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

Impact of Quantitative ST-T Analysis in Patients With Suspected Myocardial Infarction Presenting With Right Bundle Branch Block

BACKGROUND

While left bundle branch block (LBBB) is a well-known risk feature in patients with acute myocardial infarction, and a rapid invasive management is recommended, data supporting this strategy for patients with right bundle branch block (RBBB) is less robust.

METHODS

In total, 2139 patients with suspected ST-elevation myocardial infarction (STEMI) were triaged to acute coronary angiography based on a prehospital 12-lead electrocardiogram (ECG). Sensitivity and specificity for STEMI-ECG criteria were compared in RBBB and non-BBB patients. Adjusted hazard ratios for 1-year overall mortality were computed.

RESULTS

STEMI was adjudicated in 1832/2139 (85.6%) of all patients and in 102/117 (87.2%) of RBBB patients. ST-segment deviation followed typical ST-T patterns in most RBBB patients. Of 17 RBBB patients without significant ST changes, STEMI was adjudicated in 14 (82%). Diagnostic accuracy of STEMI criteria was comparable in RBBB and non-RBBB patients for inferior (sensitivity: 51.1% vs 59.1%, P = .14; specificity: 66.7% vs 52.1%, P = .33) and anterior STEMI (sensitivity: 35.2% vs 36.6%, P = .80; specificity: 58.3% vs 49.5%, P = .55). Diagnostic performance was lower for lateral STEMI in RBBB patients (sensitivity: 14.8% vs 4.4%, P = .001; specificity: 75.0% vs 98.4%, P < .001). Patients with RBBB had higher 1-year mortality compared with non-BBB patients (hazard ratio 2.3%; 95% confidence interval, 1.25-4.21.

CONCLUSION

ECG criteria used for detection of STEMI showed comparable diagnostic accuracy in RBBB and non-BBB patients. However, STEMI was frequently present in RBBB patients not fulfilling diagnostic ECG criteria. RBBB patients showed poorer outcome after 1 year. Consequently, the presence of RBBB in suspected STEMI cases signifies a high-risk feature, aligning with established guidelines.

Attitude of Emergency Medical Technicians Toward Electrocardiogram - Needs of Electrocardiogram Training Courses and Other Learning Opportunities

BACKGROUND

Early intervention in prehospital settings is important for treating patients with acute coronary syndrome (ACS). Emergency medical technicians (EMTs) are the essential first responders who treat these patients, and their current attitudes towards electrocardiograms (ECGs) have not been identified. This study investigated the awareness of EMTs of ECGs to shorten hospital arrival time, improving patient prognosis.Methods and Results: An anonymous questionnaire survey, with 27 statements about ECG and ACS response, was administered to 395 EMTs. The statements were related to interest, motivation, learning status, confidence, and norms pertaining to ECGs, a sense of perceived behavioral control, and training courses. The primary outcome was the perception of EMTs that their interpretation of an ECG affected patient prognosis (Statement [S] 1). Participants assigned scores for each statement using a scale ranging from 1 (strongly disagree) to 10 (strongly agree). The mean score for S1 was 7.09. Mean scores for statements regarding confidence and learning status were 3.96 and 3.53, respectively. The participants had a positive impression of training courses (score >5.5).

CONCLUSIONS

The EMTs experience was that their interpretation of an ECG could affect the prognosis of patients with ACS. Conversely, they lacked confidence reading ECGs, but were willing to attend ECG training courses. Therefore, efficient training programs need to be established to achieve a better prognosis for ACS patients.

Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

The blockage of blood in the vessels results in heart attacks and cardiac arrests which are referred to as myocardial infarction. Early detection of such infarction is feasible through percutaneous coronary intervention (PCI) based on electrocardiogram (ECG) monitoring. The variations in blood flow and clot are precisely observed through periodic ECG monitoring and previous correlations. This article introduces a concentrated value assessment model (CVAM) for determining PCI levels in treating myocardial infarction. The ECG observations from the previous observation sessions are accumulated and organized for validating the infarction rate. This requires the accompanying concentrated data like a heartbeat, blood pressure, and flow rate observed in different sessions. Based on the session observation and normal data correlation, the PCI level is recommended for the patient. In this analysis process, the value shift due to blocks and high and low blood pressure is accounted for through the deep learning paradigm. This paradigm correlates the above factors with the ECG values for precisely determining PCI from the last known concentration. The learning paradigm is trained based on session and normal observation data through different intervals. This model is validated using the metrics precision, analysis rate, diagnosis recommendation, and complexity.