Predictors of Malignant Cerebral Edema Following Mechanical Thrombectomy in Acute Ischemic Stroke: A Retrospective Study and Nomogram Development
Bibliographic record
Abstract
OBJECTIVE: To study the risk factors of malignant cerebral edema (MCE) after mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and middle cerebral artery/internal carotid artery occlusion, and to establish a clinical prediction nomogram. METHODS: We retrospectively analyzed patients with AIS who achieved successful recanalization after MT at our hospital from March 2019 to December 2024. These patients were randomly divided into a training cohort (80%) and an internal validation cohort (20%). Independent risk factors associated with MCE were determined using logistic regression analysis, and a nomogram was created on the basis of these factors. The discriminative performance was quantified by the area under the receiver operating characteristic curve (AUC). RESULTS: Among the 284 enrolled patients, 49 (17%) developed MCE. Patients in the MCE group had a significantly higher percentage of a poor functional outcome at 90 days than those in the non-MCE group (88% vs. 45%, P<0.001). Predictors of MCE after MT in patients with AIS included the Alberta Stroke Project Early CT Score, Hyperdensity on CT Score, baseline National Institutes of Health Stroke Scale score, neutrophil-lymphocyte ratio, and puncture-to-reperfusion time >120 min. The nomogram showed excellent discrimination, with an area under the curve of 0.941 (95% CI: 0.909-0.972) in the training cohort and 0.964 (95% CI: 0.921-1.000) in the validation cohort. CONCLUSIONS: We developed a nomogram incorporating 5 readily available clinical and imaging variables that predicts the risk of MCE after MT, which may assist in early clinical decision-making.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".