COVID‐19 Infection Is Associated With Poor Outcomes in Patients With Intracerebral Hemorrhage

Background Patients with ischemic stroke and concomitant COVID‐19 infection have worse outcomes than those without this infection, but the impact of COVID‐19 on hemorrhagic stroke remains unclear. We aimed to assess if COVID‐19 worsens outcomes in intracerebral hemorrhage (ICH). Methods and Results We conducted an observational study of ICH outcomes using Get With The Guidelines Stroke data. We compared patients with ICH who were COVID‐19 positive and negative during the pandemic (March 2020–February 2021) and prepandemic (March 2019–February 2020). Main outcomes were poor functional outcome (defined as a modified Rankin scale score of 4 to 6 at discharge), mortality, and discharge to a skilled nursing facility or hospice. The first stage included 60 091 patients with ICH who were COVID‐19 negative and 1326 COVID‐19 positive. In multivariable analyses, patients with ICH with versus without COVID‐19 infection had 68% higher odds of poor outcome (odds ratio [OR], 1.68 [95% CI, 1.41–2.01]), 51% higher odds of mortality (OR, 1.51 [95% CI, 1.33–1.71]), and 66% higher odds of being discharged to a skilled nursing facility/hospice (OR, 1.66 [95% CI, 1.43–1.93]). The second stage included 62 743 prepandemic and 64 681 intrapandemic cases with ICH. In multivariable analyses, patients with ICH admitted during versus before the COVID‐19 pandemic had 10% higher odds of poor outcomes (OR, 1.10 [95% CI, 1.07–1.14]), 5% higher mortality (OR, 1.05 [95% CI, 1.02–1.08]), and no significant difference in the risk of being discharged to a skilled nursing facility/hospice (OR, 0.93 [95% CI, 0.90–0.95]). Conclusions The pathophysiology of the COVID‐19 infection and changes in health care delivery during the pandemic played a role in worsening outcomes in the patient population with ICH

Brain care score and neuroimaging markers of brain health in asymptomatic middle-age persons

Objectives: To investigate associations between health-related behaviors as measured using the Brain Care Score (BCS) and neuroimaging markers of white matter injury. Methods: This prospective cohort study in the UK Biobank assessed the BCS, a novel tool designed to empower patients to address 12 dementia and stroke risk factors. The BCS ranges from 0 to 21, with higher scores suggesting better brain care. Outcomes included white matter hyperintensities (WMH) volume, fractional anisotropy (FA), and mean diffusivity (MD) obtained during 2 imaging assessments, as well as their progression between assessments, using multivariable linear regression adjusted for age and sex. Results: We included 34,509 participants (average age 55 years, 53% female) with no stroke or dementia history. At first and repeat imaging assessments, every 5-point increase in baseline BCS was linked to significantly lower WMH volumes (25% 95% CI [23%–27%] first, 33% [27%–39%] repeat) and higher FA (18% [16%–20%] first, 22% [15%–28%] repeat), with a decrease in MD (9% [7%–11%] first, 10% [4%–16%] repeat). In addition, a higher baseline BCS was associated with a 10% [3%–17%] reduction in WMH progression and FA decline over time. Discussion: This study extends the impact of the BCS to neuroimaging markers of clinically silent cerebrovascular disease. Our results suggest that improving one's BCS could be a valuable intervention to prevent early brain health decline

Impact of sleep quality and physical activity on blood pressure variability

Increased blood pressure variability (BPV) is linked to cardiovascular disease and mortality, yet few modifiable BPV risk factors are known. We aimed to assess the relationship between sleep quality and activity level on longitudinal BPV in a cohort of community-dwelling adults (age ≥18) from 17 countries. Using Withings home measurement devices, we examined sleep quality and physical activity over one year, operationalized as mean daily step count and number of sleep interruptions, both transformed into tertiles. The primary study outcome was high BPV, defined as the top tertile of systolic blood pressure standard deviation. Our cohort comprised 29,375 individuals (mean age = 58.6 years) with 127.8±90.1 mean days of measurements. After adjusting for age, gender, country, body mass index, measurement days, mean blood pressure, and total time in bed, the odds ratio of having high BPV for those in the top tertile of sleep interruptions (poor sleep) was 1.37 (95% CI, 1.28–1.47) and 1.44 (95% CI, 1.35–1.54) for those in the lowest tertile of step count (physically inactive). Combining these exposures revealed a significant excess relative risk of 0.20 (95% CI, 0.04–0.35, p = 0.012), confirming their super-additive effect. Comparing individuals with the worst exposure status (lowest step count and highest sleep interruptions, n = 2,690) to those with the most optimal status (highest step count and lowest sleep interruptions, n = 3,531) yielded an odds ratio of 2.01 (95% CI, 1.80–2.25) for high BPV. Our findings demonstrate that poor sleep quality and physical inactivity are associated with increased BPV both independently and super-additively

Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan.

