Accuracy of OMI ECG findings versus STEMI criteria for diagnosis of acute coronary occlusion myocardial infarction

Beyond STEMI-NSTEMI Paradigm: Dante Pazzanese's Proposal for Occlusion Myocardial Infarction Diagnosis

Although the existing framework for classifying acute myocardial infarction (AMI) into STEMI and NSTEMI has been beneficial, it is now considered to be falling short in addressing the complexity of acute coronary syndromes. The study aims to scrutinize the current STEMI-NSTEMI paradigm and advocate for a more nuanced framework, termed as occlusion myocardial infarction (OMI) and non-occlusion myocardial infarction (NOMI), for a more accurate diagnosis and management of AMI. A comprehensive analysis of existing medical literature was conducted, with a focus on the limitations of the STEMI-NSTEMI model. The study also outlines a new diagnostic approach for patients presenting with chest pain in emergency settings. The traditional STEMI-NSTEMI model falls short in diagnostic precision and effective treatment, especially in identifying acute coronary artery occlusions. The OMI-NOMI framework offers a more anatomically and physiologically accurate model, backed by a wealth of clinical research and expert opinion. It underscores the need for quick ECG assessments and immediate reperfusion therapies for suspected OMI cases, aiming to improve patient outcomes. The OMI-NOMI framework offers a new avenue for future research and clinical application. It advocates for a more comprehensive understanding of the underlying mechanisms of acute coronary syndromes, leading to individualized treatment plans. This novel approach is expected to ignite further scholarly debate and research, particularly in the Brazilian cardiology sector, with the goal of enhancing diagnostic accuracy and treatment effectiveness in AMI patients.

Life-saving fall detection by a smartwatch in a case of ventricular fibrillation

New smart devices that have the potential to support the health and well-being of their owners have become available. In particular, smart watches are able to identify a fall by the person who is wearing the watch and report it to pre-defined contacts and the local emergency control center. Falls in older people are common and only rarely caused by malignant cardiac arrhythmia. The case of an elderly male whose smart watch automatically reported his fall due to ventricular fibrillation to the local emergency control center is described. Through the intervention of the wearer's device, the activated dispatcher called the patient's wife, who found her husband lying unresponsive on the floor. Emergency medical services responded immediately and were able to successfully resuscitate the patient. A hospital discharge without any long-term complications was achieved.

Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms

Preclinical management of patients with acute chest pain and their identification as candidates for urgent coronary revascularization without the use of high sensitivity troponin essays remains a critical challenge in emergency medicine. We enrolled 2760 patients (average age 70 years, 58.6% male) with chest pain and suspected ACS, who were admitted to the Emergency Department of the University Hospital Tübingen, Germany, between August 2016 and October 2020. Using 26 features, eight Machine learning models (non-deep learning models) were trained with data from the preclinical rescue protocol and compared to the "TropOut" score (a modified version of the "preHEART" score which consists of history, ECG, age and cardiac risk but without troponin analysis) to predict major adverse cardiac event (MACE) and acute coronary artery occlusion (ACAO). In our study population MACE occurred in 823 (29.8%) patients and ACAO occurred in 480 patients (17.4%). Interestingly, we found that all machine learning models outperformed the "TropOut" score. The VC and the LR models showed the highest area under the receiver operating characteristic (AUROC) for predicting MACE (AUROC = 0.78) and the VC showed the highest AUROC for predicting ACAO (AUROC = 0.81). A SHapley Additive exPlanations (SHAP) analyses based on the XGB model showed that presence of ST-elevations in the electrocardiogram (ECG) were the most important features to predict both endpoints.

Detection of acute coronary occlusion with a novel mobile electrocardiogram device: a pilot study

Aims

Many portable electrocardiogram (ECG) devices have been developed to monitor patients at home, but the majority of these devices are single lead and only intended for rhythm disorders. We developed the miniECG, a smartphone-sized portable device with four dry electrodes capable of recording a high-quality multi-lead ECG by placing the device on the chest. The aim of our study was to investigate the ability of the miniECG to detect occlusive myocardial infarction (OMI) in patients with chest pain.

