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