Cryptogenic Stroke
Cryptogenic strokes are defined as symptomatic cerebral infarcts for
which no probable cause is identified after thorough standard
evaluation.59 About one-third of transient ischemic
attacks 60 and ischemic strokes are of undetermined
etiology (cryptogenic).60 These numbers have decreased
over time from as high as 40% in 1970’s61 to as low
as 10-15% today in advanced centers with extensive testing
modalities.29,62 This highlights the importance of
better ways to investigate patients in order to initiate an appropriate
and timely secondary prevention strategy.
AF prevalence as high as 24.6% has been noted in patients presenting
with first time ischemic strokes and was especially high amongst elderly
females.63,64 In CRYSTAL AF (Cryptogenic Stroke and
Underlying AF) trial, AF detection was compared between insertable
cardiac monitors 65 and conventional follow-up in
patients with cryptogenic stroke or TIA.65 Results
showed detection rate of AF at 8.9% vs 1.4% at 6 months and 12.4% vs
2% at 12 months for ICM vs conventional follow-up respectively. This
proves that many cases go undetected after the first thromboembolic
event and foreshadow a recurrence which could have been prevented. In
the same trial, mean time in AF was only 4.3 minutes a day and about
74% patients were asymptomatic, highlighting the increasingly difficult
diagnosis of paroxysmal AF in these patients.65 Since
prolonged monitoring is inconvenient and expensive, the authors called
for further studies to determine which risk factors could better
delineate which patients would derive the most clinical benefit from
extensive monitoring.
Current recommendation for patients with cryptogenic stroke is that a
prolonged rhythm monitoring of about 30 days within 6 months of index
event is reasonable.66 Furthermore, even though a
single 1-hour episode of AF during 2 years of monitoring doubles the
risk of stroke, the treatment benefit of anticoagulants vs antiplatelet
agents is not clearly defined amongst low burden paroxysmal AF patients.
A large subgroup of cryptogenic strokes (80-90%), in which the cause is
almost always embolic (superficial or deep large infarcts) is termed
embolic stroke of undetermined significance (ESUS).59Low burden paroxysmal AF forms an important underlying cause for these
patients. However, empiric anticoagulation is not recommended as
bleeding risk seems to outweigh the benefits.67,68
Therefore, knowledge gaps remain regarding the best way to monitor
patients with ESUS and who would benefit most from oral anticoagulation.
The AI-ECG AF model is one potential way to tackle this complex problem
as it could be used to identify a subset of high-risk ESUS patients most
likely to benefit from empirical oral anticoagulation. In a
retrospective study of stroke patients, we found a strong association of
probability output >0.2 as noted by the AI-ECG AF model and
detection of AF by ambulatory cardiac monitoring OR of 5.47 (95% CI
1.51-22.51; P = 0.01).52 However, we were limited by
the detection rate of AF amongst the ESUS patients due to a shorter
average monitoring time. A clinical trial ‘batch enrollment for
AI-guided intervention to lower neurologic events in unrecognized AF’
(BEAGLE) is currently underway to assess the use of this
model.34 Participants are selected using natural
language processing tools if they are at high risk for incident AF as
identified using the algorithm and are eligible to receive
anticoagulation if AF is detected. Enrolled patients are then mailed a
device to continuously monitor for up to 30 days. This is a completely
off-site trial and will allow vetting and validation of the model in a
real-world scenario.34