Diagnosis Of Valvular Heart Disease Research Articles

Meta-Analysis of the Performance of AI-Driven ECG Interpretation in the Diagnosis of Valvular Heart Diseases

Valvular heart diseases (VHDs) significantly impact morbidity and mortality rates worldwide. Early diagnosis improves patient outcomes. Artificial intelligence (AI) applied to electrocardiogram (ECG) interpretation presents a promising approach for early VHD detection. We conducted a meta-analysis on the efficacy of AI models in this context. We reviewed databases including PubMed, MEDLINE, Embase, Scopus, and Cochrane until August 20, 2023, focusing on AI for ECG-based VHD detection. The outcomes included pooled accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value. The pooled proportions were derived using a random-effects model with 95% confidence intervals (CIs). Study heterogeneity was evaluated with the I-squared statistic. Our analysis included 10 studies, involving ECG data from 713,537 patients. The AI algorithms mainly screened for aortic stenosis (n=6), mitral regurgitation (n=4), aortic regurgitation (n=3), mitral stenosis (n=1), mitral valve prolapse (n=2), and tricuspid regurgitation (n=1). A total of 9 studies used convolution neural network models, whereas 1 study combined the strengths of support vector machine logistic regression and multilayer perceptron for ECG interpretation. The collective AI models demonstrated a pooled accuracy of 81% (95% CI 73 to 89, I²=92%), sensitivity was 83% (95% CI 77 to 88, I²=86%), specificity was 72% (95% CI 68 to 75, I²=52%), PPV was 13% (95% CI 7 to 19, I²=90%), and negative predictive value was 99% (95% CI 97 to 99, I²=50%). The subgroup analyses for aortic stenosis and mitral regurgitation detection yielded analogous outcomes. In conclusion, AI-driven ECG offers high accuracy in VHD screening. However, its low PPV indicates the need for a combined approach with clinical judgment, especially in primary care settings.

High-Throughput Metabolomics Applications in Pathogenesis and Diagnosis of Valvular Heart Disease.

High-throughput metabolomics techniques are a useful tool to understand many disease conditions including cardiovascular disease such as valvular heart disease(s) (VHD). VHD involves damage to heart valves, mostly presenting as stenosis, regurgitation or prolapse and can be classified into degenerative, rheumatic, congenital, or prosthetic valve disease. Gaps remain in our understanding of the pathogenesis of the common VHD. It is now fitting to place into perspective the contribution of metabolomics in the mechanism of development, diagnosis, and prognosis of VHD. A structured search for metabolomics studies centred on human VHD was undertaken. Biomarkers associated with the pathogenesis of bicuspid aortic valve disease, mitral valve disease, rheumatic heart disease, and degenerative aortic valve stenosis are reviewed and discussed. In addition, metabolic biomarkers reported to prognosticate patient outcomes of post-valve repair or replacement are highlighted. Finally, we also review the pitfalls and limitations to consider when designing metabolomics studies, especially from a clinician's viewpoint. In the future, reliable and simple metabolic biomarker(s) may supplement the existing diagnostic tools in the early diagnosis of VHD.

Risk of Gastrointestinal Bleeding in Patients with End-Stage Renal Disease: The Link between Gut, Heart, and Kidneys.

