Faculty Bibliography

IMPORTANCE/OBJECTIVE:It is uncertain whether coronavirus disease 2019 (COVID-19) is associated with a higher risk of ischemic stroke than would be expected from a viral respiratory infection. OBJECTIVE:To compare the rate of ischemic stroke between patients with COVID-19 and patients with influenza, a respiratory viral illness previously associated with stroke. DESIGN, SETTING, AND PARTICIPANTS/METHODS:This retrospective cohort study was conducted at 2 academic hospitals in New York City, New York, and included adult patients with emergency department visits or hospitalizations with COVID-19 from March 4, 2020, through May 2, 2020. The comparison cohort included adults with emergency department visits or hospitalizations with influenza A/B from January 1, 2016, through May 31, 2018 (spanning moderate and severe influenza seasons). EXPOSURES/METHODS:COVID-19 infection confirmed by evidence of severe acute respiratory syndrome coronavirus 2 in the nasopharynx by polymerase chain reaction and laboratory-confirmed influenza A/B. MAIN OUTCOMES AND MEASURES/METHODS:A panel of neurologists adjudicated the primary outcome of acute ischemic stroke and its clinical characteristics, mechanisms, and outcomes. We used logistic regression to compare the proportion of patients with COVID-19 with ischemic stroke vs the proportion among patients with influenza. RESULTS:Among 1916 patients with emergency department visits or hospitalizations with COVID-19, 31 (1.6%; 95% CI, 1.1%-2.3%) had an acute ischemic stroke. The median age of patients with stroke was 69 years (interquartile range, 66-78 years); 18 (58%) were men. Stroke was the reason for hospital presentation in 8 cases (26%). In comparison, 3 of 1486 patients with influenza (0.2%; 95% CI, 0.0%-0.6%) had an acute ischemic stroke. After adjustment for age, sex, and race, the likelihood of stroke was higher with COVID-19 infection than with influenza infection (odds ratio, 7.6; 95% CI, 2.3-25.2). The association persisted across sensitivity analyses adjusting for vascular risk factors, viral symptomatology, and intensive care unit admission. CONCLUSIONS AND RELEVANCE/CONCLUSIONS:In this retrospective cohort study from 2 New York City academic hospitals, approximately 1.6% of adults with COVID-19 who visited the emergency department or were hospitalized experienced ischemic stroke, a higher rate of stroke compared with a cohort of patients with influenza. Additional studies are needed to confirm these findings and to investigate possible thrombotic mechanisms associated with COVID-19.

Time is of the essence in evaluating potential drugs and biologics for the treatment and prevention of COVID-19. There are currently over 400 clinical trials (phase 2 and 3) of treatments for COVID-19 registered on clinicaltrials.gov. Covariate adjustment is a statistical analysis method with potential to improve precision and reduce the required sample size for a substantial number of these trials. Though covariate adjustment is recommended by the U.S. Food and Drug Administration and the European Medicines Agency, it is underutilized, especially for the types of outcomes (binary, ordinal and time-to-event) that are common in COVID-19 trials. To demonstrate the potential value added by covariate adjustment in this context, we simulated two-arm, randomized trials comparing a hypothetical COVID-19 treatment versus standard of care, where the primary outcome is binary, ordinal, or time-to-event. Our simulated distributions are derived from two sources: longitudinal data on over 500 patients hospitalized at Weill Cornell Medicine New York Presbyterian Hospital, and a Centers for Disease Control and Prevention (CDC) preliminary description of 2449 cases. We found substantial precision gains from using covariate adjustment--equivalent to 9-21% reductions in the required sample size to achieve a desired power--for a variety of estimands (targets of inference) when the trial sample size was at least 200. We provide an R package and practical recommendations for implementing covariate adjustment. The estimators that we consider are robust to model misspecification.

Background and Purpose- Patients who continue to smoke after a stroke face a higher risk of recurrent stroke. While several effective drugs for smoking cessation became available over the past 2 decades, whether active smoking has decreased among stroke survivors is unknown. We, therefore, evaluated trends in active smoking among stroke survivors during this period. Methods- We performed trends analyses using cross-sectional data collected every 1 to 2 years from 2 US health surveys spanning 1999 to 2018. In the National Health and Nutrition Examination Survey (NHANES) and the Behavioral Risk Factor Surveillance System (BRFSS) survey, participants were asked about prior stroke and active tobacco smoking. In NHANES, serum cotinine levels were available as a secondary measure of active smoking. We used multivariable logistic regression models for survey data to assess trends in active smoking among participants with and without prior stroke. Results- Among 49 375 participants in NHANES during 1999 to 2016 and 3 621 741 participants in BRFSS during 2011 to 2018, the prevalence of stroke was ≈3%. The overall prevalence of active smoking among stroke survivors was 24% in NHANES and 23% in BRFSS. Among individuals without prior stroke, the odds of smoking decreased over time in both NHANES (odds ratio, 0.95 per 2 years [95% CI, 0.93-0.96]) and BRFSS (odds ratio, 0.96 per year [95% CI, 0.96-0.96]). In contrast, there was no decrease in smoking among stroke survivors in NHANES (odds ratio, 1.00 [95% CI, 0.93-1.07]) or BRFSS (odds ratio, 0.99 [95% CI, 0.98-1.004]). Results were consistent in secondary analysis using biochemical ascertainment of active smoking in NHANES and in sensitivity analyses accounting for potential demographic changes in stroke epidemiology. Conclusions- In contrast to the general population, the prevalence of active smoking among stroke survivors has not decreased during the past 2 decades.

