Abstract 141: Door-to-Puncture: A Practical Metric for Capturing and Enhancing System Processes Associated With Endovascular Stroke Care, Preliminary Results From the Rapid Reperfusion Registry
Bibliographic record
Abstract
Background/Purpose: In 2011, the Brain Attack Coalition proposed an “Arrival-to-Treatment” of two hours as a benchmark for ischemic stroke patients undergoing intra-arterial therapy (IAT). We designed the Rapid Reperfusion Registry to capture the percentage of stroke patients being treated within the targeted time frame, and to assess the clinical impact of the metric on patient outcomes. Methods: This is a retrospective analysis of consecutive anterior circulation patients treated with IAT within 9 hours of symptom onset from nine institutions. Data was collected from December 31, 2011 to Dec 31, 2012 at two centers and from July 1, 2012 to December 31, 2012 at seven centers. Short “Door to Puncture” (D2P) time was hypothesized to be associated with good patient outcomes (90 day modified Rankin Scale score of 0-2), which was confirmed on logistic regression modeling. Results: A total of 478 patients were analyzed, with a mean age of 68±14 years and median NIHSS of 18 (IQR 14-21). The median times for IAT delivery were 234 minutes (IQR 164-304) from ‘last known normal to puncture’ (LKN-to-GP) and 111 minutes (IQR 65-173) from D2P. The overall good outcome rate was 39.7% for the entire cohort. In a multivariable model adjusting for age, NIHSS, hypertension, diabetes, reperfusion status, and symptomatic hemorrhage, both short LKN-to-GP (OR 0.996; 95%CI [0.994-0.998]; p<0.001) and short D2P times (OR 0.993, 95%CI [0.990-0.996]; p<0.001) were associated with good outcomes. Only 52% of all patients in the registry achieved the targeted D2P time of two hours. Conclusions: The pre-treatment time interval of D2P presents a clinically relevant time frame by which system processes can be targeted on a national level to streamline the delivery of IAT care. At present, there is much opportunity to reduce delays within this narrow time window, thus, providing an opportunity to enhance patient 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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".