Abstract T P287: Reasons for Delays in Door to Needle Time
Bibliographic record
Abstract
Background: The National Quality Forum recently endorsed a performance measure for door-to-needle (DTN) time less than 60 minutes, with exclusion criteria for acceptable reasons for delay. However, the reasons for delay in timely treatment with tPA, and their impact on DTN times, are largely unknown. At our center we initiated the Hurry Acute Stroke Treatment and Evaluation-2 (HASTE-2) project to identify reasons for longer DTN. Methods: From 06/2012 to 06/2013 we encouraged treating physicians to fill out a 1-page case report form on opportunities for improvements and reasons for delays in treatment times, categorized according to systems-related delays (e.g. in patient registration, or in recognition of stroke symptoms and activation of the stroke) vs. medical/eligibility delays (e.g. management of concomitant emergent conditions or initial patient refusal). DTN data were analyzed from 113 consecutive patients presenting directly to the ER, treated with IV tPA within 4.5 hours of symptom onset. Results: Mean age was 71, 60/113 (53%) were women, median NIHSS was 13.5 and DTN was 57 minutes. Prospective data were recorded on the presence of absence of potential delays in 48/113 (42.5%); patients with missing data were somewhat younger (mean age 68.5 vs. 75.3, p=0.04) but did not differ in DTN or NIHSS. 52 different systems delays were identified in 30/48 patients (63%), and 15 different medical/eligibility delays were identified in 13/48 patients (27%). Medical/eligibility delays had the greatest impact on DTN: median 67 min [interquartile range 54-120] in patients with medical/eligibility delays, 47.5 min [40-66] in patients with only systems delays and 51 min [34-65] in patients with no delays (p=0.01). Conclusions: DTN may be prolonged for a variety of reasons. Up to 27% of patients have delays due to medical or eligibility-related causes that may be legitimate reasons for providing tPA later than the benchmark time of 60 minutes. Our difficulty in obtaining complete prospective physician documentation of reasons for delays suggests a need for improved chart documentation regarding DTN times.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".