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Aims: Physiological activation of the heart using algorithms to minimize right ventricular pacing (RVPm) may be an effective strategy to reduce adverse events in patients requiring anti-bradycardia therapies. This systematic review and meta-analysis aimed to evaluate current evidence on clinical outcomes for patients treated with RVPm algorithms compared to dual-chamber pacing (DDD).
Methods And Results: We conducted a systematic search of the PubMed database. The predefined endpoints were the occurrence of persistent/permanent atrial fibrillation (PerAF), cardiovascular (CV) hospitalization, all-cause death, and adverse symptoms. We also aimed to explore the differential effects of algorithms in studies enrolling a high percentage of atrioventricular block (AVB) patients. Eight studies (7229 patients) were included in the analysis. Compared to DDD pacing, patients using RVPm algorithms showed a lower risk of PerAF [odds ratio (OR) 0.74, 95% confidence interval (CI) 0.57-0.97] and CV hospitalization (OR 0.77, 95% CI 0.61-0.97). No significant difference was found for all-cause death (OR 1.01, 95% CI 0.78-1.30) or adverse symptoms (OR 1.03, 95% CI 0.81-1.29). No significant interaction was found between the use of the RVPm strategy and studies enrolling a high percentage of AVB patients. The pooled mean RVP percentage for RVPm algorithms was 7.96% (95% CI 3.13-20.25), as compared with 45.11% (95% CI 26.64-76.38) of DDD pacing.
Conclusion: Algorithms for RVPm may be effective in reducing the risk of PerAF and CV hospitalization in patients requiring anti-bradycardia therapies, without an increased risk of adverse symptoms. These results are also consistent for studies enrolling a high percentage of AVB patients.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11346371 | PMC |
http://dx.doi.org/10.1093/europace/euae212 | DOI Listing |
Europace
August 2024
Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Via del Pozzo 71, Modena 41121, Italy.
Cortex
June 2013
Department of Experimental Psychology, Ghent University, Ghent, Belgium.
In order to choose the best action for maximizing fitness, mammals can estimate the reward expectations (value) linked to available actions based on past environmental outcomes. Value updates are performed by comparing the current value with the actual environmental outcomes (prediction error). The anterior cingulate cortex (ACC) has been shown to be critically involved in the computation of value and its variability across time (volatility).
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