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

Recognizing cardiac murmurs in childhood: a survey of physicians' approaches and knowledge levels

OBJECTIVE

This study aimed to assess physicians' approach to cardiac murmurs and their level of knowledge about this sign, which is a crucial finding in childhood cardiac anomalies.

METHODS

The study intended to include all family physicians in the Adıyaman province of Turkey, but ultimately 150 out of 210 physicians participated and was completed with a percentage response rate of 71%. Participants were asked about their approach to cardiac murmurs, answered knowledge questions, and completed a questionnaire on demographic characteristics. Subsequently, eight heart sounds were played, and participants were asked to identify the nature of each sound.

RESULTS

Family medicine specialists (all scores were p < 0.001) and physicians who completed a pediatric internship lasting over a month (knowledge score p = 0.012, behavioral score p = 0.021, recording score p = 0.01) demonstrated significantly higher knowledge, approach, and recording scores. Age and years in the profession showed a negative correlation with recording scores.

CONCLUSIONS

The study highlights the significant impact of various factors such as gender, specialization, internship duration, experience, and theoretical knowledge on the ability to recognize and approach cardiac murmurs. These findings underscore the importance of incorporating these factors into medical education and development programs, especially those aimed at improving cardiac examination skills.

Implementing a clinical scientist-led screening clinic for hypertrophic and dilated cardiomyopathies

BACKGROUND

The burden of screening for inherited cardiac conditions on health services grows ever larger, with each new diagnosis necessitating screening of additional family members. Screening these usually asymptomatic, low-risk individuals is currently performed by consultant cardiologists, consuming vital clinic resources that could otherwise be diverted to sicker patients requiring specialist consultant input. Clinical scientists now constitute a highly skilled and often underutilised group of individuals with training in areas such as clinical evaluation, 12-lead electrocardiography (ECG) interpretation, and echocardiography. These skills place them in a unique position to offer a full screening evaluation in a single consultation. The aim of this study was to implement and evaluate a novel clinical scientist-led screening clinic for first-degree relatives of patients with hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM). The clinical scientist-led screening clinic was established at a London tertiary centre to allow review of asymptomatic, first-degree relatives of patients with a confirmed diagnosis of HCM or DCM, independent of a cardiology consultant. Patients were evaluated with history, examination, ECG, and echocardiography, with further investigations if deemed necessary. A retrospective review was performed of the first 200 patients seen in the clinic.

RESULTS

Of the 200 individuals reviewed between September 2019 and July 2022, 99 had a proband with HCM and 101 a proband with DCM. Overall, 169 individuals (85%) revealed normal screenings and were discharged. Thirty-one individuals (15.5%), all asymptomatic, revealed ECG changes and/or significant echocardiographic findings. Of these, 21 individuals (10.5% of the total cohort) were subsequently diagnosed with a cardiomyopathy or early phenotypic changes consistent with a cardiomyopathy (11 with HCM and 10 with DCM). These individuals were referred on to an inherited cardiac conditions consultant clinic for regular follow-up. Overall, 179 consultant clinic appointments were saved which could instead be allocated to patients requiring specialist consultant input.

CONCLUSIONS

This is the first description of a clinical scientist-led screening clinic for first-degree relatives of patients with HCM and DCM. The findings demonstrate that implementation of such a service into routine clinical practice is feasible, effective, safe, and can free up capacity in consultant clinics for patients requiring specialist input.

The Effectiveness of Different Teaching Modalities for the Detection of Heart Murmurs in Undergraduate Medical Education: A Review

One of the many physical exam skills introduced to medical students during their pre-clerkship education is cardiac auscultation, one purpose of which is to teach the detection and identification of heart murmurs. Cardiac auscultation with a stethoscope has been the standard method of teaching. Another method, point-of-care ultrasound (POCUS), has been recently introduced as another modality by which students learn to detect and identify murmurs. The emerging popularity of POCUS in undergraduate medical curricula has led many institutions to include it in their curricula; however, doing so is challenging. Not only is cost a major factor, but reorganizing curricula to allow sufficient time for POCUS training has proven to be difficult. Additionally, the presence of notable gaps in the literature regarding the efficacy of POCUS for teaching the detection and identification of heart murmur has increased scrutiny of its value. Studies that assessed teaching cardiac auscultation to medical students in their pre-clinical years via stethoscope have used different teaching methods. However, evaluation of these studies identified numerous limitations, one being little long-term retention of cardiac auscultation knowledge. Furthermore, several barriers to integration of POCUS in undergraduate medical education were identified. The purpose of this review is to synthesize the literature comparing the effectiveness of these different tools of a cardiac exam for detection of heart murmurs in undergraduate medical education and identify gaps in literature requiring future exploration.

