Markers of Increased Risk of Intracerebral Hemorrhage After Intravenous Recombinant Tissue Plasminogen Activator Therapy for Acute Ischemic Stroke in Clinical Practice
Bibliographic record
Abstract
BACKGROUND: Intravenous recombinant tissue plasminogen activator (rtPA) is an effective therapy for acute ischemic stroke, but it is associated with risk of intracerebral hemorrhage (ICH). Our aim was to identify, in a large cohort of patients, readily available baseline factors that are associated with thrombolysis-related ICH. METHODS AND RESULTS: In a multicenter retrospective and prospective investigation of individual data from 1205 patients treated in routine clinical practice with intravenous rtPA within 3 hours of stroke symptom onset, 72 patients (6%) developed symptomatic ICH and 86 additional patients (7%) had asymptomatic ICH identified on a routine follow-up CT. In analyses based on clinical variables alone, the main attributes associated with ICH were a history of diabetes mellitus and cardiac disease, increasing stroke severity, advancing age, use of antiplatelet agents other than aspirin before stroke onset, and elevated pretreatment mean blood pressure. In additional analyses that incorporated baseline CT and laboratory findings (in a subset of patients), the main associations were early ischemic CT changes, in particular if exceeding one third of middle cerebral artery territory; increasing stroke severity; diabetes mellitus or elevated serum glucose; and lower platelet counts. Final independent attributes associated with parenchymatous hematoma, defined by purely radiologically based criteria, were similar to those of symptomatic ICH. CONCLUSIONS: Readily available factors can identify acute ischemic stroke patients at high and low risk for rtPA-related ICH. These factors require confirmation in a prospective cohort before clinical implementation.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 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".