Valvular Heart Disease Research Articles

Successful Anaesthetic Management of Triple Valve Surgery (Double Valve Replacement and Tricuspid Valve Ring Repair)- Challenges and Troubleshooting

Heart valve diseases are extensive and include double (mitral-aortic, mitral-tricuspid), or triple (mitral, aortic, and tricuspid) valvular regurgitation, stenotic or mixed lesions. The surgical correction of valvular lesions usually consists of the repair or replacement of all valves affected by a pathologic process.We present a challenging complex valvular case of a 61 year old hypothyroid female diagnosed with severe mitral stenosis with moderate mitral regurgitation, severe aortic stenosis with mild aortic regurgitation and severe tricuspid regurgitation with high pulmonary artery pressures.She successfully underwent Double valve replacement (Mitral and aortic) and Tricuspid Annuloplasty Ring repair.

Machine learning approach for detection of valvular heart disease through digital cardiac auscultation: results from multi-center prospective cross-sectional study

Abstract Background Cardiac auscultation, an essential bedside diagnostic tool, often identifies significant valvular heart disease through murmurs. However, its accuracy is contingent upon the clinician's expertise, leading to a growing dependence on expensive imaging techniques. This study aimed to develop and validate a machine learning model for detecting valvular heart disease from cardiac auscultation. Methods The digital cardiac auscultation and associated echocardiograms were prospectively collected from three sites between May 1, 2022, and Dec 31, 2022. We trained, validated, and externally tested a machine learning based model to classify aortic stenosis, mitral stenosis, aortic regurgitation, mitral regurgitation, tricuspid regurgitation, oveall valvular heart disease from cardiac auscultation, age, sex, and comorbidity. We condcted training, and internal testing using datasets for eXtreme Gradient Boosting (XGBoost) machine learning model from hospital one and hospital two. The dataset from hospital three was employed for external testing. We evaluated the area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), accuracy, sensitivity, specificity. Result We selected the cardiac auscultation and echocardiograms of a total of 4,321 patients, excluding those without valvular heart disease information, congenital heart disease, a history of valvular operation, and cardiac auscultation could not be interpreted. The diagnostic performance of heart murmurs for valvular heart disease across the three datasets generally demonstrated high sensitivity and PPV. Specifically, the sensitivity for aortic stenosis was 89.1%, mitral stenosis 88.8%, aortic regurgitation 88.5%, mitral regurgitation 89.8%, tricuspid regurgitation 90.1%, and for any valvular heart disease, it was 95.0%. However, the NPV was consistently lower, with values of 16.4%, 14.2%, 12.2%, 23.1%, 26.2%, and 65.3% for the respective conditions. In XGBoost machine learning model, the AUROC values for internal testing (N=1,403), along with their 95% confidence intervals, were as follows: aortic stenosis (0.93, 0.88–0.97), mitral stenosis (0.91, 0.81–0.97), aortic regurgitation (0.84, 0.79–0.90), mitral regurgitation (0.94, 0.90–0.98), tricuspid regurgitation (0.81, 0.75–0.87), and overall valvular heart disease (0.86, 0.82–0.89). For external testing (N=814), the AUROC values were: aortic stenosis (0.90, 0.83–0.96), mitral stenosis (0.88, 0.79–0.95), aortic regurgitation (0.94, 0.93–0.96), mitral regurgitation (0.93, 0.92–0.95), tricuspid regurgitation (0.89, 0.87–0.91), and overall valvular heart disease (0.92, 0.90–0.94). Conclusion The machine learning based model can accurately classify valvular heart diseases using information from digital cardiac auscultation.Figure 1.Study FlowFigure 2.AUC curve

Prevalence of multiple native valvular heart disease in TAVR patients: a multicenter study assessing the impact of staged transcatheter edge-to-edge repair

Abstract Background Multiple valvular heart disease (VHD) is associated with poor prognosis in patients undergoing TAVR. Existing research focuses either on mitral or tricuspid regurgitation (MR, TR), without providing comparative analysis of their long-term effects. Moreover, whether staged transcatheter edge-to-edge repair (TEER) improves outcomes in these patients is unknown. Purpose This multicenter study aimed to evaluate the prevalence of multiple VHD within a recent TAVR cohort and to assess and compare the impact of concomitant severe MR and TR on outcomes at both one and five years post-TAVR. Additionally, we aimed to examine the potential impact of staged TEER for severe VHD in patients treated with TAVR. Methods The study cohort consisted of 2,823 patients undergoing TAVR at our Heart Center between 2015 and 2022. This representative cohort was used to investigate the prevalence of multiple VHD in a contemporary TAVR population and to assess and compare the impact of severe MR and TR on outcomes following TAVR. Additionally, this cohort, along with TAVR cohorts from other Heart Centers, was screened for additional valvular intervention. Patients who underwent staged transcatheter edge-to-edge repair for severe MR (n=147) or TR (n=59) were included. Results A concomitant persistent severe VHD was observed in 369 (13.1%) patients, with 208 (7.4%) having a severe MR, and 161 (5.7%) having a severe TR. The one-year mortality was significantly higher in patients with a severe VHD compared to the overall cohort (9.0 vs 5.2 per 100 PY, p<0.01). Notably, the highest one-year mortality was observed in patients with severe TR (13.3 per 100 PY) followed by patients with severe MR (6.4 per 100 PY) and those with no or a mild VHD (3.9 per 100 PY, p<0.01). This difference persisted over five years, with mortality rates of 27.8 per 100 PY in TR patients compared to 16.7 and 10.6 per 100 PY in MR patients and those with no or a mild VHD (p<0.01), Figure 1A. The landmark analysis revealed lower one-year mortality in patients who underwent staged TEER compared to those with severe VHD (4.1 vs 12.1 per 100 PY, p<0.001). This trend continued at the five-year follow-up, with a mortality rate of 11.6 per 100 PY in the staged TEER group versus 25.8 per 100 PY in the group with severe VHD (p<0.001), Figure 1B. The multivariate analyses revealed that concomitant severe TR was independently associated with one-year mortality (HR: 1.75 [95% CI: 1.14 – 2.67], p<0.01). Conclusion A concomitant clinically significant VHD was prevalent among patients with AS undergoing TAVR. The persistence of severe VHD was associated with increased one- and five-year mortality following TAVR. Specifically, severe TR was associated with higher mortality rates compared to severe MR. The potential benefit of an additional staged TEER for concomitant VHD should be carefully considered, as it shows promise in improving outcomes for patients with persistent severe VHD after TAVR.

