Contemporary Trends in the Nationwide Incidence of Primary Intracerebral Hemorrhage.

BACKGROUND: We report contemporary trends in nationwide incidence of intracerebral hemorrhage (ICH) across demographic and regional strata over a 15-year period. METHODS: Utilizing the Nationwide Inpatient Sample (2004-2018) and US Census Bureau data, we calculated ICH incidence rates for age, race/ethnicity, sex, and hospital region sub-cohorts across 5 consecutive 3-year periods (2004-2006 to 2016-2018). We fit Poisson and log binomial regression models to evaluate demographic and regional differences in ICH incidence and trends in prevalence of hypertension and past/current anticoagulant use among hospitalized ICH patients. RESULTS: Overall, the annual incidence rate (95% CI) of ICH per 100 000 was 23.15 (23.10-23.20). The 3-year incidence of ICH (per 100 000) increased from 62.79 in 2004 to 2006 to 78.86 in 2016 to 2018 (adjusted incidence rate ratio, CI: 1.11 [1.02-1.20]), coinciding with increased 3-year prevalence of hypertension and anticoagulant use among hospitalized ICH patients (adjusted risk ratio, CI: hypertension-1.16 [1.15-1.17]; anticoagulant use-2.30 [2.14-2.47]). We found a significant age-time interaction, whereby ICH incidence increased significantly faster among those aged 18 to 44 years (adjusted incidence rate ratio, CI: 1.10 [1.05-1.14]) and 45 to 64 years (adjusted incidence rate ratio, CI: 1.08 [1.03-1.13]), relative to those aged ≥75 years. CONCLUSIONS: Rising ICH incidence among young and middle-aged Americans warrants ICH prevention strategies targeting these economically productive age groups.

Seasonal variation in the incidence of primary intracerebral hemorrhage: a 16-year nationwide analysis.

Introduction: Data on nationwide trends and seasonal variations in the incidence of Intracerebral Hemorrhage (ICH) in the United States (US) are lacking. Methods: We used the Nationwide Inpatient Sample (2004-2019) and Census Bureau data to calculate the quarterly (Q1:January-March; Q2:April-June; Q3:July-September; Q4:October-December) incidence rates (IR) of adult (≥18 years) ICH hospitalizations, aggregated across Q1-Q4 and Q2-Q3. We report adjusted incidence rate ratios (aIRR) and 95% confidence intervals (CI) for differences in the quarterly incidence of ICH, as compared to acute ischemic stroke (AIS), between Q1Q4 and Q2Q3 using a multivariable Poisson regression model. We additionally performed stratified analyses across the four US regions. Results: Among 822,143 (49.0% female) ICH and 6,266,234 (51.9% female) AIS hospitalizations, the average quarterly crude IR of ICH was consistently higher in Q1Q4 compared to Q2Q3 (5.6 vs. 5.2 per 100,000) (aIRR, CI: 1.09, 1.08-1.11)-this pattern was similar across all four US regions. However, a similar variation pattern was not observed for AIS incidence. The incidence (aIRR, CI) of both ICH (1.01, 1.00-1.02) and AIS (1.03, 1.02-1.03) is rising. Conclusion: Unlike AIS, ICH incidence is consistently higher in colder quarters, underscoring the need for evaluation and prevention of factors driving seasonal variations in ICH incidence.

Stroke severity mediates the effect of socioeconomic disadvantage on poor outcomes among patients with intracerebral hemorrhage.

Background: Socioeconomic deprivation drives poor functional outcomes after intracerebral hemorrhage (ICH). Stroke severity and background cerebral small vessel disease (CSVD) burden have each been linked to socioeconomic status and independently contribute to worse outcomes after ICH, providing distinct, plausible pathways for the effects of deprivation. We investigate whether admission stroke severity or cerebral small vessel disease (CSVD) mediates the effect of socioeconomic deprivation on 90-day functional outcomes. Methods: Electronic medical record data, including demographics, treatments, comorbidities, and physiological data, were analyzed. CSVD burden was graded from 0 to 4, with severe CSVD categorized as ≥3. High deprivation was assessed for patients in the top 30% of state-level area deprivation index scores. Severe disability or death was defined as a 90-day modified Rankin Scale score of 4-6. Stroke severity (NIH stroke scale (NIHSS)) was classified as: none (0), minor (1-4), moderate (5-15), moderate-severe (16-20), and severe (21+). Univariate and multivariate associations with severe disability or death were determined, with mediation evaluated through structural equation modelling. Results: A total of 677 patients were included (46.8% female; 43.9% White, 27.0% Black, 20.7% Hispanic, 6.1% Asian, 2.4% Other). In univariable modelling, high deprivation (odds ratio: 1.54; 95% confidence interval: [1.06-2.23]; p = 0.024), severe CSVD (2.14 [1.42-3.21]; p < 0.001), moderate (8.03 [2.76-17.15]; p < 0.001), moderate-severe (32.79 [11.52-93.29]; p < 0.001), and severe stroke (104.19 [37.66-288.12]; p < 0.001) were associated with severe disability or death. In multivariable modelling, severe CSVD (3.42 [1.75-6.69]; p < 0.001) and moderate (5.84 [2.27-15.01], p < 0.001), moderate-severe (27.59 [7.34-103.69], p < 0.001), and severe stroke (36.41 [9.90-133.85]; p < 0.001) independently increased odds of severe disability or death; high deprivation did not. Stroke severity mediated 94.1% of deprivation's effect on severe disability or death (p = 0.005), while CSVD accounted for 4.9% (p = 0.524). Conclusion: CSVD contributed to poor functional outcome independent of socioeconomic deprivation, while stroke severity mediated the effects of deprivation. Improving awareness and trust among disadvantaged communities may reduce admission stroke severity and improve outcomes.

