To examine the association of co-morbidity with home-time after acute stroke and whether the association is influenced by age.

We conducted a province-wide study using linked administrative databases to identify all admissions for first acute ischemic stroke or intracerebral hemorrhage between 2007 and 2018 in Alberta, Canada. We used ischemic stroke-weighted Charlson Co-morbidity Index of 3 or more to identify those with severe co-morbidity. We used zero-inflated negative binomial models to determine the association of severe co-morbidity with 90-day and 1-year home-time, and logistic models for achieving ≥ 80 out of 90 days of home-time, assessing for effect modification by age and adjusting for sex, stroke type, comprehensive stroke center care, hypertension, atrial fibrillation, year of study, and separately adjusting for estimated stroke severity. We also evaluated individual co-morbidities.

Among 28,672 patients in our final cohort, severe co-morbidity was present in 27.7% and was associated with lower home-time, with a greater number of days lost at younger age (−13 days at age < 60 compared to −7 days at age 80+ years for 90-day home-time; −69 days at age < 60 compared to −51 days at age 80+ years for 1-year home-time). The reduction in probability of achieving ≥ 80 days of home-time was also greater at younger age (−22.7% at age < 60 years compared to −9.0% at age 80+ years). Results were attenuated but remained significant after adjusting for estimated stroke severity and excluding those who died. Myocardial infarction, diabetes, and cancer/metastases had a greater association with lower home-time at younger age, and those with dementia had the greatest reduction in home time.

Severe co-morbidity in acute stroke is associated with lower home-time, more strongly at younger age.

Depressive symptoms are common in stroke survivors. While obesity has been associated with stroke and depression, its influence on the association between stroke and depressive symptoms is unknown.

Cross-sectional data from 2015 to 2016 Canadian Community Health Survey was used. History of stroke was self-reported and our outcome of interest was depressive symptoms in the prior 2 weeks, measured using the 9-item Patient Health Questionnaire. Self-reported body mass index (BMI) was modeled as cubic spline terms to allow for nonlinear associations. We used multivariable logistic regression to evaluate the association between stroke and depressive symptoms and added an interaction term to evaluate the modifying effect of BMI.

Of the 47,521 participants, 694 (1.0%) had a stroke and 3314 (6.5%) had depressive symptoms. Those with stroke had a higher odds of depressive symptoms than those without (aOR = 3.13, 95% CI 2.48, 3.93). BMI did not modify the stroke-depressive symptoms association (P interaction = 0.242) despite the observed variation in stroke-depressive symptoms association across BMI categories,: normal BMI [18.5–25 kg/m2] (aOR† = 3.91, 95% CI 2.45, 6.11), overweight [25–30 kg/m2] (aOR† = 2.63, 95% CI 1.58, 4.20), and obese [>30 kg/m2] (aOR† = 2.76, 95% CI 1.92, 3.94). Similar results were found when depressive symptoms were modeled as a continuous measure.

The association between stroke and depressive symptoms is not modified by BMI, needing additional work to understand the role of obesity on depression after stroke.

Limited evidence exists regarding care pathways for stroke survivors who do and do not receive poststroke spasticity (PSS) treatment.

Administrative data was used to identify adults who experienced a stroke and sought acute care between 2012 and 2017 in Alberta, Canada. Pathways of stroke care within the health care system were determined among those who initiated PSS treatment (PSS treatment group: outpatient pharmacy dispensation of an anti-spastic medication, focal chemo-denervation injection, or a spasticity tertiary clinic visit) and those who did not (non-PSS treatment group). Time from the stroke event until spasticity treatment initiation, and setting where treatment was initiated were reported. Descriptive statistics were performed.

Health care settings within the pathways of stroke care that the PSS (n = 1,079) and non-PSS (n = 22,922) treatment groups encountered were the emergency department (86 and 84%), acute inpatient care (80 and 69%), inpatient rehabilitation (40 and 12%), and long-term care (19 and 13%), respectively. PSS treatment was initiated a median of 291 (interquartile range 625) days after the stroke event, and most often in the community when patients were residing at home (45%), followed by “other” settings (22%), inpatient rehabilitation (18%), long-term care (11%), and acute inpatient care (4%).

To our knowledge, this is the first population based cohort study describing pathways of care among adults with stroke who subsequently did or did not initiate spasticity treatment. Areas for improvement in care may include strategies for earlier identification and treatment of PSS.

Contrast-induced encephalopathy (CIE) is an adverse event associated with diagnostic and therapeutic endovascular procedures. Decades of animal and human research support a mechanistic role for pathological blood-brain barrier dysfunction (BBBd). Here, we describe an institutional case series and review the literature supporting a mechanistic role for BBBd in CIE.

