Myocardial work and risk stratification in patients with severe aortic valve stenosis referred for transcatheter aortic valve replacement.

Background: Transcatheter aortic valve replacement(TAVR) has shown clear survival benefits in severe aortic valve stenosis(AS). However, patients unable to recover left ventricle function remain at risk with poor long-term survival. This single-center prospective study aims to analyze the supplementary benefits of myocardial work(MW) assessment for baseline risk stratification in patients with severe AS referred for TAVR. Methods: A total of 110 patients with severe AS referred for TAVR were included in the study. Baseline ECG data, transthoracic echocardiographic(TTE) images and blood samples were obtained. The TTE examination was repeated one day and one month after valve replacement. The primary outcome of the study was a composite endpoint consisting of all-cause mortality and HF hospitalization. Results: During a mean follow-up period of 521 ± 343 days, 29patients(26.4 %) reached the composite endpoint. Baseline troponins, NT-proBNP, sST2, GWI and GCW showed statistically significant differences between groups. Patients with a baseline GWI<2323 mmHg% (sensitivity 0.63 and specificity 0.76)had significantly worse outcome following TAVR. A basic predictive model included QRS-length, TAPSE, LAVI and E/e'. The addition of biomarkers did not yield any further advantages whereas incorporating the GWI cut-off value of 2323 mmHg% significantly enhanced the predictive value. Although there were no significant changes in LVEF and GLS, all patients exhibited a significant reduction in GWI and GCW immediately after TAVR. Conclusion: Our findings provide evidence for the enhanced usefulness of MW analysis in the initial risk stratification of patients with severe AS referred for TAVR. Specifically, a baseline GWI<2323 mmHg% demonstrates an independent predictor associated with increased incidence of all-cause mortality and HF hospitalization following TAVR.

CIED-based remote monitoring in heart failure using the HeartLogic™ algorithm: Which patients benefit most?

BACKGROUND & AIMS: Early identification of worsening HF enables timely adjustments to prevent hospitalization. Recent studies show the HeartLogic™ algorithm detects congestion and reduces HF events. However, it is unclear which patients benefit most. Therefore, this study aims to identify and characterize HF patients who benefit most from CIED-based remote monitoring with HeartLogic™. METHODS: In this multicenter retrospective study, patients with a CIED and HeartLogic™ algorithm under structured follow-up were included. Patients were classified as having "substantial benefit" or "no benefit" from monitoring. RESULTS: In total, 242 patients were included (male n = 190, 79%, median age 61 years [IQR 61-77]). Median follow-up was 1.2 years [IQR 1.1-2.7]. Among 378 alerts, 266 were true positive (70%) and 112 false positive (30%). Of the 242 patients, 69 (29%) were classified as having "substantial benefit", while 173 (71%) had "no benefit" from HeartLogic™ monitoring. Univariate and multivariate analysis showed that patients with "substantial benefit" had higher NYHA functional class (OR 2.64, P = 0.004), higher NT-ProBNP (OR 1.02, P = 0.003), higher serum creatinine (OR 1.10, P < 0.001), lower LVEF (OR 1.19, P = 0.004), more severe mitral regurgitation (OR 2.16, P = 0.006), higher right ventricular end diastolic volume (OR 1.05, P = 0.040), higher pulmonary artery pressures (OR 1.19, P = 0.003), and were more likely to use loop diuretics (OR 2.79, P = 0.001). Among patients with "substantial benefit," the positive predictive value (PPV) of HeartLogic™ to detect congestion was 92%. CONCLUSION: The utilization of CIED-based HeartLogic™ driven HF care demonstrated pronounced efficacy, predominantly in patients exhibiting characteristics of HF at a more advanced disease stage.

Combined Cardiac Damage Staging by Echocardiography and Cardiac Catheterization in Patients With Clinically Significant Aortic Stenosis.