Methods and results

Patients presenting with acute chest pain at the emergency department of the University Medical Center Utrecht or Meander Medical Center, between May 2021 and February 2022, were included in the study. The clinical 12-lead ECG and the miniECG before coronary intervention were recorded. The recordings were evaluated by cardiologists and compared the outcome of the coronary angiography, if performed. A total of 369 patients were measured with the miniECG, 46 of whom had OMI. The miniECG detected OMI with a sensitivity and specificity of 65 and 92%, compared with 83 and 90% for the 12-lead ECG. Sensitivity of the miniECG was similar for different culprit vessels.

Conclusion

The miniECG can record a multi-lead ECG and rule-in ST-segment deviation in patients with occluded or near-occluded coronary arteries from different culprit vessels without many false alarms. Further research is required to add automated analysis to the recordings and to show feasibility to use the miniECG by patients at home.

The impact of Twitter/X promotion on visibility of research articles: Results of the #TweetTheJournal study

Aim

Social media (SoMe) are emerging as important tools for research dissemination. Twitter/X promotion has been shown to increase citation rates in well-established journals. We aimed to test the effect of a SoMe promotion strategy on the Mendeley reader counts, the Altmetric Attention Score and the number of citations in an upcoming open-access journal.

Methods

The #TweetTheJournal study is a randomized, controlled study. Articles published in seven subsequent issues of the International Journal of Cardiology Heart & Vasculature (April 2021-April 2022) were randomized to a Twitter/X promotion arm (articles were posted four times) and to a control arm (without active posting). Articles with accompanied editorials were excluded. Primary endpoint of the study was Mendeley reader count, secondary endpoints were Altmetric Attention Score and number of citations. Follow-up was one year.

Results

SoMe promotion of articles showed no statistically significant difference in Mendeley reader counts or number of citations at one year follow up. SoMe promotion resulted in a statistically significant higher Altmetric Attention Score in the intervention compared to the control group (RR 1.604, 95 % CI 1.024-2.511, p = 0.039). In the overall group, Altmetric Attention Score showed a correlation with Mendeley reader counts (Spearman's ρ = 0.202, p = 0.010) and Mendeley reader counts correlated significantly with number of citations (Spearman's ρ = 0.372, p < 0.001).

Conclusion

A dedicated SoMe promotion strategy did not result in statistically significant differences in early impact indicators as the Mendeley reader count in a upcoming journal, but increased the Altmetric Attention Score.

Validity of Computer-interpreted "Normal" and "Otherwise Normal" ECG in Emergency Department Triage Patients

Introduction

Chest pain is the second most common chief complaint for patients undergoing evaluation in emergency departments (ED) in the United States. The American Heart Association recommends immediate physician interpretation of all electrocardiograms (ECG) performed for adults with chest pain within 10 minutes to evaluate for the finding of ST-elevation myocardial infarction (STEMI). The ECG machines provide computerized interpretation of each ECG, potentially obviating the need for immediate physician analysis; however, the reliability of computer-interpreted findings of "normal" or "otherwise normal" ECG to rule out STEMI requiring immediate intervention in the ED is unknown.

Methods

We performed a prospective cohort analysis of 2,275 ECGs performed in triage in the adult ED of a single academic medical center, comparing the computerized interpretations of "normal" and "otherwise normal" ECGs to those of attending cardiologists. ECGs were obtained with a GE MAC 5500 machine and interpreted using Marquette 12SL.

Results

In our study population, a triage ECG with a computerized interpretation of "normal" or "otherwise normal" ECG had a negative predictive value of 100% for STEMI (one-sided, lower 97.5% confidence interval 99.6%). None of the studied patients with these ECG interpretations had a final diagnosis of STEMI, acute coronary syndrome, or other diagnosis requiring emergent cardiac catheterization.

Conclusion

In our study population, ECG machine interpretations of "normal" or "otherwise normal" ECG excluded findings of STEMI. The ECGs with these computerized interpretations could safely wait for physician interpretation until the time of patient evaluation without delaying an acute STEMI diagnosis.