Patients with end-stage renal disease (ESRD) have a five times higher risk of gastrointestinal bleed (GIB) and mortality than the general population. Aortic stenosis (AS) has been associated with GIB from intestinal angiodysplasia. In this retrospective analysis, we obtained data from the 2012 and 2019 National Inpatient Sample. The primary outcome of interest was all-cause in-hospital mortality and risk factors of mortality in patients with ESRD with GIB with aortic valve disorders especially AS. We identified all patients (≥18 years of age) with ESRD (n = 1,707,452) and analyzed based on discharge diagnosis of valvular heart disease (n = 6521) in patients with GIB compared with those without GIB (n = 116,560). Survey statistical methods accounting for strata and weighted data were used for analysis using survey packages in R (version 4.0). Baseline categorical data were compared using Rao-Scott chi square test, and continuous data were compared using Student's t-test. Covariates were assessed using univariate regression analysis, and factors with p value less than 0.1 in the univariate analysis were entered in the final model. The univariate and multivariable associations of presumed risk factors of mortality in ESRD with GIB patients were performed by Cox proportional hazards model censored at length of stay. Propensity score matching was done using MatchIt package in R (version 4.3.0). 1 : 1 nearest neighbour matching was done with propensity scores estimated through logistic regression, in which occurrence of GIB, valvular lesions, and AS was regressed according to other patient characteristics. Among patients with ESRD with valvular heart diseases, AS was found to be associated with increased risk of GIB (adj.OR = 1.005; 95% CI 1.003-1.008; p < 0.01). ESRD patients with AS showed increased risk of lower GIB (OR = 1.04; 95% CI 1.01-1.06; p = 0.02), colonic angiodysplasia (OR = 1.03; 95% CI 1.01-1.05; p < 0.01), stomach and duodenal angiodysplasia (OR = 1.03; 95% CI 1.02-1.06; p < 0.01), need for blood transfusion add pressors as compared to those without AS. However, there was no increased risk of mortality (OR = 0.97; 95% CI 0.95-0.99; p < 0.01).

Machine Learning for Pediatric Echocardiographic Mitral Regurgitation Detection

Echocardiography-based screening for valvular disease in at-risk asymptomatic children can result in early diagnosis. These screening programs, however, are resource intensive and may not be feasible in many resource-limited settings. Automated echocardiographic diagnosis may enable more widespread echocardiographic screening, early diagnosis, and improved outcomes. In this feasibility study, the authors sought to build a machine learning model capable of identifying mitral regurgitation (MR) on echocardiography. Echocardiograms were labeled by clip for view and by frame for the presence of MR. The labeled data were used to build two convolutional neural networks to perform the stepwise tasks of classifying the clips (1) by view and (2) by the presence of any MR, including physiologic, in parasternal long-axis color Doppler views. The view classification model was developed using 66,330 frames, and model performance was evaluated using a hold-out testing data set with 45 echocardiograms (11,730 frames). The MR detection model was developed using 938 frames, and model performance was evaluated using a hold-out testing data set with 42 echocardiograms (182 frames). Metrics to evaluate model performance included accuracy, precision, recall, F1 score (average of precision and recall, ranging from 0 to 1, with 1 suggesting perfect precision and recall), and receiver operating characteristic analysis. For the parasternal long-axis view with color Doppler, the view classification convolutional neural network achieved an F1 score of 0.97. The MR detection convolutional neural network achieved testing accuracy of 0.86 and an area under the receiver operating characteristic curve of 0.91. A machine learning model is capable of discerning MR on transthoracic echocardiography. This is an encouraging step toward machine learning-based diagnosis of valvular heart disease on pediatric echocardiography.

Artificial Intelligence-Based Stethoscope for the Diagnosis of Aortic Stenosis

BackgroundThe diagnostic accuracy of the stethoscope is limited and highly dependent on clinical expertise. Our purpose was to develop an electronic stethoscope, based on artificial intelligence (AI) and infrasound, for the diagnosis of aortic stenosis (AS). MethodsWe used an electronic stethoscope (VoqX; Sanolla, Nesher, Israel) with subsonic capabilities and acoustic range of 3-2000 Hz. The study had 2 stages. In the first stage, using the VoqX, we recorded heart sounds from 100 patients referred for echocardiography (derivation group), 50 with moderate or severe AS and 50 without valvular disease. An AI-based supervised learning model was applied to the auscultation data from the first 100 patients used for training, to construct a diagnostic algorithm that was then tested on a validation group (50 other patients, 25 with AS and 25 without AS). In the second stage, conducted at a different medical center, we tested the device on 106 additional patients referred for echocardiography, which included patients with other valvular diseases. ResultsUsing data collected at the aortic and pulmonic auscultation points from the derivation group, the AI-based algorithm identified moderate or severe AS with 86% sensitivity and 100% specificity. When applied to the validation group, the sensitivity was 84% and specificity 92%; and in the additional testing group, 90% and 84%, respectively. The sensitivity was 55% for mild, 76% for moderate, and 93% for severe AS. ConclusionOur initial findings show that an AI-based stethoscope with infrasound capabilities can accurately diagnose AS. AI-based electronic auscultation is a promising new tool for automatic screening and diagnosis of valvular heart disease.

