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Record W4392478722 · doi:10.1161/svin.03.suppl_2.040

Abstract 040: Hemorrhagic Conversion Patterns After Transition of Stroke Thrombolysis from Alteplase to Tenecteplase; Real‐World Experience

2023· article· en· W4392478722 on OpenAlexaff
Mohamad Ezzeldin, Courtney Hill, Eryn Percenti, Ali Kerro, Adam Delora, Juan Santos, Hamza Saei, Lisa Greco, Rime Ezzeldin, Mohammad El‐Ghanem, Yazan J. Alderazi, Yana Kim, Cathleen Poitevint, Osman Mir

Bibliographic record

VenueStroke Vascular and Interventional Neurology · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsCARE Canada
Fundersnot available
KeywordsTenecteplaseThrombolysisMedicineStroke (engine)CardiologyInternal medicineAcute strokeTissue plasminogen activatorEngineeringMyocardial infarctionMechanical engineering

Abstract

fetched live from OpenAlex

Introduction Importance:Tenecteplase (TNK) use is more prevalent as the thrombolytic drug of choice for acute ischemic stroke (AIS), given its ease of use with results from randomized trials showing non‐ inferiority and comparable safety to Alteplase (tPA). However, there is conflicting data in terms of intracranial hemorrhage risk. Objective: We are reporting the rate of symptomatic intracranial hemorrhage(sICH) in TNK and tPA treated stroke populations across two large hospital systems. Methods Design: Retrospective cohort observational study. Data was collected from April 1, 2022 through March 29, 2023. Setting: Data was collected from 15 stroke centers: 10 primary and 5 comprehensive stroke centers in Texas. Participants: Inclusion criteria: 18 years or older, suspected to have an AIS were eligible to receive thrombolytic therapy, and received either IV TNK or tPA at the standard dose. A total of 431 patients were included. 216 patients received alteplase and 215 patients received tenecteplase. Exposure: Data was collected 90 days before and 90 days after the stroke center changed from tPA to TNK. Main Outcomes: The primary endpoint was to compare the incidence of sICH according to SITS‐MOST/ECASS‐3 criteria in the tPA and TNK groups. Secondary endpoints included the radiographic pattern of hemorrhagic conversion according to the Heidelberg bleeding classification (HBc). Results A total of 431 patients; half of them had been administered Alteplase (n=216) and the other half had Tenecteplase (n=215). Approximately half of them were females 110 (51%) for alteplase and 117 (54%) for Tenecteplase. Almost 2/3 of the study population never smoked; 66% for alteplase 64% for Tenecteplase. Majority of the patient population got thrombolytic therapy within 3 hours 174 (81%) for alteplase versus 176 (82%) for Tenecteplase. 34 patients (15%) in the alteplase group as compared to 26 patients (12%) in the Tenecteplase group had endovascular thrombectomy attempted. 7 patients in the tPA group (3.2%) and 14 patients (6.5%) in the TNK group had sICH. An increase in the NIHSS on arrival (p=0.048) was a statistically significant predictor of sICH. A two sample proportion test on TNK produced a statistically significant increase in Heidelberg Bleed class 3 (HBc3) (p=0.040) over tPA. Conclusion We observed increased cases of bleeding associated with TNK administration with statistically significant increase in the HBc3 when compared to patients who received tPA. Suggested mechanisms of bleeding are hemorrhagic conversion in clinically silent infarcts, and contusions underlying the lesions. These findings suggest a potential need to reevaluate the criteria for administering TNK to patients. Larger studies are required to confirm this data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.265
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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