New insights on the predictive value of hypoperfusion intensity ratio in thrombectomy: an updated systematic review and meta-analysis with multiple cut-offs
Bibliographic record
Abstract
BACKGROUND: The hypoperfusion intensity ratio (HIR) has emerged as a vital measure of tissue-level collateral blood flow, helping to identify patients who are likely to benefit from mechanical thrombectomy (MT). We aimed to assess the HIR's predictive accuracy for clinical outcomes following MT in patients with acute ischemic stroke. METHODS: PubMed, Embase, and Scopus were searched to identify studies comparing good versus poor HIR groups based on studies' reported cut-offs. We pooled binary outcomes to calculate odds ratios (OR) and continuous outcomes to calculate mean differences (MD) with 95% confidence intervals (95% CI) using random-effects models. PROSPERO registration code: CRD42024609185. RESULTS: 14 studies with 2987 patients, 1553 with good HIR and 1434 with poor HIR, were included in this meta-analysis. Patients with poor HIR exhibited a significantly higher baseline infarct volume compared with those with good HIR (MD 30.6 mL, 95% CI 20.8 mL to 40.3 mL, P<0.01), though baseline National Institutes of Health Stroke Scale (NIHSS) (P=0.12) and Alberta Stroke Program Early CT Score (ASPECTS) (P=0.35) were comparable between groups. The rates of infarct growth (MD 22.4 mL, 95% CI 6.7 mL to 38.0 mL, P<0.01) and 3-month mortality (OR 2.18, 95% CI 1.04 to 4.58, P=0.04) were higher among the poor HIR group and good functional recovery (modified Rankin Scale 0-2 at 3 months) was lower (OR 0.58, 95% CI 0.42 to 0.80, P<0.01). The rates of symptomatic intracranial hemorrhage (P=0.37) and successful reperfusion (P=0.47) were comparable among groups. CONCLUSION: This meta-analysis highlights the significant negative impact of poor HIR on patient outcomes. These findings emphasize the need for personalized treatment strategies for patients with poor HIR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".