Increased risk of stroke in non-rheumatic valvular disease associated with antiphospholipid syndrome (APS): a US nationwide study 2017-2019

Abstract Introduction Antiphospholipid syndrome (APS) is an autoimmune syndrome characterized by the presence of antiphospholipid autoantibodies, arterial and venous thrombosis, recurrent pregnancy loss and thrombocytopenia. Cardiac manifestations are also common which include coronary artery disease (CAD), non rheumatic valvular heart disorders (NRVHD), myocardial dysfunction, intracardiac thrombi and pulmonary hypertension. Valvular lesions occur likely due to immune complex deposition on the valves, causing fibrosis, calcification and subsequently valvular deformities. Literature review suggests prevalence of NRVHD in APS population is 30% and left sided valves are more involved than right. An increased risk of stroke has also been seen in APS patients with valvular disorders. Purpose Studies report variations in incidence of NRVHD in association with APS. To estimate the prevalence of NRVHD and their correlation with APS, we conducted a retrospective database analysis of hospitalizations across the United States. Additionally we investigated whether this association indicates a increased risk of ischemic stroke in this specific subpopulation. Methods The national inpatient sample (NIS) from 2017-2019 was queried for adult patients (age>18 yrs) admitted with a primary or secondary diagnosis of APS. These patients were stratified for different NRVHD including tricuspid stenosis, tricuspid insufficiency, pulmonary stenosis, pulmonary regurgitation, mitral stenosis, mitral insufficiency (MI), aortic stenosis (AS), and aortic regurgitation. Baseline risk factors and cardiac complications were compared. Multiple regression analysis was performed to study the association of NRVHD with stroke as the primary outcome in the subpopulation of APS hospitalizations. Results A total of 567600 adult patients hospitalized with diagnosis of APS were identified from the NIS 2017-2019 database. Among those, 33,095 had NRVHD with a mean age of 69 years. Of these, prevalence of males was 52.41% compared to females of 47.59%. In the cohort of APS, patients with NRVHD had a higher prevalence of coronary artery disease (43.78% vs 20.41%), diabetes mellitus (30.65% vs 27.56%), prior stroke (10.42% vs 7.78%) than those without valvular disorders. Among the APS population the most prevalent NRVHD were mitral insufficiency (MI), 2.45% and aortic stenosis (AS), 2.13%. In the cohort of NRVHD in APS hospitalizations, the odds of stroke was found to be 1.3 times higher with statistical significance (confidence interval (CI): 1.13-1.48, p <0.001) adjusted for relevant clinical and demographic risk factors such as tobacco abuse, CAD, diabetes mellitus, prior stroke. Conclusion This study shows 6.2% prevalence of NRVHD in APS population, of which MI and AS were common. NRVHD is also associated with higher odds of stroke in APS. Further studies are warranted in identifying the risk of stroke with NRVHD in APS population and emphasizing the need for early detection.

Valvular heart disease and out-of-hospital cardiac arrest

Abstract Background The prevalence of valvular heart disease is on the rise. Severe valvular diseases are generally associated with high mortality, and aortic stenosis (AS) is associated with halved survival after out-of-hospital cardiac arrest (OHCA). Despite the severity of the prognosis of valvular heart diseases, their association with the risk of OHCA is not well-elucidated. Purpose The study aimed to examine the association between valvular heart disease and OHCA. Method We conducted a study using data from the nationwide Danish Cardiac Arrest Registry. We included adult OHCA patients with presumed cardiac origin. We compared OHCA cases with and without valvular heart disease, stratified by valvular disease type (AS, aortic regurgitation (AR), mitral stenosis (MS), and mitral regurgitation (MR) or multiple valvular diseases (more than 1 concomitant type of valvular disease)). We calculated the hazard ratios of OHCA using time-varying Cox regression models fitted with a nested case-control design. For each case, we matched up to five controls based on age, sex, year of OHCA, and two comorbidities: ischemic heart disease and congestive heart failure. Results We included 43,967 OHCA cases and 219,772 controls matched from the general population. In the total case population, the median age was 72 years, 68% were male, 26% had ischemic heart disease, 21% had congestive heart failure, and 57% had cardiovascular risk factors. We identified 1862 (4.23%) cases with AS, 336 (<1%) with AR, 31 with MS (<1%), 710 with MR (1.6%), and 605 with multiple valvular diseases (1.4%). Compared with cardiac arrest cases without valvular disease, cases with valvular disease were more likely to have ischemic heart disease (e.g., AS vs controls: 6.5% vs 1.7%, p<0.001) and congestive heart failure (e.g., MR vs controls: 3.5% vs 1%, p<0,001; AS vs controls: 8.2% vs 2.8%, p<0.001). AS, MS (HR: 1.67 [95%CI: 1.12; 2.51]), MR (HR: 1.38 [95%CI: 1.27; 1.50]) and multiple valvular diseases (HR: 1.36 [95%CI: 1.24; 1.49]) were significantly associated with higher hazard of OHCA (Figure 1); the strongest association was seen for AS (HR: 1.66 [95% CI: 1.58; 1.76]) (Figure 1). AR was not significantly associated with OHCA (HR: 1.05 [95%CI: 0.94; 1.19]). Conclusion In this Danish nationwide cardiac arrest cohort, aortic stenosis, aortic regurgitation, and mitral regurgitation were associated with increased rates of OHCA. The association was strongest in aortic stenosis patients. Focus on risk factors of OHCA in patients with valvular heart disease is warranted.Figure 1

Causes and predictors of long-term mortality in patients referred to a cardiac rehabilitation program

