Safety and efficacy of intra-arterial fibrinolytics as adjunct to mechanical thrombectomy: a systematic review and meta-analysis of observational data
Bibliographic record
Abstract
BACKGROUND: Achieving the best possible reperfusion is a key determinant of clinical outcome after mechanical thrombectomy (MT). However, data on the safety and efficacy of intra-arterial (IA) fibrinolytics as an adjunct to MT with the intention to improve reperfusion are sparse. METHODS: We performed a PROSPERO-registered (CRD42020149124) systematic review and meta-analysis accessing MEDLINE, PubMed, and Embase from January 1, 2000 to January 1, 2020. A random-effect estimate (Mantel-Haenszel) was computed and summary OR with 95% CI were used as a measure of added IA fibrinolytics versus control on the risk of symptomatic intracranial hemorrhage (sICH) and secondary endpoints (modified Rankin Scale ≤2, mortality at 90 days). RESULTS: The search identified six observational cohort studies and three observational datasets of MT randomized-controlled trial data reporting on IA fibrinolytics with MT as compared with MT alone, including 2797 patients (405 with additional IA fibrinolytics (100 urokinase (uPA), 305 tissue plasminogen activator (tPA)) and 2392 patients without IA fibrinolytics). Of 405 MT patients treated with additional IA fibrinolytics, 209 (51.6%) received prior intravenous tPA. We did not observe an increased risk of sICH after administration of IA fibrinolytics as adjunct to MT (OR 1.06, 95% CI 0.64 to 1.76), nor excess mortality (0.81, 95% CI 0.60 to 1.08). Although the mode of reporting was heterogeneous, some studies observed improved reperfusion after IA fibrinolytics. CONCLUSION: The quality of evidence regarding peri-interventional administration of IA fibrinolytics in MT is low and limited to observational data. In highly selected patients, no increase in sICH was observed, but there is large uncertainty.
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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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".