Cardiac auscultation via simulation: a survey of the approach of UK medical schools

Comparison of Cardiac Auscultation Features on Four Different Simulation Mannequins Performed by Pediatric Residents

INTRODUCTION

Cardiac murmurs are a common problem in pediatric clinical practice. Studies demonstrated low accuracy in detecting and diagnosing various cardiac murmurs at all levels of medical training. So, supplementary training methods started to evolve, including simulation for auscultation skills training. Over the years, mannequins have evolved with different types of technology. Therefore, we decided to compare cardiac auscultation accuracy among high-fidelity mannequins as the primary objective and compare the performance of various postgraduate-level residents as a secondary objective.

METHOD

Pediatric residents at King Abdulaziz University Hospital were given a lecture on the basics of cardiac auscultation and then requested to auscultate four mannequins, namely SimJumior® (Laerdal Medical, Stavanger, Norway), SimBaby™ (Laerdal Medical), Pediatric HAL® (Gaumard Scientific, Miami, FL, USA), and Cardiac Patient Simulator K-Plus (Kyoto Kagaku Co. Ltd., Kyoto, Japan). The accuracies of murmur type, diagnosis, and auscultation time were compared.  Results: A total of 56 pediatric residents were enrolled. Median murmur accuracy ranged from 50% to 53% (p-value 0.79), and median diagnosis accuracy ranged from 33% to 36% (p-value 0.77), with a nonsignificant difference between mannequins. Comparing resident levels in all mannequins, median murmur accuracy ranged from 49% to 56% (p-value 0.70), and median diagnosis accuracy ranged from 29% to 41% (p-value 0.09). While the median average auscultation time was between 41 and 50 seconds (p-value 0.34).

CONCLUSION

Auscultation skills can be taught through simulation on any mannequin used in this comparison, not necessarily the cardiac one. For better accuracy, future comparisons might include more advanced cardiac mannequins based on cardiac auscultation expertise (i.e., consultant level). The introduction of an auscultation program from the undergraduate level throughout the training process and monitoring of these skills are mandated.

The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification

Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can support physicians in their decisions. Unfortunately, the application of such systems in clinical trials is still minimal since most of them only aim to detect the presence of extra or abnormal waves in the phonocardiogram signal, i.e., only a binary ground truth variable (normal vs abnormal) is provided. This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e.g., cardiac murmurs) exists. To pave the way to more effective research on healthcare recommendation systems based on auscultation, our team has prepared the currently largest pediatric heart sound dataset. A total of 5282 recordings have been collected from the four main auscultation locations of 1568 patients, in the process, 215780 heart sounds have been manually annotated. Furthermore, and for the first time, each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading, and quality. In addition, the auscultation locations where the murmur is present were identified as well as the auscultation location where the murmur is detected more intensively. Such detailed description for a relatively large number of heart sounds may pave the way for new machine learning algorithms with a real-world application for the detection and analysis of murmur waves for diagnostic purposes.

Use of Simulation Based Training to Enhance Cardiac Auscultation Proficiency

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INTRODUCTION

Cardiopulmonary auscultation is an important skill for medical professionals with deficiencies being well documented with broad implications on healthcare. Providing ideal bedside auscultatory teaching presents many difficulties. Simulation has been used as a means to improve cardiac auscultatory training and circumvent some of these limitations.

METHODS

We studied the use of Harvey © simulation in teaching cardiac murmurs and whether there was improvement in short term knowledge, as well as testing long term knowledge retention, as measured through a standardized test. From 2014-2019, 124 medical students in their 2 nd and 3 rd year of school (during the clinical portion of medical school curriculum) rotating through an Internal Medicine rotation completed a 2 hour training course on cardiac auscultation using Harvey © to identify six common cardiac murmurs. The session contained a pretest, didactic session, and posttest.

RESULTS

124 students participated in the auscultatory training session. Of them, 42 (34%) underwent the session a second time at an average of 1.29 months from their first session. There was statistically significant improvement between tests. Notably, the most often missed murmurs were mitral stenosis and benign (innocent) flow murmur.

DISCUSSION/CONCLUSIONS

As shown in our study, simulation based cardiac auscultatory education is feasible and likely beneficial in medical education as it can be delivered to a large group of trainees and overcomes the challenges of bedside teaching.

Computer model for the cardiovascular system: development of an e-learning tool for teaching of medical students

BACKGROUND

This study combined themes in cardiovascular modelling, clinical cardiology and e-learning to create an on-line environment that would assist undergraduate medical students in understanding key physiological and pathophysiological processes in the cardiovascular system.

METHODS

An interactive on-line environment was developed incorporating a lumped-parameter mathematical model of the human cardiovascular system. The model outputs were used to characterise the progression of key disease processes and allowed students to classify disease severity with the aim of improving their understanding of abnormal physiology in a clinical context. Access to the on-line environment was offered to students at all stages of undergraduate training as an adjunct to routine lectures and tutorials in cardiac pathophysiology. Student feedback was collected on this novel on-line material in the course of routine audits of teaching delivery.

RESULTS

Medical students, irrespective of their stage of undergraduate training, reported that they found the models and the environment interesting and a positive experience. After exposure to the environment, there was a statistically significant improvement in student performance on a series of 6 questions based on cardiovascular medicine, with a 33% and 22% increase in the number of questions answered correctly, p < 0.0001 and p < 0.001 respectively.

CONCLUSIONS

Considerable improvement was found in students' knowledge and understanding during assessment after exposure to the e-learning environment. Opportunities exist for development of similar environments in other fields of medicine, refinement of the existing environment and further engagement with student cohorts. This work combines some exciting and developing fields in medical education, but routine adoption of these types of tool will be possible only with the engagement of all stake-holders, from educationalists, clinicians, modellers to, most importantly, medical students.