PubMed Health⌕ Search

PubMed · 7639445

Expanding indications for permanent pacemakers.

Abstract

PURPOSE: To review the current clinical experience with new and expanding indications for permanent cardiac pacing. DATA SOURCES: A MEDLINE search was done of the English-language literature published from 1980 through 1994 about indications for permanent pacing. Five major areas were identified and searched: cardiomyopathies, atrial fibrillation, the long QT syndrome, cardiac transplantation, and vasovagal syncope. A manual search was then done for other contributions, including abstracts. STUDY SELECTION: Because published reports in these areas are scarce, all of the peer-reviewed articles and most of the relevant abstracts found were reviewed. DATA EXTRACTION: Data were manually extracted from the various sources, and the reports were classified and summarized according to specific indications. RESULTS: Pacing is becoming an important option in the treatment of patients with symptomatic drug-resistant hypertrophic obstructive cardiomyopathy. Symptomatic and hemodynamic benefits have been shown in patients with pacing over various periods of follow-up. In patients with the long QT syndrome in whom medical therapy had failed, pacing at relatively fast rates markedly reduced symptoms and almost completely abolished fainting spells. Preliminary results suggest that pacing may be beneficial in dilated cardiomyopathy and in preventing episodes of paroxysmal atrial fibrillation. Further studies are needed to clarify the mechanisms of and to improve selection criteria for pacing in these conditions. Our ability to select cardiac transplant recipients for permanent pacing and our ability to optimize the timing of pacing in these patients have recently improved considerably. The role of pacing therapy in patients with neurally mediated (vasovagal) syncope remains incompletely understood. Better classification of these patients, made according to the sequence of hemodynamic events leading to syncope, is likely to clarify the potential benefit of pacing in these patients and improve the selection of patients for pacing. CONCLUSION: Few peer-reviewed clinical trials have been done, and further studies are needed to confirm the promising effects of pacing in patients with these newly recognized and expanding indications for pacing.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M Glikson, R E Espinosa, D L Hayes. 1995-09-15. Expanding indications for permanent pacemakers.. https://doi.org/10.7326/0003-4819-123-6-199509150-00009

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

RR-interval-based atrial fibrillation detection and burden estimation: cross-dataset validation and calibration-aware probability analysis.

Objective.Atrial fibrillation (AF) burden has become an increasingly important endpoint in long-duration rhythm monitoring, but reliable burden estimation requires more than accurate AF detection alone. In particular, when burden is derived by aggregating predicted AF probabilities over time, probability calibration may directly affect burden validity under external dataset shift.Approach.This study developed an interpretable-interval feature model for AF detection and evaluated it using record-wise cross-validation on a development cohort and independent cross-dataset external validation on public Holter electrocardiographic databases. Window-level performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), area under the precision-recall curve (PR-AUC), Brier score, expected calibration error (ECE), and calibration intercept and calibration slope. Recording-level AF burden was estimated using both probability-based and hard-label aggregation and evaluated using mean absolute error (MAE) and agreement analyses.Main results.The model showed high discrimination in both development and external evaluation, with external ROC-AUC ofand PR-AUC of. However, external calibration deteriorated despite preserved ranking performance, with Brier score of, ECE(15) of, calibration intercept of, and calibration slope of. In the external cohort, probability-based burden estimation preserved strong association with reference burden but showed weaker raw agreement than hard-label aggregation, with MAE ofversus, consistent with systematic probability underprediction. Repeated external recalibration across record-level splits substantially improved probability quality and probability-based burden estimation. Median probability-burden MAE decreased fromwithout recalibration toafter Platt recalibration andafter isotonic recalibration, while median ECE(15) decreased fromtoand, respectively.Significance.These findings indicate that-interval-based AF detection maintained strong ranking performance in the tested external cohort, but probability calibration should be evaluated explicitly when predicted probabilities are aggregated into AF-burden estimates.

Atrial Fibrillation↗

Research on identification of key genes and immune-metabolic mechanisms in atrial fibrillation through integrated multi-cohort transcriptomic analysis and machine learning.

This study aimed to integrate multiple datasets for the identification of atrial fibrillation (AF)-related differentially expressed genes (DEGs), analyze their underlying mechanisms through functional enrichment and machine learning, construct diagnostic models, and explore immune-metabolic interactions to provide novel biomarkers and theoretical foundations. Gene expression datasets were integrated and normalized, with batch effects removed using principal component analysis. Differential expression analysis, functional enrichment analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways), and machine learning-based feature gene selection and model construction were performed. Shapley additive explanations analysis was utilized to interpret the constructed models, while gene set enrichment analysis, gene set variation analysis, and immune cell infiltration analysis were conducted to investigate the associations between feature genes and immune infiltration. After integrating and normalizing gene expression data and eliminating batch effects via principal component analysis, 6 DEGs were identified, including 4 upregulated and 2 down-regulated ones. Functional enrichment analysis showed these DEGs were significantly enriched in neuro-related biological processes and pathways, indicating their key roles in AF pathogenesis. Five key feature genes were selected using LASSO, random forest, and support vector machine-recursive feature elimination algorithms. They had significant expression differences between the AF and control groups (P&#x2005;<&#x2005;.001) and were located on distinct chromosomes. The constructed random forest and support vector machine models performed excellently (area under the curve&#x2005;&#x2265;&#x2005;0.85). Shapley additive explanations analysis revealed TNNI1 contributed most to model prediction, with its expression significantly positively correlated with immune cell infiltration. Gene set enrichment analysis and gene set variation analysis analyses further showed feature genes participated in AF pathogenesis by regulating immune modulation, metabolic pathways, and autophagy. Immune cell infiltration analysis found altered proportions of T-cell subsets and M0 macrophages in the AF group, along with complex links between feature gene expression and immune cell function. This study systematically elucidated the unique gene expression patterns and key regulatory pathways associated with AF, clarifying the crucial roles of feature genes in immune regulation, metabolic imbalance, and cellular dysfunction. These findings provide a theoretical basis and potential therapeutic targets for understanding AF pathogenesis and developing targeted treatment strategies.

Atrial Fibrillation↗

Horse model of spontaneous atrial fibrillation share proteomic changes with humans.

Horses and humans are among the few mammals susceptible to spontaneous atrial fibrillation (AF), both suffering from high recurrence rates after treatment. Treatment resistance is often attributed to progressive atrial remodeling, but current treatment options fail to effectively address this aspect. Here, we introduce a novel horse model of spontaneous AF to investigate the biological pathway changes in early stages of the disease. Through data-independent acquisition mass spectrometry on biopsies from the right and left atrium and left ventricular chamber of horses with early-stage persistent AF (n&#x2009;=&#x2009;8) and controls (n&#x2009;=&#x2009;8), we identify several differentially regulated proteins across all three chambers. Pathway enrichment analyses and histological stainings highlight a significant role of atrial extracellular matrix (ECM) remodeling in early AF. Other key proteomic changes relate to metabolism, contractility, and protein-folding, and overlap with findings from publicly available human datasets. Our results demonstrate that horses and humans share several AF-related proteomic changes, providing translational insights into the early atrial remodeling processes that are likely to contribute to treatment resistance. These protein-level changes could serve as biomarkers or pharmacological targets for preventing AF-associated atrial remodeling and improve treatment outcomes across species.

Atrial Fibrillation↗