Causes of Prehospital Misinterpretations of ST Elevation Myocardial Infarction

A Novel Algorithm for Improving the Prehospital Diagnostic Accuracy of ST-Segment Elevation Myocardial Infarction

INTRODUCTION

Early detection of ST-segment elevation myocardial infarction (STEMI) on the prehospital electrocardiogram (ECG) improves patient outcomes. Current software algorithms optimize sensitivity but have a high false-positive rate. The authors propose an algorithm to improve the specificity of STEMI diagnosis in the prehospital setting.

METHODS

A dataset of prehospital ECGs with verified outcomes was used to validate an algorithm to identify true and false-positive software interpretations of STEMI. Four criteria implicated in prior research to differentiate STEMI true positives were applied: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. The test characteristics were calculated and regression analysis was used to examine the association between the number of criteria included and test characteristics.

RESULTS

There were 44,611 cases available. Of these, 1,193 were identified as STEMI by the software interpretation. Applying all four criteria had the highest positive likelihood ratio of 353 (95% CI, 201-595) and specificity of 99.96% (95% CI, 99.93-99.98), but the lowest sensitivity (14%; 95% CI, 11-17) and worst negative likelihood ratio (0.86; 95% CI, 0.84-0.89). There was a strong correlation between increased positive likelihood ratio (r2 = 0.90) and specificity (r2 = 0.85) with increasing number of criteria.

CONCLUSIONS

Prehospital ECGs with a high probability of true STEMI can be accurately identified using these four criteria: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. Applying these criteria to prehospital ECGs with software interpretations of STEMI could decrease false-positive field activations, while also reducing the need to rely on transmission for physician over-read. This can have significant clinical and quality implications for Emergency Medical Services (EMS) systems.

Implementation of an All-Day Artificial Intelligence-Based Triage System to Accelerate Door-to-Balloon Times

OBJECTIVE

To implement an all-day artificial intelligence (AI)-based system to facilitate chest pain triage in the emergency department.

METHODS

The AI-based triage system encompasses an AI model combining a convolutional neural network and long short-term memory to detect ST-elevation myocardial infarction (STEMI) on electrocardiography (ECG) and a clinical risk score (ASAP) to prioritize patients for ECG examination. The AI model was developed on 2907 twelve-lead ECGs: 882 STEMI and 2025 non-STEMI ECGs.

RESULTS

Between November 1, 2019, and October 31, 2020, we enrolled 154 consecutive patients with STEMI: 68 during the AI-based triage period and 86 during the conventional triage period. The mean ± SD door-to-balloon (D2B) time was significantly shortened from 64.5±35.3 minutes to 53.2±12.7 minutes (P=.007), with 98.5% vs 87.2% (P=.009) of D2B times being less than 90 minutes in the AI group vs the conventional group. Among patients with an ASAP score of 3 or higher, the median door-to-ECG time decreased from 30 minutes (interquartile range [IQR], 7-59 minutes) to 6 minutes (IQR, 4-30 minutes) (P<.001). The overall performances of the AI model in identifying STEMI from 21,035 ECGs assessed by accuracy, precision, recall, area under the receiver operating characteristic curve, F1 score, and specificity were 0.997, 0.802, 0.977, 0.999, 0.881, and 0.998, respectively.

CONCLUSION

Implementation of an all-day AI-based triage system significantly reduced the D2B time, with a corresponding increase in the percentage of D2B times less than 90 minutes in the emergency department. This system may help minimize preventable delays in D2B times for patients with STEMI undergoing primary percutaneous coronary intervention.