BACKGROUND: Cardiac damage (CD) staging enhances risk stratification in patients with clinically significant aortic stenosis (AS). We aimed to assess the prognostic value and reclassification rate of right heart catheterization (RHC) compared with transthoracic echocardiography (TTE) in characterising CD staging at 3-year follow-up in patients with clinically significant AS, to identify patients that would benefit from RHC for prognostic stratification, and to test the prognostic value of combined CD staging. METHODS: An observational cohort study of 432 AS patients undergoing TTE and RHC were divided into moderate or asymptomatic severe (m/asAS) and symptomatic severe (ssAS) AS. Kaplan-Meier curves were used to compare survival. The accuracy in prognostic stratification was tested by area under the receiver operating characteristic curve analysis and Delong test. RESULTS: In both cohorts, TTE- and RHC-derived staging systems had prognostic value, although the agreement between them appeared moderate. A higher proportion of patients were assigned to stage 2 by TTE than by RHC. Patients in TTE-derived stage 2 had a high reclassification rate, with 40%-50% presenting with right chamber involvement (stages 3-4) according to RHC. Discordant cases were significantly older, with higher prevalence of atrial fibrillation, markedly elevated N-terminal pro-B-type natriuretic peptide, and higher indexed left atrial volume, E/e', and systolic pulmonary artery pressure vs concordant cases (P < 0.05). The combined CD staging, integrating TTE and RHC, was more accurate in predicting mortality than the TTE-derived system (P < 0.05). CONCLUSIONS: In patients with m/asAS and ssAS, the combined CD staging, derived from TTE and RHC, was more accurate in predicting mortality than TTE alone. In a subset of AS patients, the integration of RHC may significantly improve prognostic stratification.

Validation of an automated artificial intelligence system for 12­lead ECG interpretation.

BACKGROUND: The electrocardiogram (ECG) is one of the most accessible and comprehensive diagnostic tools used to assess cardiac patients at the first point of contact. Despite advances in computerized interpretation of the electrocardiogram (CIE), its accuracy remains inferior to physicians. This study evaluated the diagnostic performance of an artificial intelligence (AI)-powered ECG system and compared its performance to current state-of-the-art CIE. METHODS: An AI-powered system consisting of 6 deep neural networks (DNN) was trained on standard 12­lead ECGs to detect 20 essential diagnostic patterns (grouped into 6 categories: rhythm, acute coronary syndrome (ACS), conduction abnormalities, ectopy, chamber enlargement and axis). An independent test set of ECGs with diagnostic consensus of two expert cardiologists was used as a reference standard. AI system performance was compared to current state-of-the-art CIE. The key metrics used to compare performances were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. RESULTS: A total of 932,711 standard 12­lead ECGs from 173,949 patients were used for AI system development. The independent test set pooled 11,932 annotated ECG labels. In all 6 diagnostic categories, the DNNs achieved high F1 scores: Rhythm 0.957, ACS 0.925, Conduction abnormalities 0.893, Ectopy 0.966, Chamber enlargement 0.972, and Axis 0.897. The diagnostic performance of DNNs surpassed state-of-the-art CIE for the 13 out of 20 essential diagnostic patterns and was non-inferior for the remaining individual diagnoses. CONCLUSIONS: Our results demonstrate the AI-powered ECG model's ability to accurately identify electrocardiographic abnormalities from the 12­lead ECG, highlighting its potential as a clinical tool for healthcare professionals.

Association of Mild-to-Moderate Aortic Regurgitation With Outcomes in Heart Failure With Preserved Ejection Fraction.

OBJECTIVE: To assess aortic regurgitation (AR) prevalence, its hemodynamic effect, and long-term prognostic implications in patients admitted with de novo or worsened heart failure with preserved ejection fraction (HFpEF). METHODS: Consecutive patients hospitalized with de novo or worsened HFpEF between 2014 and 2020 were enrolled. Patients with more than moderate aortic and/or mitral valve disease were excluded. Based on the presence and degree of AR, patients were divided into those without AR, those with mild, and those with moderate AR. Data on cardiovascular death, heart failure (HF) rehospitalization, and their composite (major adverse cardiovascular events) were collected. RESULTS: The final study population consisted of 458 HFpEF patients: 156 (34.1%) with mild-AR, 153 (33.4%) with moderate-AR, and the remaining 149 (32.5%) with no AR. Mild-to-moderate AR patients were older, with larger left atrium-left ventricle (LV) volumes, greater LV mass index, higher filling pressure, and prevalence of diastolic dysfunction compared with the no-AR group (all P<.05). During 5-year follow-up, 113 patients died of cardiovascular causes, 124 patients were rehospitalized for HF, whereas 196 experienced the composite endpoint. Mild-to-moderate AR was identified as an independent predictor of all-cause death (HR, 1.62; 95% CI, 1.14 to 1.58; P=.04) and major adverse cardiovascular event occurrence (HR, 1.48; 95% CI, 1.05 to 2.09; P=.02). A total of 126 (35.5%) of 355 patients showed progression of AR at follow-up echocardiography. CONCLUSION: Mild-to-moderate AR is common among patients hospitalized for HFpEF. It is associated with adverse LV remodeling and worse long-term outcomes. These findings warrant further prospective studies addressing the importance of AR in prognostic stratification and exploring therapeutic strategies to mitigate its hemodynamic effect on HF.

