Pre-morbid Risk Factor Control Differences by Age in Patients Undergoing Thrombectomy for Acute Ischemic Stroke (P8-5.014)
Bibliographic record
Abstract
Objective: We proposed to estimate the percentage of large vessel strokes that are potentially attributable to unmanaged vascular risk factors and how this may differ by age to improve stroke prevention and patient outcomes. Background: Controlling metabolic and behavioral risk factors may avert 75% of the world stroke burden. We anticipate risk factors, and their management may differ depending on age of patient and may offer insight into preventative strategies. Design/Methods: A retrospective chart review was conducted on patients undergoing endovascular therapy from 2012–2019 at University of Maryland. Patients were stratified based on age into three groups: <50, 50–70, and >70 years of age. Risk factors prior to admission including hypertension, diabetes, hyperlipidemia, atrial fibrillation, history of vascular disease, and current smoking were recorded. A possibly preventable stroke was defined as having a history of at least one of these risk factors that was not being optimally controlled according to current guidelines. Chi squared tests of proportion were used to evaluate differences between age groups. Results: 396 patients underwent thrombectomy (50% female, mean age 64.7). The most prominent etiology of stroke across all age groups was cardioembolic. 62% of strokes in patients <50 years of age were preventable as compared to 82% in patients 50–70 years old and 79% in patients older than 70. Smoking was a common risk factor in all age groups. Despite overall lower incidence of diabetes and hyperlipidemia in patients <50 with stroke, a large proportion of these patients presented with poor control of these risk factors (p=0.04 and p=0.01). Conclusions: Most patients who underwent thrombectomy had at least one cardiovascular risk factor that was uncontrolled despite age group. This illustrates the importance of cardiovascular risk factor management in primary prevention of stroke, especially control of diabetes and hyperlipidemia in the younger population. Disclosure: Dr. Grimes has nothing to disclose. Dr. Mehndiratta has nothing to disclose. Dr. Chaturvedi has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Astra Zeneca. Dr. Chaturvedi has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for University of Calgary. Dr. Chaturvedi has received personal compensation in the range of $10,000-$49,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for American Heart Association. Dr. Chaturvedi has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Ramar & Paradiso. Dr. Chaturvedi has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Cole, Scott, Kissane. The institution of Dr. Chaturvedi has received research support from NINDS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".