Difficult ECGs in STEMI: lessons learned from serial sampling of pre- and in-hospital ECGs

Compared with physician overread, computer is less accurate but helpful in interpretation of electrocardiography for ST-segment elevation myocardial infarction

INTRODUCTION

Previous studies have demonstrated varying sensitivity and specificity of computer-interpreted electrocardiography (CIE) in identifying ST-segment elevation myocardial infarction (STEMI). This study aims to evaluate the accuracy of contemporary computer software in recognizing electrocardiography (ECG) signs characteristic of STEMI compared to emergency physician overread in clinical practice.

MATERIAL AND METHODS

In this retrospective observational single-center study, we reviewed the records of patients in the emergency department (ED) who underwent ECGs and troponin tests. Both the Philips DXL 16-Lead ECG. Algorithm and on-duty emergency physicians interpreted each standard 12‑lead ECG. The sensitivity and specificity of computer interpretation and physician overread ECGs for the definite diagnosis of STEMI were calculated and compared.

RESULTS

Among the 9340 patients included in the final analysis, 133 were definitively diagnosed with STEMI. When "computer-reported infarct or injury" was used as the indicator, the sensitivity was 87.2% (95% CI 80.3% to 92.4%) and the specificity was 86.2% (95% CI 85.5% to 86.9%). When "physician-overread STEMI" was used as the indicator, the sensitivity was 88.0% (95% CI 81.2% to 93.0%) and the specificity was 99.9% (95% CI 99.8% to 99.9%). The area under the receiver operating characteristic curve for physician-overread STEMI and computer-reported infarct or injury were 0.939 (95% CI 0.907 to 0.972) and 0.867 (95% CI 0.834 to 0.900), respectively.

CONCLUSIONS

This study reveals that while the sensitivity of the computer in recognizing ECG signs of STEMI is similar to that of physicians, physician overread of ECGs is more specific and, therefore, more accurate than CIE.

Early prediction of ventricular fibrillation using electrocardiographic characteristics in prehospital suspected ST-segment elevation myocardial infarction: a case-control study

PURPOSE

Our objective was to determine characteristics of electrocardiograms (ECG) that predict ventricular fibrillation (VF) among prehospital patients with suspected ST-segment elevation myocardial infarction (STEMI) in Québec.

METHODS

We performed a matched case-control study of prehospital adult suspected with STEMI. Patients in case group (STEMI/VF+) were matched with controls (STEMI/VF-) for age and sex and then compared for ECG characteristics, including ST-segment elevations (STE) and depressions (STD), duration of interval complexes, general characteristics, and several calculated variables. Logistic regression was used to measure the association between ECG characteristics and VF development.

RESULTS

Overall, 310 prehospital patients with suspected STEMI were included in the analysis (case group, n = 155; control group, n = 155). We confirmed that the presence of TW-pattern complex (OR 7.0, 95% CI 1.55-31.58), premature ventricular contraction (PVC) (OR 5.5, 95% CI 2.04-14.82), and STE in V2-V6 (OR 3.8, 95% CI 1.21-11.74) were electrocardiographic predictors of VF. We also observed that STD in V3-V5 (OR 6.5, 95% CI 1.42-29.39), atrial fibrillation (AF) ≥ 100 beats per minute (bpm) (OR 6.3, 95% CI 1.80-21.90), the combination of STE in V4 and V5, and STD in II, III and aVF (OR 4.8, 95% CI 1.01-22.35), and the presence of STD in ≥ 6 leads (OR 4.2, 95% CI 1.33-13.13) were also associated with VF development. Finally, simultaneous association of 2 (OR 2.3, 95% CI 1.13-4.06) and 3 (OR 11.6, 95% CI 3.22-41.66) predictors showed significant association with VF.

CONCLUSIONS

In addition to some already known predictors, we have identified several ECG findings associated with the development of VF in patients with suspected STEMI. Early identification of patients with STEMI at increased risk of VF should help EMS providers anticipate adverse events and encourage use of defibrillation pads.

ST-Segment Elevation Followed by de Winter Electrocardiogram Pattern in a Patient With Chest Pain

This case report describes a patient in their 40s with no history of cardiovascular disease who presented to the emergency department with chest pain accompanied by sweating.

Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography

OBJECTIVE

Chest pain is common, and current risk-stratification methods, requiring 12-lead electrocardiograms (ECGs) and serial biomarker assays, are static and restricted to highly resourced settings. Our objective was to predict myocardial injury using continuous single-lead ECG waveforms similar to those obtained from wearable devices and to evaluate the potential of transfer learning from labeled 12-lead ECGs to improve these predictions.