Detection of transthyretin amyloid cardiomyopathy by automated data extraction from electronic health records.

AIMS: Transthyretin amyloid cardiomyopathy (ATTR-CM), a progressive and fatal cardiomyopathy, is frequently misdiagnosed or entails diagnostic delays, hindering patients from timely treatment. This study aimed to generate a systematic framework based on data from electronic health records (EHRs) to assess patients with ATTR-CM in a real-world population of heart failure (HF) patients. Predictive factors or combinations of predictive factors related to ATTR-CM in a European population were also assessed. METHODS AND RESULTS: Retrospective unstructured and semi-structured data from EHRs of patients from OLV Hospital Aalst, Belgium (2012-20), were processed using natural language processing (NLP) to generate an Observational Medical Outcomes Partnership Common Data Model database. NLP model performance was assessed on a random subset of EHRs by comparing algorithm outputs to a physician-generated standard (using precision, recall, and their harmonic mean, or F1-score). Of the 3127 HF patients, 103 potentially had ATTR-CM (age 78 ± 9 years; male 55%; ejection fraction of 48% ± 16). The mean diagnostic delay between HF and ATTR-CM diagnosis was 1.8 years. Besides HF and cardiomyopathy-related phenotypes, the strongest cardiac predictor was atrial fibrillation (AF; 72% in ATTR-CM vs. 60% in non-ATTR-CM, P = 0.02), whereas the strongest non-cardiac predictor was carpal tunnel syndrome (21% in ATTR-CM vs. 3% in non-ATTR-CM, P < 0.001). The strongest combination predictor was AF, joint disorders, and HF with preserved ejection fraction (29% in ATTR-CM vs. 18% in non-ATTR-CM: odds ratio = 2.03, 95% confidence interval = 1.28-3.22). CONCLUSIONS: Not only well-known variables associated with ATTR-CM but also unique combinations of cardiac and non-cardiac phenotypes are able to predict ATTR-CM in a real-world HF population, aiding in early identification of ATTR-CM patients.

Fewer Worsening Heart Failure Events With HeartLogic on top of Standard Care: a Propensity-Matched Cohort Analysis.

BACKGROUND: The implantable cardiac defibrillator-based HeartLogic algorithm aims to detect impending fluid retention in patients with heart failure (HF). Studies show that HeartLogic is safe to integrate into clinical practice. The current study investigates whether HeartLogic provides clinical benefit on top of standard care and device telemonitoring in patients with HF. METHODS: A multicenter, retrospective, propensity-matched cohort analysis was performed in patients with HF and implantable cardiac defibrillators, and it compared HeartLogic to conventional telemonitoring. The primary endpoint was the number of worsening HF events. Hospitalizations and ambulatory visits due to HF were also evaluated. RESULTS: Propensity score matching yielded 127 pairs (median age 68 years, 80% male). Worsening HF events occurred more frequently in the control group (2; IQR 0-4) compared to the HeartLogic group (1; IQR 0-3; P = 0.004). The number of HF hospitalization days was higher in controls than in the HeartLogic group (8; IQR 5-12 vs 5; IQR 2-7; P = 0.023), and ambulatory visits for diuretic escalation were more frequent in the control group than in the HeartLogic group (2; IQR 0-3 vs 1; IQR 0-2; P = 0.0001). CONCLUSION: Integrating the HeartLogic algorithm in a well-equipped HF care path on top of standard care is associated with fewer worsening HF events and shorter duration of fluid retention-related hospitalizations.

Cardio-renal-metabolic syndrome: clinical features and dapagliflozin eligibility in a real-world heart failure cohort.

