Admission glucose is a significant outcome predictor in anterior circulation stroke: approaching the sweet spot
Bibliographic record
Abstract
BACKGROUND: Admission glycemia has emerged as an important outcome predictor in the context of mechanical thrombectomy (MT) for large vessel occlusions (LVO) in ischemic stroke. However, a clinically relevant threshold of glucose levels to identify patients at risk for poor functional outcome has yet to be established. METHODS: We conducted a retrospective, monocentric, consecutive registry-based analysis of patients who underwent MT for anterior circulation LVO. Good outcome was defined as functional independence after 90 days (90d mRS < 3) or no deterioration from premorbid mRS. We performed a multiple logistic regression analysis to assess the association between admission glucose levels and functional outcome, including for well-established outcome predictors, i.e. age, NIHSS, Alberta Stroke Program Early CT Score (ASPECTS), time to reperfusion, unsuccessful recanalization, presence of bleeding, and diabetes. In addition, we conducted a receiver operating characteristic (ROC) analysis to determine the optimal admission glucose threshold that best discriminates patients at risk for poor outcome, maximizing sensitivity and specificity. RESULTS: We analyzed 509 patients (mean age = 74.3 ± 12.6 years, median previous mRS = 1.5, 48% male). 194 patients (38.1%) had good outcome and 315 (61.9%) had poor outcome. According to the logistic regression admission glucose (p = 0.012, OR 1.009 95% CI [1.002 1.016]) contributed to predicting poor outcome, while known diabetes did not show a significant contribution. The ROC analysis revealed an admission glucose of 117 mg/dL (59.7% sensitivity; 58% specificity) to be the optimal cut-off value to discriminate patients at risk for poor outcome with an OR of 2.3. CONCLUSION: Admission hyperglycemia is an independent predictor of poor outcome after MT for LVO in the anterior circulation. We hypothesize, that optimal glucose values in patients undergoing MT will likely be in the low normoglycemic range. Prospective controlled studies with targeted glucose values will be needed for validation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".