Abstract DP088: The Role of Sex and Age in Acute Ischemic Stroke

Introduction: A substantial number of articles, case studies, literature reviews, and meta-analyses have explored the topic of acute ischemic stroke (AIS) in relation to women and age. These factors interact to influence AIS risk, incidence, and outcome. However, women remain underrepresented in clinical trials, and sex disparities in AIS remain poorly understood. This study aims to evaluate symptoms of AIS, care provided, and outcomes after treatment in male and female patients across the adult lifespan (18-89+ years). This study will add to the knowledge of sex-related differences and help guide the focus of interventions to improve AIS care. Methods: This secondary data analysis study, supported by retrospective chart review, was conducted at a Comprehensive Stroke Center (CSC) within a large Midwest academic medical center. It included patients diagnosed with AIS and admitted between January 1, 2022, and December 31, 2024. Approval was granted by the university’s Institutional Review Board (IRB). Data were obtained from the Get With the Guidelines-Stroke Registry (GWTG-Stroke). Patient records were grouped by arrival method: those presenting directly to the CSC and those transferred to the CSC from an outside hospital. Data in the sub-groups will be compared to identify differences between male and female patients. The researchers hypothesize that significant differences exist between males and females for age, last known well to arrival, initial NIHSS score, pre-stroke mRS, discharge mRS, and 90-day mRS. NIHSS sub-scores are being collected and analyzed to determine potential differences in presenting symptoms between male and female patients. Results: A total of 1,801 patient records were received from GWTG-Stroke. Five records were excluded for not meeting inclusion criteria. Missing data points were extracted using retrospective chart review. Initial descriptive statistics are included below in Table 1 and Table 2. Conclusion: Initial analysis using independent samples t-tests (p-value < 0.05) indicates statistically significant differences for age (p <0.001), initial NIHSS (p 0.016), and discharge mRS (p <0.001) between the male and female patients presenting directly to the CSC. Data capture is ongoing, and final conclusions will be presented at the 2026 International Stroke Conference. IBM SPSS Statistics version 28 will be used for data analysis.

Authors

Institutions

Publication Details

Journal
Stroke
Published
2026-01-29
DOI
https://doi.org/10.1161/str.57.suppl_1.dp088
Primary Topic
Acute Ischemic Stroke Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract DP088: The Role of Sex and Age in Acute Ischemic Stroke

Anika Zainab, Lisa E. Guthrie, Sarah Langston, Chaeli Dougherty-Stenuf et al.
Stroke
Acute Ischemic Stroke Management
article

Abstract DP088: The Role of Sex and Age in Acute Ischemic Stroke

Anika Zainab, Lisa E. Guthrie, Sarah Langston, Chaeli Dougherty-Stenuf, Margaret Houghton, Colleen Lechtenberg
article en

Abstract

Introduction: A substantial number of articles, case studies, literature reviews, and meta-analyses have explored the topic of acute ischemic stroke (AIS) in relation to women and age. These factors interact to influence AIS risk, incidence, and outcome. However, women remain underrepresented in clinical trials, and sex disparities in AIS remain poorly understood. This study aims to evaluate symptoms of AIS, care provided, and outcomes after treatment in male and female patients across the adult lifespan (18-89+ years). This study will add to the knowledge of sex-related differences and help guide the focus of interventions to improve AIS care. Methods: This secondary data analysis study, supported by retrospective chart review, was conducted at a Comprehensive Stroke Center (CSC) within a large Midwest academic medical center. It included patients diagnosed with AIS and admitted between January 1, 2022, and December 31, 2024. Approval was granted by the university’s Institutional Review Board (IRB). Data were obtained from the Get With the Guidelines-Stroke Registry (GWTG-Stroke). Patient records were grouped by arrival method: those presenting directly to the CSC and those transferred to the CSC from an outside hospital. Data in the sub-groups will be compared to identify differences between male and female patients. The researchers hypothesize that significant differences exist between males and females for age, last known well to arrival, initial NIHSS score, pre-stroke mRS, discharge mRS, and 90-day mRS. NIHSS sub-scores are being collected and analyzed to determine potential differences in presenting symptoms between male and female patients. Results: A total of 1,801 patient records were received from GWTG-Stroke. Five records were excluded for not meeting inclusion criteria. Missing data points were extracted using retrospective chart review. Initial descriptive statistics are included below in Table 1 and Table 2. Conclusion: Initial analysis using independent samples t-tests (p-value < 0.05) indicates statistically significant differences for age (p <0.001), initial NIHSS (p 0.016), and discharge mRS (p <0.001) between the male and female patients presenting directly to the CSC. Data capture is ongoing, and final conclusions will be presented at the 2026 International Stroke Conference. IBM SPSS Statistics version 28 will be used for data analysis.

StrokeVol. 57(Suppl_1)
University of Kansas (US), University of Kansas Medical Center (US)
Openalex Percentile: Top 72%
Acute Ischemic Stroke Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.