Daily Cardiology Research Analysis

BACKGROUND: Conduction system pacing (CSP), including His bundle pacing (HBP) and left bundle branch area pacing (LBBAP), offers a physiological alternative to conventional pacing. However, current evidence is limited by small sample sizes, short follow-up, and inconsistent LBBAP definitions. OBJECTIVE: This study evaluated the long-term outcomes, safety, and lead performance of CSP in a large multicenter cohort, and provided a precise LBBAP classification for investigating its impact on clinical outcomes. METHODS: This prospective registry-based study included patients receiving CSP at 5 Chinese centers from 2019 to 2021. LBBAP was classified as left bundle branch pacing (LBBP), left ventricular septal pacing (LVSP), or unclassified LBBAP based on the presence, absence, or uncertainty of left bundle branch capture. Pacing and clinical outcomes were analyzed. RESULTS: Of 3,336 enrolled patients, 3,167 successfully received CSP (557 HBP, 2,610 LBBAP), with a mean follow-up of 41.3 ± 14.0 months. LBBAP comprised LBBP (84.2%), unclassified (12.5%), and LVSP (3.3%). In patients with LBBB and heart failure with reduced ejection fraction, LBBP and HBP achieved the greatest LVEF improvements (+20.7% and +21.9%), while LVSP showed the least (+12.1%). LVSP was associated with higher mortality or heart failure hospitalization (33.3%) compared with LBBP (8.6%) and unclassified LBBAP (15.4%). Threshold increases ≥1 V/0.5 ms occurred in 5.03% HBP vs 1.80% LBBAP (P < 0.001). Procedural complications (excluding threshold rise) occurred in 1.3% of both groups. CONCLUSIONS: CSP demonstrated long-term safety and stability. Subclassification of LBBAP enhances clinical precision, with LBBP capture yielding a higher positive clinical outcomes and LVSP with inferior outcomes, especially in cardiac resynchronization therapy patients.

AIMS: Diagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging, particularly in older individuals. We hypothesized that machine learning (ML) approaches could improve diagnostic accuracy compared with HFpEF scores. METHODS: We evaluated the diagnostic performance of four supervised ML algorithms (random forest [RF], extreme gradient boosting [XGBoost], support vector machines, and decision trees) to identify HFpEF in individuals aged 60 to 80 years. The models were trained on three derivation cohorts (N = 1474; HFpEF: KaRen, MEDIA cohorts; community-based without HF: Malmö Preventive Project) and validated in two independent cohorts (N = 542; HFpEF: HF-Nancy cohort; community-based without HF: STANISLAS cohort). Performance metrics included accuracy, F-measure, area under the receiver operating characteristic curve (AUC), and C-index. ML models were also compared with HFA-PEFF, H2FPEF, and HFpEF-ABA scores. RESULTS: Among 2017 participants, RF and XGBoost demonstrated the highest diagnostic value, outperforming traditional HFpEF scores (AUC: RF, 0.98; XGBoost, 0.96; HFA-PEFF, 0.86; H2FPEF, 0.79). RF and XGBoost also showed the greatest gain in discriminative capacity among ML algorithms when compared with H2FPEF (ΔC-index: RF +0.20, XGBoost +0.18), HFA-PEFF (ΔC-index: RF +0.12, XGBoost +0.10), and HFpEF-ABA score (ΔC-index: RF +0.17, XGBoost +0.15). Elevated natriuretic peptides were by far the most influential feature in both RF and XGBoost models (36% of model explainability). CONCLUSIONS: Machine learning algorithms, particularly RF and XGBoost, demonstrated superior diagnostic accuracy compared to established HFpEF scoring systems. These findings support the potential integration of ML-based tools into clinical workflows to facilitate earlier identification of HFpEF and prompt initiation of guideline-recommended therapies.

BACKGROUND: Stroke is a possible complication after bioprosthetic aortic valve replacement (AVR) for severe aortic stenosis (AS), impacting morbidity and mortality. Accurate estimates of the proportion of individuals who experience stroke within and beyond the periprocedural period after transcatheter AVR (TAVR), surgical AVR, and valve-in-valve (ViV) replacement are essential for management and prognostication. The objective was to determine the proportion of adults aged >18 who experienced an ischemic stroke after bioprosthetic AVR for AS. METHODS: A systematic search of MEDLINE, Embase, and Web of Science was conducted from database inception through March 2024. Studies reporting on stroke rates at least 90 days after bioprosthetic AVR for severe AS, including ViV procedures, and meeting predefined eligibility criteria were included. The pooled proportion of individuals experiencing a stroke was estimated for TAVR and ViV procedures, whereas comparative analyses between TAVR and surgical AVR were performed using mixed-effects models in studies directly comparing both procedures. RESULTS: Twenty-seven studies were included in the native AS treatment cohort, and 5 in the ViV subanalysis. In native AS, the pooled 30-day proportion of individuals who had a stroke after TAVR was 3.0% (95% CI, 2.5-3.9), with different studies reporting major and minor stroke proportions of 1.7% each. At 1 year, all stroke proportion was 5.0% (95% CI, 4.0-6.0), major stroke was 3.0%, and minor stroke was 2.0%. Comparative analysis demonstrated that TAVR was associated with significantly lower odds of all stroke at 30 days compared with surgical AVR (odds ratio, 0.73 [95% CI, 0.57-0.93]). No significant difference in the proportions of individuals who had a stroke was observed in TAVR versus surgical AVR at 1, 2, or 5 years. In the ViV cohort, the pooled 30-day and 1-year all stroke proportion after ViV was 2.0% (95% CI, 1.0-3.0) and 3.0% (95% CI, 2.0-6.0), respectively. CONCLUSIONS: This meta-analysis provides updated estimates of stroke after bioprosthetic AVR for AS, capturing risk beyond the early periprocedural period. Future studies should investigate the causes of long-term stroke post-AVR, the effects of different antithrombotic therapies on the risk of stroke, as well as the potential impact of these procedures on short and long-term cognitive function.