AIMS: The Cardiovascular Outcomes Retrospective Data analysIS in Heart Failure (CORDIS-HF) is a single-centre retrospective study aimed to (i) clinically characterize a real-world population with heart failure (HF) with reduced (HFrEF) and mildly reduced ejection fraction (HFmrEF), (ii) evaluate impact of renal-metabolic comorbidities on all-cause mortality and HF readmissions, and (iii) determine patients' eligibility for sodium-glucose cotransporter 2 inhibitors (SGLT2is). METHODS AND RESULTS: Using a natural language processing algorithm, clinical data of patients diagnosed with HFrEF or HFmrEF were retrospectively collected from 2014 to 2018. Mortality and HF readmission events were collected during subsequent 1 and 2 year follow-up periods. The predictive role of patients' baseline characteristics for outcomes of interest was assessed using univariate and multivariate Cox proportional hazard models. Kaplan-Meier analysis was used to determine if type 2 diabetes (T2D) and chronic kidney disease (CKD) impacted mortality and HF readmission rates. The European SGLT2i label criteria were used to assess patients' eligibility. The CORDIS-HF included 1333 HF patients with left ventricular ejection fraction (LVEF) < 50% (413 HFmrEF and 920 HFrEF), who were predominantly male (69%) with a mean [standard deviation (SD)] age of 74.7 (12.3) years. About one-half (57%) of patients presented CKD and 37% T2D. The use of guideline-directed medical therapy (GDMT) was high (76-90%). HFrEF patients presented lower age [mean (SD): 73.8 (12.4) vs. 76.7 (11.6) years, P < 0.05], higher incidence of coronary artery disease (67% vs. 59%, P < 0.05), lower systolic blood pressure [mean (SD): 123 (22.6) vs. 133 (24.0) mmHg, P < 0.05], higher N-terminal pro-hormone brain natriuretic peptide (2720 vs. 1920 pg/mL, P < 0.05), and lower estimated glomerular filtration rate [mean (SD): 51.4 (23.3) vs. 54.1 (22.3) mL/min/1.73 m2 , P < 0.05] than those with HFmrEF. No differences in T2D and CKD were detected. Despite optimal treatment, event rates for the composite endpoint of HF readmission and mortality were 13.7 and 8.4/100 patient years. The presence of T2D and CKD negatively impacted all-cause mortality [T2D: hazard ratio (HR) = 1.49, P < 0.01; CKD: HR = 2.05, P < 0.001] and hospital readmission events in all patients with HF. Eligibility for SGLT2is dapagliflozin and empagliflozin was 86.5% (n = 1153) and 97.9% (n = 1305) of the study population, respectively. CONCLUSIONS: This study identified high residual risk for all-cause mortality and hospital readmission in real-world HF patients with LVEF < 50% despite GDMT. T2D and CKD aggravated the risk for these endpoints, indicating the intertwinement of HF with CKD and T2D. SGLT2i treatment that clinically benefits these different disease conditions can be an important driver to lower mortality and hospitalizations in this HF population.

Same Day Discharge Strategy by Default in a Tertiary Catheterization Laboratory in Belgium: Value Based Healthcare-Change in Practice.

AIMS: To assess the effects on outcomes and hospital revenues (societal cost) of a by default strategy of same day discharge (SDD) in patients undergoing a cardiac catheterization procedure in a Belgian Hospital. METHODS AND RESULTS: Outcome and complete financial data were obtained in all consecutive patients with a cardiac catheterization performed in 2019 (n=5237) and in 2021 (n=5377). Patient-reported experience, patient satisfaction and Net promotor score were obtained prospectively for the SDD cohort in 2021. The proportion of patients receiving catheterization procedure in SDD increased from 28 to 44 % (p<0.001). This translates to the saving of 889 conventional hospitalizations in 2021. All-cause death and readmission rate remained unchanged (0,17% vs 0,15% (p=0,004); and 0,7% vs 1,8% (p>0,05)) in 2019 and 2021, respectively. Patients satisfaction top box score was 91% and the Net Promotor Score was 89,5. The by default SDD strategy was associated with reduction in in-hospital health care spending, on average 3206€ per procedure is saved. This means a 57% decrease in hospital revenues and translates into an important decrease in physician income. CONCLUSION: Implementing a by default SDD cardiac catheterization strategy results in a reduction of societal cost, excellent patient satisfaction and unchanged clinical outcome. Yet, in the given context this approach negatively impacts hospital and physician revenues precluding the sustainability of such protocol.