A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram

(1) Background: Acute pericarditis is often confused with ST-segment elevation myocardial infarction (STEMI) among patients presenting with acute chest pain in the emergency department (ED). Since a deep learning model (DLM) has been validated to accurately identify STEMI cases via 12-lead electrocardiogram (ECG), this study aimed to develop another DLM for the detection of acute pericarditis in the ED. (2) Methods: This study included 128 ECGs from patients with acute pericarditis and 66,633 ECGs from patients visiting the ED between 1 January 2010 and 31 December 2020. The ECGs were randomly allocated based on patients to the training, tuning, and validation sets, at a 3:1:1 ratio. We used raw ECG signals to train a pericarditis-DLM and used traditional ECG features to train a machine learning model. A human–machine competition was conducted using a subset of the validation set, and the performance of the Philips automatic algorithm was also compared. STEMI cases in the validation set were extracted to analyze the DLM ability of differential diagnosis between acute pericarditis and STEMI using ECG. We also followed the hospitalization events in non-pericarditis cases to explore the meaning of false-positive predictions. (3) Results: The pericarditis-DLM exceeded the performance of all participating human experts and algorithms based on traditional ECG features in the human–machine competition. In the validation set, the pericarditis-DLM could detect acute pericarditis with an area under the receiver operating characteristic curve (AUC) of 0.954, a sensitivity of 78.9%, and a specificity of 97.7%. However, our pericarditis-DLM also misinterpreted 10.2% of STEMI ECGs as pericarditis cases. Therefore, we generated an integrating strategy combining pericarditis-DLM and a previously developed STEMI-DLM, which provided a sensitivity of 73.7% and specificity of 99.4%, to identify acute pericarditis in patients with chest pains. Compared to the true-negative cases, patients with false-positive results using this strategy were associated with higher risk of hospitalization within 3 days due to cardiac disorders (hazard ratio (HR): 8.09; 95% confidence interval (CI): 3.99 to 16.39). (4) Conclusions: The AI-enhanced algorithm may be a powerful tool to assist clinicians in the early detection of acute pericarditis and differentiate it from STEMI using 12-lead ECGs.

Systematic Review and Meta-Analysis of Diagnostic Accuracy to Identify ST-Segment Elevation Myocardial Infarction on Interpretations of Prehospital Electrocardiograms

Background: The aim of this study was to assess and discuss the diagnostic accuracy of prehospital ECG interpretation through systematic review and meta-analyses.

Methods and Results: Relevant literature published up to July 2020 was identified using PubMed. All human studies of prehospital adult patients suspected of ST-segment elevation myocardial infarction in which prehospital electrocardiogram (ECG) interpretation by paramedics or computers was evaluated and reporting all 4 (true-positive, false-positive, false-negative, and true-negative) values were included. Meta-analyses were conducted separately for the diagnostic accuracy of prehospital ECG interpretation by paramedics (Clinical Question [CQ] 1) and computers (CQ2). After screening, 4 studies for CQ1 and 6 studies for CQ2 were finally included in the meta-analysis. Regarding CQ1, the pooled sensitivity and specificity were 95.5% (95% confidence interval [CI] 82.5–99.0%) and 95.8% (95% CI 82.3–99.1%), respectively. Regarding CQ2, the pooled sensitivity and specificity were 85.4% (95% CI 74.1–92.3%) and 95.4% (95% CI 87.3–98.4%), respectively.

Conclusions: This meta-analysis suggests that the diagnostic accuracy of paramedic prehospital ECG interpretations is favorable, with high pooled sensitivity and specificity, with an acceptable estimated number of false positives and false negatives. Computer-assisted ECG interpretation showed high pooled specificity with an acceptable estimated number of false positives, whereas the pooled sensitivity was relatively low.

Analysis of the accuracy of automatic electrocardiogram interpretation in ST-segment elevation myocardial infarction

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.

ST-Segment Depression in Leads I and aVL: Artifactual or Pathophysiological Findings?