Abstract Introduction Cardiac rehabilitation (CR) programs have proven to reduce short-term mortality in patients with ischemic heart disease (IHD) and hospitalization rates in patients with heart failure (HF) (1-3). However, there is insufficient evidence about long-term mortality (>24-36 months) for patients receiving treatments based on recent clinical practice recommendations. Purpose To assess the long-term prognosis and predictors of mortality in a heterogenous group of patients referred to cardiac rehabilitation, and to obtain a detailed overview of death causes. Methods An inferential analysis of consecutive patients referred for exercise-based CR at a single site between January 2016 and January 2021 was performed. Included subjects were classified attending to four indications for referral: IHD, HF, valvular heart disease (VHD) and cancer therapy-related cardiovascular toxicity (CTR-CVT). Patients who did not initiate the CR program or were referred for other indications were excluded. The primary endpoint was all cause death. The secondary endpoint was cardiovascular (CV) death. A survival analysis was performed using Kaplan Meier estimates for each referral indication and a Cox proportional hazards model was fitted to investigate mortality predictors. The initiation of the exercise program was considered the inclusion date, and patients were followed until they met the primary endpoint or censored by January 2024. Results A total of 1180 patients were included in the analysis (81% male, mean age 61 ± 11 years), with a median follow-up of 5.3 years. Most of the patients had been referred due to IHD (87%), while the proportion for HF, VHD, and CTR-CVT was 9.9%, 1.8% and 1.4%, respectively. Seventy-eight patients (6.6%) died during follow-up, with a Kaplan-Meier estimated survival of 94.5% at 5 years (95% CI 93.2%-95.9%). Nineteen patients (24.4%) died due to a cardiovascular event, mostly sudden cardiac death (47%). The predominant non-CV causes of death were infectious disease (36%) and cancer (34%). A multivariate Cox model analysis (Table 1) showed an increased risk of all cause death for patients referred due to HF (HR 2.95, 95% CI 1.33 - 6.55), VHD (HR 5.55, 95% CI 1.19 - 25.9) and CTR-CVT (22.47, 95% IC 7.32 - 68.95) when compared to IHD (Figure 1). Other significant predictors of mortality were LVEF <40%, male sex, age, diabetes, peripheral vascular disease, and multivessel coronary disease. Only LVEF <40%, VHD, CTR-CVT and peripheral vascular disease were independent predictors of CV death. Conclusion Patients who initiated an exercise-based CR program had a low risk of all cause death at 5 years. Indications for CR other than IHD were associated with higher mortality rates after adjusting for confounder variables, likely due to a selection bias of patients in advanced stages of the disease.Table 1.Cox models for all-cause deathFigure 1.KM plot for all-cause death

Risk assessment using DEVI and CARPREG -1 score to predict maternal adverse cardiac cvents in pregnancies with valvular heart disease: external validation study from MPAC registry

Abstract Background Even though many risk assessment tools in pregnant women with valvular heart disease (VHD) are available, validation studies are few that assess their performance in diverse settings, which is needed prior to the application of routine clinical practice. Objectives To validate and establish the clinical utility of two risk stratification tools – DEVI (VHD-specific tool) and CARPREG-I in predicting adverse cardiac events in pregnant patients with VHD. Methods This cohort study involved consecutive pregnancies complicated with VHD enrolled in the prospective MPAC registry from July 2016 to December 2019(1). The ability to discriminate those with and without adverse cardiac outcomes was assessed using the area under the curve for both the DEVI and CARPREG-1 scores. Performance was assessed through discrimination and calibration characteristics. Clinical utility was evaluated with Decision Curve Analysis. Results Among 1029 pregnancies complicated with heart disease in the MPAC registry cohort, 604(52.3%) with valvular heart disease were included in this analysis. One or more composite adverse cardiac events occurred in 70 (11.6%) pregnancies during antenatal or the early postpartum period. Mitral regurgitation was the most common lesion (67.7%). In assessing the pregnancy outcomes, those who continued past 20 weeks were included, with the majority (92.0%) delivered at term, and the cesarean rates were 33.6% (n=203). The most common adverse event was heart failure (n=32, 5.3%), most of which (68.6%) occurred in the postnatal period. There were 11 maternal deaths (1.8%) during the study period, six among those with mechanical heart valves (four prosthetic valve thrombosis), two among those with severe mitral stenosis and pulmonary hypertension and two with severe mitral regurgitation and infective endocarditis. The area under the receiver operating characteristic curve (AUC) was 0.766, with 95% confidence intervals (CI) 0.703- 0.828 for DEVI and 0.703 (95%CI 0.644- 0.763) for CARPREG-I models. Calibration plots suggested that the DEVI score overestimates risk at higher probabilities, whereas the CARPREG-I score underestimates the risk at most probabilities. Decision curve analysis demonstrated that both models were useful across predicted probability thresholds between 10% and 60%. Conclusion In this external validation study in pregnant women with primary rheumatic VHD, DEVI and CARPREG-I scores showed good discrimination ability and clinical utility across various probabilities. Both can be used to classify those who develop adverse cardiac outcomes during pregnancy and childbirth. However, both models need recalibration to improve the agreement between the predicted and observed events prior to routine application in diverse clinical settings.Figure 1.a and b ROC and Decision Curves

Predicting the severity of valvular heart diseases using ai integrated with ECG and anthropometric data

Abstract Background AI has been used in studies to predict the severity of aortic stenosis (AS) from electrocardiography (ECG), but the application of AI to predict the severity of aortic regurgitation (AR), mitral regurgitation (MR) and tricuspid regurgitation (TR) has not been widely explored. This study aimed to determine whether deep learning can predict the severity of valvular heart disease (AS, AR, MR and TR) assessed by echocardiography using ECG data. Methods and Results We analyzed data from 31,787 adult patients who underwent ECG and echocardiography within three days of each other between 2014 and 2019 at two hospitals. Valvular heart diseases, including AS, AR, MR, and TR, were classified into categories; normal, mild, moderate, and severe; according to guidelines. ECGs, along with patient age, gender, height, and weight, were inputs to a LightGBM machine learning model. The model was trained on 80.6% of the data, with the remaining 19.4% used for validation. The sensitivity for predicting moderate or severe valvular heart disease was 85% for AS, 88% for AR, 85% for MR, and 85% for TR. The negative predictive values for AS, AR, MR, and TR were all 100%. Prediction accuracies for the severity of each disease were AS at 0.857, AR at 0.871, MR at 0.855, and TR at 0.890. Predictions based on AS diagnostic parameters; mean pressure gradient, valve area, and maximum blood flow velocity also showed favorable accuracies at 0.891, 0.821, and 0.877, respectively. Conclusion Integrating AI with standard clinical parameters, including ECG data, height, and weight, showed potential in accurately predicting the severity of various valvular heart diseases. Further researches across diverse populations and settings are necessary to validate the approach's effectiveness and explore its impact on patient care.