Multi-modality imaging in aortic stenosis: an EACVI clinical consensus document

In this EACVI clinical scientific update, we will explore the current use of multi-modality imaging in the diagnosis, risk stratification, and follow-up of patients with aortic stenosis, with a particular focus on recent developments and future directions. Echocardiography is and will likely remain the key method of diagnosis and surveillance of aortic stenosis providing detailed assessments of valve haemodynamics and the cardiac remodelling response. Computed tomography (CT) is already widely used in the planning of transcutaneous aortic valve implantation. We anticipate its increased use as an anatomical adjudicator to clarify disease severity in patients with discordant echocardiographic measurements. CT calcium scoring is currently used for this purpose; however, contrast CT techniques are emerging that allow identification of both calcific and fibrotic valve thickening. Additionally, improved assessments of myocardial decompensation with echocardiography, cardiac magnetic resonance, and CT will become more commonplace in our routine assessment of aortic stenosis. Underpinning all of this will be widespread application of artificial intelligence. In combination, we believe this new era of multi-modality imaging in aortic stenosis will improve the diagnosis, follow-up, and timing of intervention in aortic stenosis as well as potentially accelerate the development of the novel pharmacological treatments required for this disease.

Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease

Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration-cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board-certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.

Valve Surgery in a Low-Volume Center in a Low- and Middle-Income Country: A Retrospective Cross-Sectional Study

Background

Valvular heart disease (VHD) has a significant prevalence and mortality rate with surgical intervention continuing to be a cornerstone of therapy. We aim to report the outcome of patients undergoing heart valve surgery (HVS) in a low-volume center (LVC) in a low- and middle-income country (LMIC).

Methods

A cross-sectional retrospective study was conducted at the Jordan University Hospital (JUH), a tertiary teaching hospital in a developing country, between April 2014 and December 2019. Patients who underwent mitral valve replacement (MVR), aortic valve replacement (AVR), tricuspid valve replacement (TVR), double valve replacement (DVR), CABG + MVR, and CABG + AVR patients were included. Thirty-day and two-year mortalities were taken as the primary and secondary outcomes, respectively.

Results

A total number of 122 patients were included, and the mean age was 54.46 ± 14.89 years. AVR was most common (42.6%). There was no significant association between STS mortality score or Euroscore II with 30-day and 2-year mortality.

Conclusion

LVC will continue to have a role in LMICs, especially during development to HICs. Further global studies are needed to assert the safety of HVS in LVC and LMICs.

Diagnostic accuracy of heart auscultation for detecting valve disease: a systematic review

OBJECTIVE

The objective of this study was to determine the diagnostic accuracy in detecting valvular heart disease (VHD) by heart auscultation, performed by medical doctors.

DESIGN/METHODS

A systematic literature search for diagnostic studies comparing heart auscultation to echocardiography or angiography, to evaluate VHD in adults, was performed in MEDLINE (1947-November 2021) and EMBASE (1947-November 2021). Two reviewers screened all references by title and abstract, to select studies to be included. Disagreements were resolved by consensus meetings. Reference lists of included studies were also screened. The results are presented as a narrative synthesis, and risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2.

MAIN OUTCOME MEASURES

Sensitivity, specificity and likelihood ratios (LRs).

RESULTS

We found 23 articles meeting the inclusion criteria. Auscultation was compared with full echocardiography in 15 of the articles; pulsed Doppler was used as reference standard in 2 articles, while aortography and ventriculography was used in 5 articles. One article used point-of-care ultrasound. The articles were published from year 1967 to 2021. Sensitivity of auscultation ranged from 30% to 100%, and specificity ranged from 28% to 100%. LRs ranged from 1.35 to 26. Most of the included studies used cardiologists or internal medicine residents or specialists as auscultators, whereas two used general practitioners and two studied several different auscultators.

