Laura Heitsch

Genetic variation at 16q24.2 is associated with small vessel stroke

Genome‐wide association studies (GWAS) have been successful at identifying associations with stroke and stroke subtypes, but have not yet identified any associations solely with small vessel stroke (SVS). SVS comprises one quarter of all ischemic stroke and is a major manifestation of cerebral small vessel disease, the primary cause of vascular cognitive impairment. Studies across neurological traits have shown that younger‐onset cases have an increased genetic burden. We leveraged this increased genetic burden by performing an age‐at‐onset informed GWAS meta‐analysis, including a large younger‐onset SVS population, to identify novel associations with stroke.

How much would performing diffusion-weighted imaging for all transient ischemic attacks increase MRI utilization?

Objectives— The American Heart Association recently redefined TIA to exclude patients with infarction on neuroimaging. Given its advantages, MRI/diffusion-weighted imaging (DWI) was recommended as the preferred imaging modality. We determined how frequently MRI/DWI was performed for TIA and ascertained the proportion of clinically defined TIA patients who had ischemic lesions on DWI in our community in 2005. Methods— All clinically defined TIA cases among residents of a 5-county region around Cincinnati who presented to emergency departments were identified during 2005. Demographics and medical history, whether MRI/DWI was performed, and DWI findings were recorded. Generalized estimating equations were used to compare groups to account for the design of the study and multiple events per patient. Results— Of 834 TIA events in 799 patients, 323 events (40%) had MRI/DWI performed. Patients who had MRI/DWI were younger (mean, 66 vs 70 years; P=0.03), had less severe prestroke disability (baseline modified Rankin Scale score, 0; 44% vs 34%; P=0.02), were less likely to have previous stroke or TIA (42% vs 56%; P=0.002), and were less likely to have atrial fibrillation (10% vs 16%; P=0.01). Of the 323 events with DWI, 51 (15%) had evidence of acute infarction. Patients with positive DWI were older (75 vs 64 years; P=0.0001) and more likely to have atrial fibrillation (21% vs 7%; P=0.002). Conclusion— Performing MRI/DWI on all clinically defined TIA patients in our community would reveal more cases of actual infarction but would more than double current use. Future studies should assess whether MRI/DWI is warranted for all TIA patients.

Streamlined Hyperacute Magnetic Resonance Imaging Protocol Identifies Tissue-Type Plasminogen Activator–Eligible Stroke Patients When Clinical Impression Is Stroke Mimic

Background and Purpose— Stroke mimics (SM) challenge the initial assessment of patients presenting with possible acute ischemic stroke (AIS). When SM is considered likely, intravenous tissue-type plasminogen activator (tPA) may be withheld, risking an opportunity to treat AIS. Although computed tomography is routinely used for tPA decision making, magnetic resonance imaging (MRI) may diagnose AIS when SM is favored but not certain. We hypothesized that a hyperacute MRI (hMRI) protocol would identify tPA-eligible AIS patients among those initially favored to have SM. Methods— A streamlined hMRI protocol was designed based on barriers to rapid patient transport, MRI acquisition, and post-MRI tPA delivery. Neurologists were trained to order hMRI when SM was favored and tPA was being withheld. The use of hMRI for tPA decision making, door-to-needle times, and outcomes were compared before hMRI implementation (pre-hMRI: August 1, 2011 to July 31, 2013) and after (post-hMRI, August 1, 2013, to January 15, 2015). Results— Post hMRI, 57 patients with suspected SM underwent hMRI (median MRI-order-to-start time, 29 minutes), of whom, 11 (19%) were diagnosed with AIS and 7 (12%) received tPA. Pre-hMRI, no tPA-treated patients were screened with hMRI. Post hMRI, 7 of 106 (6.6%) tPA-treated patients underwent hMRI to aid in decision making because of suspected SM (0% versus 6.6%; P=0.001). To ensure standard care was maintained after implementing the hMRI protocol, pre- versus post-hMRI tPA-treated cohorts were compared and did not differ: door-to-needle time (39 versus 37 minutes; P=0.63), symptomatic hemorrhage rate (4.5% versus 1.9%; P=0.32), and favorable discharge location (85% versus 89%; P=0.37). Conclusions— A streamlined hMRI protocol permitted tPA administration to a small, but significant, subset of AIS patients initially considered to have SM.