Context-independent identification of myocardial ischemia in the prehospital ECG of chest pain patients

Non-traumatic chest pain is a frequent reason for an urgent ambulance visit of a patient by the emergency medical services (EMS). Chest pain (or chest pain-equivalent symptoms) can be innocent, but it can also signal an acute form of severe pathology that may require prompt intervention. One of these pathologies is cardiac ischemia, resulting from a disbalance between blood supply and demand. One cause of a diminished blood supply to the heart is acute coronary syndrome (ACS, i.e., cardiac ischemia caused by a reduced blood supply to myocardial tissue due to plaque instability and thrombus formation in a coronary artery). ACS is dangerous due to the unpredictable process that drives the supply problem and the high chance of fast hemodynamic deterioration (i.e., cardiogenic shock, ventricular fibrillation). This is why an ECG is made at first medical contact in most chest pain patients to include or exclude ischemia as the cause of their complaints. For speedy and adequate triaging and treatment, immediate assessment of this prehospital ECG is necessary, still during the ambulance ride. Human diagnostic efforts supported by automated interpretation algorithms seek to answer questions regarding the urgency level, the decision if and towards which healthcare facility the patient should be transported, and the indicated acute treatment and further diagnostics after arrival in the healthcare facility. In the case of an ACS, a catheter intervention room may be activated during the ambulance ride to facilitate the earliest possible in-hospital treatment. Prehospital ECG assessment and the subsequent triaging decisions are complex because chest pain is not uniquely associated with ACS. The differential diagnosis includes other cardiac, pulmonary, vascular, gastrointestinal, orthopedic, and psychological conditions. Some of these conditions may also involve ECG abnormalities. In practice, only a limited fraction (order of magnitude 10%) of the patients who are urgently transported to the hospital because of chest pain are ACS patients. Given the relatively low prevalence of ACS in this patient mix, the specificity of the diagnostic ECG algorithms should be relatively high to prevent overtreatment and overflow of intervention facilities. On the other hand, only a sufficiently high sensitivity warrants adequate therapy when needed. Here, we review how the prehospital ECG can contribute to identifying the presence of myocardial ischemia in chest pain patients. We discuss the various mechanisms of myocardial ischemia and infarction, the typical patient mix of chest pain patients, the shortcomings of the ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) ECG criteria to detect a completely occluded culprit artery, the OMI ECG criteria (including the STEMI-equivalent ECG patterns) in detecting completely occluded culprit arteries, and the promise of neural networks in recognizing ECG patterns that represent complete occlusions. We also discuss the relevance of detecting any ACS/ischemia, not necessarily caused by a total occlusion, in the prehospital ECG. In addition, we discuss how serial prehospital ECGs can contribute to ischemia diagnosis. Finally, we discuss the diagnostic contribution of a serial comparison of the prehospital ECG with a previously made nonischemic ECG of the patient.

Prehospital Electrocardiogram Transmission and Prehospital Scene Time: A Retrospective Cohort Study

Background: Telemedical transmission of prehospital electrocardiograms (ECGs) to a target clinic may improve clinical workflows and speed of intervention. However, whether ECG transmission delays prehospital workflows remains controversial. Therefore, we aimed to clarify whether ECG transmission prolongs prehospital scene time in patients diagnosed with acute coronary syndrome (ACS). Methods: We retrospectively included all patients diagnosed with ACS by prehospital emergency physicians from July 2016 to June 2019 at a single academic center. The primary endpoint was the effect of ECG transmission on prehospital scene time. The secondary endpoints were the effects of ECG diagnosis on prehospital scene time and quality of care. Multivariable regression was used to account for patients' age, physician specialty, completion of specialty training, and whether emergencies occurred throughout the day or night shifts as potential confounders. Results: A total of 1,106 cases were included, of which 154 ECG transmissions were performed. ECG transmission prolonged prehospital scene time by an average of 3 min: adjusted regression coefficient [95% confidence interval (95% CI)]: 3.24 (1.7-4.8), p < 0.001. Prehospital treatment time was not influenced by prehospital ECG-based diagnosis (ST-elevation myocardial infarction [STEMI] vs. non-ST-elevation ACS [NSTE-ACS]): adjusted regression coefficient (95% CI): 0.7 (-1.3 to 2.7), p = 0.490. Emergency physicians adhered to local standard operating procedures in 739 of 1,007 (73%) patients diagnosed with NSTE-ACS and 93 of 99 (94%) patients diagnosed with STEMI. A STEMI diagnosis compared with NSTE-ACS was associated with five times higher odds of adhering to standard operating procedures (odds ratio [95% CI]: 5.6 [2.7-14.6], p < 0.001). Conclusion: The observed delay of ∼3 min in the prehospital scene time by ECG transmission is clinically irrelevant. For patients prehospitally diagnosed with NSTE-ACS who do not meet STEMI criteria, adherence to standard operating procedures seems unjustifiably low and should be improved.

