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Williams–Beuren syndrome (WBS) is a rare genetic condition caused by a chromosomal microdeletion at 7q11.23. It is a multisystem disorder characterized by distinct facies, intellectual disability, and supravalvar aortic stenosis (SVAS). Those with WBS are at increased risk of sudden death, but mechanisms underlying this remain poorly understood. We recently demonstrated autonomic abnormalities in those with WBS that are associated with increased susceptibility to arrhythmia and sudden cardiac death (SCD). A recently introduced method for heart rate variability (HRV) analysis called "heart rate fragmentation" (HRF) correlates with adverse cardiovascular events (CVEs) and death in studies where heart rate variability (HRV) failed to identify high-risk subjects. Some argue that HRF quantifies nonautonomic cardiovascular modulators. We, therefore, sought to apply HRF analysis to a WBS cohort to determine 1) if those with WBS show differences in HRF compared with healthy controls and 2) if HRF helps characterize HRV abnormalities in those with WBS. Similar to studies of those with coronary artery disease (CAD) and atherosclerosis, we found significantly higher HRF (4 out of 7 metrics) in those with WBS compared with healthy controls. Multivariable analyses showed a weak-to-moderate association between HRF and HRV, suggesting that HRF may reflect HRV characteristics not fully captured by traditional HRV metrics (autonomic markers). We also introduce a new metric inspired by HRF methodology, significant acute rate drop (SARD), which may detect vagal activity more directly. HRF and SARD may improve on traditional HRV measures to identify those at greatest risk for SCD both in those with WBS and in other populations. NEW & NOTEWORTHY: This work is the first to apply heart rate fragmentation analyses to individuals with Williams syndrome and posits that the heart rate fragmentation parameter W3 may enable detection and investigation of phenomena underlying the proarrhythmic short-long-short RR interval sequences paradigm known to precede ventricular fibrillation and ventricular tachycardia. It also forwards a novel method for quantifying sinus arrhythmia and sinus pauses that likely correlate with parasympathetic activity. [ABSTRACT FROM AUTHOR]

Direct regression modeling of the subdistribution has become popular for analyzing data with multiple, competing event types. All general approaches so far are based on non-likelihood based procedures and target covariate effects on the subdistribution. We introduce a novel weighted likelihood function that allows for a direct extension of the Fine-Gray model to a broad class of semiparametric regression models. The model accommodates time-dependent covariate effects on the subdistribution hazard. To motivate the proposed likelihood method, we derive standard nonparametric estimators and discuss a new interpretation based on pseudo risk sets. We establish consistency and asymptotic normality of the estimators and propose a sandwich estimator of the variance. In comprehensive simulation studies we demonstrate the solid performance of the weighted NPMLE in the presence of independent right censoring. We provide an application to a very large bone marrow transplant dataset, thereby illustrating its practical utility.