Abstract 398: Serum Magnesium Levels and Intraparenchymal Hemorrhage After Mechanical Thrombectomy
Bibliographic record
Abstract
Purpose Experimental evidence suggests that magnesium plays a hemostatic role by enhancing activation of clotting factors, increasing platelet adhesion, and shortening coagulation times. Given its potential to reduce hemorrhagic complications, we investigated the association between baseline serum magnesium levels and risk of intraparenchymal hemorrhage following mechanical thrombectomy (MT) in patients with anterior circulation large vessel occlusion (LVO) stroke. Methods We conducted a retrospective analysis of a prospectively maintained MT registry (2010‐2024). Consecutive patients with internal carotid artery terminus (ICA‐T), M1, or M2 occlusions were included if serum magnesium levels were available within 48 hours of admission. Hemorrhagic outcomes were defined according to ECASS III criteria: any hemorrhagic transformation (HT; HI‐1, HI‐2, PH‐1, or PH‐2) and parenchymal hematoma (PH; PH‐1, or PH‐2). ROC curve analysis assessed discriminatory ability of hypomagnesemia (<1.5 mEq/L) and continuous magnesium levels for predicting hemorrhage. Multivariable binary logistic regression was used to analyze the association between hypomagnesemia and hemorrhagic outcomes, adjusted for age, diabetes mellitus (DM), Alberta Stroke Program Early CT Score (ASPECTS), baseline National Institutes of Health Stroke Scale (NIHSS), and intravenous thrombolysis administration. Nonlinear models (restricted cubic splines and log‐spline models) were applied to explore the relationship between magnesium levels and the risk of HT or PH. Results Among 1,546 patients screened, 1,311 met inclusion criteria. Median age was 69 years (IQR 59‐80), median NIHSS 17 (IQR 13‐21), and median ASPECTS 8 (IQR 7‐9). IV thrombolysis was administered in 34.5%. Median magnesium was 1.8 mEq/L (IQR 1.6‐1.9), with 110 patients (8.4%) classified as hypomagnesemic. Overall, 628 patients (48.0%) developed HT, including 121 (9.2%) with PH. ROC analysis demonstrated poor discriminatory value for hypomagnesemia: AUC 0.49 for HT and 0.51 for PH (Figure 1). Continuous magnesium levels yielded similarly poor performance (AUC 0.50 for HT, 0.49 for PH). In adjusted logistic regression, hypomagnesemia was not associated with risk of HT (aOR 0.83, 95% CI 0.55‐1.24; p=0.36) or PH (aOR 1.27, 95% CI 0.67‐2.40; p=0.47). Nonlinear spline models revealed no significant association between serum magnesium and hemorrhagic risk. Conclusion Baseline serum magnesium levels were not associated with risk of intraparenchymal hemorrhage following MT for anterior circulation LVO stroke. Although magnesium demonstrates hemostatic effects in experimental settings, these findings did not translate into clinically measurable protection against hemorrhage after MT. Future work should evaluate ionized magnesium, serial level monitoring, and individualized thresholds, leveraging nonlinear modeling to refine patient risk stratification and potential therapeutic interventions. image
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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 | 0.003 |
| 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) | 0.002 | 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".