Point-of-care cardiac ultrasound in acute medicine--the quick scan

Establishing a Novel Diagnostic Framework Using Handheld Point-of-Care Focused-Echocardiography (HoPE) for Acute Left-Sided Cardiac Valve Emergencies: A Bayesian Approach for Emergency Physicians in Resource-Limited Settings

Acute severe cardiac valve emergencies, such as acute severe mitral regurgitation (AMR) and acute severe aortic regurgitation (AAR), present significant challenges in terms of diagnosis and management. Handheld point-of-care ultrasound devices have emerged as potentially pivotal tools in ensuring the prompt and accurate diagnosis of these left-sided valve emergencies by emergency physicians, particularly in resource-limited settings. Despite the increased utilisation of point-of-care ultrasound by emergency physicians for the management of patients in states of acute cardiorespiratory failure, current diagnostic protocols cannot perform sufficient quantitative assessments of the left-sided cardiac valves. This review elucidates and evaluates the diagnostic utility of handheld point-of-care focused-echocardiography (HoPE) in native AMR and AAR by reviewing the relevant literature and the use of clinical case examples from the Emergency Department at Port Shepstone Regional Hospital (PSRH-ED)-a rural, resource-limited hospital located in KwaZulu-Natal, South Africa. Combining the findings of the review and clinical case illustrations, this review proceeds to synthesise a novel, Bayesian-inspired, iterative diagnostic framework that integrates HoPE into the evaluation of patients with acute cardiorespiratory failure and suspected severe left-sided valve lesions.

Utility of Non-invasive Cardiac Imaging Assessment in Coronavirus Disease 2019

Coronavirus disease 2019 (COVID-19) was initially regarded as a disease of the lungs, which manifests as an acute respiratory illness and pneumonia, although more recently cardiac complications have been well-characterised. Serological cardiac biomarkers have been used to define acute myocardial injury, with significant elevation of high-sensitivity cardiac troponin (hs-cTn) associated with poor prognosis. Accordingly, 20-25% patients with acute myocardial injury (as defined by an elevated hs-cTn greater than the 99th percentile) have clinical signs of heart failure and increased mortality. An important outstanding clinical question is how best to determine the extent and nature of cardiac involvement in COVID-19. Non-invasive cardiac imaging has a well-established role in assessing cardiac structure and function in a wide range of cardiac diseases. It offers the potential to differentiate between direct and indirect COVID-19 effects upon the heart, providing incremental diagnostic and prognostic utility beyond the information yielded by elevated cardiac biomarkers in isolation. This review will focus on the non-invasive imaging assessment of cardiac involvement in COVID-19.

Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings

Valvular heart diseases (VHD) are one of the major causes of cardiovascular diseases that are having high mortality rates worldwide. The early diagnosis of VHD prevents the development of cardiac diseases and allows for optimum medication. Despite of the ability of current gold standards in identifying VHD, they still lack the required accuracy and thus, several cases go misdiagnosed. In this vein, a study is conducted herein to investigate the efficiency of deep learning models in identifying VHD through phonocardiography (PCG) recordings. PCG heart sounds were obtained from an open-access data-set representing normal heart sounds along with four major VHD; namely aortic stenosis (AS), mitral stenosis (MS), mitral regurgitation (MR), and mitral valve prolapse (MVP). A total of 1,000 patients were involved in the study with 200 recordings for each class. All recordings were initially trimmed to have 9,600 samples ensuring their coverage of at least 1 cardiac cycle. In addition, they were pre-processed by applying maximal overlap discrete wavelet transform (MODWT) smoothing algorithm and z-score normalization. The neural network architecture was designed to reduce the complexity often found in literature and consisted of a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) based on Bi-directional long short-term memory (BiLSTM). The model was trained and tested following a k-fold cross-validation scheme of 10-folds utilizing the CNN-BiLSTM network as well as the CNN and BiLSTM, individually. The highest performance was achieved using the CNN-BiLSTM network with an overall Cohen's kappa, accuracy, sensitivity, and specificity of 97.87%, 99.32%, 98.30%, and 99.58%, respectively. In addition, the model had an average area under the curve (AUC) of 0.998. Furthermore, the performance of the model was assessed on the PhysioNet/Computing in Cardiology 2016 challenge data-set and reached an overall accuracy of 87.31% with an AUC of 0.900. This study paves the way towards implementing deep learning models in VHD identification under clinical settings to assist clinicians in decision making and prevent many cases from cardiac abnormalities development.

