Abstract WP214: Risk Factors for Recurrence of Vascular Events in Middle-Aged Stroke Survivors
Bibliographic record
Abstract
Background: Stroke in middle-aged persons is emerging as a distinct entity from previously described "stroke in the young.” Risk factors for recurrence of vascular events in middle-aged stroke survivors have not been well described in a large, racially diverse US-based population. Methods: We performed a retrospective longitudinal analysis of claims data from all hospitalizations in California (2005-2011), Florida (2005-2014), and New York (2005-2014). We included survivors of first-ever ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH). We used validated ICD-9 codes to identify recurrent vascular events, defined as a readmission with a primary diagnosis of IS, ICH, or myocardial infarction (MI). We used survival analysis and Cox proportional hazards regression to determine factors associated with recurrence risk in middle-aged (40-60 years) versus older (>60 years) adults. Results: We identified 123,212 non-fatal hospitalizations for first-ever IS or ICH in middle-aged adults. There were 14,721 readmissions for recurrent vascular events (11.9%) over a mean follow-up of 4.0 years (standard deviation [SD] 2.8 years). The 1-year and 10-year recurrence rates were 5.5% (95% confidence interval [CI], 5.3%-5.6%) and 19.4% (95% CI, 19.0%-19.8%) respectively. In multivariable analysis, black (hazard ratio [HR] 1.44; 95% CI, 1.39-1.50; p<0.001) and Hispanic race (HR 1.10; 95% CI, 1.05-1.16; p<0.001), diabetes (HR 1.76; 95% CI, 1.70-1.82; p<0.001), smoking (HR 1.22; 95% CI, 1.18-1.27; p<0.001), hyperlipidemia (HR 1.08; 95% CI, 1.04-1.12; p<0.001), and hypertension (HR 1.06; 95% CI, 1.03-1.10; p<0.001) were associated with increased risk. The associations of black race and diabetes were modified by age category (both multivariable interactions p<0.001). Black race and diabetes were associated with greater increases in risk in middle-aged versus older adults (in older adults: black race HR 1.28; 95% CI, 1.25-1.31; p<0.001; diabetes HR 1.27; 95% CI, 1.25-1.30; p<0.001). Conclusions: Black race and diabetes were the strongest risk factors for recurrence of vascular events in a middle-aged population. Further study of interventions to reduce recurrence and resulting disability in this younger, higher risk population is needed.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".