The 12-lead electrocardiogram (ECG) is a routinely performed test but is susceptible to misinterpretation even by experienced physicians. We report a case of a 72-year-old lady with no prior cardiac history presented to our hospital with atypical chest pain. Her initial electrocardiogram shows an initial ST depression followed by positive deflections leads I and aVL. Non-physiological ST segment and T-wave changes are also observed in the precordial leads V2 to V6. By contrast, these abnormalities are notably absent in lead II. A repeat of the ECG taken 30 minutes later reveals the resolution of most abnormalities seen in the initial ECG on a background of high-frequency noise in the limb leads. She was referred to the cardiology department for further management. An urgent echocardiogram revealed no regional wall motion abnormalities with preserved ejection fraction, and her coronary angiogram revealed no significant coronary stenosis. This case illustrates the importance of understanding different factors that can cause ST segment abnormalities, notably artifactual changes that can mimic ST segment myocardial infarction.

Prehospital Predictors of Atypical STEMI Symptoms

Introduction: Rapid prehospital identification of patients with ST-elevation myocardial infarction (STEMI) is a critical step to reduce time to treatment. Broad screening with field 12-lead ECGs can lead to a high rate of false positive STEMI activations due to low prevalence. One strategy to reduce false positive STEMI interpretations is to limit acquisition of 12-lead ECGs to patients who have symptoms strongly suggestive of STEMI, but this may delay care in patients who present atypically and lead to disparities in populations with more atypical presentations. We sought to assess patient factors associated with atypical STEMI presentation.Methods: We retrospectively analyzed consecutive adult patients for whom Los Angeles Fire Department paramedics obtained a field 12-lead ECG from July 2011 through June 2012. The regional STEMI receiving center registry was used to identify patients with STEMI. Patients were designated as having typical symptoms if paramedics documented provider impressions of chest pain/discomfort, cardiac arrest, or cardiac symptoms, otherwise they were designated as having atypical symptoms. We utilized logistic regression to determine patient factors (age, sex, race) associated with atypical STEMI presentation.Results: Of the 586 patients who had STEMI, 70% were male, 43% White, 16% Black, 20% Hispanic, 5% Asian and 16% were other or unspecified race. Twenty percent of STEMI patients (n = 117) had atypical symptoms. Women who had STEMI were older than men (74 years [IQR 62-83] vs. 60 years [IQR 53-70], p < 0.001). Univariate predictors of atypical symptoms were older age and female sex (p < 0.0001), while in multivariable analysis older age [odd ratio (OR) 1.05 per year, [95%CI 1.04-1.07, p < 0.0001] and black race (OR vs White 2.18, [95%CI 1.20-3.97], p = 0.011) were associated with atypical presentation.Conclusion: Limiting prehospital acquisition of 12-lead ECGs to patients with typical STEMI symptoms would result in one in five patients with STEMI having delayed recognition, disproportionally impacting patients of older age, women, and Black patients. Age, not sex, may be a better predictor of atypical STEMI presentation.

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.

Usefulness of multi-labelling artificial intelligence in detecting rhythm disorders and acute ST-elevation myocardial infarction on 12-lead electrocardiogram

Aims

To develop an artificial intelligence-based approach with multi-labelling capability to identify both ST-elevation myocardial infarction (STEMI) and 12 heart rhythms based on 12-lead electrocardiograms (ECGs).

Methods and results

We trained, validated, and tested a long short-term memory (LSTM) model for the multi-label diagnosis of 13 ECG patterns (STEMI + 12 rhythm classes) using 60 537 clinical ECGs from 35 981 patients recorded between 15 January 2009 and 31 December 2018. In addition to the internal test above, we conducted a real-world external test, comparing the LSTM model with board-certified physicians of different specialties using a separate dataset of 308 ECGs covering all 13 ECG diagnoses. In the internal test, the area under the curves (AUCs) of the LSTM model in classifying the 13 ECG patterns ranged between 0.939 and 0.999. For the external test, the LSTM model for multi-labelling of the 13 ECG patterns evaluated by AUC was 0.987 ± 0.021, which was superior to those of cardiologists (0.898 ± 0.113, P < 0.001), emergency physicians (0.820 ± 0.134, P < 0.001), internists (0.765 ± 0.155, P < 0.001), and a commercial algorithm (0.845 ± 0.121, P < 0.001). Of note, the LSTM model achieved an accuracy of 0.987, AUC of 0.997, and precision, recall, and F 1 score of 0.952, 0.870, and 0.909, respectively, in detecting STEMI.