Review of Health Economics of Point-of-Care Testing Worldwide and Its Efficacy of Implementation in the Primary Health Care Setting in Remote Australia

There are important differences concerning health outcomes between the Australian population living in rural/remote regions and the urban population. Health care provision in remote areas, particularly in regions with a low number of inhabitants, is not without challenges. Aboriginal, rural and remote communities are therefore affected, as they face various obstacles in accessing health services, owing to geographical settings, difficulties in transportation to nearby hospitals, limited or inexistent local qualified personnel. The implementation of point-of-care testing could be a plausible solution to these challenges, as various point-of-care services that have been successfully put into action worldwide indicate towards positive clinical outcomes. Point-of-care units have a real potential in reducing morbidity and mortality in all population groups. This article aims to review the published literature on point-of-care testing around the world, with a focus on health economics and the feasibility of its implementation in Australian rural and remote regions.

A systematic review of pocket-sized imaging devices: small and mighty?

Introduction

Hand-held imaging devices are widely used in clinical practice and are a useful tool. There is no published review examining the diagnostic parameters achieved with these devices in clinical practice.

Methods

We searched three online medical literature databases (PubMed, EMBASE and MEDLINE) for all literature published up until January 2018. We selected studies that (1) were conducted in the adult population; (2) used a truly hand-held device; (3) featured sensitivities and/or specificities on the use of the hand-held scanner. We extracted and summarised the diagnostic metrics from the literature.

Results

Twenty-seven articles were excluded from the initial 56 relevant articles, as the device featured was not truly hand-held. Ultimately a total of 25 studies were analysed. Sixteen studies were carried out by experienced users, seven by users with little previous experience and two studies by nurses. High diagnostic parameters were achieved by all three groups when scanning cardiac pathology and intra-abdominal structures. Training of non-expert users varied, taking a mean of 21.6 h. These hand-held devices can change diagnoses at the bedside and be used as gate-keepers to formal echocardiography. Individual studies show them to be cost-effective.

Conclusion

Hand-held echocardiography is a useful tool in the hands of experts and novices alike. Studies conducted are highly heterogeneous making it difficult to pool data for the diagnostic metrics. Further studies with rigorous methodology are needed to evaluate the true diagnostic potential in the hands of non-experts and in the community as well as to validate training protocols.

Self-learning of point-of-care cardiac ultrasound - Can medical students teach themselves?

Background

Point-of-care ultrasonography (PoCUS) is a rapidly evolving discipline that aims to train non-cardiologists, non-radiologists clinicians in performing bedside ultrasound to guide clinical decision. Training of PoCUS is challenging, time-consuming and requires large amount of resources. The objective of our study was to evaluate if this training process can be simplified by allowing medical students self-train themselves with a web-based cardiac ultrasound software.

Methods

A prospective, single blinded, cohort study, comparing performance of 29 medical students in performing a six-minutes cardiac ultrasound exam. Students were divided into two groups: self-learning group, using a combination of E-learning software and self-practice using pocket ultrasound device compared to formal, frontal cardiac ultrasound course.

