Cerebral Embolism in the Michael Reese Stroke Registry

Infarction Secondary to Cerebral Embolism Was Diagnosed in 127 (23.5%) of 540 Patients in the Michael Reese Stroke Registry. Coronary Artery Disease, Atrial Fibrillation, Valvular Heart Disease, Mitral Annulus Calcification, and Cardiomyopathy Were the Commonest Etiologies. Echocardiography Documented a Potential Embolic Source in 7 Patients Without Previously Known Heart Disease and Clarified the Cardiac Pathology in Many of the Patients with Known Heart Disease. the Left Anterior Circulation Was Affected in 48%, Right Anterior in 37%, and Posterior Circulation in 15% of patients. CT Was Abnormal in 71% of the Patients and Was Approximately Equally Helpful in All Locations. Nineteen Percent of Emboli Presented with a Deficit that Was Other Than Maximal at Onset. Concurrent Systemic Embolism Was Unusual (2.3%). Prognosis Was Somewhat Worse Than in Thrombotic Stroke. Grouping of Patients According to Embolic Source (Intra-Arterial, Cardiac, and Uncertain Source) Showed No Differences in Activity at Onset, Early Course, or in Subsequent Course of the Illness

Current Concepts of Cerebrovascular Disease - Stroke: Stroke and Drug Abuse

This Review Summarizes Available Information Concerning Cerebral Vascular Complications of the Most Commonly Abused Substances and Discusses Possible Mechanisms of Vascular Injury and Cerebral Damage. Although Alcohol is Frequently Abused and May Have Important Cerebrovascular Effects, its Consideration is Beyond the Scope of This Review

The visualization of Orphadata neurology phenotypes

Disease phenotypes are characterized by signs (what a physician observes during the examination of a patient) and symptoms (the complaints of a patient to a physician). Large repositories of disease phenotypes are accessible through the Online Mendelian Inheritance of Man, Human Phenotype Ontology, and Orphadata initiatives. Many of the diseases in these datasets are neurologic. For each repository, the phenotype of neurologic disease is represented as a list of concepts of variable length where the concepts are selected from a restricted ontology. Visualizations of these concept lists are not provided. We address this limitation by using subsumption to reduce the number of descriptive features from 2,946 classes into thirty superclasses. Phenotype feature lists of variable lengths were converted into fixed-length vectors. Phenotype vectors were aggregated into matrices and visualized as heat maps that allowed side-by-side disease comparisons. Individual diseases (representing a row in the matrix) were visualized as word clouds. We illustrate the utility of this approach by visualizing the neuro-phenotypes of 32 dystonic diseases from Orphadata. Subsumption can collapse phenotype features into superclasses, phenotype lists can be vectorized, and phenotypes vectors can be visualized as heat maps and word clouds

Inter-rater agreement for the annotation of neurologic signs and symptoms in electronic health records

The extraction of patient signs and symptoms recorded as free text in electronic health records is critical for precision medicine. Once extracted, signs and symptoms can be made computable by mapping to signs and symptoms in an ontology. Extracting signs and symptoms from free text is tedious and time-consuming. Prior studies have suggested that inter-rater agreement for clinical concept extraction is low. We have examined inter-rater agreement for annotating neurologic concepts in clinical notes from electronic health records. After training on the annotation process, the annotation tool, and the supporting neuro-ontology, three raters annotated 15 clinical notes in three rounds. Inter-rater agreement between the three annotators was high for text span and category label. A machine annotator based on a convolutional neural network had a high level of agreement with the human annotators but one that was lower than human inter-rater agreement. We conclude that high levels of agreement between human annotators are possible with appropriate training and annotation tools. Furthermore, more training examples combined with improvements in neural networks and natural language processing should make machine annotators capable of high throughput automated clinical concept extraction with high levels of agreement with human annotators

High Throughput Neurological Phenotyping with MetaMap

The phenotyping of neurological patients involves the conversion of signs and symptoms into machine readable codes selected from an appropriate ontology. The phenotyping of neurological patients is manual and laborious. MetaMap is used for high throughput mapping of the medical literature to concepts in the Unified Medical Language System Metathesaurus (UMLS). MetaMap was evaluated as a tool for the high throughput phenotyping of neurological patients. Based on 15 patient histories from electronic health records, 30 patient histories from neurology textbooks, and 20 clinical summaries from the Online Mendelian Inheritance in Man repository, MetaMap showed a recall of 61-89%, a precision of 84-93%, and an accuracy of 56-84% for the identification of phenotype concepts. The most common cause of false negatives (failure to recognize a phenotype concept) was an inability of MetaMap to find concepts that were represented as a description or a definition of the concept. The most common cause of false positives (incorrect identification of a concept in the text) was a failure to recognize that a concept was negated. MetaMap shows potential for high throughput phenotyping of neurological patients if the problems of false negatives and false positives can be solved

