Background His-bundle pacing (HBP) is a physiological form of pacing. Although high capture thresholds are common, few predictors of low HBP threshold have been determined. We aimed to identify electrophysiological predictors. Methods Fifty-one patients (53% with atrioventricular block) underwent HBP for bradycardia with an intrinsic QRS duration of <120 ms. Attempts to anchor the HBP lead were guided by unipolar His-bundle electrograms (HB EGMs) recorded with an electrophysiology recording system. Patients were followed-up for >6 months. Results In total, 153 attempts at anchoring the HBP lead were made, of which, 45 achieved acceptable HBP thresholds (≤2.5 V at 1 ms). The amplitude of negative deflection in HB EGM and the selective HBP form at fixation were independently associated with achieving an acceptable threshold. A negative amplitude of ≥0.060 mV in HB EGM was determined as the optimal value for identifying the acceptable threshold. This deep negative HB EGM was recorded with an HBP threshold of 1.4±1.3 V (in 34 attempts), significantly lower than that of positive HB EGM without deep negative deflection (2.8±1.3 V, in 31 trials; or >5 V, in 38 trials). The permanent HBP lead remained with deep negative (≥0.060 mV) or positive HB EGMs in 28 and 14 patients, respectively, and with positive or negative HB injury current in 19 and 23 patients, respectively. During follow-up, increased HBP threshold of >1 V was significantly more prevalent in the positive HB EGM group. The HBP thresholds of deep negative HB EGM and HB injury current, but not of the selective HBP group, were significantly lower than the other subgroups during follow-up. Conclusions Deep negative HB EGM at fixation was associated with an excellent short-term HBP threshold, similar to HB injury current. Analysis of unipolar HB EGM postfixation may enable prediction of permanent HBP threshold.
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http://dx.doi.org/10.1161/CIRCEP.119.007415 | DOI Listing |
Brain Behav Immun Health
February 2025
Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases-IRCAD, University of Eastern Piedmont, 28100, Novara, Italy.
Major Depressive Disorder (MDD) is a widespread psychiatric condition impacting social and occupational functioning, making it a leading cause of disability. The diagnosis of MDD remains clinical, based on the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 criteria, as biomarkers have not yet been validated for diagnostic purposes or as predictors of treatment response. Traditional treatment strategies often follow a one-size-fits-all approach obtaining suboptimal outcomes for many patients who fail to experience response or recovery.
View Article and Find Full Text PDFJBMR Plus
February 2025
Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki 00014, Finland.
Hypophosphatemic rickets is a rare bone disease characterized by short stature, bone deformities, impaired bone mineralization, and dental problems. Most commonly, hypophosphatemic rickets is caused by pathogenic variants in the X-chromosomal gene, but autosomal dominant and recessive forms also exist. We investigated a Finnish family in which the son (index, 29 yr) and mother (56 yr) had hypophosphatemia since childhood.
View Article and Find Full Text PDFJ Comput Assist Tomogr
January 2025
Department of Radiological Sciences.
Objective: This study evaluated the performance of a deep learning-based vertebral compression fracture (VCF) detection tool in patients with incidental VCF. The purpose of this study was to validate this tool across multiple sites and multiple vendors.
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Int Dent J
January 2025
Department of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China. Electronic address:
Introduction And Aim: The assessment of gingival inflammation surface features mainly depends on subjective judgment and lacks quantifiable and reproducible indicators. Therefore, it is a need to acquire objective identification information for accurate monitoring and diagnosis of gingival inflammation. This study aims to develop an automated method combining intraoral scanning (IOS) and deep learning algorithms to identify the surface features of gingival inflammation and evaluate its accuracy and correlation with clinical indicators.
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