An intelligent probe with dual-emission in water and oil for lipid droplet specific imaging in human fibrocalcific aortic valvular leaflet

Lipid droplets (LDs) have been regarded as potential marker for study the pathologic processes and diagnosis of valvular heart disease. While conventional imaging strategy fail to precisely locate LDs in pathological tissues. Herein, a LDs specific probe ECPID with special feature of single-excitation but dual-emission in oil (520 nm) and water (628 nm) was prepared for LDs imaging. ECPID exhibited good biocompatibility, great performance in intracellular and tissular LDs imaging, which would help to reveal the pathologic process of human fibrocalcific aortic valvular leaflet. Our work offers a novel approach for accurate imaging LDs in situ and paves a way to study the pathologic processes of valvular disease.

Effect of Different Nursing Interventions on Discharged Patients with Cardiac Valve Replacement Evaluated by Deep Learning Algorithm-Based MRI Information.

This study was aimed to explore the application of cardiac magnetic resonance imaging (MRI) image segmentation model based on U-Net in the diagnosis of a valvular heart disease. The effect of continuous nursing on the survival of discharged patients with cardiac valve replacement was analyzed in this study. In this study, the filling completion operation, cross entropy loss function, and guidance unit were introduced and optimized based on the U-Net network. The heart MRI image segmentation model ML-Net was established. We compared the Dice, Hausdorff distance (HD), and percentage of area difference (PAD) values between ML-Net and other algorithms. The MRI image features of 82 patients with valvular heart disease who underwent cardiac valve replacement were analyzed. According to different nursing methods, they were randomly divided into the control group (routine nursing) and the intervention group (continuous nursing), with 41 cases in each group. The Glasgow Outcome Scale (GOS) score and the Self-rating Anxiety Scale (SAS) were compared between the two groups to assess the degree of anxiety of patients and the survival status at 6 months, 1 year, 2 years, and 3 years after discharge. The results showed that the Dice coefficient, HD, and PAD of the ML-Net algorithm were (0.896 ± 0.071), (5.66 ± 0.45) mm, and (15.34 ± 1.22) %, respectively. The Dice, HD, and PAD values of the ML-Net algorithm were all statistically different from those of the convolutional neural networks (CNN), fully convolutional networks (FCN), SegNet, and U-Net algorithms (P < 0.05). Atrial, ventricular, and aortic abnormalities can be seen in MRI images of patients with valvular heart disease. The cardiac blood flow signal will also be abnormal. The GOS score of the intervention group was significantly higher than that of the control group (P < 0.01). The SAS score was lower than that of the control group (P < 0.05). The survival rates of patients with valvular heart disease at 6 months, 1 year, 2 years, and 3 years after discharge were significantly higher than those in the control group (P < 0.05). The abovementioned results showed that an effective segmentation model for cardiac MRI images was established in this study. Continuous nursing played an important role in the postoperative recovery of discharged patients after cardiac valve replacement. This study provided a reference value for the diagnosis and prognosis of valvular heart disease.