A Neuro-Informatics Pipeline for Cerebrovascular Disease: Research Registry Development.

BACKGROUND: Although stroke is well recognized as a critical disease, treatment options are often limited. Inpatient stroke encounters carry critical information regarding the mechanisms of stroke and patient outcomes; however, these data are typically formatted to support administrative functions instead of research. To support improvements in the care of patients with stroke, a substantive research data platform is needed. OBJECTIVE: To advance a stroke-oriented learning health care system, we sought to establish a comprehensive research repository of stroke data using the Houston Methodist electronic health record (EHR) system. METHODS: Dedicated processes were developed to import EHR data of patients with primary acute ischemic stroke, intracerebral hemorrhage (ICH), transient ischemic attack, and subarachnoid hemorrhage under a review board-approved protocol. Relevant patients were identified from discharge diagnosis codes and assigned registry patient identification numbers. For identified patients, extract, transform, and load processes imported EHR data of primary cerebrovascular disease admissions and available data from any previous or subsequent admissions. Data were loaded into patient-focused SQL objects to enable cross-sectional and longitudinal analyses. Primary data domains (admission details, comorbidities, laboratory data, medications, imaging data, and discharge characteristics) were loaded into separate relational tables unified by patient and encounter identification numbers. Computed tomography, magnetic resonance, and angiography images were retrieved. Imaging data from patients with ICH were assessed for hemorrhage characteristics and cerebral small vessel disease markers. Patient information needed to interface with other local and national databases was retained. Prospective patient outreach was established, with patients contacted via telephone to assess functional outcomes 30, 90, 180, and 365 days after discharge. Dashboards were constructed to provide investigators with data summaries to support access. RESULTS: The Registry of Neurological Endpoint Assessments among Patients with Ischemic and Hemorrhagic Stroke (REINAH) database was constructed as a series of relational category-specific SQL objects. Encounter summaries and dashboards were constructed to draw from these objects, providing visual data summaries for investigators seeking to build studies based on REINAH data. As of June 2022, the database contains 18,061 total patients, including 1809 (10.02%) with ICH, 13,444 (74.43%) with acute ischemic stroke, 1221 (6.76%) with subarachnoid hemorrhage, and 3165 (17.52%) with transient ischemic attack. Depending on the cohort, imaging data from computed tomography are available for 85.83% (1048/1221) to 98.4% (1780/1809) of patients, with magnetic resonance imaging available for 27.85% (340/1221) to 85.54% (11,500/13,444) of patients. Outcome assessment has successfully contacted 56.1% (240/428) of patients after ICH, with 71.3% (171/240) of responders providing consent for assessment. Responders reported a median modified Rankin Scale score of 3 at 90 days after discharge. CONCLUSIONS: A highly curated and clinically focused research platform for stroke data will establish a foundation for future research that may fundamentally improve poststroke patient care and outcomes.

Sex differences in post-stroke cognitive decline: A population-based longitudinal study of nationally representative data.

BACKGROUND: Sex differences in post-stroke cognitive decline have not been systematically evaluated in a nationally representative cohort. We use a quasi-experimental design to investigate sex differences in rate of post-stroke cognitive decline. METHODS: Utilizing the event study design, we use the Health and Retirement Study (HRS) data (1996-2016) to evaluate the differences (percentage points [95% Confidence interval]) in the rate of change in cognitive function, measured using the modified version of the Telephone Interview for Cognitive Status (TICS-m) score, before and after incident stroke, and among patients with and without incident stroke. We estimated this event study model for the overall study population and separately fit the same model for male and female participants. RESULTS: Of 25,872 HRS participants included in our study, 14,459 (55.9%) were females with an overall mean age (SD) of 61.2 (9.3) years. Overall, 2,911 (11.3%) participants reported experiencing incident stroke. Participants with incident stroke (vs. no stroke) had lower baseline TICS-m score (15.6 vs. 16.1). Among participants with incident stroke, the mean pre-stroke TICS-m score was higher than the mean post-stroke TICS-m score (14.9 vs. 12.7). Event study revealed a significant short-term acceleration of cognitive decline for the overall population (4.2 [1.7-6.6] percentage points, p value = 0.001) and among female participants (5.0 [1.7-8.3] percentage points, p value = 0.003). We, however, found no evidence of long-term acceleration of cognitive decline after stroke. Moreover, among males, incident stroke was not associated with significant changes in rate of post-stroke cognitive decline. CONCLUSION: Females, in contrast to males, experience post-stroke cognitive deficits, particularly during early post-stroke period. Identifying the sex-specific stroke characteristics contributing to differences in post stroke cognitive decline may inform future strategies for reducing the burden of post-stroke cognitive impairment and dementia.