Conclusions

We demonstrated the usefulness of an LSTM model in the multi-labelling detection of both rhythm classes and STEMI in competitive testing against board-certified physicians. This AI tool exceeding the cardiologist-level performance in detecting STEMI and rhythm classes on 12-lead ECG may be useful in prioritizing chest pain triage and expediting clinical decision-making in healthcare.

Low diagnostic yield of ST elevation myocardial infarction amplitude criteria in chest pain patients at the emergency department

OBJECTIVES

To evaluate the diagnostic yield of the ECG criteria for ST-elevation myocardial infarction in a large cohort of emergency department chest pain patients, and to determine whether extended ECG criteria or reciprocal ST depression can improve accuracy. Design: Observational, register-based diagnostic study on the accuracy of ECG criteria for ST-elevation myocardial infarction. Between Jan 2010 and Dec 2014 all patients aged ≥30 years with chest pain who had an ECG recorded within 4 h at two emergency departments in Sweden were included. Exclusion criteria were: ECG with poor technical quality; QRS duration ≥120 ms; ECG signs of left ventricular hypertrophy; or previous coronary artery bypass surgery. Conventional and extended ECG criteria were applied to all patients. The main outcome was acute myocardial infarction (AMI) and an occluded/near-occluded coronary artery at angiography. Results: Finally, 19932 patients were included. Conventional ECG criteria for ST elevation myocardial infarction were fulfilled in 502 patients, and extended criteria in 1249 patients. Sensitivity for conventional ECG criteria in diagnosing AMI with coronary occlusion/near-occlusion was 17%, specificity 98% and positive predictive value 12%. Corresponding data for extended ECG criteria were 30%, 94% and 8%. When reciprocal ST depression was added to the criteria, the positive predictive value rose to 24% for the conventional and 23% for the extended criteria. Conclusions: In unselected chest pain patients at the emergency department, the diagnostic yield of both conventional and extended ECG criteria for ST-elevation myocardial infarction is low. The PPV can be increased by also considering reciprocal ST depression.

P2Y12 inhibitor loading dose before catheterization in ST-segment elevation myocardial infarction: Is this the best strategy?

INTRODUCTION AND OBJECTIVES

In ST-segment elevation myocardial infarction (STEMI) the benefit of dual antiplatelet therapy is unequivocal, but the optimal time to administer the loading dose (LD) of a P2Y₁₂ inhibitor is the subject of debate and disagreement. The main aim of this study was characterize current practice in Portugal and to assess the prognostic impact of P2Y₁₂ inhibitor LD administration strategy, before versus during or after primary percutaneous coronary intervention (PCI).

METHODS

This multicenter retrospective study based on the Portuguese National Registry on Acute Coronary Syndromes included patients with STEMI and PCI performed between October 1, 2010 and September 19, 2017. Two groups were established: LD before PCI (LD-PRE) and LD during or after PCI (LD-CATH).

RESULTS

A total of 4123 patients were included, 66.3% in the LD-PRE group and 32.4% in the LD-CATH group. Prehospital use of a P2Y₁₂ inhibitor was a predictor of the composite bleeding endpoint (major bleeding, need for transfusion or hemoglobin [Hb] drop >2g/dl), Hb drop >2g/dl and reinfarction. There were no differences between groups in major adverse events (MAE) (in-hospital mortality, reinfarction and stroke) or in-hospital mortality.

CONCLUSIONS

Prehospital use of a P2Y₁₂ inhibitor was associated with an increased risk of bleeding, predicting the composite bleeding outcome and Hb drop >2g/dl, with no differences in mortality or MAE, calling into question the benefit of this strategy.