Artificial intelligence-based stethoscope for the diagnosis of aortic stenosis

Abstract Background Although the stethoscope is used for auscultation in the diagnosis of valvular heart disease for more than 200 years, its diagnostic accuracy is limited and highly dependent on clinical expertise and acoustic range of the human ear. Purpose To develop an electronic stethoscope, based on artificial intelligence (AI), for the diagnosis of aortic stenosis (AS). Methods We developed an electronic stethoscope (VoqxTM, Sanolla) with subsonic capabilities and acoustic range of 0–2,000 Hz. Using the VoqxTM, we recorded heart sounds from 100 patients referred for echocardiography (derivation group), 50 with moderate or severe AS (aortic valve area (AVA) ≤1.5 cm2) and 50 without valvular disease, using the 5 standard auscultation points. An AI based supervised learning model was applied to the auscultation data from the first 100 patients, to construct a diagnostic algorithm that was then tested on a validation group (50 other patients, 25 with AS and 25 without AS). Results The derivation group included 50 patients with AS (age 78±9 years, 28 males, AVA=0.87±0.19 cm2, mean gradient 44±16 mmHg, 39 with severe AS) and 50 without AS (age 58±16 years, 39 males). The AI based algorithm using 1–4 auscultation position (all except the mitral position), correctly identified 47/50 AS patients (sensitivity 94%) and 49/50 patients without AS (specificity 98%), total accuracy 96% (Table). The algorithm was then applied to the validation group (AS: n=25, age 76±9 years,12 males, AVA=0.88±0.21 cm2, mean gradient 41±15 mmHg, 18 with severe AS; No AS: n=25, age = 59±18 years, 17 males). The algorithm correctly identified 21/25 AS patients (sensitivity 84%) and 24/25 patients without AS (specificity 96%), total accuracy 90% (Table 1). Conclusion Our initial findings show that an AI based stethoscope can accurately diagnose AS. AI based electronic auscultation is a promising new tool for automatic diagnosis of valvular heart disease. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Sanolla Company Table 1

Algorithm for primary care physicians in the management of valvular heart disease in India

In India, the burden of valvular heart disease (VHD) is high due to rheumatic heart disease (RHD). Factors like poverty, overcrowded dwellings, under-nutrition, cultural constraints, and suboptimal medical care contribute to the high prevalence of RHD. The diagnosis of VHD is a problem in routine practice and many cases are either missed in early stages. Also, late identification/ referral of complications in post valvular surgery patients of VHD by physicians working in the periphery to a large extent but not limited to periphery only. Hence, the need of the hour is to enhance the knowledge of Primary care physicians and create an easy-to-use algorithm for physicians to facilitate early identification and diagnosis of VHD and complications in post cardiac surgery patients for timely referral to reduce mortality and morbidity associated with VHD. The algorithm will act as a tool to recognize early signs and symptoms of VHD for early diagnosis of VHD & its management. The algorithm will also facilitate early referral to specialist and identification of post-operative complications in patients who have undergone surgery for VHD. Valvular heart disease patients after prosthetic valve implantation are at a risk of thrombo-embolic and therefore require lifelong follow up and anticoagulant therapy in order to alleviate morbidity and mortality which also needs to be cascaded down to the primary care physicians based on evidence-based recommendations on anti-coagulation management. This easy-to-use guide/algorithm will help physicians better their clinical practice in India, ultimately benefiting the patient outcomes.

A052 Investigation of Exhaled Nitric Oxide as a Measure of Left Atrial Pressure

During left-sided heart failure, left atrial pressure (LAP) increases, producing pulmonary congestion. Previous studies examining patients with symptomatic heart failure or rheumatic heart disease suggest a relationship between increased left atrial pressure (LAP) and fractional exhaled nitric oxide (FeNO), which can be measured by a reliable portable hand-held analyzer. If such a relationship existed, a portable diagnostic test could be used to supplement current assessments. This observational study examined a subset of the OxVALVE cohort, a UK population-based cohort aged 65 years and older. Participants were eligible for inclusion if they did not have a previous diagnosis of valvular heart disease, and could provide informed consent. Each participant had echocardiography performed at their local general practice. The E/e' ratio, measured via transthoracic echocardiography, was used as a surrogate of LAP. A consecutive subset of 277 participants had FeNO measurement attempted using a NIOX VERO electrochemical analyzer. Mean age of participants was 73.5±6.3 years, with 45% female. Only 10 participants had an NYHA class of III or more. FeNO was successfully measured in 227 participants, with a mean FeNO of 24.2 + 15.6ppb. Mean E/e' was 11.5±3.5. There were 58 participants with high E/e' (>14), and these were older than those with normal E/e' (<8) (76.8±7.4years vs 72.0±5.5 years, p<0.001). No relationship between FeNO and E/e' was seen on regression analysis. FeNO is not an accurate predictor of elevated E/e' in predominantly asymptomatic patients in a general practice setting.

