Heart rhythm最新文献

Sudden cardiac death (SCD) remains a pressing health issue, affecting hundreds of thousands each year globally. The heterogeneity among SCD victims, ranging from individuals with severe heart failure to seemingly healthy individuals, poses a significant challenge for effective risk assessment. Conventional risk stratification, which primarily relies on left ventricular ejection fraction, has resulted in only modest efficacy of implantable cardioverter-defibrillators (ICD) for SCD prevention. In response, artificial intelligence (AI) holds promise for personalised SCD risk prediction and tailoring preventive strategies to the unique profiles of individual patients. Machine and deep learning algorithms have the capability to learn intricate non-linear patterns between complex data and defined endpoints, and leverage these to identify subtle indicators and predictors of SCD that may not be apparent through traditional statistical analysis. However, despite the potential of AI to improve SCD risk stratification, there are important limitations that need to be addressed. We aim to provide an overview of the current state-of-the-art of AI prediction models for SCD, highlight the opportunities for these models in clinical practice, and identify the key challenges hindering widespread adoption.

Background: Lead-related venous stenosis and occlusion can complicate the insertion or replacement of transvenous leads in patients with cardiac implantable electronic devices (CIED). A possible solution is to tunnel the lead from the contralateral vasculature to the ipsilateral generator. Procedural complications and long term outcomes remain unclear with this technique.

Objective: We sought to assess outcomes of tunneled transvenous leads.

Methods: We retrospectively identified all patients who underwent transvenous CIED lead tunneling to a contralateral pocket at our institution between 2014 and 2024. Clinical characteristics, indications for lead implant, post-operative complications and long-term outcomes were collected.

Results: We identified 27 patients underwent transvenous lead tunneling at our institution. Most patients were males (74%) with an average age of 68.8 ± 18.3 years old. Most patients had non-ischemic cardiomyopathy (59%) with an average ejection fraction of 29.3 ± 11.3 %. The tunneled leads were coronary sinus leads (20), followed by defibrillator leads (5) and RV pacing leads (2). Implants were primarily for device upgrade (18), lead revisions (8), or de-novo lead placement (1). No post-operative complications were seen. Patients were followed for an average of 2.2 ± 1.4 years. One tunneled defibrillator lead (3.7%) had low shock impedance 3 years after implant which was monitored and did not require an intervention.

Conclusion: In patients with ipsilateral venous occlusion, contralateral lead tunneling appears to be an effective and safe approach to manage CIED patients with occluded ipsilateral subclavian veins.

Background: The influence of hemodynamic parameters on the recurrence of atrial fibrillation (AF) following catheter ablation is not well known, and it remains unclear whether a nomogram combining risk factors and hemodynamic parameters improves prediction accuracy.

Objective: This study aimed to develop a nomogram based on echocardiographic hemodynamic parameters for predicting AF recurrence following catheter ablation in non-valvular atrial fibrillation (NVAF).

Methods: A total of 380 consecutive NVAF patients undergoing AF catheter ablation treatment were prospectively collected. Patients were divided into training and validation cohorts at a 7:3 ratio. The follow-up duration averaged 9 months with a median of 12 months, during which 132 patients (34.7%) experienced a recurrence of AF.

Results: LASSO regression and Cox regression analyses identified four significant predictors of AF recurrence: persistent AF (HR=1.63, 95% CI=1.02∼2.61, P=0.041), the systolic/diastolic (S/D) ratio (HR=0.50, 95% CI=0.30∼0.84, P=0.009), left atrial acceleration factor α (HR=1.31, 95% CI=1.02∼1.68, P=0.032), and left atrial appendage peak emptying flow velocity (HR=0.98, 95% CI=0.97∼0.99, P=0.004). Based on these four variables, a predictive nomogram was constructed. The nomogram demonstrated C-indexes of 0.664 and 0.728 for predicting 1-year and 2-year AF recurrence, respectively, in the validation cohort. The Kaplan-Meier survival analysis indicated that a Nomo-score greater than 128 was associated with a higher risk of AF recurrence.

Conclusion: Hemodynamic parameters may offer valuable insight in predicting AF recurrence following catheter ablation. Our study successfully developed a reliable nomogram based on echocardiographic hemodynamic parameters to estimate the risk of AF recurrence after catheter ablation in NVAF patients.

Backgroud: Left bundle branch pacing (LBBP) is a novel physiological pacing modality. However, whether it delivers comparable efficacy with different capture sites in heart failure (HF) patients remains unclear.

Objective: This study aims to assess the association between different pacing sites and the response of LBBP .

Methods: Forty-three consecutive HF patients , referred for successful LBBP implantation, were prospectively recruited in this study. Patients were assigned to 3 subgroups according to the paced QRS complex morphology (left bundle branch trunk pacing (LBTP), left posterior fascicular pacing (LPFP) or left anterior fascicular pacing (LAFP) groups). Echocardiograms, electrocardiograms were recorded and analyzed at baseline and 6-month follow-up.

Results: The response rate was 95.0%, 88.2%, 83.3% in LBTP, LPFP and LAFP groups, respectively. All subgroups were efficient in narrowing QRS complex(ΔQRS: 62.4±10.4 ms, 54.7±14.2ms, 58.2±14.5ms) and improving cardiac function (ΔLVEF: 25.7±8.1%, 15.3±8.1%, 18.8±4.4%). Compared with left fascicle pacing(LFP), LBTP resulted in longer LVAT (76.5±10.2ms vs 82.3±6.5ms; P=0.037) , shorter QRSid (128.0±6.0ms vs 113.3±5.2ms; P＜0.0001),along with better improvement in septal systolic longitudinal strain(P=0.007) and lateral-septal myocardial loading inhomogeneity (P=0.036). Linear regression analysis further revealed LBB capture sites was strongly associated with the improvement of peak strain dispersion (PSD) (model R2 = 0.586, P =0.042) and LVEF (model R2 = 0.425, P <0.0001).

Conclusions: Different LBB capture sites led to subtle difference in mechanical synchrony , which may in-turn affect LVEF improvement in HF patients.