Systematic Review of Clinical Decision Support Systems for Prehospital Acute Coronary Syndrome Identification

OBJECTIVES

Timely prehospital diagnosis and treatment of acute coronary syndrome (ACS) are required to achieve optimal outcomes. Clinical decision support systems (CDSS) are platforms designed to integrate multiple data and can aid with management decisions in the prehospital environment. The review aim was to describe the accuracy of CDSS and individual components in the prehospital ACS management.

METHODS

This systematic review examined the current literature regarding the accuracy of CDSS for ACS in the prehospital setting, the influence of computer-aided decision-making and of 4 components: electrocardiogram, biomarkers, patient history, and examination findings. The impact of these components on sensitivity, specificity, and positive and negative predictive values was assessed.

RESULTS

A total of 11,439 articles were identified from a search of databases, of which 199 were screened against the eligibility criteria. Eight studies were found to meet the eligibility and quality criteria. There was marked heterogeneity between studies which precluded formal meta-analysis. However, individual components analysis found that patient history led to significant improvement in the sensitivity and negative predictive values. CDSS which incorporated all 4 components tended to show higher sensitivities and negative predictive values. CDSS incorporating computer-aided electrocardiogram diagnosis showed higher specificities and positive predictive values.

CONCLUSIONS

Although heterogeneity precluded meta-analysis, this review emphasizes the potential of ACS CDSS in prehospital environments that incorporate patient history in addition to integration of multiple components. The higher sensitivity of certain components, along with higher specificity of computer-aided decision-making, highlights the opportunity for developing an integrated algorithm with computer-aided decision support.

Prevalence of ECGs Exceeding Thresholds for ST-Segment-Elevation Myocardial Infarction in Apparently Healthy Individuals: The Role of Ethnicity

Background

Early prehospital recognition of critical conditions such as ST‐segment–elevation myocardial infarction (STEMI) has prognostic relevance. Current international electrocardiographic STEMI thresholds are predominantly based on individuals of Western European descent. However, because of ethnic electrocardiographic variability both in health and disease, there is a need to reevaluate diagnostic ST‐segment elevation thresholds for different populations. We hypothesized that fulfillment of ST‐segment elevation thresholds of STEMI criteria (STE‐ECGs) in apparently healthy individuals is ethnicity dependent.

Methods and Results

HELIUS (Healthy Life in an Urban Setting) is a multiethnic cohort study including 10 783 apparently healthy subjects of 6 different ethnicities (African Surinamese, Dutch, Ghanaian, Moroccan, South Asian Surinamese, and Turkish). Prevalence of STE‐ECGs across ethnicities, sexes, and age groups was assessed with respect to the 2 international STEMI thresholds: sex and age specific versus sex specific. Mean prevalence of STE‐ECGs was 2.8% to 3.4% (age/sex‐specific and sex‐specific thresholds, respectively), although with large ethnicity‐dependent variability. Prevalences in Western European Dutch were 2.3% to 3.0%, but excessively higher in young (<40 years) Ghanaian males (21.7%–27.5%) and lowest in older (≥40 years) Turkish females (0.0%). Ethnicity (sub‐Saharan African origin) and other variables (eg, younger age, male sex, high QRS voltages, or anterolateral early repolarization pattern) were positively associated with STE‐ECG occurrence, resulting in subgroups with >45% STE‐ECGs.

Conclusions

The accuracy of diagnostic tests partly relies on background prevalence in healthy individuals. In apparently healthy subjects, there is a highly variable ethnicity‐dependent prevalence of ECGs with ST‐segment elevations exceeding STEMI thresholds. This has potential consequences for STEMI evaluations in individuals who are not of Western European descent, putatively resulting in adverse outcomes with both over‐ and underdiagnosis of STEMI.

Electrocardiographic changes in the differentiation of ischemic and non-ischemic ST elevation

Identification of true STEMI among patients with different ST-elevation etiology may be improved by considering reciprocal ST depression, ST depression in aVR and chest-lead PR depression.