Profiles of the National Institutes of Health Stroke Scale Items as a Predictor of Patient Outcome

Background and Purpose— Initial National Institutes of Health Stroke Scale (NIHSS) score is highly predictive of outcome after ischemic stroke. We examined whether grouping strokes by presence of individual NIHSS symptoms could provide prognostic information additional or alternative to the NIHSS total score. Methods— Ischemic strokes from the Greater Cincinnati Northern Kentucky Stroke Study in 2005 were used to develop the model. Latent class analysis was implemented to form groups of patients with similar retrospective NIHSS (rNIHSS) item responses. Profile group was then used as an independent predictor of discharge modified Rankin and mortality, using logistic regression and Cox proportional hazards model. Results— A total of 2112 stroke patients were identified in 2005. Six distinct profiles were characterized. Consistent with the profile patterns, the median rNIHSS total score decreased from profile A “most severe” (median [interquartile range], 20 [15–25]) to profile F “mild” (1[1–2]). Two profiles falling between these extremes, C and D, both had median rNIHSS total score of 5, but different survival rates. Compared with A, C was associated with 59% risk reduction for death, whereas D with 70%. C patients were more likely to have decreased level of consciousness and abnormal language, whereas D patients were more likely to have abnormal right arm and right leg motor function. Conclusions— Six rNIHSS profiles were identifiable using latent class analysis. In particular, 2 symptom profiles with identical median rNIHSSS were observed with widely disparate outcomes, which may prove useful both clinically and for research studies as an enhancement to the overall NIHSS score.

Genetic studies of plasma analytes identify novel potential biomarkers for several complex traits

Genome-wide association studies of 146 plasma protein levels in 818 individuals revealed 56 genome-wide significant associations (28 novel) with 47 analytes. Loci associated with plasma levels of 39 proteins tested have been previously associated with various complex traits such as heart disease, inflammatory bowel disease, Type 2 diabetes, and multiple sclerosis. These data suggest that these plasma protein levels may constitute informative endophenotypes for these complex traits. We found three potential pleiotropic genes: ABO for plasma SELE and ACE levels, FUT2 for CA19-9 and CEA plasma levels, and APOE for ApoE and CRP levels. We also found multiple independent signals in loci associated with plasma levels of ApoH, CA19-9, FetuinA, IL6r, and LPa. Our study highlights the power of biological traits for genetic studies to identify genetic variants influencing clinically relevant traits, potential pleiotropic effects, and complex disease associations in the same locus.

Accuracy of emergency medical services-reported last known normal times in patients suspected with acute stroke.

Background and Purpose— The last known normal (LKN) time is a critical determinant of IV tissue-type plasminogen activator (IV tPA) eligibility; however, the accuracy of emergency medical services (EMS)-reported LKN times is unknown. We determined the congruence between neurologist-determined and EMS-reported LKN times and identified predictors of incongruent LKN times. Methods— We prospectively collected EMS-reported LKN times for patients brought into the emergency department with suspected acute stroke and calculated the absolute difference between the neurologist-determined and EMS-reported LKN times (|&Dgr;LKN|). We determined the rate of inappropriate IV tPA use if EMS-reported times had been used in place of neurologist-determined times. Univariate and multivariable linear regression assessed for any predictors of prolonged |&Dgr;LKN|. Results— Of 251 patients, mean and median |&Dgr;LKN| were 28 and 0 minutes, respectively. |&Dgr;LKN| was <15 minutes in 91% of the entire cohort and <15 minutes in 80% of patients with a diagnosis of stroke (n=86). Of patients who received IV tPA, none would have been incorrectly excluded from IV tPA if the EMS LKN time had been used. Conversely, of patients who did not receive IV tPA, 6% would have been incorrectly included for IV tPA consideration had the EMS time been used. In patients with wake-up stroke symptoms, EMS underestimated LKN times: mean neurologist LKN time−EMS LKN time =208 minutes. The presence of wake-up stroke symptoms (P<0.0001) and older age (P=0.019) were independent predictors of prolonged |&Dgr;LKN|. Conclusions— EMS-reported LKN times were largely congruent with neurologist-determined times. Focused EMS training regarding wake-up stroke symptoms may further improve accuracy.

