Literature DB >> 19557969

Paramedic identification of electrocardiograph J-point and ST-segments.

Brett Williams1, Mal Boyle, Bill Lord.   

Abstract

INTRODUCTION: Correct identification of the J-Point and ST-segment on an electrocardiograph (ECG) is an important clinical skill for paramedics working in acute healthcare settings. The skill of ECG analysis and interpretation is known to be challenging to learn and often is a difficult concept to teach.
OBJECTIVES: The objective of the study was to determine if undergraduate paramedic students could accurately identify ECG ST-segment elevation and J-Point location.
METHODS: A convenience sample of undergraduate paramedic students (n = 148) was provided with four enlarged ECGs (ECG1-4) that illustrated different levels, patterns, and characteristics of ST-segment elevation. Participants were asked to identify whether ST-elevation was present, and if so, height in millimeters (mm) and the correct location of the J-Point.
RESULTS: There were significant variations in students' accuracy with both J-Point and ST-segment determination. Eleven (10%) students correctly identified the ST-segment being present in all ECGs. Also, ECG 2 reflected 6 mm of ST-elevation; however, only one student correctly identified this. Overall the students were 0.55 mm (95% CI = 0.29-0.81 mm, range = -6.5-5.8 mm) from the J-point on the horizontal and -0.18 mm (95% CI = -0.31-0.04 mm, range = -2.8-2.3 mm) on the vertical axis.
CONCLUSIONS: Undergraduate paramedic students recognize ST-segment elevation. However, inaccuracies occurred with measurements of ST-segment and precise location of J-Points. Errors in ECG analysis may reflect weaknesses in teaching this skill. Consideration should be given to the design of an educational program that can reliably improve performance of this skill.

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Year:  2008        PMID: 19557969     DOI: 10.1017/s1049023x00006361

Source DB:  PubMed          Journal:  Prehosp Disaster Med        ISSN: 1049-023X            Impact factor:   2.040


  1 in total

1.  Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder.

Authors:  Jong-Hwan Jang; Tae Young Kim; Hong-Seok Lim; Dukyong Yoon
Journal:  PLoS One       Date:  2021-12-01       Impact factor: 3.240

  1 in total

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