Teaching medical students the QT interval and its autonomous regulation in a practical ECG course in physiology

Background: The QT interval is a well established marker for ventricular repolarization. As the QT interval is inversely regulated with the heart rate, several correction formula are established to calculate corrected QT intervals (QT c ). In practical courses of physiology, medical students learn the correct measurement of the QT interval and its autonomous regulation. For this, students record and analyse ECGs under resting conditions and during/following a workout. However, little is known about teaching QT/QT c skills in students. In particular, it is not established whether untrained medical students can determine the length of the QT interval in rest and following an exercise with sufficient accuracy to allow discussion of its physiological regulation. Methods: A full cohort of 3rd year medical students (n=380) participated in a practical ECG course. In each course (20 students), ECGs of volunteers were recorded in small groups (2-4 students) under resting conditions and immediately following standardised physical exercise (squats and push-ups). Students calculated HR, QT intervals and QT c intervals using Bazett’s exponential correction. Automatically calculated QT intervals were retrieved from the ECG devices. Following the course, QT intervals of the same ECG passages were measured by experienced physiology teachers and expert clinical electrophysiologists. To compare effects of established QT c correction formulas, QT c was calculated for all retrieved intervals using Bazett, Framingham, Fridericia and Hodges. Data were analysed on the level of individual experiments and as pooled data of student groups. Results: In 117 experiments, students reported resting HRs of ~70 bpm and corresponding QT intervals of ~360 ms. Following exercise, students found an average increase of the HR by ~75% and a decrease of the QT interval by ~25%. Using pooled data sets of each course, the overall efficacy of the Bazett formula could well be demonstrated. Students HR data were highly concordant with the data generated by the automated ECG routines, the experienced physiologists and the clinical electrophysiologists. While average QT and QT c intervals were also comparable between the groups, the variance was substantially higher in students data than in the other groups, especially in the exercise ECGs. In the HR ranges generated in the course, QT correction by Fridericia and Framingham was more efficient than by Bazett and Hodges. Conclusions: Untrained students can determine QT intervals from ECGs recorded under resting conditions and following a workout with sufficient accuracy to demonstrate the autonomous regulation of this important (bio)marker on the level of individual data sets. Due to the high individual variance of the QT c , however, we recommend QT c correction based on data pooled within each student group to discuss the relevance of the QT correction formulas. The study was financed by institutional budget. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.