IMPORTANCE/OBJECTIVE:Case series without control groups suggest that Covid-19 may cause ischemic stroke, but whether Covid-19 is associated with a higher risk of ischemic stroke than would be expected from a viral respiratory infection is uncertain. OBJECTIVE:To compare the rate of ischemic stroke between patients with Covid-19 and patients with influenza, a respiratory viral illness previously linked to stroke. DESIGN/METHODS:A retrospective cohort study. SETTING/METHODS:Two academic hospitals in New York City. PARTICIPANTS/METHODS:We included adult patients with emergency department visits or hospitalizations with Covid-19 from March 4, 2020 through May 2, 2020. Our comparison cohort included adult patients with emergency department visits or hospitalizations with influenza A or B from January 1, 2016 through May 31, 2018 (calendar years spanning moderate and severe influenza seasons). Exposures: Covid-19 infection confirmed by evidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the nasopharynx by polymerase chain reaction, and laboratory-confirmed influenza A or B. Main Outcomes and Measures: A panel of neurologists adjudicated the primary outcome of acute ischemic stroke and its clinical characteristics, etiological mechanisms, and outcomes. We used logistic regression to compare the proportion of Covid-19 patients with ischemic stroke versus the proportion among patients with influenza. RESULTS:Among 2,132 patients with emergency department visits or hospitalizations with Covid-19, 31 patients (1.5%; 95% confidence interval [CI], 1.0%-2.1%) had an acute ischemic stroke. The median age of patients with stroke was 69 years (interquartile range, 66-78) and 58% were men. Stroke was the reason for hospital presentation in 8 (26%) cases. For our comparison cohort, we identified 1,516 patients with influenza, of whom 0.2% (95% CI, 0.0-0.6%) had an acute ischemic stroke. After adjustment for age, sex, and race, the likelihood of stroke was significantly higher with Covid-19 than with influenza infection (odds ratio, 7.5; 95% CI, 2.3-24.9). CONCLUSIONS AND RELEVANCE/CONCLUSIONS:Approximately 1.5% of patients with emergency department visits or hospitalizations with Covid-19 experienced ischemic stroke, a rate 7.5-fold higher than in patients with influenza. Future studies should investigate the thrombotic mechanisms in Covid-19 in order to determine optimal strategies to prevent disabling complications like ischemic stroke.

Background and Purpose- The risk of arterial ischemic events after subdural hemorrhage (SDH) is poorly understood. This study aimed to evaluate the risk of acute ischemic stroke and myocardial infarction among patients with and without nontraumatic SDH. Methods- We performed a retrospective cohort study using claims data from 2008 through 2014 from a nationally representative sample of Medicare beneficiaries. The exposure was nontraumatic SDH. Our primary outcome was an arterial ischemic event, a composite of acute ischemic stroke and acute myocardial infarction. Secondary outcomes were ischemic stroke alone and myocardial infarction alone. We used validated International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes to identify our predictor and outcomes. Using Cox regression and corresponding survival probabilities, adjusted for demographics and vascular comorbidities, we computed the hazard ratio in 4-week intervals after SDH discharge. We performed secondary analyses stratified by strong indications for antithrombotic therapy (composite of atrial fibrillation, peripheral vascular disease, valvular heart disease, and venous thromboembolism). Results- Among 1.7 million Medicare beneficiaries, 2939 were diagnosed with SDH. In the 4 weeks after SDH, patients' risk of an arterial ischemic event was substantially increased (hazard ratio, 3.6 [95% CI, 1.9-5.5]). There was no association between SDH diagnosis and arterial ischemic events beyond 4 weeks. In secondary analysis, during the 4 weeks after SDH, patients' risk of ischemic stroke was increased (hazard ratio, 4.2 [95% CI, 2.1-7.3]) but their risk of myocardial infarction was not (hazard ratio, 0.8 [95% CI, 0.2-1.7]). Patients with strong indications for antithrombotic therapy had increased risks for arterial ischemic events similar to patients in the primary analysis, but those without such indications did not demonstrate an increased risk for arterial ischemic events. Conclusions- Among Medicare beneficiaries, we found a heightened risk of arterial ischemic events driven by an increased risk of ischemic stroke, in the 4 weeks after nontraumatic SDH. This increased risk may be due to interruption of antithrombotic therapy after SDH diagnosis.

