Contemporary trends, treatments, and outcomes in theUnited States

Background Nationwide data on patients with cryptogenic stroke (CS) are lacking. We evaluated patient and hospital characteristics, in-hospital treatments, and discharge outcomes among patients with CS compared with other subtypes in the GetWith TheGuidelines (GWTG)Stroke registry. Methods We identified patients with ischemic stroke (IS) admitted to GWTGStroke participating hospitals between January 1, 2016, and September 30, 2017, with documented National Institutes of Health Stroke Scale (NIHSS) scale and stroke etiology (cardioembolic [CE], large artery atherosclerosis [LAA], small vessel occlusion [SVO], other determined etiology [OTH], or CS). Usingmultivariable logistic regression, we compared hospital treatments and discharge outcomes by subtype, adjusted for patient and hospital characteristics. Results Among 316,623 patients from 1,687 hospitals, there were 63,301 (20.0%) patients with CS. In multivariable analysis, patients with CS received IV thrombolysis more often than other subtypes and had lower mortality than CE, LAA, and OTH but higher mortality than SVO. They weremore likely to be discharged home than all other subtypes and be independent at discharge than LAA, OTH, or SVO. Conclusions In a large contemporary nationwide registry, CS accounted for 20% of ISs among patients with a documented stroke etiology. Patients with CS had a distinct profile of treatments and outcomes relative to other subtypes. Improved subtype documentation and further research into CS are warranted to improve care and outcomes for patients with stroke. Ischemic stroke (IS) is a leading cause of disability in the United States. Outcomes and secondary prevention strategies vary by IS subtype or etiology; for example, anticoagulation is reserved primarily for cardioembolic (CE) subtype. In addition, IS subtype has research implications. There are several ongoing and recently completed trials for cryptogenic stroke (CS), as classified by the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) criteria that encompasses patients with an in-depth evaluation in whom no cause was identified or in University of Chicago (SP), Pritzker School of Medicine; University of Pennsylvania (SRM); University of Cincinnati (DK); Hotchkiss Brain Institute (EES), University of Calgary, Calgary, Alberta, Canada; Ahmanson Cardiomyopathy Center (GCF), UCLA, Los Angeles, CA; Duke Clinical Research Institute (HX, XZ, BL), Durham, NC; Knight Cardiovascular Institute (JC), Oregon Health and Sciences University; and Department of Neurology (LHS), Massachusetts General Hospital. Funding information and disclosures are provided at the end of the article. Full disclosure form information provided by the authors is available with the full text of this article at Neurology.org/cp. 396 Copyright © 2019 American Academy of Neurology Copyright © 2020 American Academy of Neurology. Unauthorized reproduction of this article is prohibited. whom multiple potential causes were identified. For many patients, multiple specific phenotypes exist that may respond to targeted approaches including anticoagulation or patent foramen ovale (PFO) closure. Previous population-based and hospital-based cohorts have found that CS accounts for approximately 25%–40% of IS. However, there are relatively few data from the United States, mostly from geographically constrained population studies. We therefore sought to evaluate patient and hospital characteristics, in-hospital treatments, and discharge outcomes among patients with CS compared with other subtypes in the Get With The Guidelines (GWTG)-Stroke registry, a large database of acute stroke discharges at over 2,000 hospitals in the United States, accounting for ;60% of incident stroke cases. Methods Data source The GWTG-Stroke registry is a quality improvement program for stroke care in the United States that promotes consistent adherence to the latest scientific management guidelines. Hospitals enter clinical and outcomes data via a web-based Patient Management Tool (PMT; IQVIA, Cambridge, MA). Standard protocol approvals, registrations, and patient consents Data analysis was performed by the Duke Clinical Research Institute with local institutional review board approval. Under the common rule, sites are granted a waiver of informed consent, as the data are collected frommedical records for use locally for quality improvement. Data collection and study population Using the GWTG-Stroke registry, we identified patients with final diagnosis of IS at participating hospitals between January 1, 2016, and September 30, 2017, with documented stroke etiology. The documentation of stroke etiology data collection questions and accompanying coding instructions were developed and finalized in 2015 and implemented in January 2016. Primary variable of interest CS subtype was the primary variable of interest and defined as occurring when physician documentation stated that a potential cause of stroke