Clinical trial NCT06998082: AI-Driven Early Warning System for Perioperative Risks in Acute Hemorrhagic Stroke
Official title: A Large Language Model-Driven Multimodal Early Warning Platform for Perioperative Complications in Acute Hemorrhagic Cerebrovascular Disease
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Acute Hemorrhagic Stroke
Keywords: Perioperative Complications
Artificial intelligence in Medicine
Acute hemorrhagic stroke
Lead sponsor: Beijing Tiantan Hospital
Sponsor class: OTHER
First posted: 2025-05-31
Enrollment: 1533
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.


Eligibility criteria: 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.

Primary outcomes:
measure: 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.
timeFrame: Within 30 days after surgery
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Beijing Tiantan Hospital
state: Beijing
Study officials:
References: