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Cardiac Auscultation Lab Using a Heart Sounds Auscultation Simulation Manikin.

ABSTRACT: Introduction:Cardiac auscultation skills are essential to the development of a competent physician. We created a hypothesis-driven cardiac auscultation laboratory session utilizing a high-fidelity simulator to teach these skills to second-year medical students at our institution. This program was grounded in deliberate practice opportunities to aid in the acquisition of cardiac auscultation skills. Methods:This session aimed to help students identify and discriminate between normal and pathologic heart sounds in the context of a clinical vignette. Faculty facilitators guided students through unknown patient cases and utilized the auscultation manikin to simulate corresponding heart sounds. Time was also allotted for students to auscultate the manikins and practice their cardiac physical examination skills. Results:This program has been in place at our institution since 2016 and has been well received by students and facilitators. Since its initial introduction in 2016, 183 second-year medical students have completed the cardiac auscultation lab session each year, for a total of 549 students. Evaluations of the session have improved as faculty have become more familiar with the mechanics of operating the auscultation manikin. Discussion:The cardiac exam and heart sounds lab can be adapted to any simulator model that is capable of producing heart sounds and can be done in a large- or small-group format. Enough time should be allotted to adequately work through all components of the laboratory. Student and faculty feedback has helped us further refine the session since its initial introduction to the curriculum.

SUBMITTER: Quinn A

PROVIDER: S-EPMC6974355 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Cardiac Auscultation Lab Using a Heart Sounds Auscultation Simulation Manikin.

Quinn Antonia A Kaminsky Jennifer J Adler Andrew A Eisner Shirley S Ovitsh Robin R

MedEdPORTAL : the journal of teaching and learning resources 20191018

<h4>Introduction</h4>Cardiac auscultation skills are essential to the development of a competent physician. We created a hypothesis-driven cardiac auscultation laboratory session utilizing a high-fidelity simulator to teach these skills to second-year medical students at our institution. This program was grounded in deliberate practice opportunities to aid in the acquisition of cardiac auscultation skills.<h4>Methods</h4>This session aimed to help students identify and discriminate between norma ...[more]

PMID: 31976362

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Project description:BackgroundCardiac auscultation is a cost-effective, noninvasive screening tool that can provide information about cardiovascular hemodynamics and disease. However, with advances in imaging and laboratory tests, the importance of cardiac auscultation is less appreciated in clinical practice. The widespread use of smartphones provides opportunities for nonmedical expert users to perform self-examination before hospital visits.ObjectiveThe objective of our study was to assess the feasibility of cardiac auscultation using smartphones with no add-on devices for use at the prehospital stage.MethodsWe performed a pilot study of patients with normal and pathologic heart sounds. Heart sounds were recorded on the skin of the chest wall using 3 smartphones: the Samsung Galaxy S5 and Galaxy S6, and the LG G3. Recorded heart sounds were processed and classified by a diagnostic algorithm using convolutional neural networks. We assessed diagnostic accuracy, as well as sensitivity, specificity, and predictive values.ResultsA total of 46 participants underwent heart sound recording. After audio file processing, 30 of 46 (65%) heart sounds were proven interpretable. Atrial fibrillation and diastolic murmur were significantly associated with failure to acquire interpretable heart sounds. The diagnostic algorithm classified the heart sounds into the correct category with high accuracy: Galaxy S5, 90% (95% CI 73%-98%); Galaxy S6, 87% (95% CI 69%-96%); and LG G3, 90% (95% CI 73%-98%). Sensitivity, specificity, positive predictive value, and negative predictive value were also acceptable for the 3 devices.ConclusionsCardiac auscultation using smartphones was feasible. Discrimination using convolutional neural networks yielded high diagnostic accuracy. However, using the built-in microphones alone, the acquisition of reproducible and interpretable heart sounds was still a major challenge.Trial registrationClinicalTrials.gov NCT03273803; https://clinicaltrials.gov/ct2/show/NCT03273803 (Archived by WebCite at http://www.webcitation.org/6x6g1fHIu).