Subtypes of Relapsing-Remitting Multiple Sclerosis Identified by Network Analysis

We used network analysis to identify subtypes of relapsing-remitting multiple sclerosis subjects based on their cumulative signs and symptoms. The electronic medical records of 113 subjects with relapsing-remitting multiple sclerosis were reviewed, signs and symptoms were mapped to classes in a neuro-ontology, and classes were collapsed into sixteen superclasses by subsumption. After normalization and vectorization of the data, bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using NetworkX and visualized in Gephi. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps visualized differences in features by community. Network analysis of the unipartite graph yielded a higher modularity score (0.49) than the bipartite graph (0.25). The bipartite network was partitioned into five communities which were named fatigue, behavioral, hypertonia/weakness, abnormal gait/sphincter, and sensory, based on feature characteristics. The unipartite network was partitioned into five communities which were named fatigue, pain, cognitive, sensory, and gait/weakness/hypertonia based on features. Although we did not identify pure subtypes (e.g., pure motor, pure sensory, etc.) in this cohort of multiple sclerosis subjects, we demonstrated that network analysis could partition these subjects into different subtype communities. Larger datasets and additional partitioning algorithms are needed to confirm these findings and elucidate their significance. This study contributes to the literature investigating subtypes of multiple sclerosis by combining feature reduction by subsumption with network analysis

Determinants of Early Recurrence of Cerebral Infarction: The Stroke Data Bank

We Studied 1,273 Patients with Ischemic Cerebral Infarction Who Were Entered into the Stroke Data Bank, a Prospective, Observational Study Involving Four University Hospitals and the Biometry and Field Studies Branch of the National Institute of Neurological Disorders and Stroke. Forty Patients Had Non-Iatrogenic Recurrent Stroke within 30 Days after the Index Cerebral Infarction. using Life Tables, the 30-Day Cumulative± SE Risk of Early Recurrence for All Infarctions Was 3.3±0.4%. the Risk of Early Recurrence Was Greatest for Atherothrombotic Infarction (7.9±2.2%, Eight of 113 Patients) and Least for Lacunar Infarction (2.2±1.2%, Eight of 337 Patients). Both Cardioembolic Infarction (4.3±0.9%, 10 of 246 Patients) and Infarction of Undetermined Cause (3.0±0.5%, 14 of 508 Patients) Had Intermediate Risks. History of Hypertension and Diabetes Mellitus, as Well as Diastolic Hypertension and Elevated Blood Sugar Concentration at Admission, Were Associated with Early Recurrence. Logistic Regression Analysis Estimated the Risk of Early Recurrence to Be 8.56% in Those with Coexisting Hypertension and a Glucose Concentration of 300 Mg/dl Versus 0.77% in the Absence of These Two Abnormalities. Early Recurrence Was Associated with Longer Median Duration of Initial Hospital Stay (27 vs.. 14 Days) and a Higher 30-Day Case—fatality Rate (20% vs.. 7.4%). Increased Weakness Scores Were Associated with Early Recurrent Stroke. Identification of the Determinants of Early Recurrent Stroke May Lead to Better Secondary Prevention and May Help Select High-Risk Patients for Further Study. © 1989 American Heart Association, Inc

Stroke Recurrence within 2 Years after Ischemic Infarction

We Prospectively Studied Stroke Recurrence in 1,273 Patients with Ischemic Stroke Who Were Entered into the Stroke Data Bank. Median Follow-Up Was 13 Months. the 2-Year Cumulative Recurrence Rate among These Patients Was 14.1%. Age, Sex, Race, History of Hypertension, Atrial Fibrillation, or Transient Ischemic Attacks, and Stroke Location Were Not Associated with a Higher Risk of Stroke Recurrence. Patients with an Elevated Blood Pressure, an Abnormal Initial Computed Tomogram, or a History of Diabetes Mellitus Were at a Higher Risk of Stroke Recurrence. in Contrast, Patients with an Infarct of Unknown Cause Were at a Lower Risk of Stroke Recurrence Than Patients with a Denned Stroke Mechanism, Such as Lacune, Embolism, or Atherosclerosis. Amultivaria Te Model Suggests that Patients at the Lowest Risk for Stroke Recurrence Have a Low Diastolic Blood Pressure, No History of Stroke, No History of Diabetes Mellitus, and an Infarct of Unknown Cause. © 1991 American Heart Association, Inc