METHODS

We studied 10 874 Emergency Department (ED) patients who received continuous ECG monitoring and troponin testing from 2020 to 2021. We defined myocardial injury as newly elevated troponin in patients with chest pain or shortness of breath. We developed deep learning models of myocardial injury using continuous lead II ECG from bedside monitors as well as conventional 12-lead ECGs from triage. We pretrained single-lead models on a pre-existing corpus of labeled 12-lead ECGs. We compared model predictions to those of ED physicians.

RESULTS

A transfer learning strategy, whereby models for continuous single-lead ECGs were first pretrained on 12-lead ECGs from a separate cohort, predicted myocardial injury as accurately as models using patients' own 12-lead ECGs: area under the receiver operating characteristic curve 0.760 (95% confidence interval [CI], 0.721-0.799) and area under the precision-recall curve 0.321 (95% CI, 0.251-0.397). Models demonstrated a high negative predictive value for myocardial injury among patients with chest pain or shortness of breath, exceeding the predictive performance of ED physicians, while attending to known stigmata of myocardial injury.

CONCLUSIONS

Deep learning models pretrained on labeled 12-lead ECGs can predict myocardial injury from noisy, continuous monitor data early in a patient's presentation. The utility of continuous single-lead ECG in the risk stratification of chest pain has implications for wearable devices and preclinical settings, where external validation of the approach is needed.

LASSO Regression-Based Diagnosis of Acute ST-Segment Elevation Myocardial Infarction (STEMI) on Electrocardiogram (ECG)

Electrocardiogram (ECG) is an important tool for the detection of acute ST-segment elevation myocardial infarction (STEMI). However, machine learning (ML) for the diagnosis of STEMI complicated with arrhythmia and infarct-related arteries is still underdeveloped based on real-world data. Therefore, we aimed to develop an ML model using the Least Absolute Shrinkage and Selection Operator (LASSO) to automatically diagnose acute STEMI based on ECG features. A total of 318 patients with STEMI and 502 control subjects were enrolled from Jan 2017 to Jun 2019. Coronary angiography was performed. A total of 180 automatic ECG features of 12-lead ECG were input into the model. The LASSO regression model was trained and validated by the internal training dataset and tested by the internal and external testing datasets. A comparative test was performed between the LASSO regression model and different levels of doctors. To identify the STEMI and non-STEMI, the LASSO model retained 14 variables with AUCs of 0.94 and 0.93 in the internal and external testing datasets, respectively. The performance of LASSO regression was similar to that of experienced cardiologists (AUC: 0.92) but superior (p < 0.05) to internal medicine residents, medical interns, and emergency physicians. Furthermore, in terms of identifying left anterior descending (LAD) or non-LAD, LASSO regression achieved AUCs of 0.92 and 0.98 in the internal and external testing datasets, respectively. This LASSO regression model can achieve high accuracy in diagnosing STEMI and LAD vessel disease, thus providing an assisting diagnostic tool based on ECG, which may improve the early diagnosis of STEMI.

Wellen’s Syndrome: A Rare Case

Wellen’s syndrome is associated with critical stenosis of the left anterior descending coronary artery. Based on the electrocardiography (ECG) pattern, Wellen’s syndrome can be classified into type 1 (deeply inverted T-waves, mainly in lead V2 and V3) or type 2 (biphasic T-waves). T-wave abnormalities are often also found in V1 and V4 and rarely in V5 and V6. The pattern of ECG changes correlates with proximal left anterior descending artery stenosis. This characteristic ECG pattern is a sign of impending myocardial infarction and is equivalent to ST-elevated myocardial infarction. Often, these subtle findings in ECG get misinterpreted or the severity associated with this goes unrecognized. Hence, for emergency physicians, it is important to recognize such uncharacteristic ECG changes for better and timely management of patients. We present this case of Wellen’s pattern in which the coronary lesion was in the left circumflex coronary artery, right coronary artery, and diagonal-1.

The de Winter electrocardiogram pattern in a 52-year-old-male: a case report

Background 

The 12-lead electrocardiogram (ECG) remains the primary test for diagnosis of acute myocardial infarction (MI) secondary to acute coronary occlusion or near occlusion, with insufficient collateral circulation. Decisions regarding urgent reperfusion are based on whether or not there’s new ST-segment elevation. The de Winter ECG pattern is a distinct ECG pattern without any ST-segment elevation, it may be missed by anyone unfamiliar with it.