CONCLUSION

Sensitivity, specificity and LRs of auscultation varied considerably across the different studies. There is a sparsity of data from general practice, where auscultation of the heart is usually one of the main methods for detecting VHD. Based on this review, the diagnostic utility of auscultation is unclear and medical doctors should not rely too much on auscultation alone. More research is needed on how auscultation, together with other clinical findings and history, can be used to distinguish patients with VHD.

PROSPERO REGISTRATION NUMBER

CRD42018091675.

An efficient and robust Phonocardiography (PCG)-based Valvular Heart Diseases (VHD) detection framework using Vision Transformer (ViT)

BACKGROUND AND OBJECTIVES

Valvular heart diseases (VHDs) are one of the dominant causes of cardiovascular abnormalities that have been associated with high mortality rates globally. Rapid and accurate diagnosis of the early stage of VHD based on cardiac phonocardiogram (PCG) signal is critical that allows for optimum medication and reduction of mortality rate.

METHODS

To this end, the current study proposes novel deep learning (DL)-based high-performance VHD detection frameworks that are relatively simpler in terms of network structures, yet effective for accurately detecting multiple VHDs. We present three different frameworks considering both 1D and 2D PCG raw signals. For 1D PCG, Mel frequency cepstral coefficients (MFCC) and linear prediction cepstral coefficients (LPCC) features, whereas, for 2D PCG, various deep convolutional neural networks (D-CNNs) features are extracted. Additionally, nature/bio-inspired algorithms (NIA/BIA) including particle swarm optimization (PSO) and genetic algorithm (GA) have been utilized for automatic and efficient feature selection directly from the raw PCG signal. To further improve the performance of the classifier, vision transformer (ViT) has been implemented levering the self-attention mechanism on the time frequency representation (TFR) of 2D PCG signal. Our extensive study presents a comparative performance analysis and the scope of enhancement for the combination of different descriptors, classifiers, and feature selection algorithms.

MAIN RESULTS

Among all classifiers, ViT provides the best performance by achieving mean average accuracy A_cc of 99.90 % and F1-score of 99.95 % outperforming current state-of-the-art VHD classification models.

CONCLUSIONS

The present research provides a robust and efficient DL-based end-to-end PCG signal classification framework for designing a automated high-performance VHD diagnosis system.

Correlation between Murmurs and Echocardiographic Findings; From an Imaging Cardiologist Point of View

A heart murmur in adults is a common reason for referral for echocardiography at most general cardiology clinics in Europe. A murmur may indicate either a mild age-related valvular calcification or regurgitation, or represent a significant heart valve disease requiring valvular intervention. Generally, the correlation between murmurs by auscultation and severity of heart valve disease by echocardiography is poor. Particularly, the severity and characterization of diastolic murmurs by auscultation may poorly correlate with echocardiographic findings. This narrative review aims to summarize the differential diagnoses of physiological and pathological murmurs, describes the current referral practice of murmur patients for echocardiography, and presents a single-center experience on the correlation of auscultation and echocardiographic findings with a particular focus on aortic and mitral valve diseases. A careful auscultation of the heart prior to the echocardiogram is mandatory and may help to predict the echocardiographic findings and their interpretation in view of the clinical information. The correlation between clinical examination, point of care ultrasound and standard echocardiography is a matter of continued exploration.

Identifying Patients with Bicuspid Aortic Valve Disease in UK Primary Care: A Case-Control Study and Prediction Model

Bicuspid aortic valve disease (BAV) is the most common congenital heart condition, and early detection can improve outcomes for patients. In this case−control study, patients with a diagnosis of BAV were identified from their electronic primary-care records in the UK’s Clinical Practice Research Datalink (CPRD). Each case was propensity-score matched to up to five controls. The clinical features recorded before diagnosis were compared. The proposed clinical features shown to be associated with BAV (p < 0.05) were incorporated into a multivariable regression model. We identified 2898 cases. The prevalence of BAV in the CPRD was 1 in 5181, significantly lower than expected, suggesting that diagnosis and/or recording could be improved. The following biologically plausible clinical features were associated with a subsequent diagnosis of BAV: palpitations (OR: 2.86 (95% CI: 1.60, 3.16)), atrial fibrillation (AF) (OR: 2.25 (95% CI: 1.60, 3.16)) and hypertension (OR: 1.72 (1.48, 2.00)). The best model had an AUC of 0.669 (95% CI: 0.658 to 0.680), a positive predictive value (PPV) of 5.9% (95% CI: 4.0% to 8.7%) and a negative predictive value (NPV) of 99% (95% CI: 99% to 99%) at a population prevalence of 1%. This study indicates that palpitations, hypertension and AF should trigger a clinical suspicion of BAV and assessment via echocardiography. It also demonstrates the potential to develop a prediction model for BAV to stratify patients for echocardiography screening.