Good agreement between cardiac magnetic imaging protocols using free-breathing and breath-holding techniques for flow quantification in valvular heart disease

Abstract Background Cardiac magnetic resonance (CMR) evaluation of valvular heart disease is an important diagnostic tool when echocardiography is inconclusive. Flow quantification is usually performed during breath hold, which can be challenging in valvular heart disease patients often suffering from dyspnea and heart failure. Purpose The purpose of the present study is to evaluate a free-breathing protocol of flow measurement in the aortic, pulmonary and tricuspid valves in healthy subjects (HS) and patients with tricuspid regurgitation (TR). Methods Phase contrast flow volume measurements were performed in 20 HS and 25 patients with TR using standard breath-holding (BH) and a free-breathing (FB) technique. Two venc-adjusted volume acquisition were acquired in the aortic, pulmonary and tricuspid valve during BH and one during FB. Mean, standard deviation (SD) and limits of agreement (LoA) for aortic forward flow volume (AoFF), pulmonary forward flow volume (PuFF) and tricuspid inflow volume (TrIF) were evaluated. Results Figure 1 presents flow volume (ml) of AoFF, PuFF and TrIF during BH (mean of 2 measurements) and FB. Conclusions The present study demonstrated good agreements between phase contrast flow measurements using FB and BH techniques. These parameters are used in the evaluation of aortic, mitral, pulmonary and tricuspid regurgitations using CMR imaging. Thus, free-breathing CMR acquisition can be an important complement in the assessment of valvular heart disease and may be applied in patients with difficulty to hold their breath.

Impaired right ventricular strain is associated with worse prognosis in patients with liver cirrhosis

Abstract Introduction Cirrhotic cardiomyopathy is a complication of liver cirrhosis accounting for significant morbidity and associated with reduced patient survival. It is characterized by reduced afterload, increased cardiac output, and diastolic dysfunction. However, there are limited data regarding the prognostic significance of right ventricular (RV) mechanics. Novel echocardiographic techniques such as speckle tracking echocardiography can detect subclinical myocardial dysfunction early in the course of non-ischemic myocardiopathies. Purpose To investigate the prognostic role of RV free wall strain (RVFWS) in patients with liver cirrhosis. Methods We prospectively enrolled 70 patients with liver cirrhosis in stable clinical condition. Patients with coronary artery disease, valvular heart disease, ongoing alcohol consumption or poor acoustic window were excluded from the study. Transthoracic echocardiography was performed and RVFWS was calculated from RV focused 4-chamber views using a dedicated software. Since RVFWS was as expected negative in all patients, absolute values were used for all calculations. Clinical and laboratory examination was also performed in all patients and the Model for End-Stage Liver Disease (MELD) score a widely used tool for assessing liver disease severity was calculated. All-cause mortality at 24 months was the primary endpoint and was available in all patients. Results Mean RVFWS in the cohort was 27.4±6.3% and using a lower normal limit of 20%, reduced RVFWS was detected in six patients. According to the Spearman coefficient analysis, RVFWS was correlated with MELD (rho=0.274, p=0.023) indicating increased values in patients with more advanced liver disease. According to the univariate Cox-regression proportion hazard models RVFWS as a continuous variable was not significantly correlated with worse two year survival (HR: 1.01 (0.95-1.08, p=0.658). Interestingly, among conventional echocardiographic parameters basal RV diameter (p=0.004) and right atrial end-systolic area (p=0.008) were associated with increased risk for the primary endpoint. The association of the primary endpoint with other parameters of RV systolic function was also not statistically significant. The Multivatiate Cox Regression survival analysis, which incorporated age, sex, MELD, hemoglobin, systolic blood pressure and cardiac output, showed that among echocardiographic parameters for the evaluation of the RV, only RVFWS showed a tendency to be associated with worse survival (p=0.062). When patients were stratified into tertiles according to RVFWS values, those in the first tertile (with lower absolute RVFWS values) had significantly increased hazard for the primary endpoint (HR: 5.82, 2.18-15.8, P<0.001) (figure 1). Conclusions RVFWS values are within normal limits in most patients with liver cirrhosis. When adjusted for the severity of the liver disease, reduced RVFWS values are associated with worse prognosis in cirrhotics.

Differences in clinical features of patients with heart failure and reduced, mildly reduced, and preserved ejection fraction in a contemporary cohort: is the clinical profile changing?