Case summary 

We present a case whose chief complaint was severe central chest pain, the patient was diagnosed with acute MI secondary to a culprit lesion in the left anterior descending artery, despite the ECG not meeting standard STEMI criteria. After the ECG’s significance was recognized by paramedics, the patient received immediate percutaneous coronary intervention with stenting and was discharged home after a brief hospital admission.

Discussion 

In some cases, acute MI presents with ECG features that do not meet the standard criteria for STEMI diagnosis. The de Winter ECG pattern is one such example. This pattern should be immediately recognizable to those responsible for the activation of the catheterization laboratory, physicians, and paramedics included.

Feasibility of CardioSecur®, a Mobile 4-Electrode/22-Lead ECG Device, in the Prehospital Emergency Setting

Background: This study explores the application of CardioSecur® (CS-ECG), a hand-held 4-electrode/22-lead ECG-device, in comparison with conventional 12-lead electrocardiogram (c12L-ECG) in patients with acute chest pain in the prehospital emergency setting.

Methods: CS-ECG systems were provided for two physician-staffed emergency ambulances and parallel recordings of c12L-ECG and CS-ECG were obtained from all patients with acute chest pain. Treating emergency physicians were asked to evaluate the CS-ECG system with a standardized questionnaire. Following study completion, acquired ECGs were analyzed separately by two independent cardiologists blinded to all other medical records.

Results: Over a period of 20 months a total of 203 patients were included in our study. According to a standardized questionnaire, 79% of emergency medical professionals preferred application of CS-ECG, with 87% of teams judging CS-ECG to be beneficial for patients. Morover, 79% of physicians reported a reduction in time to definitive diagnosis with implementation of CS-ECG. The majority of professional users attested user-friendliness and feasibility of CS-ECG in terms of easy general handling (94%), application (93%), and placement of electrodes (98%). During prehospital triage, both c12L-ECG and CS-ECG correctly identified 31 (91%) patients with ST-elevation myocardial infarction (STEMI).

Conclusion: In this first pilot study, implementation of the CardioSecur®-ECG system in the prehospital emergency setting demonstrated feasibility and user-friendliness so that emergency teams generally preferred CS-ECG to c12L-ECG. Diagnostic yield of CS-ECG was similar to c12L-ECG recordings.

Change in T/QRS ratio can be a supplementary diagnostic tool in predicting coronary artery disease in patients with NSTEMI

BACKGROUND

Changes in the electrocardiographic findings, namely the ratio T sum to QRS sum (T/QRS ratio), between the initial electrocardiogram (ECG) and the baseline ECG have rarely been investigated in patients with non-ST elevation myocardial infarction (NSTEMI). Thus, we aimed to determine whether changes in various parameters on ECG, including T/QRS ratio, can assist in distinguishing between coronary artery disease (CAD) and NSTEMI without CAD with low to moderate risk.

METHODS

This retrospective study enrolled 2572 patients who presented ischemic symptoms, who were diagnosed with NSTEMI, and who underwent coronary angiography. Overall, 388 patients had prior ECG and echocardiography data available; 110 patients were included after excluding patients with other cardiac diseases except CAD. The population divided into two groups: a coronary stenosis group (n = 78); normal coronary group (n = 32) were analyzed.

RESULTS

We found that acute dynamic change in the most deviated T/QRS ratio in each region of leads of initial ECG from those of remote/recent ECG was an extremely strong predictor of acute CAD (odds ratio, 110; p < .001) compared to that of initial serum troponin I levels, new-onset regional wall motion abnormalities, and new-onset T inversion or ST depression. T/QRS ratio change > 1.5 or <0.5 times in injured regional leads was a significant predictor of CAD.

CONCLUSION

Change in the most deviated T/QRS ratio in the regional leads on initial ECG from the T/QRS ratio in the same lead on remote ECG can assist in predicting CAD risk between patients with CAD and patients with no CAD in NSTEMI.

The 'de Winter' electrocardiogram pattern as a ST-elevation myocardial infarction equivalent: a case report

Background

A ‘STEMI equivalent’ electrocardiogram (ECG) pattern reflects an acute thrombotic occlusion of a large epicardial coronary artery without ST-segment elevation. These ECG patterns are less known by caregivers.

Case summary

We describe the case of a 56-year-old patient suffering from acute chest pain, presenting in our emergency department with a ‘de Winter’ ECG pattern: an upsloping ST-segment depression with tall symmetrical T waves associated with left anterior descending artery occlusion.

Discussion

The ‘de Winter’ ECG pattern, as other ‘STEMI equivalent’, must be recognized promptly and treated as soon as possible with emergent reperfusion by percutaneous coronary intervention.