Project description:OBJECTIVES: The current review examines the effectiveness of simulation-based medical education (SBME) for training health professionals in cardiac physical examination and examines the relative effectiveness of key instructional design features. METHODS: Data sources included a comprehensive, systematic search of MEDLINE, EMBASE, CINAHL, PsychINFO, ERIC, Web of Science, and Scopus through May 2011. Included studies investigated SBME to teach health profession learners cardiac physical examination skills using outcomes of knowledge or skill. We carried out duplicate assessment of study quality and data abstraction and pooled effect sizes using random effects. RESULTS: We identified 18 articles for inclusion. Thirteen compared SBME to no-intervention (either single group pre-post comparisons or SBME added to other instruction common to all learners, such as traditional bedside teaching), three compared SBME to other educational interventions, and two compared two SBME interventions. Meta-analysis of the 13 no-intervention comparison studies demonstrated that simulation-based instruction in cardiac auscultation was effective, with pooled effect sizes of 1.10 (95 % CI 0.49-1.72; p?<?0.001; I(2)?=?92.4 %) for knowledge outcomes and 0.87 (95 % CI 0.52-1.22; p?<?0.001; I(2) = 91.5 %) for skills. In sub-group analysis, hands-on practice with the simulator appeared to be an important teaching technique. Narrative review of the comparative effectiveness studies suggests that SBME may be of similar effectiveness to other active educational interventions, but more studies are required. LIMITATIONS: The quantity of published evidence and the relative lack of comparative effectiveness studies limit this review. CONCLUSIONS: SBME is an effective educational strategy for teaching cardiac auscultation. Future studies should focus on comparing key instructional design features and establishing SBME's relative effectiveness compared to other educational interventions.

Project description:IntroductionThe LuboTM collar is a cervical motion restriction device featuring a unique external jaw-thrust mechanism designed to provide non-invasive airway patency. In addition, tracheal intubation is facilitated by releasing an anterior chin strap; this allows better mouth opening than the previous generation of semi-rigid cervical collars. This study aimed to compare tracheal intubation using the LuboTM collar combined with manual in-line stabilization (MILS) to intubation with MILS alone. The primary outcome was the time to successful intubation. Secondary outcomes compared intubation success rate, Cormack-Lehane grade, ease of intubation and dental trauma.MethodsA randomized, cross-over, equivalence study was performed. Eighty full-time physician anaesthesia providers were recruited. Participants performed tracheal intubation using direct laryngoscopy on a manikin under two different scenarios: with the LuboTM collar and MILS applied, and with MILS and no cervical collar. The time to successful intubation was measured and compared using two-one-sided and paired t-tests.ResultsIntubation times fell well within the a priori equivalence limits of 10 seconds, with a mean difference (95% CI) of 0.52 seconds (-1.30 to 2.56). There was no significant difference in intubation time with the LuboTM collar (mean [SD] 19.2 [4.5] seconds) compared to the MILS alone group (19.7 [5.2] seconds). The overall success rate was 98.7% in the Lubo group and 100% in the MILS group. Adequate laryngoscopy views (Cormack-Lehane grades I to IIb) were equivalent between groups (Lubo 92.5% versus MILS alone 93.7%).ConclusionIn this manikin-based study, the time to intubation with the LuboTM collar and MILS applied was equivalent to time to intubation with MILS alone, with similar intubating conditions. Thus, the LuboTM collar and MILS may simplify airway management by reducing the number of steps required to perform intubation in patients requiring cervical motion restriction.