Abstract Introduction Heart failure is a serious health problem and continues to have a high mortality and incidence of decompensations, despite advances in its management. The type of HF, defined by the LVEF value, has a great influence on treatment, since not all drugs and procedures have been shown to improve prognosis in the different subtypes of HF (reduced-HFrEF, mildly reduced-HFmrEF, and preserved-HFpEF). In addition, there are differences in clinical characteristics and comorbidities that may also influence these aspects, although the demographic and social changes that have occurred in recent years may have changed this perspective. Purpose To analyze in a contemporary registry of HF patients followed in specialized HF units in Spain the differences in clinical and demographic characteristics and comorbidities among the three subtypes of HF. Methods We analyzed data from the registry of the SEC-Excelente-IC quality accreditation program of the Spanish Society of Cardiology, with 1716 patients with HF included between 2019 and 2021 by 45 specialized HF units accredited by the SEC. Patients were included consecutively in two 1-month cutoffs (March and October) in that period. The clinical and demographic characteristics and comorbidities of the patients were compared according to the type of HF. Results Of the 1716 patients, 55.5% were HFrEF, 11.9% HFmrEF and 32.6% HFpEF. Figure 1 shows the main demographic and cardiovascular characteristics of the three subgroups. The median LVEFs were 30% (23-35), 45% (42-48) and 58% (53-65), respectively. Figure 2 shows the prevalence of major comorbidities. Patients with HFpEF were older and female in higher proportion, and presented a significantly higher prevalence of atrial fibrillation, valvular heart disease, obesity, hypertension, and anemia. Patients with HFrEF had a higher prevalence of coronary artery disease, smoking, admissions for HF in the previous year, and left bundle branch block. There were no differences between the three groups in the remaining comorbidities, including chronic kidney disease and chronic respiratory diseases. The characteristics of HFmrEF were intermediate between HFrEF and HFpEF (figures 1 and 2). Conclusions In our contemporary cohort of real-life HF patients, slight differences were observed between the 3 types of HF, particularly in relation to age, proportion of women, and prevalence of cardiac diseases. However, the prevalence of most of the major comorbidities did not show significant differences between the 3 groups, suggesting that a shift toward greater similarity in clinical characteristics between the 3 types of HF may be occurring.

Incidence and clinical characteristics of cardiac amyloidosis in Tuscany and Umbria

Abstract Background Recent advances in imaging and increased disease awareness have led to a significant rise in newly diagnosed cases of cardiac amyloidosis (CA) over the past decade. However, there remains a lack of epidemiological data regarding the true incidence of the disease. Purpose To determine the incidence and clinical characteristics of newly diagnosed cases of CA in a large Italian population. Methods Subjects with suspected CA underwent diagnostic work-up in 20 Centers in Tuscany and Umbria regions within the Cardiac Amyloidosis RegistRY (CARRY), between January 1st 2022 and December 31st 2022. Results A total of 551 subjects were enrolled in the registry, with complete data on the final diagnosis available for 448 individuals. CA was diagnosed in 179 patients (40%), with 28 (16%) classified as light chain CA (AL-CA), 114 (64%) wild-type transthyretin CA (ATTR-CA), 12 (7%) variant ATTR-CA, and 25 (13%) undetermined CA. Considering a reference population in Tuscany and Umbria in 2022 (4,518,388, according to the Italian National Institute of Statistics), this yields an estimated cumulative incidence of 39.6 cases per million inhabitants per year. Alternative diagnoses in the remaining patients mostly included hypertensive heart disease (n=157, 58%), hypertrophic cardiomyopathy (n=25, 9%), moderate-to-severe valvular heart disease (n=11, 4%), or a mixed phenotype (n=72, 27%). The primary referral pathway for the suspicion of CA was cardiological (n=345, 77%), followed by incidental finding during screening visits (n=58, 13%), and haematology referral (n=33, 7%). Patients with confirmed CA were older (81 [75-85] vs. 77 [72-83] years, p<0.001), and more frequently reported musculoskeletal red flags of CA, such as carpal tunnel syndrome (85 [48%] vs. 53 [20%] years, p<0.001), and spontaneous tendon rupture (39 [22%] vs. 18 [7%] years, p<0.001). Median time to diagnosis from clinical suspicion was 73 (37-140) days for CA and 84 (36-156) days for alternative diagnoses (p=0.564). Conclusions The diagnosis of CA is on the rise globally, with an estimated incidence of 39.6 new cases per million inhabitants per year in two large regions of Italy. These findings should inform policymakers in redefining the status of CA as a rare disease, potentially influencing patient care and treatment costs.

Anabolic steroids as the substrate for atrial fibrillation: A case report

Abstract Background Atrial fibrillation (AF) is the most frequently encountered sustained arrhythmia worldwide. This supraventricular rhythm disorder is precipitated by advanced age, valvular heart disease, hypertension, heart failure, congenital heart defects and others. However, the role of anabolic steroids abuse in precipitating AF remains insufficiently researched and largely underreported, despite their known cardiovascular risks. Case summary We present the case of a 40-year-old male bodybuilder who was admitted to the emergency department with symptomatic atrial fibrillation. His medical history revealed the use of anabolic steroids, which was suspected to be the trigger. A thorough biological evaluation and echocardiography were performed, revealing no structural or functional cardiac abnormalities. After electrical cardioversion and discontinuation of anabolic steroid use, the patient's rhythm returned to normal, with no recurrence of arrhythmia during follow-up. Discussion This case highlights the potential but underexplored link between anabolic steroid use and the onset of atrial fibrillation. Although anabolic steroids are known to affect cardiovascular health by promoting hypertension, left ventricular hypertrophy, and endothelial dysfunction, their role in arrhythmogenesis, particularly in AF, remains unclear. In this patient, the absence of other identifiable triggers, combined with the resolution of symptoms upon steroid cessation, strongly suggests a causal relationship. Further research is needed to clarify the mechanisms through which anabolic steroids may contribute to the development of AF, particularly in younger, otherwise healthy individuals such as athletes and bodybuilders. This case underscores the importance of awareness among clinicians regarding the potential cardiac risks associated with anabolic steroid use.