In recent decades, the fields of statistical and machine learning have seen a revolution in the development of data-adaptive regression methods that have optimal performance under flexible, sometimes minimal, assumptions on the true regression functions. These developments have impacted all areas of applied and theoretical statistics and have allowed data analysts to avoid the biases incurred under the pervasive practice of parametric model misspecification. In this commentary, I discuss issues around the use of data-adaptive regression in estimation of causal inference parameters. To ground ideas, I focus on two estimation approaches with roots in semi-parametric estimation theory: targeted minimum loss-based estimation (TMLE; van der Laan and Rubin, 2006) and double/debiased machine learning (DML; Chernozhukov and others, 2018). This commentary is not comprehensive, the literature on these topics is rich, and there are many subtleties and developments which I do not address. These two frameworks represent only a small fraction of an increasingly large number of methods for causal inference using machine learning. To my knowledge, they are the only methods grounded in statistical semi-parametric theory that also allow unrestricted use of data-adaptive regression techniques.

Background and Purpose- It is unknown whether admission systolic blood pressure (SBP) differs among causes of intracerebral hemorrhage (ICH). We sought to elucidate an association between admission BP and ICH cause. Methods- We compared admission SBP across ICH causes among patients in the Cornell Acute Stroke Academic Registry, which includes all adults with ICH at our center from 2011 through 2017. Trained analysts prospectively collected demographics, comorbidities, and admission SBP, defined as the first recorded value in the emergency department or on transfer from another hospital. ICH cause was adjudicated by a panel of neurologists using the SMASH-U criteria. We used ANOVA to compare mean admission SBP among ICH causes. We used multiple linear regression to adjust for age, sex, race, Glasgow Coma Scale score, and hematoma size. In secondary analyses, we compared hourly SBP measurements during the first 72 hours after admission, using mixed-effects linear models adjusted for the covariates above plus antihypertensive agents. Results- Among 484 patients with ICH, admission SBP varied significantly across ICH causes, ranging from 138 (±24) mm Hg in those with structural vascular lesions to 167 (±35) mm Hg in those with hypertensive ICH (P<0.001). The mean admission SBP in hypertensive ICH was 17 (95% CI, 11-24) mm Hg higher than in ICH of all other causes combined. These differences remained significant after adjustment for age, sex, race, Glasgow Coma Scale score, and hematoma size (P<0.001), and this persisted throughout the first 72 hours of hospitalization (P<0.001). Conclusions- In a single-center ICH registry, SBP varied significantly among ICH causes, both on admission and during hospitalization. Our results suggest that BP in the acute post-ICH setting is at least partly associated with ICH cause rather than simply representing a physiological reaction to the ICH itself.

Background and Purpose- Carotid artery plaque with <50% luminal stenosis may be an underappreciated stroke mechanism. We assessed how many stroke causes might be reclassified after accounting for nonstenosing plaques with high-risk features. Methods- We included patients enrolled in the Cornell Acute Stroke Academic Registry from 2011 to 2015 who had anterior circulation infarction, magnetic resonance imaging of the brain, and magnetic resonance angiography of the neck. High-risk plaque was identified by intraplaque hemorrhage ascertained from routine neck magnetic resonance angiography studies using validated methods. Infarct location was determined from diffusion-weighted imaging. Intraplaque hemorrhage and infarct location were assessed separately in a blinded fashion by a neuroradiologist. We used the McNemar test for matched data to compare the prevalence of intraplaque hemorrhage ipsilateral versus contralateral to brain infarction. We reclassified stroke subtypes by including large-artery atherosclerosis as a cause if there was intraplaque hemorrhage ipsilateral to brain infarction, regardless of the degree of stenosis. Results- Among the 1721 acute ischemic stroke patients registered in the Cornell Acute Stroke Academic Registry from 2011 to 2015, 579 were eligible for this analysis. High-risk plaque was more common ipsilateral versus contralateral to brain infarction in large-artery atherosclerotic (risk ratio [RR], 3.7 [95% CI, 2.2-6.1]), cryptogenic (RR, 2.1 [95% CI, 1.4-3.1]), and cardioembolic strokes (RR, 1.7 [95% CI, 1.1-2.4]). There were nonsignificant ipsilateral-contralateral differences in high-risk plaque among lacunar strokes (RR, 1.2 [95% CI, 0.4-3.5]) and strokes of other determined cause (RR, 1.5 [95% CI, 0.7-3.3]). After accounting for ipsilateral high-risk plaque, 88 (15.2%) patients were reclassified: 38 (22.6%) cardioembolic to multiple potential etiologies, 6 (8.5%) lacunar to multiple, 3 (15.8%) other determined cause to multiple, and 41 (20.8%) cryptogenic to large-artery atherosclerosis. Conclusions- High-risk carotid plaque was more prevalent ipsilateral to brain infarction across several ischemic stroke subtypes. Accounting for such plaques may reclassify the etiologies of up to 15% of cases in our sample.