Back

Low-Dose Tenecteplase for Acute Ischemic Stroke Treatment in Aging Patients DATE-AGING Trial: Rationale and Design

Zhou, Z.; Hou, X.; Mi, S.; Chen, X.; He, Y.; Chen, L.; Liu, Q.; Wu, J.; Lv, T.; Kong, L.; Xu, R.; Wu, M.; Wang, D.

2026-10-11 neurology
10.64898/2026.10.08.26361416 medRxiv
Show abstract

Abstract Background: Despite the high stroke burden in the elderly, their thrombolysis utilization is suboptimal partly due to heightened hemorrhagic risk. Observational data suggest that low-dose thrombolysis may offer comparable efficacy to standard-dose with a trend toward reduced symptomatic intracranial hemorrhage (sICH), yet high-level evidence for low-dose thrombolysis in this broad population remains absent. Objective: To evaluate the safety and efficacy of low-dose intravenous tenecteplase thrombolysis in elderly stroke patients within 4.5 hours of ischaemic stroke onset. Methods and design: The DATE-AGING trial is a multicenter, prospective, randomized, open-label, blinded-end point (PROBE) non-inferiority study. Eligible participants are elderly patients (aged [≥]70 years)with acute ischemic stroke who meet current guideline recommendations for intravenous thrombolysis within 4.5 hours of symptom onset will be randomly assigned to Low-dose Tenecteplase group (0.175 mg/kg, max 17.5 mg) or Standard-dose Tenecteplase group (0.25 mg/kg, max 25 mg). The primary outcome was excellent functional outcome, defined as a mRS 0-1 assessed at 90-day post-randomization. Safety outcomes included sICH within 36 hours and mortality at 90 days. Discussions: The DATE-AGING trial is intended to provide high-quality evidence for low-dose tenecteplase intravenous thrombolysis in elderly patients with acute ischemic stroke. Trial registry number: NCT07294209(https://clinicaltrials.gov/study/07294209). Keywords: elderly patients, acute ischemic stroke, intravenous thrombolysis, low-dose tenecteplase

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.