Project description:Objective:Tracheal sounds were used to detect apnea on various occasions. However, ambient noises can contaminate tracheal sounds which result in poor performance of apnea detection. The objective of this paper was to apply the adaptive filtering (AF) algorithm to improve the quality of tracheal sounds and examine the accuracy of the apnea detection algorithm using tracheal sounds after AF. Method:Tracheal sounds were acquired using a primary microphone encased in a plastic bell, and the ambient noises were collected using a reference microphone resting outside the plastic bell in quiet and noisy environments, respectively. Simultaneously, the flow pressure signals and thoracic and abdominal movement were obtained as the standard signals to determine apnea events. Then, the normalized least mean square (NLMS) AF algorithm was applied to the tracheal sounds mixed with noises. Finally, the algorithm of apnea detection was used to the tracheal sounds with AF and the tracheal sounds without AF. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and Cohen's kappa coefficient of apnea detection were calculated. Results:Forty-six healthy subjects, aged 18-35 years and with BMI < 21.4, were included in the study. The apnea detection performance using tracheal sounds was as follows: in the quiet environment, the tracheal sounds without AF detected apnea with 97.2% sensitivity, 99.9% specificity, 99.8% PPV, 99.4% NPV, 99.5% accuracy, and 0.982 kappa coefficient. The tracheal sounds with AF detected apnea with 98.2% sensitivity, 99.9% specificity, 99.4% PPV, 99.6% NPV, 99.6% accuracy, and 0.985 kappa coefficient. While in the noisy environment, the tracheal sounds without AF detected apnea with 81.1% sensitivity, 96.9% specificity, 85.1% PPV, 96% NPV, 94.2% accuracy, and 0.795 kappa coefficient and the tracheal sounds with AF detected apnea with 91.5% sensitivity, 97.4% specificity, 88.4% PPV, 98.2% NPV, 96.4% accuracy, and 0.877 kappa coefficient. Conclusion:The performance of apnea detection using tracheal sounds with the NLMS AF algorithm in the noisy environment proved to be accurate and reliable. The AF technology could be applied to the respiratory monitoring using tracheal sounds.

Project description:The precordial mechanical vibrations generated by cardiac contractions have a rich frequency spectrum. While the lowest frequencies can be palpated, the higher infrasonic frequencies are usually captured by the seismocardiogram (SCG) signal and the audible ones correspond to heart sounds. Forcecardiography (FCG) is a non-invasive technique that measures these vibrations via force sensing resistors (FSR). This study presents a new piezoelectric sensor able to record all heart vibrations simultaneously, as well as a respiration signal. The new sensor was compared to the FSR-based one to assess its suitability for FCG. An electrocardiogram (ECG) lead and a signal from an electro-resistive respiration band (ERB) were synchronously acquired as references on six healthy volunteers (4 males, 2 females) at rest. The raw signals from the piezoelectric and the FSR-based sensors turned out to be very similar. The raw signals were divided into four components: Forcerespirogram (FRG), Low-Frequency FCG (LF-FCG), High-Frequency FCG (HF-FCG) and heart sounds (HS-FCG). A beat-by-beat comparison of FCG and ECG signals was carried out by means of regression, correlation and Bland-Altman analyses, and similarly for respiration signals (FRG and ERB). The results showed that the infrasonic FCG components are strongly related to the cardiac cycle (R 2 > 0.999, null bias and Limits of Agreement (LoA) of ± 4.9 ms for HF-FCG; R 2 > 0.99, null bias and LoA of ± 26.9 ms for LF-FCG) and the FRG inter-breath intervals are consistent with ERB ones (R 2 > 0.99, non-significant bias and LoA of ± 0.46 s). Furthermore, the piezoelectric sensor was tested against an accelerometer and an electronic stethoscope: synchronous acquisitions were performed to quantify the similarity between the signals. ECG-triggered ensemble averages (synchronized with R-peaks) of HF-FCG and SCG showed a correlation greater than 0.81, while those of HS-FCG and PCG scored a correlation greater than 0.85. The piezoelectric sensor demonstrated superior performances as compared to the FSR, providing more accurate, beat-by-beat measurements. This is the first time that a single piezoelectric sensor demonstrated the ability to simultaneously capture respiration, heart sounds, an SCG-like signal (i.e., HF-FCG) and the LF-FCG signal, which may provide information on ventricular emptying and filling events. According to these preliminary results the novel piezoelectric FCG sensor stands as a promising device for accurate, unobtrusive, long-term monitoring of cardiorespiratory functions and paves the way for a wide range of potential applications, both in the research and clinical fields. However, these results should be confirmed by further analyses on a larger cohort of subjects, possibly including also pathological patients.