Comprehensive risk factor analysis for sick sinus syndrome: an analysis of genetic, sociodemographic, clinical and laboratory data from the UK Biobank

Abstract Background Sick sinus syndrome (SSS) and atrial fibrillation (AF) frequently coexist and interact bidirectionally, often to initiate and perpetuate each other. The polygenic risk score (PRS) assesses the cumulative risk of disease onset based on variations at multiple genetic loci. To date, no specific PRS has been established for SSS. This study investigates the impact of clinical, sociodemographic, laboratory, and genetic factors, which were represented by PRS for AF due to the absence of a specific PRS for SSS on predicting the incidence of SSS. Methods The UK biobank enrolled 502,421 individuals, aged 38 to 73, across 22 centres in the UK locations, from 2006 to 2010. After excluding cases with incomplete data, prior history of AF, and valvular heart disease, 405,869 participants without AF (median age, 58.0 [interquartile range (IQR), 50.0–64.0] years; 213,684 [45.2%] male) were analyzed. Participants were categorized into three groups according to a validated PRS for AF: the first quintile the middle three quintiles, and the fifth quintile. Throughout the observation period, the incidence of SSS event was documented. Results During a follow-up period of 11.9 (IQR 11.1-12.6) years, a total of 769 cases of Sick sinus syndrome were identified. The Kaplan-Meier analysis demonstrated a progressive increase in the incidence of SSS as the quintiles of AF-related PRS increased. (Log-rank: P <0.001) Among 24 variables, statistically significant hazard ratios were observed with age, male sex, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), previous myocardial infarction (MI), history of malignant neoplasm, hypertension, diabetes mellitus, cystatin C, and AF PRS. In the prediction model for Sick Sinus Syndrome, the C-index for the sociodemographic model was 0.753. This increased to 0.764 with the addition of clinical factors, to 0.765 with the inclusion of biomarkers, and further to 0.769 upon adding genetic factors. Conclusion Our study found significant associations between the incidence of SSS and a range of sociodemographic, clinical, laboratory, and genetic factors, including age, male sex, BMI, blood pressure, and medical history. Notably, the AF PRS also showed relevance, suggesting its potential utility in developing one for SSS in the absence of a dedicated PRS for SSS.

Global longitudinal strain measurement in the modern era - is artificial intelligence the answer ?

Abstract Background Global longitudinal strain (GLS) enhances the assessment of left ventricular (LV) function beyond ejection fraction (EF) in 2D echocardiography (Echo). However, manual GLS measurement, the most common technique, is operator dependent. A novel vendor-independent artificial intelligence (AI)-based software streamlines automated 2D-Echo exam evaluation and GLS calculation. Purpose We sought to evaluate the ability of the AI to automatically identify LV 4-, 2-, and 3-chamber views (CV) from 2D-Echo exams and to validate GLS against the conventional manual method. Methods 540 patients underwent standard 2D-Echo exams. The server-based AI recognized the optimal LV CVs from 2D-Echo exams and automatically calculated triplane GLS by endocardial delineation (n=489, 91%). Manual LV GLS was performed independently by accredited cardiologists. Pearson’s correlation (R) and Bland-Altman analysis were performed to assess bias and limits of agreement (LOA) for GLS. Multivariate between-method regression was performed using ordinary least squares to disclose possible sources of possible bias. Significance was defined as a 2-tailed P value < 0.05. Results Patients’ median age was 70 years [18-93], 59% were males. The 2D-Echo indications were coronary artery disease (36%), valvular heart disease (15%), non-ischemic heart failure (15%) and others (34%). The feasibility of the AI to calculate GLS was 91% (n=489) due to endocardial border delineation issues. AI correctly classified all CVs. An excellent correlation (r=0.92, p<0.001) was observed between AI and manual GLS, with low bias (0.5%) and narrow LOAs (-3.1 and +4.2%) (Figure 1, top row). We identified LV end-diastolic volume and EF as statistically significant confounders (p < 0.05) with only small effect as depicted in Figure 1 (bottom row). Neither sex, age, acoustic window, body mass index, nor heart rate, along with the interaction with sex, end-diastolic volume, or EF, were significant (p > 0.23). Conclusion AI-based LV GLS calculation in 2D-Echo shows promising feasibility and accuracy. Strong agreement between AI-derived GLS and manual measurements supports its reliability. The AI's ability to identify optimal LV views underscores its robustness. Further validation and refinement are warranted, highlighting the transformative potential of AI in advancing cardiovascular diagnostics.

Global myocardial work index is an independent predictor of functional capacity on 6-minute walk test in stable non-ischaemic cardiomyopathy

Abstract Background Reduction in 6-minute walk test distance (6MWD) in heart failure (HF) is associated with impaired quality of life and adverse cardiovascular outcomes. Myocardial work (MW) indices are novel non-invasive measures of left ventricular (LV) function which characterise myocardial contractile efficiency and energy utility. Purpose We aim to assess the predictive capacity of MW indices for functional capacity in stable non-ischaemic cardiomyopathy (NICM). Methods We prospectively recruited consecutive patients with stable NICM from our HF service from January 2021 to December 2023. All patients received optimal guideline direct therapy (OGDT) for 3-months before undergoing a comprehensive transthoracic echocardiogram and functional assessment with 6-minute walk test (6MWT). We excluded patients with prior cardiac surgery, severe airways disease, valvular heart disease, and those in atrial fibrillation (AF) or had mechanical limitations to their mobility at time of 6MWT. We collected demographic, clinical, and echocardiographic factors to identify the determinates of reduced 6MWD defined as <300 metres. Results Of 249-patients included (69.9% male; age 57.3±16.5yrs), 65-patients had reduced 6MWD. They were significantly older (62.3±17.2 vs 55.5±16.0yrs, p=0.005), had shorter height (166.3±10.3 vs 171.3±9.8cm, p=0.001), higher prevalence of AF history (39.1 vs 21.4%, p=0.001), lower LV-ejection-fraction (38.1±13.3 vs 43.3±10.2%, p=0.001), lower LV-global-longitudinal-strain (LV-GLS; 10.5±4.4 vs 12.7±3.9%, p<0.001), lower global-work-index (GWI; 943±487 vs 1230±455mmHg%, p<0.001), lower global-constructive-work (GCW; 1286±561 vs 1590±494mmHg%, p<0.001), lower global-work-efficiency (GE; 81.2±9.9 vs 85.4±8.1mmHg%, p=0.04), larger indexed left atrial volume (LAV; 45.0±18.4 vs 40.1±24.5ml/m2, p=0.038), higher E/e’ ratio (14.6±8.9 vs 11.8±6.0, p=0.003), and lower right-ventricular-free-wall-strain (RV-FWS; 19.2±6.7 vs 21.8±6.4, p=0.008; Table 1.). On receiver-operating-characteristics-curve analysis, GWI best predicted 6MWD reduction (Figure 1; AUC 0.68) compared to GCW, GE, LVEF and LVGLS (AUC: 0.62, 0.40, 0.62, and 0.62 respectively). Accounting for covariates, our multiple regression was run to predict 6MWD from age, height, AF history, LAV, E/e’, GWI and RV-FWS. These variables statistically significantly predicted 6MWD, F(4,239)=23.5, p<0.001, R2=0.34). With each 100mmHg% rise in GWI being independently associated with a 6.5m increase in 6MWD. Conclusion GWI was independently associated with 6MWD in our cohort of NICM patients on OGDT. MW indices provide some mechanistic insights into exercise capacity in patients with NICM.Table 1.Baseline CharacteristicsFigure 1.ROC of GWI Discriminating 6MWD

