Ignite Creation Date:
2024-06-16 @ 11:47 AM
Last Modification Date:
2024-10-26 @ 3:30 PM
Study NCT ID:
NCT06423547
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-05-30
First Post:
2024-05-15
Brief Title:
Risk Warning Model of Postoperative Delirium and Long-term Cognitive Dysfunction in Elderly Patients
Sponsor:
Xuanwu Hospital Beijing
Organization:
Xuanwu Hospital Beijing
Study Overview
Official Title:
Risk Warning Model of Postoperative Delirium and Long-term Cognitive Dysfunction in Elderly Patients Based on Autonomous Evolutionary Neural Network Algorithm
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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 monitorfMRI and HIS system to explore the evolution process of data characteristics by feature fusionWe 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 analysisthe risk prediction model of postoperative delirium and long-term postoperative neurocognitive disorders will be constructed
Detailed Description:
This project intends to collect and identify clinical monitoring data of anesthesia machine multi-parameter monitor and brain function monitor on the basis of the teams previous series of studies on cognitive function protection of elderly patients in perioperative period and the research on tracking and warning of critical illness events and decision support services based on artificial intelligence HIS clinical data and classified and tracked fMRI imaging data were integrated to form a large data set related to perioperative cognitive function of elderly patients Based on pNCD clinical diagnostic information and fMRI imaging diagnostic information a brain adverse event prediction system capable of intelligent extraction of clinical key information and real-time early warning was established by using key technologies such as data quality control real-time collection and identification of multi-source clinical monitoring data and artificial intelligence adverse event prediction
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?:
None