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

Can triage nurses accurately interpret the electrocardiogram in the emergency department to predict acute cardiovascular events? A prospective observational study

AIMS

The prompt recording of the electrocardiogram (ECG) and its correct interpretation is crucial to the management of patients who present to the emergency department (ED) with cardiovascular symptoms. Since triage nurses represent the first healthcare professionals evaluating the patient, improving their ability in interpreting the ECG could have a positive impact on clinical management. This real-world study investigates whether triage nurses can accurately interpret the ECG in patients presenting with cardiovascular symptoms.

DESIGN

Prospective, single-centre observational study conducted in a general ED of General Hospital of Merano in Italy.

METHODS

For all patients included, the triage nurses and the emergency physicians were asked to independently interpret and classify the ECGs answering to dichotomous questions. We correlated the interpretation of the ECG made by the triage nurses with the occurrence of acute cardiovascular events. The inter-rater agreement in ECG interpretation between physicians and triage nurses was evaluated with Cohen's kappa analysis.

RESULTS

Four hundred and ninety-one patients were included. The inter-rater agreement between triage nurses and physicians in classifying an ECG as abnormal was good. Patients who developed an acute cardiovascular event were 10.6% (52/491), and in 84.6% (44/52) of them, the nurse accurately classified the ECG as abnormal, with a sensitivity of 84.6% and a specificity of 43.5%.

CONCLUSION

Triage nurses have a moderate ability in identifying alterations in specific components of the ECG but a good ability in identifying patterns indicative of time-dependent conditions correlated with major acute cardiovascular events.

IMPACT FOR NURSING

Triage nurses can accurately interpret the ECG in the ED to identify patients at high risk of acute cardiovascular events.

REPORTING METHOD

The study was reported according to the STROBE guidelines.

NO PATIENT OR PUBLIC CONTRIBUTION

The study did not involve any patients during its conduction.

Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients

Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially convolutional deep models have shown promise in electrocardiogram (ECG) classification, but there is a lack of high-performing models for the diagnosis of myocardial infarction in real-world scenarios. We aimed to train and validate a deep learning model using ECGs to predict myocardial infarction in real-world emergency department patients. We studied emergency department patients in the Stockholm region between 2007 and 2016 that had an ECG obtained because of their presenting complaint. We developed a deep neural network based on convolutional layers similar to a residual network. Inputs to the model were ECG tracing, age, and sex; and outputs were the probabilities of three mutually exclusive classes: non-ST-elevation myocardial infarction (NSTEMI), ST-elevation myocardial infarction (STEMI), and control status, as registered in the SWEDEHEART and other registries. We used an ensemble of five models. Among 492,226 ECGs in 214,250 patients, 5,416 were recorded with an NSTEMI, 1,818 a STEMI, and 485,207 without a myocardial infarction. In a random test set, our model could discriminate STEMIs/NSTEMIs from controls with a C-statistic of 0.991/0.832 and had a Brier score of 0.001/0.008. The model obtained a similar performance in a temporally separated test set of the study sample, and achieved a C-statistic of 0.985 and a Brier score of 0.002 in discriminating STEMIs from controls in an external test set. We developed and validated a deep learning model with excellent performance in discriminating between control, STEMI, and NSTEMI on the presenting ECG of a real-world sample of the important population of all-comers to the emergency department. Hence, deep learning models for ECG decision support could be valuable in the emergency department.

Utility of prehospital electrocardiogram interpretation in ST-segment elevation myocardial infarction utilizing computer interpretation and transmission for interventional cardiologist consultation

Objectives

We examined the appropriateness of prehospital cardiac catheter laboratory activation (CCL‐A) in ST‐segment elevation myocardial infarction (STEMI) utilizing the University of Glasgow algorithm (UGA) and remote interventional cardiologist consultation.

Background

The incremental benefit of prehospital electrocardiogram (PH‐ECG) transmission on the diagnostic accuracy and appropriateness of CCL‐A has been examined in a small number of studies with conflicting results.

Methods

We identified consecutive PH‐ECG transmissions between June 2, 2010 and October 6, 2016. Blinded adjudication of ECGs, appropriateness of CCL‐A, and index diagnoses were performed using the fourth universal definition of MI. The primary outcome was the appropriate CCL‐A rate. Secondary outcomes included rates of false‐positive CCL‐A, inappropriate CCL‐A, and inappropriate CCL nonactivation.