Role of 3-Dimensional Echocardiography in the Comprehensive Evaluation of the Tricuspid Valve in Patients With Tricuspid Regurgitation.

Three-dimensional echocardiography is one of the most promising methods for the diagnosis of valvular heart disease, and recently has become an essential clinical tool owing to the continued development of real-time transesophageal echocardiography (TEE) technology. And now an era of renewed interest and enthusiasm surrounding the diagnosis and treatment of valvular heart disease has come, which is driven by emerging trans-catheter procedures. Nonetheless, little or no attention has been given to the treatment of tricuspid regurgitation (TR). The application of 3D-TEE is useful for simultaneous visualization of all 3 leaflets in order to grasp the whole picture of the tricuspid valve (TV; “en face” view). The implications of 3-D assessment of TV annulus, leaflets, and morphology involve an improved understanding of both the mechanics and treatment of TR. This method has been useful for surgical management, including accurate measurement of tricuspid annular diameter and prediction of the post-surgical outcome. Moreover, this method may be indispensable for detailed and comprehensive evaluation of the TV in patients with TR who are candidates for trans-catheter tricuspid procedures. In addition, color Doppler 3D-TEE has been valuable to identify the location of the regurgitant orifice and the severity of the TR. It is now clear that this method will enhance the diagnosis and management of TR patients.

Automatic diagnosis of valvular heart diseases by impedance cardiography signal processing

Valvular heart diseases (VHDs) are an abnormal activity of the heart caused by a damage of one of the four heart valves. The impedance cardiography (ICG) is a non-invasive method employed to identify and classify the heart abnormalities. Despite its importance, there are not many works in scientific literature that use the ICG method in order to diagnose VHDs. Therefore, this paper deals with the ICG signal processing for the classification of normal (N) and various VHDs classes of heartbeats. In this work, six types of heartbeat classes of VHD are used, namely: aortic insufficiency (AOI), aortic stenosis (AOS), aortic disease (AOD), mitral disease (MD), mitral-aortic heart disease (MAOHD) and tricuspid insufficiency + mitral disease (TI + MD). The classification of these heartbeat classes is performed using a combination among statistical, morphological and spectral features. For each ICG heartbeat, the statistical features (median, mean, standard deviation, kurtosis, skewness, central moment and Shannon entropy) are computed from the first four intrinsic mode functions (IMFs) calculated using the empirical mode decomposition (EMD) technique. These statistical features are subjected to principal component analysis (PCA) to reduce the dimensionality. Then, the morphological features are extracted by calculating maximums and minimums of the peaks existing in the ICG heartbeat signal and determining the intervals which separate these peaks. Besides, the spectral features are calculated from the first three harmonics of each heartbeat ICG spectrum. From these three types of features, we selected the most significant of them by applying the analysis of variance (ANOVA) test. In order to obtain the best classification performance, three combination of the selected features are investigated and tested using the decision tree (DT), the random forest (RF) and the support vector machine (SVM) classifiers: i) spectral + morphological, ii) statistical + morphological, iii) statistical + morphological + spectral. The achieved results showed that the use of the third combination of features coupled with the RF classifier gave the highest overall accuracy of 96.34 %, average class-accuracy of 98.95 %, average specificity of 99.38 %, average positive predictivity of 96.73 % and average negative predictivity of 99.44 % using 10-fold cross validation. Thus, our developed method seems robust and very efficient for automatic detection of normal and VHDs ICG classes.

