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

BACKGROUNDS: Haematoma 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. METHODS: The 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 REGISTRATION: This study was registered at the Clinical Trials under registry number NCT06242938. Registered on Feb 2, 2024. https://clinicaltrials.gov/study/NCT06242938.

Authors

Institutions

Publication Details

Journal
Trials
Published
2026-06-12
DOI
https://doi.org/10.1186/s13063-026-09833-x
Primary Topic
Intracerebral and Subarachnoid Hemorrhage Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Xu Qin, Kaijiang Kang, H W Li, Xingquan Zhao et al.
Trials
Intracerebral and Subarachnoid Hemorrhage Research
article

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

Xu Qin, Kaijiang Kang, H W Li, Xingquan Zhao, Anxin Wang, Lei Wu, Shiyu Shan, Zixiao Li, Na Li, Xia Meng, Wanxing Ye
article en

Abstract

BACKGROUNDS: Haematoma 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. METHODS: The 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 REGISTRATION: This study was registered at the Clinical Trials under registry number NCT06242938. Registered on Feb 2, 2024. https://clinicaltrials.gov/study/NCT06242938.

Trials
Capital Medical University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Beijing Tian Tan Hospital (CN), National Clinical Research Center for Digestive Diseases (CN), Beijing Academy of Artificial Intelligence (CN)
National Natural Science Foundation of China, Organization Department of the Communist Party of China Beijing Municipal Committee, Beijing Nova Program, National Key Research and Development Program of China
Good health and well-being
Openalex Percentile: Top 6%
Intracerebral and Subarachnoid Hemorrhage Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.