Larger Perfusion Mismatch Volume Is Associated With Longer Hospital Length of Stay in Medium Vessel Occlusion Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Prolonged length of stay (LOS) following a stroke is associated with unfavorable clinical outcomes. Factors predicting LOS in medium vessel occlusion (MeVO), impacting up to 40% of acute ischemic stroke (AIS) cases, remain underexplored. This study aims to investigate the predictors of LOS in AIS-MeVO. METHODS: We conducted a retrospective analysis of prospectively maintained stroke databases, comprising AIS cases with MeVO in the anterior circulation, assessed by adequate CT perfusion (CTP). Baseline and clinical data were obtained from electronic health records. Alberta Stroke Program Early CT Scores (ASPECTS) were calculated from non-contrast head CT. The perfusion mismatch volume (time to maximum > 6 s minus relative cerebral blood flow <30%) volume was reported from CTP. Multiple regression was employed to examine the relationship between baseline parameters and hospital LOS. RESULTS: A total of 133 patients (median age 71 [interquartile range 63-80] years, 59.4% females) were included in the study cohort. The perfusion mismatch volume significantly positively correlated with LOS (r = 0.264, p = 0.004). After adjusting for age, sex, hypertension, diabetes, prior stroke or transient ischemic attack, admission NIHSS, ASPECTS, Tan score, intravenous thrombolysis, mechanical thrombectomy (MT), and hemorrhagic transformation, a larger mismatch volume remained independently associated with longer hospital stays (β = 0.209, 95% confidence interval [CI] 0.006-0.412, p = 0.045). Additional significant determinants of longer hospital stay included admission NIHSS (β = 0.250, 95% CI: 0.060-0.440, p = 0.010) and MT (β = 0.208, 95% CI: 0.006-0.410, p = 0.044). Among patients who underwent MT (n = 83), multiple regression analysis incorporating both perfusion mismatch volume and admission NIHSS revealed that perfusion mismatch volume remained independently associated with LOS (β = 0.248, 95% CI: 0.019-0.471, p = 0.033), while admission NIHSS did not retain significance (β = 0.208, 95% CI: 0.019-0.433, p = 0.071). CONCLUSIONS: In our cohort of AIS patients with MeVO in the anterior circulation, and particularly in those who underwent MT, the perfusion mismatch volume serves as an independent predictor of LOS. These findings offer critical valuable insights in clinical assessments and decision-making protocols of MT in AIS-MeVO.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".