Systolic Blood Pressure and Risk of Valvular Heart Disease

Modifiable risk factors for valvular heart disease remain largely unknown, which limits prevention and treatment. To assess the association between systolic blood pressure (BP) and major valvular heart disease. A UK Biobank population-based cohort of 502 602 men and women aged 40 to 96 years at baseline was evaluated through mendelian randomization using individual participant data. Inclusion criteria were valid genetic data and BP measurements. The participants were recruited between 2006 and 2010; data analysis was performed from June 2018 to January 2019. Systolic BP was measured during clinical assessment and instruments for the genetic effect of high BP were identified from variants that were independently (linkage disequilibrium threshold of r2<0.1) associated with systolic BP with minor allele frequency greater than 0.01. A total of 130 single-nucleotide polymorphisms that have been shown to be associated with systolic BP in a genome-wide association meta-analysis involving 1 million participants of European ancestry were selected. Incident aortic stenosis, aortic regurgitation, and mitral regurgitation, individually and combined. Cases were largely based on hospital records linked to the UK Biobank with International Classification of Diseases and Health Related Problems, Tenth Revision codes. Of the 502 602 individuals screened, 329 237 participants (177 741 [53.99%] women; mean [SD] age, 56.93 [7.99] years) had valid genetic data and BP measurements; of this cohort, 3570 individuals (1.08%) had a diagnosis of valvular heart disease (aortic stenosis, 1491 [0.45%]; aortic regurgitation, 634 [0.19%]; and mitral regurgitation, 1736 [0.53%]). Each genetically associated 20-mm Hg increment in systolic BP was associated with an increased risk of aortic stenosis (odds ratio [OR], 3.26; 95% CI, 1.50-7.10), aortic regurgitation (OR, 2.59; 95% CI, 0.75-8.92), and mitral regurgitation (OR, 2.19; 95% CI, 1.07-4.47), with no evidence for heterogeneity by type of valvular heart disease (P = .90). Sensitivity analyses confirmed the robustness of the association. Lifetime exposure to elevated systolic BP appears to be associated with an increased risk of major valvular heart disease.

DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds

Objective: Automatic heart sound analysis has the potential to improve the diagnosis of valvular heart diseases in the primary care phase, as well as in countries where there is neither the expertise nor the equipment to perform echocardiograms. An algorithm has been trained, on the PhysioNet open-access heart sounds database, to classify heart sounds as normal or abnormal. Approach: The heart sounds are segmented using an open-source algorithm based on a hidden semi-Markov model. Following this, the time-frequency behaviour of a single heartbeat is characterized by using a novel implementation of the continuous wavelet transform, mel-frequency cepstral coefficients, and certain complexity measures. These features help detect the presence of any murmurs. A number of other features are also extracted to characterise the inter-beat behaviour of the heart sounds, which helps to recognize diseases such as arrhythmia. The extracted features are normalized and their dimensionality is reduced using principal component analysis. They are then used as the input to a fully-connected, two-hidden-layer neural network, trained by error backpropagation, and regularized with DropConnect.Main results: This algorithm achieved an accuracy of 85.2% on the test data, which placed third in the PhysioNet/Computing in Cardiology Challenge (first place scored 86.0%). However, this is unrealistic of real-world performance, as the test data contained a dataset (dataset-e) in which normal and abnormal heart sounds were recorded with different stethoscopes. A 10-fold cross-validation study on the training data (excluding dataset-e) gives a mean score of 74.8%, which is a more realistic estimate of accuracy. With dataset-e excluded from training, the algorithm scored only 58.1% on the test data.

Epidemiology of valvular heart disease in a Swedish nationwide hospital-based register study

ObjectiveTransitions in the spectrum of valvular heart diseases (VHDs) in developed countries over the 20th century have been reported from clinical case series, but large, contemporary population-based studies are lacking.MethodsWe...