Results

Among 1088 PH‐ECG transmissions, there were 565 (52%) CCL‐As and 523 (48%) CCL nonactivations. The appropriate CCL‐A rate was 97% (550 of 565 CCL‐As), of which 4.9% (n = 27) were false‐positive. The inappropriate CCL‐A rate was 2.7% (15 of 565 CCL‐As) and the inappropriate CCL nonactivation rate was 3.6% (19 of 523 CCL nonactivations). Reasons for appropriate CCL nonactivation (n = 504) included nondiagnostic ST‐segment elevation (n = 128, 25%), bundle branch block (n = 132, 26%), repolarization abnormality (n = 61, 12%), artefact (n = 72, 14%), no ischemic symptoms (n = 32, 6.3%), severe comorbidities (n = 26, 5.2%), transient ST‐segment elevation (n = 20, 4.0%), and others.

Conclusions

PH‐ECG interpretation utilizing UGA with interventional cardiologist consultation accurately identified STEMI with low rates of inappropriate and false‐positive CCL‐As, whereas using UGA alone would have almost doubled CCL‐As. The benefits of cardiologist consultation were identifying “masquerading” STEMI and avoiding unnecessary CCL‐As.

Late Outcomes of Patients With Prehospital ST-Segment Elevation and Appropriate Cardiac Catheterization Laboratory Nonactivation

Background Patients with suspected ST-segment-elevation myocardial infarction (STEMI) and cardiac catheterization laboratory nonactivation (CCL-NA) or cancellation have reportedly similar crude and higher adjusted risks of death compared with those with CCL activation, though reasons for these poor outcomes are not clear. We determined late clinical outcomes among patients with prehospital ECG STEMI criteria who had CCL-NA compared with those who had CCL activation. Methods and Results We identified consecutive prehospital ECG transmissions between June 2, 2010 to October 6, 2016. Diagnoses according to the Fourth Universal Definition of myocardial infarction (MI), particularly rates of myocardial injury, were adjudicated. The primary outcome was all-cause death. Secondary outcomes included cardiovascular death/MI/stroke and noncardiovascular death. To explore competing risks, cause-specific hazard ratios (HRs) were obtained. Among 1033 included ECG transmissions, there were 569 (55%) CCL activations and 464 (45%) CCL-NAs (1.8% were inappropriate CCL-NAs). In the CCL activation group, adjudicated index diagnoses included MI (n=534, 94%, of which 99.6% were STEMI and 0.4% non-STEMI), acute myocardial injury (n=15, 2.6%), and chronic myocardial injury (n=6, 1.1%). In the CCL-NA group, diagnoses included MI (n=173, 37%, of which 61% were non-STEMI and 39% STEMI), chronic myocardial injury (n=107, 23%), and acute myocardial injury (n=47, 10%). At 2 years, the risk of all-cause death was higher in patients who had CCL-NA compared with CCL activation (23% versus 7.9%, adjusted risk ratio, 1.58, 95% CI, 1.24-2.00), primarily because of an excess in noncardiovascular deaths (adjusted HR, 3.56, 95% CI, 2.07-6.13). There was no significant difference in the adjusted risk for cardiovascular death/MI/stroke between the 2 groups (HR, 1.23, 95% CI, 0.87-1.73). Conclusions CCL-NA was not primarily attributable to missed STEMI, but attributable to "masquerading" with high rates of non-STEMI and myocardial injury. These patients had worse late outcomes than patients who had CCL activation, mainly because of higher rates of noncardiovascular deaths.

ST-Elevation Myocardial Infarction Complicated by Out-of-Hospital Cardiac Arrest

5-10% of ST-elevated myocardial infarctions (STEMI) present with out-of-hospital cardiac arrest (OHCA). Although this subgroup of patients carries the highest in-hospital mortality among the STEMI population, it is the least likely to undergo coronary angiography and revascularization. Due to the concomitant neurologic injury, patients with OHCA STEMI require prolonged hospitalization and adjustments to standard MI management. This review systematically assesses the course of patients with OHCA STEMI from development of the arrest to hospital discharge, assesses the limiting factors for their treatment access, and presents the evidence-based optimal intervention strategy for this high-risk MI population.