ACR Appropriateness Criteria® Dyspnea-Suspected Cardiac Origin (Ischemia Already Excluded): 2021 Update

Dyspnea is the symptom of perceived breathing discomfort and is commonly encountered in a variety of clinical settings. Cardiac etiologies of dyspnea are an important consideration; among these, valvular heart disease (Variant 1), arrhythmia (Variant 2), and pericardial disease (Variant 3) are reviewed in this document. Imaging plays an important role in the clinical assessment of these suspected abnormalities, with usually appropriate procedures including resting transthoracic echocardiography in all three variants, radiography for Variants 1 and 3, MRI heart function and morphology in Variants 2 and 3, and CT heart function and morphology with intravenous contrast for Variant 3. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Murmurs other than the early diastolic murmur in aortic dissection

The purpose of this review is to draw attention to the presence and significance of murmurs other than the murmur of aortic regurgitation, in patients with aortic dissection. For that purpose, a literature search was conducted using Pubmed and Googlescholar. The search terms were "dissecting aneurysm of the aorta", "systolic murmurs", "ejection systolic murmurs", "holosystolic" murmurs, "continuous murmurs", and "Austin-Flint" murmur. Murmurs other than the murmur of aortic regurgitation, which were associated with aortic dissection, fell into the categories of systolic murmurs, some of which were holosystolic, and continuous murmurs, the latter attributable to fistulae between the dissecting aneurysm and the left atrium, right atrium, and the pulmonary artery, respectively. Mid-diastolic murmurs were also identified, and these typically occurred in association with both the systolic and the early diastolic murmurs. Among patients with systolic murmurs clinical features which enhanced the pre-test probability of aortic dissection included back pain, stroke, paraplegia, unilateral absence of pulses, interarm differences in blood pressure, hypertension, shock, bicuspid aortic valve, aortic coarctation, Turner's syndrome, and high D-dimer levels, respectively. In the absence of the murmur of aortic regurgitation timely diagnosis of aortic dissection could be expedited by increased attention to parameters which enhance pretest probability of aortic dissection. That logic would apply even if the only murmurs which were elicited were systolic murmurs.

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.

Do 'pathologic' cardiac murmurs in adolescents identify structural heart disease? An evaluation of 15 141 active adolescents for conditions that put them at risk of sudden cardiac death

OBJECTIVES

We assessed whether the presence and character of a cardiac murmur in adolescents were associated with structural heart disease that confers risk of sudden cardiac death (SCD).

METHODS

We performed a retrospective analysis of 15 141 adolescents age 12-19 who underwent a heart screen with history, physical examination and ECG. Participants with any screening abnormality underwent an echocardiogram for the assessment of structural heart disease. Murmurs were classified as physiological or pathological according to standard clinical criteria, and participants with murmurs were compared with a comparison group without murmurs. The primary outcome was echocardiogram-detected structural heart disease associated with SCD.

RESULTS

905 participants with a cardiac murmur (mean age 15.8; 58% male) and 4333 participants without a murmur (comparison group; mean age 15.8; 55% male) had an echocardiogram to detect structural heart disease. 743 (82%) murmurs were described as physiological and 162 (18%) as pathological. Twenty-five (2.8%) participants with murmurs and 61 (1.4%) participants without murmurs had structural heart disease. Three (0.3%) participants in the murmur group were diagnosed with hypertrophic cardiomyopathy (HCM) which was the only identified condition associated with SCD. Two participants with HCM had physiological murmurs, one had a pathological murmur, and all three had an abnormal ECG. The most common minor structural heart disease was bicuspid aortic valve in both the murmur (7; 0.8%) and comparison (20; 0.5%) groups. The positive predictive value of physiological versus pathological murmurs for identifying any structural heart disease was 2.4% versus 4.3% (p=0.21), respectively. The positive predictive value of having any murmur versus no murmur for identifying structural heart disease was 2.8% versus 1.4% (p=0.003), respectively.

