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Background: We previously developed an early reconnection/dormant conduction (ERC) prediction model for cryoballoon ablation to avoid a 30-min waiting period with adenosine infusion. We now aimed to validate this model based on time to isolation, number of unsuccessful cryo-applications, and nadir balloon temperature.
Methods: Consecutive atrial fibrillation patients who underwent their first cryoballoon ablation in 2018-2019 at the Leiden University Medical Center were included. Model performance at the previous and at a new optimal cutoff value was determined.
Results: A total of 201 patients were included (85.57% paroxysmal AF, 139 male, median age 61 years (IQR 53-69)). ERC was found in 35 of 201 included patients (17.41%) and in 41 of 774 veins (5.30%). In the present study population, the previous cutoff value of - 6.7 provided a sensitivity of 37.84% (previously 70%) and a specificity of 89.07% (previously 86%). Shifting the cutoff value to - 7.2 in both study populations resulted in a sensitivity of 72.50% and 72.97% and a specificity of 78.22% and 78.63% in data from the previous and present study respectively. Negative predictive values were 96.55% and 98.11%. Applying the model on the 101 patients of the present study with all necessary data for all veins resulted in 43 out of 101 patients (43%) not requiring a 30-min waiting period with adenosine testing. Two patients (2%) with ERC would have been missed when applying the model.
Conclusions: The previously established ERC prediction model performs well, recommending its use for centers routinely using adenosine testing following PVI.
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http://dx.doi.org/10.1007/s10840-024-01811-0 | DOI Listing |
Clin Orthop Relat Res
September 2025
Leni & Peter W. May Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Peripheral nerve injury commonly results in pain and long-term disability for patients. Recovery after in-continuity stretch or crush injury remains inherently unpredictable. However, surgical intervention yields the most favorable outcomes when performed shortly after injury.
View Article and Find Full Text PDFJAMA Dermatol
September 2025
Department of Population Health, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia.
Importance: Increasingly, strategies to systematically detect melanomas invoke targeted approaches, whereby those at highest risk are prioritized for skin screening. Many tools exist to predict future melanoma risk, but most have limited accuracy and are potentially biased.
Objectives: To develop an improved melanoma risk prediction tool for invasive melanoma.
Curr Med Sci
September 2025
Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Objective: To develop a novel prognostic scoring system for severe cytokine release syndrome (CRS) in patients with B-cell acute lymphoblastic leukemia (B-ALL) treated with anti-CD19 chimeric antigen receptor (CAR)-T-cell therapy, aiming to optimize risk mitigation strategies and improve clinical management.
Methods: This single-center retrospective cohort study included 125 B-ALL patients who received anti-CD19 CAR-T-cell therapy from January 2017 to October 2023. These cases were selected from a cohort of over 500 treated patients on the basis of the availability of comprehensive baseline data, documented CRS grading, and at least 3 months of follow-up.
Mol Divers
September 2025
Department of Biotechnology, National Institute of Technology Raipur, Raipur, Chhattisgarh, 492001, India.
Traditional drug discovery methods like high-throughput screening and molecular docking are slow and costly. This study introduces a machine learning framework to predict bioactivity (pIC₅₀) and identify key molecular properties and structural features for targeting Trypanothione reductase (TR), Protein kinase C theta (PKC-θ), and Cannabinoid receptor 1 (CB1) using data from the ChEMBL database. Molecular fingerprints, generated via PaDEL-Descriptor and RDKit, encoded structural features as binary vectors.
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