Abstract-The decentralized detection of event regions is a fundamental building block for monitoring and reasoning about spatial phenomena. However, so far the problem has been studied almost exclusively for static networks. This study proposes a theoretical framework with which we can analyze event detection algorithms suitable for large-scale mobile networks. Our analysis builds on the following insight: the inherent trends of spatial events are well captured by the spectral domain of the network graph. Using this framework, we propose novel local algorithms that are location-free; that work with mobile nodes and dynamic events; that operate on 3D topologies; and that are simple to implement. We are not aware of event detection algorithms possessing all these traits. Simulations based on complex oil spill traces showcase the resilience and robustness of our methods. Additionally, we demonstrate their validity for practical scenarios by evaluating them on a 105 node testbed.
Aims
To study the impact of genotype on the performance of the 2019 risk model for arrhythmogenic right ventricular cardiomyopathy (ARVC).
Methods and results
The study cohort comprised 554 patients with a definite diagnosis of ARVC and no history of sustained ventricular arrhythmia (VA). During a median follow-up of 6.0 (3.1,12.5) years, 100 patients (18%) experienced the primary VA outcome (sustained ventricular tachycardia, appropriate implantable cardioverter defibrillator intervention, aborted sudden cardiac arrest, or sudden cardiac death) corresponding to an annual event rate of 2.6% [95% confidence interval (CI) 1.9–3.3]. Risk estimates for VA using the 2019 ARVC risk model showed reasonable discriminative ability but with overestimation of risk. The ARVC risk model was compared in four gene groups: PKP2 (n = 118, 21%); desmoplakin (DSP) (n = 79, 14%); other desmosomal (n = 59, 11%); and gene elusive (n = 160, 29%). Discrimination and calibration were highest for PKP2 and lowest for the gene-elusive group. Univariable analyses revealed the variable performance of individual clinical risk markers in the different gene groups, e.g. right ventricular dimensions and systolic function are significant risk markers in PKP2 but not in DSP patients and the opposite is true for left ventricular systolic function.
Conclusion
The 2019 ARVC risk model performs reasonably well in gene-positive ARVC (particularly for PKP2) but is more limited in gene-elusive patients. Genotype should be included in future risk models for ARVC.
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