Treating the Elderly Stroke Patient: Complications, Controversies, and Best Care Metrics

Acute stroke is a devastating disease that affects almost 800,000 Americans annually. Worldwide, the incidence of stroke is rapidly increasing. Although stroke can affect all age groups, patients over age 80 are at much higher risk for ischemic stroke. Despite this, there are disparities in thrombolytic treatment rates, and as well as outcomes, between elderly stroke patients and their younger counterparts. This article discusses what is currently known about the elderly stroke patient for a greater understanding of the disease burden, research limitations and potential treatment options.

Automated quantification of cerebral edema following hemispheric infarction: Application of a machine-learning algorithm to evaluate CSF shifts on serial head CTs

Although cerebral edema is a major cause of death and deterioration following hemispheric stroke, there remains no validated biomarker that captures the full spectrum of this critical complication. We recently demonstrated that reduction in intracranial cerebrospinal fluid (CSF) volume (∆ CSF) on serial computed tomography (CT) scans provides an accurate measure of cerebral edema severity, which may aid in early triaging of stroke patients for craniectomy. However, application of such a volumetric approach would be too cumbersome to perform manually on serial scans in a real-world setting. We developed and validated an automated technique for CSF segmentation via integration of random forest (RF) based machine learning with geodesic active contour (GAC) segmentation. The proposed RF + GAC approach was compared to conventional Hounsfield Unit (HU) thresholding and RF segmentation methods using Dice similarity coefficient (DSC) and the correlation of volumetric measurements, with manual delineation serving as the ground truth. CSF spaces were outlined on scans performed at baseline (< 6 h after stroke onset) and early follow-up (FU) (closest to 24 h) in 38 acute ischemic stroke patients. RF performed significantly better than optimized HU thresholding (p < 10− 4 in baseline and p < 10− 5 in FU) and RF + GAC performed significantly better than RF (p < 10− 3 in baseline and p < 10− 5 in FU). Pearson correlation coefficients between the automatically detected ∆ CSF and the ground truth were r = 0.178 (p = 0.285), r = 0.876 (p < 10− 6) and r = 0.879 (p < 10− 6) for thresholding, RF and RF + GAC, respectively, with a slope closer to the line of identity in RF + GAC. When we applied the algorithm trained from images of one stroke center to segment CTs from another center, similar findings held. In conclusion, we have developed and validated an accurate automated approach to segment CSF and calculate its shifts on serial CT scans. This algorithm will allow us to efficiently and accurately measure the evolution of cerebral edema in future studies including large multi-site patient populations.

Systematic Molecular Phenotyping: A Path Toward Precision Emergency Medicine?

Precision medicine is an emerging approach to disease treatment and prevention that considers variability in patient genes, environment, and lifestyle. However, little has been written about how such research impacts emergency care. Recent advances in analytical techniques have made it possible to characterize patients in a more comprehensive and sophisticated fashion at the molecular level, promising highly individualized diagnosis and treatment. Among these techniques are various systematic molecular phenotyping analyses (e.g., genomics, transcriptomics, proteomics, and metabolomics). Although a number of emergency physicians use such techniques in their research, widespread discussion of these approaches has been lacking in the emergency care literature and many emergency physicians may be unfamiliar with them. In this article, we briefly review the underpinnings of such studies, note how they already impact acute care, discuss areas in which they might soon be applied, and identify challenges in translation to the emergency department (ED). While such techniques hold much promise, it is unclear whether the obstacles to translating their findings to the ED will be overcome in the near future. Such obstacles include validation, cost, turnaround time, user interface, decision support, standardization, and adoption by end-users.