Lipid Profile, Lipid-lowering Medications, and Intracerebral Hemorrhage After tPA in Get With The Guidelines–Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Symptomatic intracerebral hemorrhage (sICH) after tissue plasminogen activator for acute ischemic stroke is associated with poor outcome. There are conflicting data on sICH risk related to lipid levels and use of lipid-lowering medications. We evaluated whether there are associations between lipid levels, lipid-lowering medications, and sICH in Get With the Guidelines-Stroke. METHODS: We identified acute ischemic stroke patients in the Get With the Guidelines-Stroke data set who were treated with IV tissue plasminogen activator between April 2003 and September 2009 and had complete data on lipid profiles and complications. Potential predictors of sICH were tested in univariate and multivariate analysis. RESULTS: The analysis included 22 216 IV tissue plasminogen activator-treated acute ischemic stroke patients. Overall, 1104 (4.97%) experienced sICH (National Institute of Neurological Disorders and Stroke definition). In univariate analysis, patients with sICH were more often taking antihypertensive, lipid-lowering, and diabetes mellitus medications. There was no relationship between low density lipoprotein or total cholesterol and sICH in univariate analysis. However, the risk of sICH increased with higher high density lipoprotein, 6.1% in Q4 versus 4.7% in Q1, P=0.0013; and lower triglyceride levels, 5.9% in Q1 versus 4.2% in Q4, P<0.0001. In multivariable models, although the high density lipoprotein and triglyceride levels were modestly associated with sICH, low density lipoprotein and total cholesterol were not. Lipid-lowering medications were not independently associated with sICH. CONCLUSIONS: We found that low density lipoprotein and total cholesterol levels are not associated with risk of sICH after tissue plasminogen activator, although higher high density lipoprotein and lower triglyceride levels were modest risk factors. Lipid-lowering medications are not associated with risk of sICH.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".