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

New ECG Algorithm for the Prediction of Culprit Vessel in Acute Myocardial Infarction Involving Lateral Part of the Ventricle: Ilkay Classification

Background

Isolated lateral myocardial infarction sometimes does not meet ST-segment elevation myocardial infarction (STEMI) criteria according to contiguous leads. This condition could cause late diagnosis and the need for revascularization therapy.

Aim

To accurately predict the occlusion of lateral surface of the left ventricle, we defined a new electrocardiogram (ECG) algorithm by using angiographic and electrocardiographic correlations.

Methods

This was a retrospective, multicenter observational study. The study population consisted of 200 patients with STEMI affecting lateral surface of myocardium, between 2021 and 2022. According to the coronary angiography results, we identified 74 eligible patients for study protocol. The study patients were divided into two groups: isolated DB (14 patients) or circumflex obtuse marginal group (60 patients).

Results

ST depression in lead V2 had high positive predictive values for the prediction of obtuse marginal occlusion (positive predictive values (PPV), 100%; negative predictive value (NPV), 90%). ST elevation in V2 in ECG, in conjunction with ST depression in lead III had high positive predictive values for prediction of diagonal branch of LAD. Moreover, the presence of hyperacute T wave (≥10 mm) in lead V2 and ≥2 mm ST depression in lead III had large diagonal branch of LAD (PPV, 98%; NPV, 100%). However, <10 mm T wave in lead V2 and <2 mm ST depression in lead III had small diagonal branch of LAD.

Conclusion

We comprehensively classified the lateral STEMI definition through new electrocardiographic scheme as Ilkay classification, whereby we could accurately predict infarct-related artery and its occlusion level in lateral myocardial infarction.

Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting ECG are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but we currently have no accurate tools to identify them during initial triage. Herein, we report the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, significantly boosting both precision and sensitivity. Our derived OMI risk score provided superior rule-in and rule-out accuracy compared to routine care, and when combined with the clinical judgment of trained emergency personnel, this score helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

Clinical Features of the Aslanger Pattern to Compensate for the Limitation of ST-Elevation Myocardial Infarction (STEMI) Criteria

BACKGROUND

ST-elevation is one of the most valuable electrocardiogram findings to diagnose acute myocardial infarction. However, more than a quarter of acute coronary occlusions are missed by this criterion, causing a delay in revascularization. Therefore, there should be awareness of the limitations of the current criteria and new electrocardiographic findings are required as a diagnostic tool to compensate for them. The Aslanger pattern is a specific electrocardiographic finding in acute inferior myocardial infarction with multivessel disease and allows the detection of inferior myocardial infarction that does not show ST-elevation, leading to rapid revascularization. However, in patients with the Aslanger pattern, the hemodynamic characteristics, such as the rate of shock and the use of mechanical circulatory support, as well as prognostic characteristics such as the in-hospital mortality rate, have not yet been clarified.

METHODS

In this study, we retrospectively surveyed the current practice on the basis of ST-elevation myocardial infarction (STEMI) criteria in patients with acute coronary artery occlusion presenting with inferior myocardial infarction. We examined the clinical characteristics of the Aslanger pattern.