Abstract 042: Association Between Preexisting Valvular Heart Disease and Takotsubo Cardiomyopathy Among Hospitalized Patients in the United States

Background: The pathogenesis of Takotsubo Cardiomyopathy (TTCM) remains poorly understood. Several case reports have described the occurrence of TTCM in patients with preexisting valvular heart disease (VHD). We sought to examine the association between preexisting VHD and TTCM in hospitalized patients. Methods: Using the Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS), all hospitalizations between 2007 and 2011 with preexisting diagnosis of VHD and admitting diagnosis of TTCM were extracted using ICD-9 CM codes. We compared patients admitted for TTCM who had preexisting VHD to those without. We excluded patients below the age of 18 as well as those diagnosed with TTCM who later underwent percutaneous coronary intervention (PCI). Multivariate logistic regression was used to assess the independent effect of preexisting VHD on both occurrence of TTCM and clinical outcomes (length of stay (LOS), stroke and in-hospital mortality). Results: In our study, 613 (0.06%) out of the 1,084,719 hospitalized patients with preexisting VHD, had TTCM, compared to 13,381 (0.04%) out of the 31,460,000 with no preexisting VHD (p&lt;0.0001). In adjusted models, patients with preexisting VHD had a lower risk of being hospitalized for TTCM in the index hospital admission (adjusted odds ratio (aOR) =0.83, 95% confidence interval (CI) =0.77-0.90), independent of potential demographic, comorbid and lifestyle confounders. However, preexisting VHD was significantly associated with longer LOS (aOR=2.59, 95% CI=1.95-3.23), increased stroke rate (aOR=2.49, 95% CI=1.75-3.54) and higher in-hospital mortality (aOR=1.48, 95% CI=1.05-2.09). Conclusion: In this large, nationwide study, preexisting VHD was associated with worse clinical outcomes in patients hospitalized for TTCM. Our results can guide future clinical decision-making regarding prompt risk factor identification for poor prognosis in TTCM patients. Future prospective studies are needed to further evaluate this association.

The year in cardiology 2016: valvular heart disease.

A large number of studies addressing various aspects of the diagnosis and treatment of valvular heart disease (VHD) have been published since the ESC Annual congress in 2015. As expected in this era of rapidly evolving therapeutic modalities and technologies, many scientific contributions are related to the expanded role of percutaneous interventions. New data are now available, consolidating the validity of the transcatheter approach in a variety of subsets of patients and therefore offering new strategies and perspectives in management. The most relevant articles have been selected for this review. It is well known that ageing of the population is associated with a higher prevalence of VHD. A large-scale echocardiographic screening involving 2500 individuals aged > 65 years was conducted to detect undiagnosed VHD ( Figure 1 ).1 Clinically significant (moderate or severe) undiagnosed VHD was identified in 6.4%. In addition, 4.9% of the cohort had pre-existing VHD (a total prevalence of 11.3%). Projecting these findings using population data, the prevalence of clinically significant VHD is estimated to double before 2050. The unique data of this study confirm the scale of the emerging epidemic of VHD, with widespread implications for clinicians and healthcare resources. In this scenario of predominantly elderly people affected by significant VHD, percutaneous modalities of treatment are expected to play a major role. Figure 1 UK population projections of diagnosed and undiagnosed significant valvular heart disease. The OxValve Population Cohort Study. Diagnosed estimates are based on the number excluded from participation in the present study due to a prior diagnosis of valvular heart disease. Undiagnosed estimates are based on the number with newly diagnosed significant valvular heart disease in OxVALVE-PCS. Reproduced with permission from D’Arcy JL et al .1 The mechanisms responsible for aortic valve calcification and development of aortic stenosis (AS) have been explored, and an association between valvular …

Healthcare utilization and costs associated with dabigatran compared to warfarin treatment in newly diagnosed patients with non-valvular atrial fibrillation

