High relative cerebral blood volume is associated with good long term clinical outcomes in acute ischemic stroke: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Endovascular therapy for acute ischemic stroke has been shown to be highly effective in selected patients. However, the ideal criteria for patient selection are still debated. It is well known that collateral flow is an important factor, but the assessment is often subjective and time-consuming. Relative cerebral blood volume (rCBV) is a putative indicator of collateral capacity and can be quickly and easily determined by automated quantitative analysis. We investigated the relationship between rCBV of the affected region and clinical outcome in patients with acute ischemic stroke after endovascular therapy. METHODS: We conducted a retrospective study on consecutive patients between January 2017 and May 2019. Patients with acute ischemic stroke of the anterior circulation who underwent imaging including computed tomography perfusion and were treated with mechanical thrombectomy (MT) were eligible for inclusion. rCBV was calculated automatically with RAPID software by dividing the average cerebral blood volume (CBV) of the affected region (time-to-maximum (Tmax) > 6 s) by the CBV of the unaffected contralateral side. The primary outcome was determined by the modified Rankin Scale (mRS) after 90 days. Good clinical outcome was defined as mRS ≤ 2. We compared means, performed mono- and multivariate logistical regression and calculated a receiver operating characteristic (ROC)-analysis to determine the ideal cutoff value to predict clinical outcomes. RESULTS: 155 patients were enrolled in this study. 66 patients (42.58%) had good clinical outcomes. Higher rCBV was associated with good clinical outcome (p < 0.001), even after adjustment for the patients' status according to mRS and National Institute of Health Stroke Scale (NIHSS) age and Alberta stroke program early computed tomography score (ASPECTS) at baseline (p = 0.006). ROC-analysis revealed 0.650 (confidence interval: 0.616-0.778) as the optimal cutoff value. CONCLUSION: Higher rCBV at baseline is associated with good clinical long-term outcomes in patients with acute ischemic stroke treated by MT. In this study we provide the biggest collective so far that gives evidence that rCBV can be a valuable tool to identify patients who might benefit from MT and are able give a threshold to help to offer patients MT in borderline cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".