RESULTS

Based on the STEMI criteria, 71.8% (51/72) of patients were diagnosed with STEMI from an acute electrocardiogram, and 28.2% (21/78) were diagnosed with non-STEMI. As expected, ruling out in all acute coronary artery occlusions using STEMI criteria alone was difficult. A total of 48% of patients with non-STEMI had the Aslanger pattern. In addition, 80% of patients with the Aslanger pattern had multivessel disease, 30% had the use of the mechanical circulatory support, and 20% had in-hospital mortality.

CONCLUSION

This study suggests that the Aslanger pattern is useful not only for diagnosis, but also for predicting hemodynamic collapse and a poor prognosis. Therefore, we should share information on Aslanger pattern with other physicians and use this pattern in daily practice.

High-risk Electrocardiogram Patterns

This case report describes a patient in their 50s who presented to the emergency department with the presence of typical retrosternal chest pain radiating to the left scapular site.

From Q/Non-Q Myocardial Infarction to STEMI/NSTEMI: Why It's Time to Consider Another Simplified Dichotomy; a Narrative Literature Review

Acute coronary syndromes (ACSs) are classified as ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) based on the presence of guideline-recommended ST-segment elevation (STE) criteria on the electrocardiogram (ECG). STEMI is associated with acute total coronary occlusion (ATO) and transmural myocardial necrosis and is managed with emergent reperfusion therapy, and NSTEMI is supposedly synonymous with subendocardial myocardial infarction without ATO. However, coronary angiograms reveal that a significant proportion of patients with NSTEMI have ATO. Here, we review articles that studied the frequency and cardiovascular outcomes of ATO in NSTEMI patients compared with those without ATO. We discuss ECG patterns of patients with suspected acute myocardial infarction that do not fulfill STEMI criteria but are associated with ATO. Under-recognition of these atypical patterns results in delays to reperfusion therapy. We also advocate revision of the current STEMI/NSTEMI paradigm because consideration of STE, by itself, out of context of other clinical and ECG features, leads to the ECG diagnosis of STEMI when the ECG actually represents a mimic ["Pseudo-STEMI"], and suggest renaming the ACSs classification as the Occlusion Myocardial Infarction (OMI)/Non-Occlusion Myocardial Infarction (NOMI) paradigm.

The Role of ECG in the Diagnosis and Risk Stratification of Acute Coronary Syndromes: an Old but Indispensable Tool

PURPOSE OF REVIEW

Since its inception in 1902 by Willem Einthoven, the electrocardiogram (ECG) has fundamentally undergone minimal technological advances. Nevertheless, its clinical utility is critical, and it remains an essential tool to diagnose, risk stratify, and guide reperfusion and invasive strategies in patients with suspected acute coronary syndromes. ECG reading can be demanding, with many healthcare professionals lacking the necessary expertise to accurately interpret them. This is exacerbated by the need to constantly revisit old dogmas pertinent to the interpretation of ECGs.

RECENT FINDINGS

Notably, ECG leads record the global electrical activity of the heart toward and away from each electrode rather than local events. The long-held central paradigm that the various ECG leads record local events underneath specific electrodes should therefore be reassessed. For example, ST segment elevation in leads V1 and V2 usually denote antero-apical rather than septal infarction, often a misnomer utilized by the majority of clinicians. The ECG diagnosis of ST-elevation myocardial infarction (STEMI) is sometimes challenging and discerning it from non-ST-elevation myocardial infarction (NSTEMI) is of paramount importance to implement timely acute reperfusion therapy. In fact, when qualifications for emergency reperfusion therapy are based on STEMI ECG criteria, nearly one-third of cases with acute coronary occlusion are missed. Diagnostic ST elevation in the absence of left ventricular (LV) hypertrophy or left bundle-branch block (LBBB) is defined by a specific set of sex-specific criteria for new ST elevation at the J point in contiguous precordial or limb leads. However, other ECG criteria need to be kept in mind. These include, but are not limited to, new or presumably new left bundle branch block (LBBB), which is often considered as an STEMI-equivalent; ST depression in two or more precordial leads (V1-V4), denoting a true inferolateral transmural myocardial infarction; and the infrequent presentation with hyperacute T-wave changes. As our understanding of the pathology of ischemic reperfusion injury has evolved and following the introduction of new imaging modalities such as cardiac magnetic resonance imaging, we need to re-assess the old dogmas pertinent to the interpretation of ECGs and update the terms and classifications.

