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

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.

Exploring decision making 'noise' when interpreting the electrocardiogram in the context of cardiac cath lab activation

In this commentary paper, we discuss the use of the electrocardiogram to help clinicians make diagnostic and patient referral decisions in acute care settings. The paper discusses the factors that are likely to contribute to the variability and noise in the clinical decision making process for catheterization lab activation. These factors include the variable competence in reading ECGs, the intra/inter rater reliability, the lack of standard ECG training, the various ECG machine and filter settings, cognitive biases (such as automation bias which is the tendency to agree with the computer-aided diagnosis or AI diagnosis), the order of the information being received, tiredness or decision fatigue as well as ECG artefacts such as the signal noise or lead misplacement. We also discuss potential research questions and tools that could be used to mitigate this 'noise' and improve the quality of ECG based decision making.

Electrocardiogram interpretation competency among emergency nurses and emergency medical service (EMS) personnel: A cross-sectional and comparative descriptive study

AIMS

The aim of this research study was to compare electrocardiogram (ECG) interpretation competency among emergency nurses and EMS personnel.

DESIGN

A cross-sectional comparative descriptive study design was used.

METHODS

This study recruited 170 participants (105 emergency nurses and 65 EMS personnel) in northwest of Iran. Data were collected during 2018 using ECG, an interpretation competency questionnaire and analysed using SPSS V.24 through independent t test, linear regression, Pearson and Spearman correlation coefficient. A statistical significance of p < .05 was assumed.

RESULTS

The study results showed a mean score of 6.65 ± 2.16 out of 10 for emergency nurses' and 4.87 ± 1.81 for EMS personnel ECG interpretation competency (p < .05).

CONCLUSIONS

Hospital emergency nurses were more qualified to interpret the ECG than the pre-hospital emergency medical personnel (p = .792 and β (SE)) = 0.22 (0.84). Active involvement in ECG interpretation and standard continued education are needed to develop and improve the emergency nurses and EMS personnel ECG interpretation competency.