A literature review was conducted by searching MEDLINE, Web of Science, Embase, CINAHL and Cochrane databases from inception to January 31, 2022. We searched our institutional neurovascular database for cases of CIE following endovascular treatment of cerebrovascular disease during a 6-month period. Informed consent was obtained in all cases.

Review of the literature revealed risk factors for BBBd and CIE, including microvascular disease, pathological neuroinflammation, severe procedural hypertension, iodinated contrast load and altered cerebral blood flow dynamics. In our institutional series, 6 of 52 (11.5%) of patients undergoing therapeutic neuroendovascular procedures developed CIE during the study period. Four patients were treated for ischemic stroke and two patients for recurrent cerebral aneurysms. Mechanical stenting or thrombectomy were utilized in all cases.

In this institutional case series and literature review of animal and human data, we identified numerous shared risk factors for CIE and BBBd, including microvascular disease, increased procedure length, large contrast volumes, severe intraoperative hypertension and use of mechanical devices that may induce iatrogenic endothelial injury.

Stroke outcomes research requires risk-adjustment for stroke severity, but this measure is often unavailable. The Passive Surveillance Stroke SeVerity (PaSSV) score is an administrative data-based stroke severity measure that was developed in Ontario, Canada. We assessed the geographical and temporal external validity of PaSSV in British Columbia (BC), Nova Scotia (NS) and Ontario, Canada.

We used linked administrative data in each province to identify adult patients with ischemic stroke or intracerebral hemorrhage between 2014-2019 and calculated their PaSSV score. We used Cox proportional hazards models to evaluate the association between the PaSSV score and the hazard of death over 30 days and the cause-specific hazard of admission to long-term care over 365 days. We assessed the models’ discriminative values using Uno’s c-statistic, comparing models with versus without PaSSV.

We included 86,142 patients (n = 18,387 in BC, n = 65,082 in Ontario, n = 2,673 in NS). The mean and median PaSSV were similar across provinces. A higher PaSSV score, representing lower stroke severity, was associated with a lower hazard of death (hazard ratio and 95% confidence intervals 0.70 [0.68, 0.71] in BC, 0.69 [0.68, 0.69] in Ontario, 0.72 [0.68, 0.75] in NS) and admission to long-term care (0.77 [0.76, 0.79] in BC, 0.84 [0.83, 0.85] in Ontario, 0.86 [0.79, 0.93] in NS). Including PaSSV in the multivariable models increased the c-statistics compared to models without this variable.

PaSSV has geographical and temporal validity, making it useful for risk-adjustment in stroke outcomes research, including in multi-jurisdiction analyses.

Prognosticating outcomes for traumatic brain injury (TBI) patients is challenging due to the required specialized skills and variability among clinicians. Recent attempts to standardize TBI prognosis have leveraged machine learning (ML) methodologies. This study evaluates the necessity and influence of ML-assisted TBI prognostication through healthcare professionals’ perspectives via focus group discussions.

Two virtual focus groups included ten key TBI care stakeholders (one neurosurgeon, two emergency clinicians, one internist, two radiologists, one registered nurse, two researchers in ML and healthcare and one patient representative). They answered six open-ended questions about their perceptions and potential ML use in TBI prognostication. Transcribed focus group discussions were thematically analyzed using qualitative data analysis software.

The study captured diverse perceptions and interests in TBI prognostication across clinical specialties. Notably, certain clinicians who currently do not prognosticate expressed an interest in doing so independently provided they had access to ML support. Concerns included ML’s accuracy and the need for proficient ML researchers in clinical settings. The consensus suggested using ML as a secondary consultation tool and promoting collaboration with internal or external research resources. Participants believed ML prognostication could enhance disposition planning and standardize care regardless of clinician expertise or injury severity. There was no evidence of perceived bias or interference during the discussions.

Our findings revealed an overall positive attitude toward ML-based prognostication. Despite raising multiple concerns, the focus group discussions were particularly valuable in underscoring the potential of ML in democratizing and standardizing TBI prognosis practices.

To assess cost-effectiveness of late time-window endovascular treatment (EVT) in a clinical trial setting and a “real-world” setting.