Ischemic ST-Segment Depression Maximal in V1-V4 (Versus V5-V6) of Any Amplitude Is Specific for Occlusion Myocardial Infarction (Versus Nonocclusive Ischemia)

Background Occlusion myocardial infarctions (OMIs) of the posterolateral walls are commonly missed by ST-segment-elevation myocardial infarction (STEMI) criteria, with >50% of patients with circumflex occlusion not receiving emergent reperfusion and experiencing increased mortality. ST-segment depression maximal in leads V1-V4 (STDmaxV1-4) has been suggested as an indicator of posterior OMI. Methods and Results We retrospectively reviewed a high-risk population with acute coronary syndrome. OMI was defined from prior studies as a culprit lesion with TIMI (Thrombolysis in Myocardial Infarction) 0 to 2 flow or TIMI 3 flow plus peak troponin T >1.0 ng/mL or troponin I >10 ng/mL. STEMI was defined by the Fourth Universal Definition of Myocardial Infarction. ECGs were interpreted blinded to outcomes. Among 808 patients, there were 265 OMIs, 108 (41%) meeting STEMI criteria. A total of 118 (15%) patients had "suspected ischemic" STDmaxV1-4, of whom 106 (90%) had an acute culprit lesion, 99 (84%) had OMI, and 95 (81%) underwent percutaneous coronary intervention. Suspected ischemic STDmaxV1-4 had 97% specificity and 37% sensitivity for OMI. Of the 99 OMIs detected by STDmaxV1-4, 34% had <1 mm ST-segment depression, and only 47 (47%) had accompanying STEMI criteria, of which 17 (36%) were identified a median 1.00 hour earlier by STDmaxV1-4 than STEMI criteria. Despite similar infarct size, TIMI flow, and coronary interventions, patients with STEMI(-) OMI and STDmaxV1-4 were less likely than STEMI(+) patients to undergo catheterization within 90 minutes (46% versus 68%; P=0.028). Conclusions Among patients with high-risk acute coronary syndrome, the specificity of ischemic STDmaxV1-4 was 97% for OMI and 96% for OMI requiring emergent percutaneous coronary intervention. STEMI criteria missed half of OMIs detected by STDmaxV1-4. Ischemic STDmaxV1-V4 in acute coronary syndrome should be considered OMI until proven otherwise.

Diagnosis of Occlusion Myocardial Infarction in Patients with Left Bundle Branch Block and Paced Rhythms

PURPOSE OF REVIEW

A number of criteria have been developed to aid with the diagnosis of occlusion myocardial infarction (OMI) in patients with left bundle branch block (LBBB) and ventricular paced rhythms (VPR). The current guidelines do not provide clear preference for any specific ECG criteria in LBBB and paced rhythm patients.

RECENT FINDINGS

This review delineates the difficulties of electrocardiographic diagnosis of OMI in both LBBB and VPR patients. We describe the original Sgarbossa and the newer criteria and their diagnostic performances. We highlight the expected changes of newer pacing modalities and how they may interfere with the electrocardiographic diagnosis of OMI. We recommend utilizing the Cai et al. algorithm, which combines clinical assessment with the Smith Modified Sgarbossa ECG criteria, for both LBBB and right ventricular pacing patients with suspected OMI. There is limited data concerning ECG changes of OMI in patients with the newer pacing modalities, such as biventricular, His-bundle, or left bundle branch pacing.

