Abstract 103: Temporal Trends in Patient Characteristics and Use of IV tPA in Acute Ischemic Stroke Patients Treated at GWTG-Stroke Hospitals
Bibliographic record
Abstract
Intro: Substantial efforts over the past decade have been made to increase rates of IV tPA use in the US. We sought to determine changes in patient characteristics and rates of tPA use over time among hospitalized stroke patients. Methods: We analyzed all acute ischemic stroke (AIS) patients (n=1,093,895) and those arriving ≤ −2 hr and treated with IV tPA ≤ 3 hr of onset (n=50,798) from 2003-2011 in the GWTG-Stroke registry. Categorical data were analyzed by Pearson Chi-square and continuous data by Wilcoxon test, with multivariable GEE models (with and without NIHSS) for analysis of calendar time effect. Results: IV tPA use increased from 4.0% to 7.0% in all AIS admissions, and 42.6% to 77.0% in AIS patients arriving ≤ 2hr and tPA treated ≤ 3 hr (p <0.001) (Figure). In univariate analysis, IV tPA use increased over time in those aged >85 yr, non-white, and with milder strokes (NIHSS 0-4) (Table). Door-to-image time (median 24 vs. 20 min) and door-to -tPA time (median 81 vs. 72 min) also improved, with ~65% of tPA treated patients getting CT <25 min of arrival (Table). Multivariable GEE analysis of the effect of calendar year on rates of tPA use (n= 45656) found an adjusted OR of 1.37 (95%CI 1.35 - 1.40; p < 0.0001) per year. Including NIHSS in the model (n=39814) did not change the results substantially (OR=1.32, CI 1.29 - 1.35; p<0.0001) per yr. Conclusion: The frequency of IV tPA use among all AIS patients, regardless of contraindications, and among eligible patients without contraindications arriving within two hours nearly doubled from 2003-2011. Treatment with tPA has expanded over the past decade to include more stroke patients with milder deficits, non-white race-ethnicity, and in the oldest old.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".