Automated Electronic Phenotyping of Cardioembolic Stroke.

BACKGROUND AND PURPOSE: Oral anticoagulation is generally indicated for cardioembolic strokes, but not for other stroke causes. Consequently, subtype classification of ischemic stroke is important for risk stratification and secondary prevention. Because manual classification of ischemic stroke is time-intensive, we assessed the accuracy of automated algorithms for performing cardioembolic stroke subtyping using an electronic health record (EHR) database. METHODS: We adapted TOAST (Trial of ORG 10172 in Acute Stroke Treatment) features associated with cardioembolic stroke for derivation in the EHR. Using administrative codes and echocardiographic reports within Mass General Brigham Biobank (N=13 079), we iteratively developed EHR-based algorithms to define the TOAST cardioembolic stroke features, revising regular expression algorithms until achieving positive predictive value ≥80%. We compared several machine learning-based statistical algorithms for discriminating cardioembolic stroke using the feature algorithms applied to EHR data from 1598 patients with acute ischemic strokes from the Massachusetts General Hospital Ischemic Stroke Registry (2002-2010) with previously adjudicated TOAST and Causative Classification of Stroke subtypes. RESULTS: Regular expression-based feature extraction algorithms achieved a mean positive predictive value of 95% (range, 88%-100%) across 11 echocardiographic features. Among 1598 patients from the Massachusetts General Hospital Ischemic Stroke Registry, 1068 had any cardioembolic stroke feature within predefined time windows in proximity to the stroke event. Cardioembolic stroke tended to occur at an older age, with more TOAST-based comorbidities, and with atrial fibrillation (82.3%). The best model was a random forest with 92.2% accuracy and area under the receiver operating characteristic curve of 91.1% (95% CI, 87.5%-93.9%). Atrial fibrillation, age, dilated cardiomyopathy, congestive heart failure, patent foramen ovale, mitral annulus calcification, and recent myocardial infarction were the most discriminatory features. CONCLUSIONS: Machine learning-based identification of cardioembolic stroke using EHR data is feasible. Future work is needed to improve the accuracy of automated cardioembolic stroke identification and assess generalizability of electronic phenotyping algorithms across clinical settings.

Design and rationale of a pragmatic trial integrating routine screening for atrial fibrillation at primary care visits: The VITAL-AF trial.

Given the preventable morbidity and mortality associated with atrial fibrillation (AF), increased awareness of undiagnosed AF, and advances in mobile electrocardiogram (ECG) technology, there is a critical need to assess the effectiveness of using such technology to routinely screen for AF in clinical practice. VITAL-AF is a pragmatic trial that will test whether screening for AF using a single-lead handheld ECG in individuals 65 years or older during primary care visits will lead to an increased rate of AF detection. The study is a cluster-randomized trial, with 8 primary care practices randomized to AF screening and 8 primary care practices randomized to usual care. We anticipate studying approximately 16,000 patients in each arm. During the 1-year enrollment period, practice medical assistants will screen eligible patients who agree to participate during office visits using a single-lead ECG device. Automated screening results are documented in the electronic health record, and patients can discuss screening results with their provider during the scheduled visit. All single-lead ECGs are overread by a cardiologist. Screen-detected AF is managed at the discretion of the patient's physician. The primary study end point is incident AF during the screening period. Key secondary outcomes include new oral anticoagulation prescriptions, incident ischemic stroke, and major hemorrhage during a 24-month period following the study start. Outcomes are ascertained based on electronic health record documentation and are manually adjudicated. The results of this pragmatic trial may help identify a model for widespread adoption of AF screening as part of routine clinical practice.

Seed-competent high-molecular-weight tau species accumulates in the cerebrospinal fluid of Alzheimer's disease mouse model and human patients.

OBJECTIVE: Cerebrospinal fluid (CSF) tau is an excellent surrogate marker for assessing neuropathological changes that occur in Alzheimer's disease (AD) patients. However, whether the elevated tau in AD CSF is just a marker of neurodegeneration or, in fact, a part of the disease process is uncertain. Moreover, it is unknown how CSF tau relates to the recently described soluble high-molecular-weight (HMW) species that is found in the postmortem AD brain and can be taken up by neurons and seed aggregates. METHODS: We have examined seeding and uptake properties of brain extracellular tau from various sources, including interstitial fluid (ISF) and CSF from an AD transgenic mouse model and postmortem ventricular and antemortem lumbar CSF from AD patients. RESULTS: We found that brain ISF and CSF tau from the AD mouse model can be taken up by cells and induce intracellular aggregates. Ventricular CSF from AD patients contained a rare HMW tau species that exerted a higher seeding activity. Notably, the HMW tau species was also detected in lumbar CSF from AD patients, and its levels were significantly elevated compared to control subjects. HMW tau derived from CSF of AD patients was seed competent in vitro. INTERPRETATION: These findings suggest that CSF from an AD brain contains potentially bioactive HMW tau species, giving new insights into the role of CSF tau and biomarker development for AD. Ann Neurol 2016;80:355-367.