Purpose:Real-world healthcare resource utilization and costs were compared among patients with non-valvular atrial fibrillation (NVAF) receiving either dabigatran or warfarin.Methods:A retrospective cohort study was conducted using administrative claims data from the United States Department of Defense (DOD) Military Health System. Patients with newly diagnosed AF initiated on dabigatran or warfarin were identified using ICD-9 diagnosis, procedure and drug codes. Patients were observed for 3 months prior to treatment initiation to ascertain a diagnosis of valvular heart disease and 12 months for exclusion of those with a history of anticoagulation therapy. Propensity score matching was used to balance baseline characteristics between the two treatment cohorts. Medical and pharmacy utilization and costs were compared between the dabigatran and warfarin treatment groups for 3 and 12 months following treatment initiation.Results:A total of 1102 patients with newly diagnosed NVAF initiated on dabigatran were matched with corresponding warfarin-treated patients. In the 12 months following initiation of anticoagulation, the mean medical costs for patients initiated on dabigatran were significantly lower than for patients initiated on warfarin (−$6299, p < 0.001), largely due to fewer hospitalizations (−0.162, p = 0.009). While pharmacy costs were higher ($4369, p < 0.001) for dabigatran, overall healthcare costs were significantly lower compared with patients on warfarin (12 months: −$1940, p < 0.001). Mean hospital length of stay between these two groups were similar (6.033 days for dabigatran vs 6.318 days for warfarin, p = 0.139).Conclusion:Despite higher pharmacy costs for NVAF patients initiated on dabigatran vs warfarin, this was more than offset by lower utilization of medical care resources.

Guidelines for the prevention, diagnosis, and treatment of infective endocarditis in adults: The Task Force for the Prevention, Diagnosis, and Treatment of Infective Endocarditis in Adults of Chinese Society of Cardiology of Chinese Medical Association, and of the Editorial Board of Chinese Journal of Cardiology

The pathogenesis of infective endocarditis (IE) is a complex process which can include tissue damage to the valves during bacteraemia, pathogen adherence leading to the formation of non-bacterial thrombotic vegetations, platelet aggregation at sites of tissue damage, and a variety of biological interactions between pathogens and host matrix molecules (e.g. platelets).1,2 In the past few decades, the incidence of IE has increased significantly, which may be the result of an aging population, delayed diagnosis of valvular heart disease, and even new methods of valve implantation, including transcatheter therapies. The incidence of IE of the right heart has also increased due to the prevalence of intravenous drug use. Overall, the epidemiological profile of IE in China remains inadequately characterized, and the precise epidemiology differs between geographic location and study populations. For example, in Europe, the incidence of IE is reported to be 3–10 episodes/100 000 person-years and increases with age—the peak incidence being 14.5 episodes/100 000 person-years in patients between 70 and 80 years of age, with a male : female ratio of ≥2 : 1. Over the course of the 20th century, due to effective antibiotic treatment, the major cause of IE has changed from rheumatic valve disease, which mainly affects young adults, to a variety of other aetiologies. In many locations, the most common pathogen of IE has changed from Streptococcus to Staphylococcus . In the USA, staphylococcal infection has become the most prevalent, and, in China, case reports show that Streptococcus and Staphylococcus are the most common pathogens.3–6 Despite advances in its management, IE is still considered a severe disease with a high mortality rate and poor prognosis. This consensus is …

Role of modern 3D echocardiography in valvular heart disease.

Three-dimensional (3D) echocardiography has been conceived as one of the most promising methods for the diagnosis of valvular heart disease, and recently has become an integral clinical tool thanks to the development of high quality real-time transesophageal echocardiography (TEE). In particular, for mitral valve diseases, this new approach has proven to be the most unique, powerful, and convincing method for understanding the complicated anatomy of the mitral valve and its dynamism. The method has been useful for surgical management, including robotic mitral valve repair. Moreover, this method has become indispensable for nonsurgical mitral procedures such as edge to edge mitral repair and transcatheter closure of paravaluvular leaks. In addition, color Doppler 3D echo has been valuable to identify the location of the regurgitant orifice and the severity of the mitral regurgitation. For aortic and tricuspid valve diseases, this method may not be quite as valuable as for the mitral valve. However, the necessity of 3D echo is recognized for certain situations even for these valves, such as for evaluating the aortic annulus for transcatheter aortic valve implantation. It is now clear that this method, especially with the continued development of real-time 3D TEE technology, will enhance the diagnosis and management of patients with these valvular heart diseases.