Abstract 2433: Outcomes Of Thrombolysis In The 0-4.5 Hour Time Window In Acute Stroke Patients Selected Using Ct Perfusion Imaging
Bibliographic record
Abstract
Background: Thrombolytic treatment within 4.5 hours of symptom onset is effective in preventing death or significant disability in 1 out of 7 stroke patients, but with a 5% risk of symptomatic intracerebral haemorrhage (sICH). We hypothesised that patient selection using CT perfusion (CTP) imaging can increase the benefit of thrombolysis and reduce the risk of sICH. Methods: We extracted data from a prospective thrombolysis database of 533 consecutive stroke patients of all ages thrombolysed with 4.5 hours of symptom onset. Thrombolysis decisions were made on the basis of a non-enhanced CT (NECT) scan but CTP was undertaken in a proportion of patients, depending upon physician decisions. In the analysis, patients with an Alberta Stroke Program Early CT Score (ASPECTS) of ≥7 on NECT and an estimated volumetric perfusion mismatch of ≥100% on PCT were defined as optimal candidates for thrombolysis. Imaging was reviewed by 2 raters masked to outcomes. Nine patients were excluded from analysis because of poor PCT quality. Findings: Of the 524 patients included, 97 patients had CTP mismatch that met the defined criteria for mismatch guided thrombolysis. Their age (72 v 70 years), sex (50% v 54% male), pre-morbid modified Rankin Scale (mRS) score, baseline National Institute of Health Stroke Scale (NIHSS) score (13.6 v 12.6,p=0.20), blood glucose (6.8 v 6.6 mmols) and blood pressure (149/84 v 148/79 mm Hg) were comparable with those thrombolysed on the basis of NECT imaging. At 3 months, the proportion of patients with modified Rankin Score of 0-1 and 0-2 was higher in those with mismatch (36% v 29%, p=0.003 and 51% v 42%, p=0.007 respectively) and there was a non-statistical trend towards reductions in any ICH (13% v 16%), sICH (1.1% v 2.8%) and mortality (23% v 18%). CTP mismatch was an independent determinant of a favourable outcome at 3 months in regression analyses to adjust for covariates. Conclusions: Patient selection based on estimation of salvageable brain tissue using CTP mismatch may improve functional outcomes at 3 months. The value of CT perfusion in increasing the effectiveness and safety of thrombolysis within established therapeutic time windows merits investigation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".