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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

1533 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-07-06

Study Completion Date

2028-12-31

Brief Summary

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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.

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Detailed Description

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Conditions

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Acute Hemorrhagic Stroke

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Patients with acute hemorrhagic cerebrovascular disease

No interventions assigned to this group

Eligibility Criteria

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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.