Abstract WMP86: Reasons for Slower Door-to-Needle Times and Their Impact on Timing of Treatment and Outcomes: Findings From Get With The Guidelines-Stroke
Bibliographic record
Abstract
Background: Despite quality improvement programs such as the American Heart Association/American Stroke Association Target_Stroke initiative, a substantial portion of acute ischemic stroke patients are still treated with alteplase later than 60 minutes, for unclear reasons. This study aims to describe the documented reasons for delays as well as the associations between reasons for delays and patient outcomes. Methods: We analyzed 55,296 patients who received intravenous alteplase in 1,422 hospitals participating in Get With The Guidelines-Stroke from October 2012 to April 2015, excluding transferred patients and inpatient strokes. We assessed eligibility, medical, and hospital reasons for delays in door-to-needle time (DTN). Multivariable models were used to evaluate associations between reasons for delays, time lost, and hospital discharge outcomes, controlling for patient and hospital characteristics. Results: There were 27,778 (50.2%) patients treated within 60 minutes, 10,086 (18.2%) treated in more than 60 minutes without documented delays, and 17,432 (31.5%) treated in more than 60 minutes with one or more documented reasons for delay. The longest DTN times were associated with inability to determine eligibility, delay in diagnosis, further diagnostic evaluation for hypoglycemia or seizure, management of emergent medical conditions and initial patient refusal (Table). One or more reason for delays was associated with in-hospital mortality (OR 1.2; 95CI 1.1-1.3), symptomatic intracranial hemorrhage (OR 1.2; 95CI 1.1-1.3), and lower odds of independent ambulation at discharge (OR 0.92; 95CI 0.9-1.0). Conclusions: Hospital and eligibility delays such as delay in diagnosis and inability to determine eligibility are common and are associated with longer DTN and poorer outcomes. Improved stroke recognition and management of acute comorbidities may help to reduce DTN times and improve outcomes.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".