A Predictive Nomogram Model Incorporating Contrast Extravasation for Early Prediction of Postthrombectomy Malignant Cerebral Edema
Bibliographic record
Abstract
OBJECTIVE: Early prediction of malignant cerebral edema (MCE) following endovascular thrombectomy (EVT) is critical for guiding timely interventions to improve outcomes; however, existing prediction models predominantly require complex algorithms. Contrast extravasation (CE) is a novel imaging biomarker. We aimed to develop a practical nomogram incorporating CE for predicting MCE to enhance postoperative management. METHODS: We reviewed the records of individuals with anterior circulation acute ischemic stroke who exhibited CE on dual-energy computed tomography after successful recanalization via EVT. The prediction nomogram was developed via multivariable logistic regression and internally validated with the Hosmer-Lemeshow test. RESULTS: The final cohort comprised 89 patients (median age, 72 [interquartile range, 67-79] years; male, 62.92%), with 35 (39.33%) patients developing MCE. After adjusting for confounding variables, the CE score in the Alberta Stroke Program Early CT Score region (CE-ASPECTS) retained independent predictive significance for MCE. It was incorporated into the nomogram with variables according to clinical relevance. The nomogram demonstrated excellent discriminative performance, with an area under the curve of 0.961 (95% confidence interval: 0.927-0.995), and good calibration accuracy according to the Hosmer-Lemeshow test (P = 0.869). CONCLUSIONS: CE-ASPECTS is an accessible and independent predictor of MCE. The nomogram, which is composed of age, admission fasting blood glucose level, Trial of ORG 10,172 in Acute Stroke Treatment classification, occlusion site, and CE-ASPECTS, may serve as a practical tool for predicting the probability of MCE in patients with acute ischemic stroke who achieved successful recanalization after EVT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".