A Novel Algorithm for Improving the Diagnostic Accuracy of Prehospital ST-Elevation Myocardial Infarction

INTRODUCTION

ST-segment elevation myocardial infarction (STEMI) is a time-sensitive entity that has been shown to benefit from prehospital diagnosis by electrocardiogram (ECG). Current computer algorithms with binary decision making are not accurate enough to be relied on for cardiac catheterization lab (CCL) activation.

HYPOTHESIS

An algorithmic approach is proposed to stratify binary STEMI computerized ECG interpretations into low, intermediate, and high STEMI probability tiers.

METHODS

Based on previous literature, a four-criteria algorithm was developed to rule out/in common causes of prehospital STEMI false-positive computer interpretations: heart rate, QRS width, ST elevation criteria, and artifact. Prehospital STEMI cases were prospectively collected at a single academic center in Salt Lake City, Utah (USA) from May 2012 through October 2013. The prehospital ECGs were applied to the algorithm and compared against activation of the CCL by an emergency department (ED) physician as the outcome of interest. In addition to calculating test characteristics, linear regression was used to look for an association between number of criteria used and accuracy, and logistic regression was used to test if any single criterion performed better than another.

RESULTS

There were 63 ECGs available for review, 39 high probability and 24 intermediate probability. The high probability STEMI tier had excellent test characteristics for ruling in STEMI when all four criteria were used, specificity 1.00 (95% CI, 0.59-1.00), positive predictive value 1.00 (0.91-1.00). Linear regression showed a strong correlation demonstrating that false-positives increased as fewer criteria were used (adjusted r-square 0.51; P <.01). Logistic regression showed no significant predictive value for any one criterion over another (P = .80). Limiting physician overread to the intermediate tier only would reduce the number of ECGs requiring physician overread by a factor of 0.62 (95% CI, 0.48-0.75; P <.01).

CONCLUSION

Prehospital STEMI ECGs can be accurately stratified to high, intermediate, and low probabilities for STEMI using the four criteria. While additional study is required, using this tiered algorithmic approach in prehospital ECGs could lead to changes in CCL activation and decreased requirements for physician overread. This may have significant clinical and quality implications.

Push Notifications Reduce Emergency Department Response Times to Prehospital ST-segment Elevation Myocardial Infarction

INTRODUCTION

Prehospital acquisition of electrocardiograms (ECG) has been consistently associated with reduced door-to-balloon times in the treatment of ST-segment myocardial infarction (STEMI). There is little evidence establishing best hospital practices once the ECG has been received by the emergency department (ED). This study evaluates the use of a push notification system to reduce delays in cardiac catheterization lab (CCL) activation for prehospital STEMI.

METHODS

In this prospective before-and-after study, we collected prehospital ECGs with computer interpretation of STEMI from May 2012 to October 2013. Push notifications were implemented June 1, 2013. During the study period, we collected timestamps of when the prehospital ECG was received (email timestamp of receiving account), CCL team activation (timestamp in paging system), and patient arrival (timestamp in ED tracking board). When prehospital ECGs were received in the ED, an audible alert was played via the Vocera WiFi communication system, notifying nursing staff that an ECG was available for physician interpretation. We compared the time from receiving the ECG to activation of the CCL before and after the audible notification was implemented.

RESULTS

Of the 56 cases received, we included 45 in our analysis (20 cases with pre-arrival CCL activation and 25 with post-arrival activation). For the pre-arrival group, the interval from ECG received to CCL activation prior to implementation was 9.1 minutes with a standard deviation (SD) of 5.7 minutes. After implementation, the interval was reduced to 3.33 minutes with a SD of 1.63 minutes. Delay was decreased by 5.8 minutes (p < 0.01). Post-implementation activation times were more consistent, demonstrated by a decrease in SD from 5.75 to 1.63 min (p < 0.01). For patients with CCL activation after arrival, there was no significant change in mean delay after implementation.

