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Importance: Sudden cardiac death (SCD) is the most common mode of death in childhood hypertrophic cardiomyopathy (HCM), but there is no validated algorithm to identify those at highest risk.
Objective: To develop and validate an SCD risk prediction model that provides individualized risk estimates.
Design, Setting, And Participants: A prognostic model was developed from a retrospective, multicenter, longitudinal cohort study of 1024 consecutively evaluated patients aged 16 years or younger with HCM. The study was conducted from January 1, 1970, to December 31, 2017.
Exposures: The model was developed using preselected predictor variables (unexplained syncope, maximal left-ventricular wall thickness, left atrial diameter, left-ventricular outflow tract gradient, and nonsustained ventricular tachycardia) identified from the literature and internally validated using bootstrapping.
Main Outcomes And Measures: A composite outcome of SCD or an equivalent event (aborted cardiac arrest, appropriate implantable cardioverter defibrillator therapy, or sustained ventricular tachycardia associated with hemodynamic compromise).
Results: Of the 1024 patients included in the study, 699 were boys (68.3%); mean (interquartile range [IQR]) age was 11 (7-14) years. Over a median follow-up of 5.3 years (IQR, 2.6-8.3; total patient years, 5984), 89 patients (8.7%) died suddenly or had an equivalent event (annual event rate, 1.49; 95% CI, 1.15-1.92). The pediatric model was developed using preselected variables to predict the risk of SCD. The model's ability to predict risk at 5 years was validated; the C statistic was 0.69 (95% CI, 0.66-0.72), and the calibration slope was 0.98 (95% CI, 0.59-1.38). For every 10 implantable cardioverter defibrillators implanted in patients with 6% or more of a 5-year SCD risk, 1 patient may potentially be saved from SCD at 5 years.
Conclusions And Relevance: This new, validated risk stratification model for SCD in childhood HCM may provide individualized estimates of risk at 5 years using readily obtained clinical risk factors. External validation studies are required to demonstrate the accuracy of this model's predictions in diverse patient populations.
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http://dx.doi.org/10.1001/jamacardio.2019.2861 | DOI Listing |
Spectrochim Acta A Mol Biomol Spectrosc
September 2025
College of Chemistry, Chemical Engineering and Material Science, Soochow University, No. 199 Ren'Ai Road, Suzhou 215123, China; Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, China. Electronic address: g
The dynamic monitoring of cell death processes remains a significant challenge due to the scarcity of highly sensitive molecular tools. In this study, two hemicyanine-based probes (5a-5b) with D-π-A structures were developed for organelle-specific viscosity monitoring. Both probes exhibited correlation with the Förster-Hoffmann viscosity-dependent relationship (R > 0.
View Article and Find Full Text PDFThromb Res
September 2025
Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg University, Mainz, Germany. Electronic address:
Warfarin is a widely used vitamin K antagonist (VKA) with known pleiotropic effects beyond anticoagulation. Preclinical and case-control evidence suggests that warfarin may affect hematopoiesis, but longitudinal human evidence is lacking. To explore this potential effect, we conducted a post-hoc analysis of participants in the Hokusai-VTE and ENGAGE AF-TIMI 48 trials, which randomized patients to warfarin or the direct oral anticoagulant edoxaban with routine laboratory testing at predefined follow-up visits.
View Article and Find Full Text PDFDriven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFAm J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.