AI-Driven Early Warning System for Perioperative Risks in Acute Hemorrhagic Stroke

NCT06998082 | Not Yet Recruiting

• 1 other identifier: 20250401
• Study Type: observational
• Target: 1,533
• Locations: 1 country
• Sites: 1

Brief Summary

Acute hemorrhagic cerebrovascular disease is a life-threatening condition characterized by sudden onset, rapid progression, multiple complications, poor prognosis, and high mortality. It presents a significant public health burden. During surgical interventions, precise risk stratification and effective perioperative management are crucial to mitigating intraoperative and postoperative complications, optimizing disease diagnosis, guiding severity assessment, and refining anesthesia strategies. Continuous real-time evaluation and dynamic perioperative adjustments are essential to minimize the influence of institutional variability and individual clinician-dependent decision-making. By harnessing big data-driven, evidence-based medical approaches, clinicians can enhance diagnostic accuracy and therapeutic precision, addressing a critical challenge in reducing morbidity and mortality in this patient population. This study aims to develop a comprehensive multimodal perioperative database and leverage large language models (LLMs) for the efficient extraction of structured demographic and clinical data throughout the perioperative course. By integrating real-time hemodynamic monitoring parameters, the investigators seek to elucidate the relationship between perioperative hemodynamic patterns and the incidence of postoperative complications affecting major organ systems, including the brain, heart, kidneys, and lungs. The ultimate goal is to construct a multimodal fusion early-warning model capable of real-time, simultaneous prediction of multiple perioperative complications. This AI-driven platform will function as a risk stratification and alert system for organ-specific perioperative complications in patients with acute hemorrhagic cerebrovascular disease. By providing evidence-based insights for optimized perioperative management-encompassing early warning mechanisms, diagnostic support, and individualized therapeutic strategies-the system aims to improve clinical outcomes, reduce perioperative morbidity, and lower overall mortality.

Conditions

Acute Hemorrhagic Stroke

Keywords

Perioperative Complications; Artificial intelligence in Medicine; Acute hemorrhagic stroke

Outcome Measures

Primary Outcomes (1)

• The primary outcome measures were postoperative complications involving the neurological, cardiac, pulmonary, and renal systems in patients with acute hemorrhagic cerebrovascular disease following surgical interventions.

  Within 30 days after surgery

Study Arms (1)

Patients with acute hemorrhagic cerebrovascular disease

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The investigators plan to recruit patients aged 18-80 years with acute hemorrhagic cerebrovascular disease from a minimum of three tertiary Grade A general hospitals.

You may qualify if:

• Patients aged 18 to 80 years.
• Diagnosis confirmed by preoperative imaging (CT or MRI) of one of the following conditions:
• Intracranial aneurysm
• Arteriovenous malformation (AVM)
• Hemorrhagic moyamoya disease
• Cavernous malformation
• Spontaneous intracerebral hemorrhage
• Undergoing surgery within seven days of symptom onset.

You may not qualify if:

• Patients who decline to provide informed consent.
• Patients enrolled in conflicting clinical studies.

Sponsors & Collaborators

• Beijing Tiantan Hospital: lead

Study Sites (1)

Beijing Tiantan Hospital

Beijing, Beijing Municipality, 100070, China

Location

Central Study Contacts

ming yu Peng, M.D, Ph.D

CONTACT

86-010-59976658; florapym766@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 3 Months
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Deputy chief of Department of Anesthesiology

Study Record Dates

• First Submitted: May 9, 2025
• First Posted: May 31, 2025
• Study Start: July 6, 2025
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2028
• Last Updated: May 31, 2025

Record last verified: 2025-04

Locations

CN (1)