Abstract WP46: Patient Characteristics, Quality and Outcomes After Endovascular Therapy for In-Hospital Ischemic Stroke
Bibliographic record
Abstract
Introduction: A significant number of acute ischemic strokes occur while patients are hospitalized for other reasons. No national data have been reported on endovascular therapy (EVT) for in-hospital onset stroke. Here we compare the patient characteristics, process measures of quality, and outcomes for in-hospital onset vs. community-onset of strokes in a large US national registry. Methods: We performed a retrospective cohort study of Get With The Guidelines-Stroke (GTWG-Stroke) from January 2008 to June 2018 from 2,333 participating sites that included 2,428,178 patients with acute ischemic stroke. Among 67,493 in-hospital onset strokes, 2494 (3.7%) underwent EVT. We examined the association between key patient characteristics (in-hospital onset, demographics, comorbidities, treatment with EVT) and functional outcomes using multivariable logistic regression models. Results: The rate of EVT increased from 2.5% in 2008 to 6.4% in 2018 (p<0.001), with a significant and sustained increase in EVT after the second quarter of 2015 (p<0.0001). Compared with patients with community-onset strokes, patients with in-hospital onset stroke had longer times to cranial imaging and arterial puncture but similar median NIHSS (16 (9 - 21) vs. 16 (10 - 21) Std Diff 1.9). Patients with in-hospital onset stroke were less likely to undergo EVT within 120 mins of symptom recognition, have symptomatic intracranial hemorrhage, or ambulate independently at discharge. They were more likely to die or be discharged to hospice. Conclusions: Though use of EVT in GWTG-Stroke for in-hospital stroke remains low, it more than doubled in the past decade. Compared with community onset stroke, these patients have longer intervals to CT and arterial puncture, with associated worse functional outcomes. While there may be important differences in baseline patient characteristics between the groups, efforts must still be made to shorten time to reperfusion for in-hospital strokes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".