Prognostic impact of lean diabetes mellitus on aortic stenosis

Abstract Background Obesity and type 2 diabetes mellitus (DM) has traditionally been thought to coexist. Lean DM is a distinct condition defined by body mass index (BMI) < 25, associated with elevated insulin resistance, rapid beta-cell failure, absence of ketosis and malnutrition. Lean DM has been linked to poor cardiovascular outcomes, but remains an understudied entity in valvular heart disease. Purpose This study aims to demonstrate the prognostic implications of lean DM on aortic stenosis (AS). Methods Between 2011 to 2021, 700 consecutive patients with index echocardiographic diagnoses of aortic stenosis were stratified into 4 groups, namely lean (BMI < 25) DM, lean non-DM, obese (BMI > 25) DM, and obese non-DM. Demographics, co-morbidities, echocardiographic findings and clinical outcomes were collected. Clinical outcomes were compared using Kaplan-Meier curves and a multivariate Cox regression model was constructed. Results 396 (56.6%) patients were female, and 435 (62.1%) patients were of Chinese ethnicity. 279 (39.9%) patients had DM, of whom 155 (55.6%) patients had lean DM. The mean BMI in the lean group was 21.4 ± 2.4 kg/m² compared to 29.5 ± 4.9 kg/m² in the obese group. Irregardless of BMI, higher proportions of DM patients were on baseline antiplatelets and statins compared to non-DM patients. Echocardiographically, obese DM had smaller indexed left ventricular volumes and mass - left ventricular end diastolic volume index (LVEDVi) (65.7 ± 21.7 ml/m² vs. 70.8 ± 23.6 ml/m², p = 0.002) and left ventricular mass index (LVMi) (115.7 ± 31.9 g/m² vs. 116.3 ± 36.5 g/m², p = 0.401) compared to lean DM respectively. Obese DM was associated with more cardiovascular risk factors compared to lean DM in terms of hypertension (91.9%, n = 114 vs. 89.0%, n = 138, p < 0.001), hyperlipidaemia (82.3%, n = 102 vs. 73.5%, n = 114, p < 0.001) and chronic kidney disease (50.8%, n = 62 vs. 45.5%, n = 70, p < 0.001). On the contrary, lean DM was associated with more cardiovascular sequelae of acute myocardial infarction (36.8%, n = 57 vs. 24.2%, n = 30, p < 0.001) compared to obese DM. AS patients with lean DM experienced the highest significant all-cause mortality (71%, n = 110 in lean DM vs. 58.9%, n = 73 in obese DM vs. 49.1%, n = 86 in obese non-DM vs. 65%, n = 160 in lean non-DM, p < 0.001) compared to all other groups. On the contrary, AS patients with lean DM had the lowest heart failure incidence (17.4%, n = 27 in lean DM vs. 23.4%, n = 29 in obese DM vs. 33.1%, n = 58 in obese non-DM vs. 23.2%, n = 57 in lean non-DM, p = 0.009). On multivariate analyses adjusted for age, comorbidities, and echocardiographic parameters, lean DM was an independent predictor of worse all-cause mortality in AS patients (HR 1.60, CI: 1.19 - 2.16, p = 0.010). Conclusion Lean DM in AS patients was associated with worse all-cause mortality. Lean DM was a prognostic marker of all-cause mortality in patients with AS.Figure 1

Accurate detection of dilated cardiomyopathy onset through machine learning predictions from ECG data

Abstract Background Dilated cardiomyopathy (DCM) is a primary heart muscle disease characterised by left-ventricular dilatation and reduced contractility, often associated with arrhythmia and conduction disease. DCM often has a genetic component, putting family members of diagnosed patients at risk for the disease. Stratification tools to provide personalized screening advice are currently lacking. Artificial intelligence (AI) based analysis of the electrocardiogram (ECG) offers a potential solution to this challenge. Purpose Our study aims to develop and validate a novel AI-ECG model capable of prediction and early detection of DCM. Methods An AI-ECG model was developed on a United States (US) based secondary care centre dataset including 337,463 ECGs from 50,932 patients within 4 months of diagnostic echocardiography and 265,576 ECGs from 51,086 patients prior to the first diagnostic echo. The data was split 50/10/40% for training, validation, and testing, with each subset containing 9-12% DCM positive labels. The model architecture was based on a convolutional neural network with residual blocks with a modified final layer for a discrete-time survival model. The AI-ECG score was linked to changes in the median ECG within the test set to provide explainability. External validation was performed on 68,885 diagnostic ECGs (0.08% DCM positive) from the UK biobank (UKB) and a Chinese general hospital dataset with 317,854 diagnostic ECGs (2.2% DCM positive) and 76,936 predictive ECGs (1.7% DCM positive). We performed common and rare variant association studies for the AI ECG scores on quality-controlled UKB data. Results The AI-ECG score for predicted future DCM was associated with diverse ECG features (Fig. 1). In the US cohort test set, the model achieved high diagnostic accuracy with an AUC of 0.9 (95% CI 0.89-0.90) overall, and 0.89 (0.85-0.92) in a subset excluding ischemic or valvular heart disease. External datasets showed AUCs of 0.86 (0.81-0.91) in the UKB and 0.97 (96-0.97) in the Chinese dataset. The predictive performance of the AI-ECG score, adjusted for age and sex, closely approximated echocardiographic traits (LVEF and LVEDD, Fig. 2) with a concordance index (CI) of 0.79 (0.76-0.83) and 0.81 (0.77-0.85), respectively, at 10 years of follow up (FU). Combining AI-ECG scores with echocardiographic traits enhanced the CI to 0.84 (0.8-0.87). Similar findings were observed in the Chinese dataset, with a CI of 0.87 (0.85-0.89) for the AI-ECG score and 0.9 (0.88-0.92) for echocardiographic traits. Genetic analyses in UKB identified associations between the AI-ECG score and rare TTN variants as well as 25 common variant loci. Conclusion Our explainable AI ECG model for future DCM prediction demonstrates robust performance across datasets, providing comparable predictive accuracy to echocardiographic traits up to 10 years pre-diagnosis. Genetic associations support the model's validity and provide new tools for DCM genetic discovery.