CONCLUSIONS

In adolescents, the traditional classification of cardiac murmurs as 'physiologic' or 'pathologic' does not differentiate for structural heart disease that puts individuals at risk for SCD. We recommend ECG evaluation in all patients with a cardiac murmur found during preparticipation screening to increase detection of HCM.

Classification of heart sound signals using a novel deep WaveNet model

BACKGROUND AND OBJECTIVES

The high mortality rate and increasing prevalence of heart valve diseases globally warrant the need for rapid and accurate diagnosis of such diseases. Phonocardiogram (PCG) signals are used in this study due to the low cost of obtaining the signals. This study classifies five types of heart sounds, namely normal, aortic stenosis, mitral valve prolapse, mitral stenosis, and mitral regurgitation.

METHODS

We have proposed a novel in-house developed deep WaveNet model for automated classification of five types of heart sounds. The model is developed using a total of 1000 PCG recordings belonging to five classes with 200 recordings in each class.

RESULTS

We have achieved a training accuracy of 97% for the classification of heart sounds into five classes. The highest classification accuracy of 98.20% was achieved for the normal class. The developed model was validated with a 10-fold cross-validation, thus affirming its robustness.

CONCLUSION

The study results clearly indicate that the developed model is able to classify five types of heart sounds accurately. The developed system can be used by cardiologists to aid in the detection of heart valve diseases in patients.

Outpatient management of heart valve disease following the COVID-19 pandemic: implications for present and future care

The established processes for ensuring safe outpatient surveillance of patients with known heart valve disease (HVD), echocardiography for patients referred with new murmurs and timely delivery of surgical or transcatheter treatment for patients with severe disease have all been significantly impacted by the novel coronavirus pandemic. This has created a large backlog of work and upstaging of disease with consequent increases in risk and cost of treatment and potential for worse long-term outcomes. As countries emerge from lockdown but with COVID-19 endemic in society, precautions remain that restrict 'normal' practice. In this article, we propose a methodology for restructuring services for patients with HVD and provide recommendations pertaining to frequency of follow-up and use of echocardiography at present. It will be almost impossible to practice exactly as we did prior to the pandemic; thus, it is essential to prioritise patients with the greatest clinical need, such as those with symptomatic severe HVD. Local procedural waiting times will need to be considered, in addition to usual clinical characteristics in determining whether patients requiring intervention would be better suited having surgical or transcatheter treatment. We present guidance on the identification of stable patients with HVD that could have follow-up deferred safely and suggest certain patients that could be discharged from follow-up if waiting lists are triaged with appropriate clinical input. Finally, we propose that novel models of working enforced by the pandemic-such as increased use of virtual clinics-should be further developed and evaluated.

Specialist valve clinic in a cardiac centre: 10-year experience

Aims

Guidelines recommend specialist valve clinics as best practice for the assessment and conservative management of patients with heart valve disease. However, there is little guidance on how to set up and organise a clinic. The aim of this study is to describe a clinic run by a multidisciplinary team consisting of cardiologists, physiologist/scientists and a nurse.

Methods

The clinical and organisational aims of the clinic, inclusion and exclusion criteria, and links with other services are described. The methods of training non-clinical staff are detailed. Data were prospectively entered onto a database and the study consisted of an analysis of the clinical characteristics and outcomes of all patients seen between 1 January 2009 and 31 December 2018.

Results

There were 2126 new patients and 9522 visits in the 10-year period. The mean age was 64.8 and 55% were male. Of the visits, 3587 (38%) were to the cardiologists, 4092 (43%) to the physiologist/scientists and 1843 (19%) to the nurse. The outcomes from the cardiologist clinics were cardiology follow-up in 460 (30%), referral for surgery in 354 (23%), referral to the physiologist/scientist clinic in 412 (27%) or to the nurse clinic in 65 (4.3%) and discharge in 230 (15%). The cardiologist needed to see 6% from the nurse clinic and 10% from the physiologist/scientist clinic, while advice alone was sufficient in 10% and 9%.