CONCLUSION

In this small, single-center observational study, we demonstrated that the use of push notifications to ED staff alerting that a prehospital STEMI ECG was received correlated with a small reduction in, and increased consistency of, ED CCL activation.

Lack of Significant Coronary History and ECG Misinterpretation Are the Strongest Predictors of Undertriage in Prehospital Chest Pain

INTRODUCTION

Appropriate prehospital (PH) triage of patients with chest pain can significantly improve outcomes in acute myocardial infarction (MI). We sought to explore how PH providers triage chest pain as high versus low risk and to evaluate the accuracy and predictors of their triage decision.

METHODS

This was a prospective, observational cohort study that enrolled consecutive patients with chest pain transported by emergency medical services (EMS) to 3 tertiary care hospitals in the US. EMS triage decision (high risk versus low-risk) was defined based on the transmission of PH electrocardiogram (ECG) to a command center for medical consultation with or without catheter laboratory activation. Two independent reviewers examined in-hospital medical records to adjudicate the presence of acute MI and to audit the findings on the presenting ECG.

RESULTS

We enrolled 2,065 patients (aged 56 ± 17, 53% male) of whom 768 (37%) were triaged as high risk. Those triaged as high risk were older, were more likely to be men or have significant cardiac history, and had a higher rate of acute MI events (14.2% versus 3.5%). The sensitivity and specificity for triaging MI events as high risk were 70% and 97%, respectively. A total of 46/155 (30%) MI events were misclassified as low risk. No previous coronary revascularization and ECG misinterpretation were strong independent predictors of such undertriage.

CONCLUSIONS

PH providers have moderate sensitivity in triaging high-risk patients; 1 in 3 MI events are undertriaged. Emergency nurses need to pay special attention to patients with benign past histories during transition of care and should always reinterpret ECGs for subtle ischemic changes.

e-Transmission of ECGs for expert consultation results in improved triage and treatment of patients with acute ischaemic chest pain by ambulance paramedics

AIMS

In pre-hospital settings handled by paramedics, identification of patients with myocardial infarction (MI) remains challenging when automated electrocardiogram (ECG) interpretation is inconclusive. We aimed to identify those patients and to get them on the right track to primary percutaneous coronary intervention (PCI).

METHODS AND RESULTS

In the Rotterdam-Rijnmond region, automated ECG devices on all ambulances were supplemented with a modem, enabling transmission of ECGs for online expert interpretation. The diagnostic protocol for acute chest pain was modified and monitored for 1 year. Patients with an ECG that met the criteria for ST-elevation myocardial infarction (STEMI) were immediately transported to a PCI hospital. ECGs that did not meet the STEMI criteria, but showed total ST deviation ≥800 µv were transmitted for online interpretation by the ECG expert. Online supervision was offered as a service if ECGs showed conduction disorders, or had an otherwise 'suspicious' pattern according to the ambulance paramedics. We enrolled 1,076 patients with acute ischaemic chest pain who did not meet the automated STEMI criteria. Their mean age was 63 years; 64% were men. After online consultation, 735 (68%) patients were directly transported to a PCI hospital for further treatment. PCI within 90 min was performed in 115 patients.

CONCLUSION

During a 1-year evaluation of the modified pre-hospital triage protocol for patients with acute ischaemic chest pain, over 100 acute MI patients with an initially inconclusive ECG received primary PCI within 90 min. Because of these results, we decided to continue the operation of the modified protocol.

Impact of Regionalization of ST-Segment–Elevation Myocardial Infarction Care on Treatment Times and Outcomes for Emergency Medical Services–Transported Patients Presenting to Hospitals With Percutaneous Coronary Intervention

Background:

Regional variations in reperfusion times and mortality in patients with ST-segment–elevation myocardial infarction are influenced by differences in coordinating care between emergency medical services (EMS) and hospitals. Building on the Accelerator-1 Project, we hypothesized that time to reperfusion could be further reduced with enhanced regional efforts.