Project description:BackgroundCardiac auscultation remains an efficient and accessible diagnostic tool, especially in resource-limited countries where modern diagnostic devices like cardiac ultrasound are expensive and difficult to access. However, cardiac auscultation skills of medical students and physicians are declining, mainly because of an ineffective teaching method for this technique. The objective of this study is to evaluate the effect of a digitally enhanced cardiac auscultation learning method on participants' theoretical knowledge and auscultation skills.MethodsThis will be a controlled study with two parallel arms (1:1). Participants (fourth-year medical students) will be divided into two groups: an intervention group (receiving additional lectures, clinical internship and audio listening sessions) and a control group (receiving additional lectures and clinical internship). At the beginning of the study, all participants will undergo a pre-test that consist of two parts: a knowledge assessment based on multiple-choice questions and a skills assessment based on recognition of cardiac sounds from audio files. Thereafter, three specific additional lectures on cardiac auscultation will be delivered and all participants will take part in their official clinical internship. During these clinical internships (eight weeks), participants of the intervention group will be invited to two listening sessions based on five digital recordings of heart sounds. At the end of the clinical internship, all participants will be invited to a post-test to evaluate their knowledge, skills and satisfaction according to their learning method. The main outcome will be the participants' knowledge progression. The other outcomes will be the participants' skills progression, participants' total progression and satisfaction. Data will be collected and analyzed in per protocol.DiscussionThis study could contribute to the development of a learning method that takes into account the advantages of the conventional method and the contribution of digital technology. Positive results could lead to improved cardiac auscultation skills among health professionals, especially in developing countries.Trial registrationThe trial is registered on the Pan-African Clinical Trials Registry ( http://www.pactr.org ) under unique identification number: PACTR202001504666847 , registered the 29 November 2019.

Project description:IntroductionThe study aimed to evaluate visualization-based training's effects on lung auscultation during clinical clerkship (CC) in the Department of Respiratory Medicine on student skills and confidence.MethodsThe study period was December 2020-November 2021. Overall, 65 students attended a lecture on lung auscultation featuring a simulator (Mr. Lung™). Among them, 35 (visualization group) received additional training wherein they were asked to mentally visualize lung sounds using a graphical visualized lung sounds diagram as an example. All students answered questions on their self-efficacy regarding lung auscultation before and after four weeks of CC. They also took a lung auscultation test with the simulator at the beginning of CC (pre-test) and on the last day of the third week (post-test) (maximum score: 25). We compared the answers in the questionnaire and the test scores between the visualization group and students who only attended the lecture (control group, n = 30). The Wilcoxon signed-rank test and analysis of covariance were used to compare the answers to the questionnaire about confidence in lung auscultation and the scores of the lung auscultation tests before and after the training.ResultsConfidence in auscultation of lung sounds significantly increased in both groups (five-point Likert scale, visualization group: pre-questionnaire median 1 [Interquartile range 1] to post-questionnaire 3 [1], p<0.001; control group: 2 [1] to 3 [1], p<0.001) and was significantly higher in the visualization than in the control group. Test scores increased in both groups (visualization group: pre-test 11 [2] to post-test 15 [4], p<0.001; control group: 11 [5] to 14 [4], p<0.001). However, there were no differences between both groups' pre and post-tests scores (p = 0.623).ConclusionVisualizing lung sounds may increase medical students' confidence in their lung auscultation skills; this may reduce their resistance to lung auscultation and encourage the repeated auscultation necessary to further improve their long-term auscultation abilities.

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Cardiac Auscultation Lab Using a Heart Sounds Auscultation Simulation Manikin.

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Cardiac Auscultation Lab Using a Heart Sounds Auscultation Simulation Manikin.

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