Abstract 142: ASPECTS Decay During Inter-Facility Transfer Predicts Patient Outcomes in Endovascular Reperfusion for Ischemic Stroke: A Unique Assessment of Dynamic Physiologic Change Over Time
Bibliographic record
Abstract
Background: In acute ischemic stroke, delays in reperfusion lead to a reduced probability of good clinical outcomes. Pre-treatment Alberta Stroke Program Early CT Scores (ASPECTS) are also associated with clinical outcomes, but the rate of change between subsequent CT images in transferred patients may be more predictive as it incorporates time. We hypothesized that patients with significant change in ASPECT scores would have worse clinical outcomes despite having a favorable pre-treatment baseline ASPECTS. Methods: A cohort of patients transferred from seven Primary Stroke Centers and treated with endovascular reperfusion (December 15, 2010 to March 15, 2013) were retrospectively studied. All patients were analyzed with respect to radiographic, demographic, and time-related variables. Absolute ASPECTS decay was defined as [(ASPECTS First CT - ASPECTS Second CT)/time elapsed between CTs in hours]. A binary logistic regression model was performed to determine if the rate of ASPECTS decay was predictive of good 90 day outcomes (mRS 0-2). Results: A total of 106 patients with a mean age of 66±14 years and median NIHSS of 19 [IQR 15-23] were analyzed. The median time between initial imaging at the outside hospital to repeat imaging at our treatment facility was 2.7 hours (IQR 2.0-3.6). Patients with good outcomes had lower rates of absolute ASPECTS decay compared to those who did not (0.14±0.23 score/hr vs. 0.49±0.39 score/hr; p<0.001). In multivariable modeling, the absolute rate of ASPECTS decay (OR 0.043; 95%CI 0.004-0.457; p=0.01) was a stronger predictor of good patient outcome than the static pre-treatment ASPECTS obtained immediately before intervention (OR 0.653; 95%CI 0.39-1.05; p=0.076). Practically, patients with a decay of two ASPECTS points per hour compared to those who decay at one point per hour had a 23 fold lower probability of a good outcome. Conclusions: Our analysis demonstrates that patients with faster rates of ASPECTS decay are associated with worse clinical outcomes, reflecting the rate of physiological infarct expansion. This metric may be valuable in selecting patients for IAT, as patients with rapid ASPECTS decay are less likely to derive treatment benefit, particularly with delays in inter-facility transfers and procedure 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".