Data are from the randomized ESCAPE trial and a prospective cohort study (ESCAPE-LATE). Anterior circulation large vessel occlusion patients presenting > 6 hours from last-known-well were included, whereby collateral status was an inclusion criterion for ESCAPE but not ESCAPE-LATE. A Markov state transition model was built to estimate lifetime costs and quality-adjusted life-years (QALYs) for EVT in addition to best medical care vs. best medical care only in a clinical trial setting (comparing ESCAPE-EVT to ESCAPE control arm patients) and a “real-world” setting (comparing ESCAPE-LATE to ESCAPE control arm patients). We performed an unadjusted analysis, using 90-day modified Rankin Scale(mRS) scores as model input and analysis adjusted for baseline factors. Acceptability of EVT was calculated using upper/lower willingness-to-pay thresholds of 100,000 USD/50,000 USD/QALY.

Two-hundred and forty-nine patients were included (ESCAPE-LATE:n = 200, ESCAPE EVT-arm:n = 29, ESCAPE control-arm:n = 20). Late EVT in addition to best medical care was cost effective in the unadjusted analysis both in the clinical trial and real-world setting, with acceptability 96.6%–99.0%. After adjusting for differences in baseline variables between the groups, late EVT was marginally cost effective in the clinical trial setting (acceptability:49.9%–61.6%), but not the “real-world” setting (acceptability:32.9%–42.6%).

EVT for LVO-patients presenting beyond 6 hours was cost effective in the clinical trial setting and “real-world” setting, although this was largely related to baseline patient differences favoring the “real-world” EVT group. After adjusting for these, EVT benefit was reduced in the trial setting, and absent in the real-world setting.

Obstructive sleep apnea (OSA) is a sleep disorder with no widely accepted pharmacological therapy. Cannabinoids have been suggested to reduce OSA severity in small human studies. The purpose of this retrospective cohort study was to explore the association of self-reported cannabis use on OSA severity and sleep parameters in a large cohort of adults undergoing in-laboratory polysomnography.

Sleep and medication data were collected for all consecutive adults who completed diagnostic polysomnography at Sunnybrook Health Sciences Centre from 2010 to 2022. Multivariable linear regression models were employed that adjusted for age, sex, and BMI (minimally adjusted model), as well as medication and comorbidity data (maximally adjusted model). An exploratory subgroup analysis was additionally run in patients with moderate to severe OSA.

Of 6,958 individuals (mean age 54.7 ± 16.3, BMI 29.1 ± 6.8, 51.0% female), 71 reported cannabis use. In our minimally adjusted models, cannabis use predicted a reduced respiratory disturbance index (RDI) (β: −4.8 [95% CI: −9.4, −0.2]; p = 0.042); this association became nonsignificant in the fully adjusted models. In an exploratory analysis of patients with moderate to severe OSA (n = 613), cannabis use (n = 7) predicted increased stage N3 sleep (β: 33.5 [95% CI: 15.6, 51.4]; p < 0.001) and decreased REM sleep (β: 16.0 [95% CI: 0.3, 31.7]; p = 0.046).

Self-reported cannabis use was not associated with OSA severity after adjusting for confounders. In an exploratory subgroup analysis of patients with moderate to severe OSA, cannabis use impacted sleep architecture. Future studies should further explore these findings.

Ocrelizumab is an effective anti-CD20 therapy approved for Relapsing Remitting (RRMS) and Primary Progressive Multiple Sclerosis (PPMS). In clinical trials, a proportion of patients developed hypogammaglobulinemia which could contribute to infection risk. This study aimed to identify hypogammaglobulinemia and its risk factors and evaluate potentially associated serious infection risk in a real-world cohort of patients.

All MS patients treated with ocrelizumab in a Quebec City MS clinic from January 2017 to August 2021 were included and detailed patient characteristics were collected by chart review. Levels of immunoglobulins (IgM, IgA and IgG) were assessed prior to each treatment. Serious infection was defined as an infection requiring hospitalization or emergency room treatment. Association between hypogammaglobulinemia and serious infection was analyzed.

A total of 266 patients (average follow-up 2.05 years) were included (87% RRMS). After 6 infusions, 32.8%, 3.5% and 4.2% of patients had at least one IgM, IgA and IgG hypogammaglobulinemia event respectively. Aside from pre-treatment hypogammaglobulinemia, there were no variables associated with on-treatment hypogammaglobulinemia. There was a total of 21 serious infections (3.36 and 12.33 per 100-person-years in RRMS and PPMS). Developing hypogammaglobulinemia during treatment was not associated with serious infection. A regression analysis did not show associations between serious infection and key disease characteristics.

Similar to ocrelizumab extension studies, our cohort demonstrated a significant rate of hypogammaglobulinemia over time, mostly with IgM. No association was found between hypogammaglobulinemia and serious infection.