BACKGROUND: Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and require multiple expert-defined inputs and interpretation that make their clinical implementation challenging. This study aimed to predict long term functional impairment of ICH patients from admission non-contrast CT scans, leveraging deep learning models in a survival analysis framework. METHODS: We used the admission non-contrast CT scans from 882 patients from the Massachusetts General Hospital ICH Study for training, hyperparameter optimization, and model selection, and 146 patients from the Yale New Haven ICH Study for external validation of a deep learning model predicting functional outcome. Disability (modified Rankin scale [mRS] > 2), severe disability (mRS > 4), and dependent living status were assessed via telephone interviews after 6, 12, and 24 months. The prediction methods were evaluated by the c-index and compared with ICH score and FUNC score. RESULTS: Using non-contrast CT, our deep learning model achieved higher prediction accuracy of post-ICH dependent living, disability, and severe disability by 6, 12, and 24 months (c-index 0.742 [95% CI -0.700 to 0.778], 0.712 [95% CI -0.674 to 0.752], 0.779 [95% CI -0.733 to 0.832] respectively) compared with the ICH score (c-index 0.673 [95% CI -0.662 to 0.688], 0.647 [95% CI -0.637 to 0.661] and 0.697 [95% CI -0.675 to 0.717]) and FUNC score (c-index 0.701 [95% CI- 0.698 to 0.723], 0.668 [95% CI -0.657 to 0.680] and 0.727 [95% CI -0.708 to 0.753]). In the external independent Yale-ICH cohort, similar performance metrics were obtained for disability and severe disability (c-index 0.725 [95% CI -0.673 to 0.781] and 0.747 [95% CI -0.676 to 0.807], respectively). Similar AUC of predicting each outcome at 6 months, 1 and 2 years after ICH was achieved compared with ICH score and FUNC score. CONCLUSION: We developed a generalizable deep learning model to predict onset of dependent living and disability after ICH, which could help to guide treatment decisions, advise relatives in the acute setting, optimize rehabilitation strategies, and anticipate long-term care needs

COVID‐19 Infection Is Associated With Poor Outcomes in Patients With Intracerebral Hemorrhage

Background Patients with ischemic stroke and concomitant COVID‐19 infection have worse outcomes than those without this infection, but the impact of COVID‐19 on hemorrhagic stroke remains unclear. We aimed to assess if COVID‐19 worsens outcomes in intracerebral hemorrhage (ICH). Methods and Results We conducted an observational study of ICH outcomes using Get With The Guidelines Stroke data. We compared patients with ICH who were COVID‐19 positive and negative during the pandemic (March 2020–February 2021) and prepandemic (March 2019–February 2020). Main outcomes were poor functional outcome (defined as a modified Rankin scale score of 4 to 6 at discharge), mortality, and discharge to a skilled nursing facility or hospice. The first stage included 60 091 patients with ICH who were COVID‐19 negative and 1326 COVID‐19 positive. In multivariable analyses, patients with ICH with versus without COVID‐19 infection had 68% higher odds of poor outcome (odds ratio [OR], 1.68 [95% CI, 1.41–2.01]), 51% higher odds of mortality (OR, 1.51 [95% CI, 1.33–1.71]), and 66% higher odds of being discharged to a skilled nursing facility/hospice (OR, 1.66 [95% CI, 1.43–1.93]). The second stage included 62 743 prepandemic and 64 681 intrapandemic cases with ICH. In multivariable analyses, patients with ICH admitted during versus before the COVID‐19 pandemic had 10% higher odds of poor outcomes (OR, 1.10 [95% CI, 1.07–1.14]), 5% higher mortality (OR, 1.05 [95% CI, 1.02–1.08]), and no significant difference in the risk of being discharged to a skilled nursing facility/hospice (OR, 0.93 [95% CI, 0.90–0.95]). Conclusions The pathophysiology of the COVID‐19 infection and changes in health care delivery during the pandemic played a role in worsening outcomes in the patient population with ICH