Nitisinone attenuates progression of aortic stenosis in patients with alkaptonuria: an analysis of the SONIA 2 study

Abstract Background/introduction Alkaptonuria (AKU) is a rare metabolic disorder caused by a defective enzyme, resulting in deposition of unmetabolised homogentisic acid in various connective tissues throughout the body (also termed "ochronosis"). Ochronosis of the aortic valve leading to progressive aortic stenosis is a rare but serious complication. Nitisinone decreases urinary and serum homogentisic acid levels and has been shown to improve morbidity and slow disease progression in AKU but the effects of this treatment on the progression of aortic stenosis have not yet been described. This review extrapolated from the data set of the SONIA 2 study, a 4-year multi-centre, randomised controlled trial investigating the effect of nitisinone on a composite clinical measure of AKU disease activity, but looked more specifically at measures of aortic stenosis disease progression. Methods Data was obtained from echocardiograms performed on 138 patients at baseline, 12, 24, 36 and 48 months of follow-up. In measuring the degree of aortic stenosis, the peak trans-aortic velocity (Vmax) was examined. A linear mixed effects regression model was used to assess the association between treatment and Vmax and to ascertain the difference in this measure at baseline and 48 months between the treatment and control groups. The mixed effects model incorporated both fixed effects for population parameters (age, sex, baseline Vmax, follow-up time) and treatment; and random effects, to account for intra-subject correlation of longitudinal observations of Vmax, inter-subject variability of baseline measurements of Vmax, and centre. Results At baseline, 19/138 patients (13.8%) had aortic stenosis, as classified by echocardiogram findings in accordance with the European Society of Cardiology (ESC) guidance on valvular heart disease, with 9/19 having mild aortic stenosis, 6/19 having moderate aortic stenosis and 4/19 having severe aortic stenosis. 25/138 had aortic sclerosis (see figure 1). The prevalence of aortic valve disease increased with age. From the 4-year follow-up period, 613 longitudinal observations of 138 subjects across all sites were obtained. At baseline, the difference in Vmax between the control and treatment groups was 0.063 m/s [95% CI: -0.054 m/s to 0.18 m/s] and did not reach statistical significance (p=0.23). At the end of the 4-year treatment period, the difference in Vmax was 0.10 m/s [95% CI: -0.0007 m/s to 0.20 m/s] and was statistically significant (p=0.05) (see figure 2). Conclusion Nitisinone slowed progression of aortic stenosis in patients with AKU. This may be grounds for timely initiation of nitisinone in those deemed to be at high risk, as identified by echocardiography. Of note, this is the first time that any medical therapy has ever been shown to affect the natural history of aortic stenosis.

Safe transition home after transcatheter aortic valve implantation feasibility (START-F) study: Very early self-reported function and recovery status after early discharge

Abstract Background Despite significant excellent outcomes and significant improvements in rapid recovery protocols and early discharge, rates of 30-day readmission after transcatheter aortic valve implantation (TAVI) remain stubbornly high. There can be numerous drivers for readmission in older people with valvular heart disease; the time of transition home is the period of greatest risk when patients are most vulnerable to repeat hospitalization. We sought to assess patients’ early recovery (< post-procedure day 3) to inform the evaluation of a nurse-led telephone intervention to mitigate the risks of 30-day readmission after early (1 day) discharge TAVI. Methods We conducted a prospective observational cohort feasibility study in five Canadian TAVI programs to ascertain acceptability, compliance, delivery of the intervention, recruitment and retention. In this preliminary report, we assessed key indicators of patients’ status at the time of discharge and functional status in their early recovery. Results We recruited 188 participants, mean age 81.6 years (SD=7.0), 41.5% women with baseline mean KKCQ 53.4 (SD=20.1). Patients were admitted on the day of the procedure, and underwent TAVI with a balloon-expandable (n=146, 77.6%) or self-expanding (n=30, 16.0%) device, with local anaesthesia +/- light sedation (n=171, 90.1%) or general anaesthesia (n=17, 9.0%). There was 1 in-hospital death (0.5%) and 182 (97.4%) were discharged home. At the time of the 3-day telephone follow-up, 168 (95.5%) reported that an individual had stayed with them during the first 24 hours [spouse: 85 (48.9%); friend: 77 (44.3%)], and 165 (93.3%; missing: 11) were able to complete all activities of daily living, including cooking their own meals (n=80, 45.5%). Self-reported energy was low/very low for 49 participants (26.1%), moderate for 93 (52.8%), and high/very high for 34 (18.1%), with 104 (55.3%) describing feeling somewhat or quite a bit better than before TAVI, and 24 (12.8%) somewhat or quite a bit worse. Bruising was present in 120 participants (68.6%) and 137 (78.8%) had removed their bandage as instructed. 18 participants were readmitted within 30 days, including 14 cardiac-related events. Conclusion This is one of the first reports of TAVI patients’ self-reported function following next-day discharge, providing novel evidence that most patients describe safe self-care and very early improved functional status. Research is needed to identify and support patients who are at higher risk of poorer trajectories of recovery and readmission, and report the feasibility of a nurse-led intervention to mitigate these risks.