Pre dicting D eterminants of A trial Fibrillation or Flutter for T herapy E lucidation in Patients at Risk for Thromboembolic Events (PREDATE AF) Study

Nasir, Javed M.; Pomeroy, William C.; Marler, Adam T.; Hann, Matthew C.; Baykaner, Tina; Jones, R. Jason; Stoll, Richard J.; Hursey, Katherine; Meadows, Angela; Walker, Jennifer; Kindsvater, Steve

doi:10.1016/j.hrthm.2017.04.026

Cited by 88 publications

(70 citation statements)

References 30 publications

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“…Most of the patients had one or more non-specific symptoms at baseline (approximately 10% had none), but there were no differences in outcome events in those with versus those without symptoms. 32,35 This finding is not really surprising, since symptoms have been proven to have a poor correlation with AF in many studies. [36][37][38][39] Thus, REVEAL AF, a somewhat larger and longer trial than PREDATE AF or ASSERT-II, confirmed a significant incidence of previously undiagnosed AF in a group of patients identified demographically to be at-risk for both AF and stroke, in whom a history of AF was absent and no previously implanted pacemaker/ICD was present.…”

Section: Prevalence Of Subclinical Atrial Fibrillation Using An Implamentioning

confidence: 87%

“…They are PREDATE AF, ASSERT-II, and REVEAL AF. [32][33][34] Additionally, a larger but similar in nature trial, LOOP (NCT02036450), is still ongoing, while another small trial, GRAF-AF (NCT01461434), has not yet been presented or published.…”

Section: Methods and Duration Of Monitoring For Silent Afmentioning

confidence: 99%

“…PREDATE AF 32 was an investigator-initiated, prospective, single-arm, open-label, single-center trial with a planned enrollment quota of 350 asymptomatic subjects with a CHA 2 DS 2 -VASc score Z 2 and no history of AF who were monitored using ICMs. However, it was stopped prior to enrolling the full 350 subjects, as it met its power analysis goals early.…”

Section: Predicting Determinants Of Atrial Fibrillation For Therapy Ementioning

confidence: 99%

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When Silence Isn’t Golden: The Case of “Silent” Atrial Fibrillation

Reiffel

2017

J Innov Cardiac Rhythm Manage

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ABSTRACT. Silent atrial fibrillation (AF) is common. In some patients, it is the only manifestation of AF, while in others, the AF may be symptomatic or both symptomatic and asymptomatic. Regardless, however, to date, the significance, detection, and management considerations for silent AF have been incompletely elucidated. This current study aimed to review, for both the current clinician and investigator, considerations and attitudes and the ongoing studies, respectively, with respect to silent AF. The methods used were a literature review and personal trial and clinical experience; the frequency of silent AF, concerns regarding silent AF, methods to detect silent AF, and prospective trials focused on the detection and management of silent AF were considered. The results of the literature search indicated that recently conducted relevant trials, such as PREDATE AF, ASSERT-II, and REVEAL AF, have shown that silent AF is frequent in patients with risk markers for AF and stroke in whom no prior AF history is present, and in whom no pacemaker or implantable cardioverter-defibrillator implantations have been previously performed. Furthermore, the GLORIA-AF Registry has reported the observance of more permanent AF and more prior strokes in asymptomatic patients. Ongoing trials such as ARTESiA and NOAH-AFNET 6 are expected to clarify the benefits and risks of oral anticoagulation in patients with silent AF. At present, when silent AF is detected in patients with stroke risk markers, most practitioners initiate an anticoagulation regimen.

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Section: Prevalence Of Subclinical Atrial Fibrillation Using An Implamentioning

confidence: 87%

Section: Methods and Duration Of Monitoring For Silent Afmentioning

confidence: 99%

Section: Predicting Determinants Of Atrial Fibrillation For Therapy Ementioning

confidence: 99%

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When Silence Isn’t Golden: The Case of “Silent” Atrial Fibrillation

Reiffel

2017

J Innov Cardiac Rhythm Manage

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“…Remote continuous monitoring has recently been shown to improve treatment outcomes [23] and to reduce time-totreatment for patients with atrial tachycardia burden [24], so it is important to develop algorithms that can monitor over longer periods of time and/or compute more advanced statistics that can better detect the arrhythmia burden.…”

Section: B Implantable Devicesmentioning

confidence: 99%

Real-Time Decision Policies With Predictable Performance

et al. 2018

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As methods and tools for Cyber-Physical Systems grow in capabilities and use, one-size-fits-all solutions start to show their limitations. In particular, tools and languages for programming an algorithm or modeling a CPS that are specific to the application domain are typically more usable, and yield better performance, than general-purpose languages and tools. In the domain of cardiac arrhythmia monitoring, a small, implantable medical device continuously monitors the patient's cardiac rhythm and delivers electrical therapy when needed. The algorithms executed by these devices are streaming algorithms, so they are best programmed in a streaming language that allows the programmer to reason about the incoming data stream as the basic object, rather than force her to think about lower-level details like state maintenance and minimization. Because these devices are resource-constrained, it is useful if the programming language allowed predictable performance in terms of processing runtime and energy consumption, or more general costs. StreamQRE is a declarative streaming programming language, with an efficient and portable implementation and strong theoretical guarantees. In particular, its evaluation algorithm guarantees constant cost (runtime, memory, energy) per data item, and also calculates upper bounds on the per-item cost. Such an estimate of the cost allows early exploration of the algorithmic possibilities, while maintaining a handle on worst-case performance, on the basis of which hardware can be designed and algorithms can be tuned. Abstract-As methods and tools for Cyber-Physical Systems grow in capabilities and use, one-size-fits-all solutions start to show their limitations. In particular, tools and languages for programming an algorithm or modeling a CPS that are specific to the application domain are typically more usable, and yield better performance, than general-purpose languages and tools. In the domain of cardiac arrhythmia monitoring, a small, implantable medical device continuously monitors the patient's cardiac rhythm and delivers electrical therapy when needed. The algorithms executed by these devices are streaming algorithms, so they are best programmed in a streaming language that allows the programmer to reason about the incoming data stream as the basic object, rather than force her to think about lower-level details like state maintenance and minimization. Because these devices are resource-constrained, it is useful if the programming language allowed predictable performance in terms of processing runtime and energy consumption, or more general costs. StreamQRE is a declarative streaming programming language, with an efficient and portable implementation and strong theoretical guarantees. In particular, its evaluation algorithm guarantees constant cost (runtime, memory, energy) per data item, and also calculates upper bounds on the per-item cost. Such an estimate of the cost allows early exploration of the algorithmic possibilities, while maintaining a handle on worst-case per...

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“…7 Another puzzle is that asymptomatic episodes of SCAF detected by continuous monitoring in many, if not most, studies seem not to correlate closely with the timing of stroke. 4,[6][7][8]15 It may be that AF and thrombus formation might occur some days, weeks, or even months before the clot dislodges from the atrium and causes an embolic stroke, but it is difficult to incriminate AF as the stroke mechanism when the AF occurs only after the stroke. In addition, there are many causes of ischemic stroke other than AF, none of which is unlikely in patients with AF.…”

mentioning

confidence: 99%