ArticleTrials2026
Early intensive antihypertensive treatment in high-risk population of intracerebral haemorrhage expansion identified by artificial intelligence (ARCHES): study protocol for a multicentre randomised controlled trial.
Article in Trials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06242938 (Early Intensive Antihypertensive Treatment in High-risk Population of Intracerebral Hemorrhage Expansion Predicted by Artificial Intelligence), which is not on this map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Early Intensive Antihypertensive Treatment in High-risk Population of Intracerebral Hemorrhage Expansion Predicted by Artificial Intelligence
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundsHaematoma expansion (HE) is a significant factor in poor outcomes following intracerebral haemorrhage (ICH). Studies have suggested that acute intensive antihypertensive treatment could reduce HE. However, the impact of early intensive blood pressure reduction on patients with ICH at high risk of HE remains unclear. Therefore, screening ICH patients for high risk of HE upon admission and initiating early intensive blood pressure reduction could improve their prognosis. In this study we utilise a five-point scoring system integrating a deep learning system based on non-contrast CT imaging and clinical predictors to identify ICH patients at high risk of HE. This study aims to compare the efficacy, safety, and feasibility of early intensive antihypertensive treatment versus standard antihypertensive treatment for patients identified as being at high risk of HE.
methodsThe early intensive antihypertensive treatment in high-risk population of intracerebral haemorrhage expansion predicted by artificial intelligence (ARCHES), is a multicentre, prospective, randomised, open-label, blinded-endpoints clinical trial that will include an estimated 680 participants. ICH patients within 6 h of symptom onset, and at high risk of haematoma expansion with ≥ 3 points on the artificial intelligence-based haematoma expansion 5-point prediction score, will be randomly assigned to receive either intensive antihypertensive treatment (targeting systolic blood pressure control between 130 and 140 mmHg within the first hour of treatment, and maintaining this level for 7 days) or standard antihypertensive treatment (targeting systolic blood pressure control between 140 and 180 mmHg, and maintaining for 7 days). The primary outcome is death or severe disability at 90 days. DISCUSSION: The ARCHES study aims to verify the hypothesis that early intensive blood pressure lowering leads to reduced HE and improved functional outcomes with good safety in ICH patients at high risk of HE.
trial registrationThis study was registered at the Clinical Trials under registry number NCT06242938. Registered on Feb 2, 2024. https://clinicaltrials.gov/study/NCT06242938 .
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.