Accuracy Screening for ST Elevation Myocardial Infarction in a Task-switching Simulation

INTRODUCTION

Interruptions in the emergency department (ED) are associated with clinical errors, yet are important when providing care to multiple patients. Screening triage electrocardiograms (ECG) for ST-segment elevation myocardial infarction (STEMI) represent a critical interrupting task that emergency physicians (EP) frequently encounter. To address interruptions such as ECG interpretation, many EPs engage in task switching, pausing their primary task to address an interrupting task. The impact of task switching on clinical errors in interpreting screening ECGs for STEMI remains unknown.

METHODS

Resident and attending EPs were invited to participate in a crossover simulation trial. Physicians first completed a task-switching simulation in which they viewed patient presentations interrupted by clinical tasks, including screening ECGs requiring immediate interpretation before resuming the patient presentation. Participants then completed an uninterrupted simulation in which patient presentations and clinical tasks were completed sequentially without interruption. The primary outcome was accuracy of ECG interpretation for STEMI during task switching and uninterrupted simulations.

RESULTS

Thirty-five participants completed the study. We found no significant difference in accuracy of ECG interpretation for STEMI (task switching 0.89, uninterrupted 0.91, paired t-test p=0.21). Attending physician status (odds ratio [OR] [2.56], confidence interval [CI] [1.66-3.94], p<0.01) and inferior STEMI (OR [0.08], CI [0.04-0.14], p<0.01) were associated with increased and decreased odds of correct interpretation, respectively. Low self-reported confidence in interpretation was associated with decreased odds of correct interpretation in the task-switching simulation, but not in the uninterrupted simulation (interaction p=0.02).

CONCLUSION

In our simulation, task switching was not associated with overall accuracy of ECG interpretation for STEMI. However, odds of correct interpretation decreased with inferior STEMI ECGs and when participants self-reported low confidence when interrupted. Our study highlights opportunities to improve through focused ECG training, as well as self-identification of "high-risk" screening ECGs prone to error during interrupted clinical workflow.

Two case reports of Wellens' syndrome

Wellens’ syndrome is characterised by particular changes in electrocardiogram (ECG) precordial lead T-waves accompanied by proximal stenosis of the left anterior descending (LAD) artery. Two cases of electrocardiographic changes associated with Wellens’ syndrome are presented here. Case 1, a 55-year-old female, was transferred to the First Affiliated Hospital of Xi’an Jiaotong University with intermittent and laborious angina pectoris. Her first ECG on admission revealed T-wave inversion in leads V1–V3 and biphasic T-waves in V4. Case 2, an 85-year-old female, presented with dyspnoea and paroxysmal chest pain. Her admission ECG displayed asymmetrical T-wave inversion in leads V1–V3, I, and aVL, and depressed ST segments in leads V2–V5. In this patient, drug-eluting stents were placed on a LAD artery lesion and right coronary artery occlusion. The potential of ECGs to aid decision-making in severe myocardial infarction is straightforward, particularly in patients with characteristic ECGs, however, Wellens’ syndrome has a wide spectrum of clinical manifestations and the ECG patterns may manifest itself persistently over a period of weeks. Therefore, ECG parameters should be combined with coronary angiography to confirm the presence of lesions.

Diagnostic accuracy of prehospital electrocardiograms interpreted remotely by emergency physicians in myocardial infarction patients

BACKGROUND

Prehospital 12‑lead electrocardiogram (ECG) is the most widely used screening tool for recognition of ST-segment elevation myocardial infarction (STEMI). However, prehospital diagnosis of STEMI based solely on ECGs can be challenging.

OBJECTIVES

To evaluate the ability of emergency department (ED) physicians to accurately interpret prehospital 12‑lead ECGs from a remote location.

METHODS

All suspected prehospital STEMI patients who were transported by EMS and underwent angiography between 2006 and 2014 were included. We reviewed prehospital ECGs and grouped them based on: 1) presence or absence of a culprit artery lesion following angiography; and 2) whether they met the 3rd Universal Definition of Myocardial Infarction. We also described characteristics of ECGs that were misinterpreted by ED physicians.

RESULTS

A total of 625 suspected STEMI cases were reviewed. Following angiography, 94% (590/625) of patients were found having a culprit artery lesion, while 6% (35/625) did not. Among these 35 patients, 24 had ECGs that mimicked STEMI criteria and 9 had non-ischemic signs. Upon ECG reinterpretation, 92% (577/625) had standard STEMI criteria while 8% (48/625) did not. Among these 48 patients, 35 had ischemic signs ECGs and 13 did not. Characteristics of misinterpreted ECGs included pericarditis, early repolarization, STE > 1 mm (1‑lead only), and negative T-wave.