Conclusion

A multidisciplinary specialist valve clinic is feasible and sustainable in the long term.

EDUCATIONAL SERIES ON THE SPECIALIST VALVE CLINIC: Challenges in the diagnosis and management of valve disease: the case for the specialist valve clinic

Valvular heart disease (VHD) is responsible for a major societal and economic burden. Incidence and prevalence of VHD are high and increase as the population ages, creating the next epidemic. In Western countries, the etiology is mostly degenerative or functional disease and strikes an elderly population with multiple comorbidities. Epidemiological studies have shown that VHD is commonly underdiagnosed, leading to patients presenting late in their disease course, to an excess risk of mortality and morbidity and to a missed opportunity for intervention. Once diagnosed, VHD is often undertreated with patients unduly denied intervention, the only available curative treatment. This gap between current recommendations and clinical practice and the marked under-treatment is at least partially related to poor knowledge of current National and International Societies Guidelines. Development of a valvular heart team involving multidisciplinary valve specialists including clinicians, imaging specialists, interventional cardiologists and surgeons is expected to fill these gaps and to offer an integrated care addressing all issues of patient management from evaluation, risk-assessment, decision-making and performance of state-of-the-art surgical and transcatheter interventions. The valvular heart team will select the right treatment for the right patient, improving cost-effectiveness and ultimately patients' outcomes.

EDUCATIONAL SERIES ON THE SPECIALIST VALVE CLINIC: How to run a specialist valve clinic: the history, examination and exercise test

Echocardiography is the key to the detection and initial assessment of valve disease. The examination helps differentiate severe from moderate disease if this is unclear from the echocardiogram, but is less useful than echocardiography for surveillance. However, the history is extremely important because symptoms are an indication for surgery in all types of valve disease. In aortic stenosis, the mortality rises soon after the onset of exertional breathlessness or chest tightness. Exercise testing is an extension of the history and may reveal symptoms in apparently asymptomatic patients. This article discusses the history, examination and exercise testing in patients either newly referred or under routine follow-up in a specialist valve clinic.

Heart Valve Clinics, Centers, and Networks

There is consensus on important aspects of managing heart valve disease. Despite this, many patients are managed by general physicians or cardiologists without specialist competencies in valve disease, which leads to suboptimal outcomes. Multidisciplinary heart valve clinics bring together cardiologists, surgeons, nurses, and in some countries scientists to deliver expert guidelines and experience-driven optimal care. Patients are referred at the optimal time for interventions at heart valve centers, defined by strict standards of facilities and processes. Valve networks link valve clinic, heart valve center, and the community to improve the passage of patients at every level of care.

Point-of-care echocardiographic screening for left-sided valve heart disease: high yield and affordable cost in an elderly cohort recruited in primary practice

Background

Data about the epidemiology of valvular heart disease (VHD) in the elderly is scarce. Hand-held ultrasound devices (HUDs) enable point-of-care ultrasound scanning (POCUS) but their use in an elderly population has not been reported for VHD screening in primary practice.

Methods

One hundred consecutive subjects aged >70 years without a VHD diagnosis had 2D and colour Doppler POCUS by an accredited sonographer, using a contemporary HUD (Vscan), in a primary practice setting. Patients with left-sided valve pathology identified by Vscan were referred for formal echo in the local tertiary cardiac centre.

Results

Mean age (s.d.) was 79.08 (3.74) years (72-92 years); 61 female. By Vscan, we found five patients with ≥moderate aortic stenosis (AS), eight with ≥moderate mitral regurgitation (MR) and none with ≥moderate aortic regurgitation. In the AS and MR groups each, one patient had valve intervention following from the initial diagnosis by Vscan, two and one respectively are under follow-up in the valve clinic, while two and four respectively refused TTE or follow-up. Two patients with moderate MR by Vscan had mild and mild/moderate MR respectively by TTE and were discharged. Total cost for scanning 100 patients was $18,201 - i.e. $182/patient.

Conclusions

Screening with a hand-held scanner (Vscan), we identified 5/100 elderly subjects who needed valve replacement or follow-up in valve clinic, at a cost of $182/patient. These findings have potential significance for the allocation of resources in the context of an ageing population.