Dataset Information

Characteristics of Recurrent Ischemic Stroke After Embolic Stroke of Undetermined Source: Secondary Analysis of a Randomized Clinical Trial.

ABSTRACT:

Importance

The concept of embolic stroke of undetermined source (ESUS) unifies a subgroup of cryptogenic strokes based on neuroimaging, a defined minimum set of diagnostic tests, and exclusion of certain causes. Despite an annual stroke recurrence rate of 5%, little is known about the etiology underlying recurrent stroke after ESUS.

Objective

To identify the stroke subtype of recurrent ischemic strokes after ESUS, to explore the interaction with treatment assignment in each category, and to examine the consistency of cerebral location of qualifying ESUS and recurrent ischemic stroke.

Design, setting, and participants

The NAVIGATE-ESUS trial was a randomized clinical trial conducted from December 23, 2014, to October 5, 2017. The trial compared the efficacy and safety of rivaroxaban and aspirin in patients with recent ESUS (n = 7213). Ischemic stroke was validated in 309 of the 7213 patients by adjudicators blinded to treatment assignment and classified by local investigators into the categories ESUS or non-ESUS (ie, cardioembolic, atherosclerotic, lacunar, other determined cause, or insufficient testing). Five patients with recurrent strokes that could not be defined as ischemic or hemorrhagic in absence of neuroimaging or autopsy were excluded. Data for this secondary post hoc analysis were analyzed from March to June 2019.

Interventions

Patients were randomly assigned to receive rivaroxaban, 15 mg/d, or aspirin, 100 mg/d.

Main outcomes and measures

Association of recurrent ESUS with stroke characteristics.

Results

A total of 309 patients (205 men [66%]; mean [SD] age, 68 [10] years) had ischemic stroke identified during the median follow-up of 11 (interquartile range [IQR], 12) months (annualized rate, 4.6%). Diagnostic testing was insufficient for etiological classification in 39 patients (13%). Of 270 classifiable ischemic strokes, 156 (58%) were ESUS and 114 (42%) were non-ESUS (37 [32%] cardioembolic, 26 [23%] atherosclerotic, 35 [31%] lacunar, and 16 [14%] other determined cause). Atrial fibrillation was found in 27 patients (9%) with recurrent ischemic stroke and was associated with higher morbidity (median change in modified Rankin scale score 2 [IQR, 3] vs 0 (IQR, 1]) and mortality (15% vs 1%) than other causes. Risk of recurrence did not differ significantly by subtype between treatment groups. For both the qualifying and recurrent strokes, location of infarct was more often in the left (46% and 54%, respectively) than right hemisphere (40% and 37%, respectively) or brainstem or cerebellum (14% and 9%, respectively).

Conclusions and relevance

In this secondary analysis of randomized clinical trial data, most recurrent strokes after ESUS were embolic and of undetermined source. Recurrences associated with atrial fibrillation were a minority but were more often disabling and fatal. More extensive investigation to identify the embolic source is important toward an effective antithrombotic strategy.

Trial registration

ClinicalTrials.gov Identifier: NCT02313909.

SUBMITTER: Veltkamp R

PROVIDER: S-EPMC7550970 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and purposeOne-fifth of ischemic strokes are embolic strokes of undetermined source (ESUS). Their theoretical causes can be classified as cardioembolic versus noncardioembolic. This distinction has important implications, but the categories' proportions are unknown.MethodsUsing data from the Cornell Acute Stroke Academic Registry, we trained a machine-learning algorithm to distinguish cardioembolic versus non-cardioembolic strokes, then applied the algorithm to ESUS cases to determine the predicted proportion with an occult cardioembolic source. A panel of neurologists adjudicated stroke etiologies using standard criteria. We trained a machine learning classifier using data on demographics, comorbidities, vitals, laboratory results, and echocardiograms. An ensemble predictive method including L1 regularization, gradient-boosted decision tree ensemble (XGBoost), random forests, and multivariate adaptive splines was used. Random search and cross-validation were used to tune hyperparameters. Model performance was assessed using cross-validation among cases of known etiology. We applied the final algorithm to an independent set of ESUS cases to determine the predicted mechanism (cardioembolic or not). To assess our classifier's validity, we correlated the predicted probability of a cardioembolic source with the eventual post-ESUS diagnosis of atrial fibrillation.ResultsAmong 1083 strokes with known etiologies, our classifier distinguished cardioembolic versus noncardioembolic cases with excellent accuracy (area under the curve, 0.85). Applied to 580 ESUS cases, the classifier predicted that 44% (95% credibility interval, 39%-49%) resulted from cardiac embolism. Individual ESUS patients' predicted likelihood of cardiac embolism was associated with eventual atrial fibrillation detection (OR per 10% increase, 1.27 [95% CI, 1.03-1.57]; c-statistic, 0.68 [95% CI, 0.58-0.78]). ESUS patients with high predicted probability of cardiac embolism were older and had more coronary and peripheral vascular disease, lower ejection fractions, larger left atria, lower blood pressures, and higher creatinine levels.ConclusionsA machine learning estimator that distinguished known cardioembolic versus noncardioembolic strokes indirectly estimated that 44% of ESUS cases were cardioembolic.

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