was not identified, or multiple possible causes were identified, following thorough diagnostic evaluation. Coding instructions noted that lowand moderate-risk potential CE sources such as PFO, heart failure with preserved ejection fraction, mitral annulus calcification, or atrial or ventricular arrhythmias other than atrial fibrillation or flutter (AF) were of uncertain significance and not adequate to ascribe stroke mechanism. The GWTGStroke coding instructions further subcategorized CS as stroke of (1) undetermined (when a potential etiology was not identified or documented following diagnostic evaluation), (2) unspecified (when there was no documentation of the results of the diagnostic evaluation), or (3) multiple competing etiologies (when a single etiology was uncertain between 2 or more possible causes following diagnostic evaluation). Other stroke subtypes, categorized according to the TOAST criteria, included CE, large artery atherosclerosis (LAA), small vessel occlusion (SVO), and other determined etiology (OTH). Patient and hospital characteristics Demographic data included age, sex, race/ethnicity, health insurance status, and arrival and admission data including onoff hours admission, mode of arrival such as emergency medical services (EMSs), walk-in, or transfer, National Institutes of Health Stroke Scale (NIHSS) score (if documented), and times for arrival and thrombolytic drug administration. Medical history included the presence of AF, coronary artery disease (CAD), previous myocardial infarction (MI), carotid stenosis, diabetes mellitus, heart failure, hypertension, prosthetic heart valve, peripheral vascular disease (PVD), smoking status, previous stroke/TIA, ambulatory status before admission, and previous medications. Hospital characteristics including geographic region, the number of beds, hospital location, teaching and primary stroke center status, and annual volume of stroke by each type were also analyzed. In-hospital treatment included antithrombotic therapy and other risk factor–modifying medications at discharge. Outcomes Quality and achievement measures included thrombolytic therapy with IV tissue plasminogen activator (tPA) within 3 hours or 4.5 hours of last known well in eligible patients, administration of tPA within 1 hour of hospital arrival, early antithrombotic therapy, anticoagulation for AF, deep venous thrombosis prophylaxis, lipid lowering, statin initiation, and low-density lipoprotein measurement, smoking cessation, dysphagia screening, stroke education, consideration for rehabilitation, and defect-free care. Ambulatory status, mortality, modified Rankin Scale (mRS) score, discharge destination, and length of stay (LOS) were also analyzed at the time of hospital discharge as outcomes. Because stroke severity is an important predictor of these outcomes, we included only the subset of patients with documented NIHSS score for these analyses (n = 316,263). Patients with cryptogenic stroke were more likely to have better outcomes than all subtypes except those with small vessel disease. Neurology.org/CP Neurology: Clinical Practice | Volume 10, Number 5 | October 2020 397 Copyright © 2020 American Academy of Neurology. Unauthorized reproduction of this article is prohibited. Statistical Analysis All statistical analyses were performed using SAS Version 9.3 software (SAS Institute, Cary, NC). Baseline patient and hospital characteristics were described overall and compared across the 5 stroke subtypes. Categorical variables were presented as counts and percentages, and the difference between groups was tested using Pearson χ tests. Continuous variables were presented as mean and SDs or medians with 25th and 75th percentiles, and the difference between groups was tested using the KruskalWallis test. To further evaluate the association between different documented stroke etiology categories with outcomes, multivariable logistic regression models were performed in those with documented NIHSS scores. Both unadjusted and adjusted odds ratios and 95% confidence intervals are reported. The generalized estimating equationmethod with exchangeable working correlation matrix was applied to provide valid inference after accounting for the within-site correlation. Covariates in the model included age, sex, race, relevant vascular risk factor medical history elements (atrial fibrillation, prosthetic heart valve, previous stroke or TIA, CAD or previous MI, carotid stenosis, PVD, hypertension, dyslipidemia, and smoking), arrival during off hours (vs regular hours), EMS arrival, initial NIHSS score, annual IS admission, bed size, region, hospital type (academic vs nonacademic), and urban/rural location. In this cohort, covariates had very low missing rates. Missing sex (0.02%) and hospital characteristics (0.01%–1.03% missing) were excluded, whereas other missing variables were imputed as follows: race (0.02% missing) imputed to “white,” mode of arrival (2.4% missing) imputed to “No EMS,” and medical history panel (missin