Conclusion Patients evaluated in the emergency department for syncope received an electrocar- diogram and cardiac biomarkers more frequently than those that had seizure.
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Methods This is a retrospective study which included patients with spontaneous pneumothorax who presented to an emergency department of a tertiary urban hospital from February 2005 to March 2015. The primary outcome was a difference in ECG findings between before and after closed thoracostomy. We specifically investigated the following ECG elements: PR, QRS, QTc, axis, ST segments, and R waves in each lead. The secondary outcomes were change in ST segment in any lead and change in axis after closed thoracostomy.
Results There were two ECG elements which showed statistically significant difference after thoracostomy. With right pneumothorax volume of greater than 80%, QTc and the R waves in aVF and V5 significantly changed after thoracostomy. With left pneumothorax volume between 31% and 80%, the ST segment in V2 and the R wave in V1 significantly changed after thoracostomy. However, majority of ECG elements did not show statistically significant alteration after thoracostomy.
Conclusion We found only minor changes in ECG after closed thoracostomy in spontaneous pneumothorax patients.
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