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

A Large Language Model-Driven Multimodal Early Warning Platform for Perioperative Complications in Acute Hemorrhagic Cerebrovascular Disease

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06998082

Enrollment

1533

Registered

2025-05-31

Start date

2025-07-06

Completion date

2028-12-31

Last updated

2025-05-31

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Acute Hemorrhagic Stroke

Keywords

Perioperative Complications, Artificial intelligence in Medicine, Acute hemorrhagic stroke

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 80 Years

Healthy volunteers

Inclusion criteria

* 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.

Exclusion criteria

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

Design outcomes

Primary

Measure	Time frame
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

Countries

China

Contacts

Primary Contactming yu Peng, M.D, Ph.D

florapym766@163.com86-010-59976658

Outcome results