Abstract WP154: Factors Associated With Prolonged Length of Intensive Unit Stay Following Mechanical Thrombectomy for Acute Ischemic Stroke
Bibliographic record
Abstract
Objective and Background: The objective of the study was to investigate factors affecting the length of stay (LOS) in the intensive care unit (ICU) following mechanical thrombectomy (MT) for acute ischemic stroke. Methods: We identified patients undergoing MT from a prospective registry at a comprehensive stroke center between January 2021 and June 2023. ICU stay for more than 48 hours was defined as prolonged ICU stay. Results: Out of 363 patients, 41.6% (n=151) required a prolonged ICU stay. Prolonged ICU stay were more likely to have higher baseline median NIHSS score (19 vs. 14%, p < 0.0001), posterior circulation stroke (12.6% vs. 5.7%, p = 0.0200), intubation for the procedure (86.7% vs. 69.3%, p = 0.0001), complications including pneumonia (18.5% vs. 6.1%, p = 0.0002), deep vein thrombosis (7.3 vs. 2.3, p = 0.0242), urinary tract infection (12.6% vs. 6.1%, p = 0.0326), and symptomatic intracerebral hemorrhage (7.3% vs. 1.9%, p = 0.0109). Patients receiving thrombolysis prior to thrombectomy were less likely to have a prolonged LOS (27.8% vs. 41.7%, p = 0.0086). Independent predictors for prolonged ICU LOC included higher NIHSS (odds ratio [OR] 2.7, p = 0.0002), intubation prior to the procedure (OR 2.7, p = 0.0002), not receiving IV thrombolysis (OR 2.3, p = 0.0069), thrombolysis in cerebral infarction (TICI) score ≥ 2B (OR 0.458, p = 0.0754), composite ICU complications (OR 2.4, p = 0.0027), and symptomatic intracranial hemorrhage (OR 5.2, p = 0.0157). Conclusions: Almost one-third of the acute ischemic stroke patients required a prolonged ICU stay following MT. A better understanding of the factors associated with prolonged ICU stay may assist in appropriate allocation of resources.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".