CONCLUSIONS

Remote interpretation of prehospital 12‑lead ECGs by ED physicians was a useful diagnostic tool in this EMS system. Even if the rate of ECG misinterpretation is low, there is still room for ED physicians operating from a remote location to improve their ability to accurately diagnose STEMI patients.

The De Winter ECG pattern: morphology and accuracy for diagnosing acute coronary occlusion: systematic review

The De Winter ECG pattern has been reported to indicate acute left anterior descending coronary artery occlusion and is often considered to be an 'ST elevation myocardial infarction (STEMI) equivalent'. We aimed to investigate the morphology of the 'De Winter ECG pattern' and evaluate the test characteristics of the De Winter pattern for the diagnosis of acute coronary occlusion. We identified papers through the Medline, EMBASE and COCHRANE databases and screened for bias using QUADAS-2. First, measurements were recorded from every ECG reported in the literature and aggregated. Second, diagnostic accuracy data from eligible cohort studies were extracted. The primary outcome was defined as at least 70% angiographic stenosis of a major epicardial vessel. Thirteen papers reported data relevant to question 1 and three papers reported data relevant to question 2. All ECGs showed maximal up-sloping ST depression in lead V3 with a median amplitude of 0.3 mV (interquartile range: 0.2-4 mV). T-wave height peaked in lead V3 with a median amplitude 0.9 mV (interquartile range: 0.8-1.1 mV). The De Winter pattern had positive predictive values of 95.2% (95% confidence interval: 76.2-99.9%), 100% (69.2-100.0%) and 100% (51.7-100%) in the three respective diagnostic studies. There is limited evidence that the De Winter ECG pattern is a 'STEMI equivalent'. The available data suggest that the pattern has high positive predictive value for acute occlusion. Further research is required to evaluate specificity and to determine whether rapid revascularization improves mortality.

Detection of STEMI Using Prehospital Serial 12-Lead Electrocardiograms

OBJECTIVE

Repeated or serial 12-lead electrocardiograms (ECGs) in the prehospital setting may improve management of patients with subtle ST-segment elevation (STE) or with a ST-segment elevation myocardial infarction (STEMI) that evolves over time. However, there is a minimal amount of scientific evidence available to support the clinical utility of this method. Our objective was to evaluate the use of serial 12-lead ECGs to detect STEMI in patients during transport in a Canadian emergency medical services (EMS) jurisdiction.

METHODS

We performed a retrospective study of suspected STEMI patients transported by EMS in the Chaudière-Appalaches region (Québec, Canada) between August 2006 and December 2013. Patients were monitored by a serial 12-lead ECG system where an averaged ECG was transmitted every 2 minutes. Following review by an emergency physician, ECGs were grouped as having either a persistent STE or a dynamic STE that evolved over time.

RESULTS

A total of 754 suspected STEMI patients were transported by EMS during the study period. Of these, 728 patients met eligibility criteria and were included in the analysis. A persistent STE was observed in 84.3% (614/728) of patients, while the remaining 15.7% (114/728) had a dynamic STE. Among those with dynamic STE, 11.1% (81/728) had 1 ST-segment change (41 no-STEMI to STEMI; 40 STEMI to no-STEMI) and 4.5% (33/728) had ≥ 2 ST-segment changes (17 no-STEMI to STEMI; 16 STEMI to no-STEMI). Overall, in 8.0% (58/728) of the cohort, STEMI was identified on a subsequent ECG following an initial no-STEMI ECG.

CONCLUSIONS

Through recognition of transient ST-segment changes during transport via the prehospital serial 12-lead ECG system, STEMI was identified in 8% of suspected STEMI patients who had an initial no-STEMI ECG. Key words: electrocardiography; emergency medical services; ST-elevation myocardial infarction; prehospital dynamic ECG.

Cardiac Monitoring in the Emergency Department

Patients present to the emergency department (ED) with a wide range of complaints and ED clinicians are responsible for identifying which conditions are life threatening. Cardiac monitoring strategies in the ED include, but are not limited to, 12-lead electrocardiography and bedside cardiac monitoring for arrhythmia and ischemia detection as well as QT-interval monitoring. ED nurses are in a unique position to incorporate cardiac monitoring into the early triage and risk stratification of patients with cardiovascular emergencies to optimize patient management and outcomes.