Results

All 29 students completed their designated courses and performed the six-minutes exam: 20 students participated in the frontal cardiac ultrasound course and 9 completed the self-learning course. The median (Q1,Q3) test score for the self-learning group was higher than the frontal course group score, 18 (15,19) versus 15 (12,19.5), respectively. Nevertheless, no statistically significant difference was found between the two study groups (p = 0.478). All students in the self-learning course group (9/9, 100%) and 16 (16/20, 80%) of students in the frontal ultrasound course group obtained correct alignment of the parasternal long axis view (p = 0.280).

Conclusions

Self-learning students combining E-learning software with self-practice cardiac ultrasound were as good as students who received a validated, bedside, frontal cardiac ultrasound course. Our findings suggest that independent cardiac ultrasound learning, combining utilization of E–learning software and self-practice, is feasible. Self-E- learning of cardiac ultrasound may serve as an important, cost-effective adjunct to heavily resource consuming traditional teaching.

Murmur clinic: validation of a new model for detecting heart valve disease

OBJECTIVES

We aimed to determine if auscultation or a point-of-care scan could reduce the need for standard echocardiography (transthoracic echocardiogram (TTE)) in community patients with asymptomatic murmurs.

METHODS

Requests from general practitioners were directed to a new murmur clinic. Auscultation and a point-of-care scan were performed by a cardiologist between 1 October 2013 and 31 December 2014 and by a scientist between 21 July 2015 and 9 May 2017.

RESULTS

In the first phase (cardiologist), there were 75 patients, mean age 54 (56 women), and in the second phase there were 100 patients, mean age 60 (76 women). In the total population of 175, abnormalities were shown on TTE in 52 (30%), on point-of-care scan in 52 (30%) and predicted on auscultation in 45 (26%) (p=0.125; 95% CI -0.02 to 0.29). The sensitivity of auscultation was not significantly different for the cardiologist (91%) as for the scientist (83%) (p=0.18; 95% CI -0.22 to 0.175) and the specificity was 100% for both. Accuracy was 97% for the cardiologist and 95% for the scientist. For the point-of-care scan, the sensitivity, specificity, positive and negative predictive values and accuracy were 100% for both cardiologist and scientist.

CONCLUSION

Most patients in a specialist murmur clinic had normal auscultation and point-of-care scans and no additional valve disease was detected by standard echocardiography. This suggests that a murmur clinic is a valid model for reducing demand on hospital echocardiography services.

Hand-held Ultrasound Scanners in Medical Education: A Systematic Review

Background

Ultrasound imaging devices are becoming popular in clinical and teaching settings, but there is no systematic information on their use in medical education. We conducted a systematic review of hand-held ultrasound (HHU) devices in undergraduate medical education to delineate their role, significance, and limitations.

Methods

We searched Cochrane, PubMed, Embase, and Medline using the strategy: [(Hand-held OR Portable OR Pocket OR "Point of Care Systems") AND Ultrasound] AND (Education OR Training OR Undergraduate OR "Medical Students" OR "Medical School"). We retained 12 articles focusing on undergraduate medical education. We summarised the patterns of HHU use, pooled and estimated sensitivity, and specificity of HHU for detection of left ventricular dysfunction.

Results

Features reported were heterogeneous: training time (1-25 hours), number of students involved (1-an entire cohort), number of subjects scanned (27-211), and type of learning (self-directed vs. traditional lectures + hands-on sessions). Most studies reported cardiac HHU examinations, but other anatomical areas were examined, e.g. abdomen and thyroid. Pooled sensitivity 0.88 [95% confidence interval (CI) 0.83-0.92] and specificity 0.86 (95% CI 0.81-0.90) were high for the detection of left ventricular systolic dysfunction by students.

Conclusion

Data on HHU devices in medical education are scarce and incomplete, but following training students can achieve high diagnostic accuracy, albeit in a limited number of (mainly cardiac) pathologies. There is no consensus on protocols best-suited to the educational needs of medical students, nor data on long-term impact, decay in proficiency or on the financial implications of deploying HHU in this setting.

Point-of-care cardiac ultrasound in acute internal medicine: how can it be delivered?

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