Diagnostic performance of a new ECG algorithm for reducing false positive cases in patients suspected acute coronary syndrome

BACKGROUND

Early and correct diagnosis of ST-segment elevation myocardial infarction (STEMI) is crucial for providing timely reperfusion therapy. Patients with ischemic symptoms presenting with ST-segment elevation on the electrocardiogram (ECG) are preferably transported directly to a catheterization laboratory (Cath-lab) for primary percutaneous coronary intervention (PPCI). However, the ECG often contains confounding factors making the STEMI diagnosis challenging leading to false positive Cath-lab activation. The objective of this study was to test the performance of a standard automated algorithm against an additional high specificity setting developed for reducing the false positive STEMI calls.

METHODS

We included consecutive patients with an available digital prehospital ECG triaged directly to Cath-lab for acute coronary angiography between 2009 and 2012. An adjudicated discharge diagnosis of STEMI or no myocardial infarction (no-MI) was assigned for each patient. The new automatic algorithm contains a feature to reduce false positive STEMI interpretation. The STEMI performance with the standard setting (STD) and the high specificity setting (HiSpec) was tested against the adjudicated discharge diagnosis in a retrospective manner.

RESULTS

In total, 2256 patients with an available digital prehospital ECG (mean age 63 ± 13 years, male gender 71%) were included in the analysis. The discharge diagnosis of STEMI was assigned in 1885 (84%) patients. The STD identified 165 true negative and 1457 true positive (206 false positive and 428 false negative) cases (77.3%, 44.5%, 87.6% and 17.3% for sensitivity, specificity, PPV and NPV, respectively). The HiSpec identified 191 true negative and 1316 true positive (180 false positive and 569 false negative) cases (69.8%, 51.5%, 88.0% and 25.1% for sensitivity, specificity, PPV and NPV, respectively). From STD to HiSpec, false positive cases were reduced by 26 (12,6%), but false negative results were increased by 33%.

CONCLUSIONS

Implementing an automated ECG algorithm with a high specificity setting was able to reduce the number of false positive STEMI cases. However, the predictive values for both positive and negative STEMI identification were moderate in this highly selected STEMI population. Finally, due the reduced sensitivity/increased false negatives, a negative AMI statement should not be solely based on the automated ECG statement.

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

BACKGROUND

The 12‑lead ECG plays an important role in triaging patients with symptomatic coronary artery disease, making automated ECG interpretation statements of "Acute MI" or "Acute Ischemia" crucial, especially during prehospital transport when access to physician interpretation of the ECG is limited. However, it remains unknown how automated interpretation statements correspond to adjudicated clinical outcomes during hospitalization. We sought to evaluate the diagnostic performance of prehospital automated interpretation statements to four well-defined clinical outcomes of interest: confirmed ST- segment elevation myocardial infarction (STEMI); presence of actionable coronary culprit lesions, myocardial necrosis, or any acute coronary syndrome (ACS).

METHODS

An observational cohort study that enrolled consecutive patients with non-traumatic chest pain transported via ambulance. Prehospital ECGs were obtained with the Philips MRX monitor from the medical command center and re-processed using manufacturer-specific diagnostic algorithms to denote the likelihood of >>>Acute MI<<< or >>>Acute Ischemia<<<. Two independent reviewers retrospectively adjudicated the study outcomes and disagreements were resolved by a third reviewer.

RESULTS

Our study included 2400 patients (age 59 ± 16, 47% females, 41% Black), with 190 (8%) patients with documented automated diagnostic statements of acute MI or acute ischemia. The sensitivity/specificity of the automated algorithm for detecting confirmed STEMI (n = 143, 6%); presence of actionable coronary culprit lesions (n = 258, 11%), myocardial necrosis (n = 291, 12%), or any ACS (n = 378, 16%) were 62.9%/95.6%; 37.2%/95.6%; 38.5%/96.4%; and 30.7%/96.3%, respectively.

CONCLUSION

Although being very specific, automated interpretation statements of acute MI/acute ischemia on prehospital ECGs are not satisfactorily sensitive to exclude symptomatic coronary disease. Patients without these automated interpretation statements should be considered further for significant underlying coronary disease based on the clinical context.

TRIAL REGISTRATION

ClinicalTrials.gov # NCT04237688.