Methods:

Between April 2015 and March 2017, we worked with 12 metropolitan regions across the United States with 132 percutaneous coronary intervention–capable hospitals and 946 EMS agencies. Data were collected in the ACTION (Acute Coronary Treatment and Intervention Outcomes Network)-Get With The Guidelines Registry for quarterly Mission: Lifeline reports. The primary end point was the change in the proportion of EMS-transported patients with first medical contact to device time ≤90 minutes from baseline to final quarter. We also compared treatment times and mortality with patients treated in hospitals not participating in the project during the corresponding time period.

Results:

During the study period, 10 730 patients were transported to percutaneous coronary intervention–capable hospitals, including 974 in the baseline quarter and 972 in the final quarter who met inclusion criteria. Median age was 61 years; 27% were women, 6% had cardiac arrest, and 6% had shock on admission; 10% were black, 12% were Latino, and 10% were uninsured. By the end of the intervention, all process measures reflecting coordination between EMS and hospitals had improved, including the proportion of patients with a first medical contact to device time of ≤90 minutes (67%–74%; P <0.002), a first medical contact to device time to catheterization laboratory activation of ≤20 minutes (38%–56%; P <0.0001), and emergency department dwell time of ≤20 minutes (33%–43%; P <0.0001). Of the 12 regions, 9 regions reduced first medical contact to device time, and 8 met or exceeded the national goal of 75% of patients treated in ≤90 minutes. Improvements in treatment times corresponded with a significant reduction in mortality (in-hospital death, 4.4%–2.3%; P =0.001) that was not apparent in hospitals not participating in the project during the same time period.

Conclusions:

Organization of care among EMS and hospitals in 12 regions was associated with significant reductions in time to reperfusion in patients with ST-segment–elevation myocardial infarction as well as in in-hospital mortality. These findings support a more intensive regional approach to emergency care for patients with ST-segment–elevation myocardial infarction.

Pre-hospital detection of acute ischemic stroke secondary to emergent large vessel occlusion: lessons learned from electrocardiogram and acute myocardial infarction

Currently, there is no device capable of detecting acute ischemic stroke (AIS) secondary to emergent large vessel occlusion (ELVO) in the pre-hospital setting. The inability to reliably identify patients that would benefit from primary treatment with endovascular thrombectomy remains an important limitation to optimizing emergency medical services (EMS) triage models and time-to-treatment. Several clinical grading scales that rely solely on clinical examination have been proposed and have demonstrated only moderate predictive ability for ELVO. Consequently, a technology capable of detecting ELVO in the pre-hospital setting would be of great benefit. An analogous scenario existed decades ago, in which pre-hospital detection of acute myocardial infarction (AMI) was unreliable until the emergence of the 12-lead ECG and its adoption by EMS providers. This review details the implementation of pre-hospital ECG (PHECG) for the detection of AMI and explores how early experience with PHECG may be applied to ELVO detection devices, once they become available.

Computer-Interpreted Electrocardiograms: Benefits and Limitations

Computerized interpretation of the electrocardiogram (CIE) was introduced to improve the correct interpretation of the electrocardiogram (ECG), facilitating health care decision making and reducing costs. Worldwide, millions of ECGs are recorded annually, with the majority automatically analyzed, followed by an immediate interpretation. Limitations in the diagnostic accuracy of CIE were soon recognized and still persist, despite ongoing improvement in ECG algorithms. Unfortunately, inexperienced physicians ordering the ECG may fail to recognize interpretation mistakes and accept the automated diagnosis without criticism. Clinical mismanagement may result, with the risk of exposing patients to useless investigations or potentially dangerous treatment. Consequently, CIE over-reading and confirmation by an experienced ECG reader are essential and are repeatedly recommended in published reports. Implementation of new ECG knowledge is also important. The current status of automated ECG interpretation is reviewed, with suggestions for improvement.