Factors associated with prolonged intensive unit stay following mechanical thrombectomy for acute ischemic stroke
Bibliographic record
Abstract
Objective and background The aim 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 conducted a retrospective analysis using data from a prospective stroke registry at a comprehensive stroke center between January 2019 and June 2024. ICU stay for more than 48 hours was defined as prolonged ICU stay.Results Out of 808 patients, 39.2% (n = 317) required a prolonged ICU LoS. Prolonged ICU stay was more likely to have a baseline National Institutes of Health Stroke Scale NIHSS ≥ 15, higher mean HbA1c levels, posterior circulation stroke, intubation for the procedure, symptomatic intracerebral hemorrhage, in-patient mortality and ICU complications including pneumonia, deep vein thrombosis, pulmonary embolism, urinary tract infection (all p value < 0.05). Patients receiving thrombolysis prior to thrombectomy were less likely to have a prolonged LoS (p = 0.0025). Independent predictors for prolonged ICU LoS included baseline NIHSS ≥ 15 (odds ratio [OR] 1.83, p = 0.0004), intubation prior to the procedure (OR 2.20, p < 0.0001), receiving IV thrombolysis (OR 0.66, p = 0.0144), recanalization (OR 0.48, p = 0.0085, composite ICU complications (OR 2.66, p < 0.0001), and symptomatic intracranial hemorrhage (sICH) (OR 3.38, p < 0.0001).Conclusions Almost one-third of the acute ischemic stroke patients required a prolonged LoS 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 | 0.001 | 0.005 |
| 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) | 0.001 | 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".