Risk Warning Model of Postoperative Delirium and Long-term Cognitive Dysfunction in Elderly Patients

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Conditions studied: Postoperative Delirium, Postoperative Neurocognitive Disorder, Surgery

In brief

The incidence of postoperative delirium in elderly patients is high, which can lead to long-term postoperative neurocognitive disorders. Its high risk factors are not yet clear. At present, there is a lack of early diagnosis and alarm technology for perioperative neurocognitive disorders, which can not achieve early intervention and effective treatment. By artificial intelligence and autonomously evolutionary neural network algorithm, relying on multi-source clinical big data, we explored the use of Bayesian network to optimize the anesthesia decision-making system in enhanced recovery after surgery, and established risk prediction model for perioperative critical events. It is expected that this method will also help to establish a risk prediction model for postoperative delirium and long-term postoperative neurocognitive disorders. This project plans to collect the perioperative sensitive parameters of anesthesia machine, multi-parameter monitor, EEG monitor,fMRI and HIS system, to explore the evolution process of data characteristics by feature fusion.We also plan to quickly screen key perioperative risk characteristics of postoperative delirium from massive clinical data through feature selection, to explore the high risk factors of long-term postoperative neurocognitive disorders developing from postoperative delirium. Finally, with multi-center intelligent analysis,the risk prediction model of postoperative delirium and long-term postoperative neurocognitive disorders will be constructed.

Key facts

Study ID
NCT06423547
Run by
Xuanwu Hospital, Beijing
People needed
10000
Starts
2024-07-30
Expected to finish
2027-12-31
Last updated by the study team
2025-04-03

Who can join

Age: 65 and older, up to 100. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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