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

Risk Warning Model of Postoperative Delirium and Long-term Cognitive Dysfunction in Elderly Patients Based on Autonomous Evolutionary Neural Network Algorithm

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.

Study Overview

• Status: Recruiting
• Conditions: Postoperative Delirium; Surgery; Postoperative Neurocognitive Disorder
• Intervention / Treatment: Other: no intervention

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 team's 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 Type: Observational
• Enrollment (Estimated): 10000

Contacts and Locations

• Study Contact: Name: lei zhao; Phone Number: +8613811035886; Email: zhaoalei@sina.com
• Study Contact Backup: Name: xia li li; Phone Number: +86818810616341; Email: 935496838@qq.com

Study Locations

• China
  • Beijing, China, 100053
    • Recruiting
    • Xuanwu Hospital, Capital Medical University
    • Contact: Lei Zhao, Doctor's; Phone Number: +8613811035886; Email: zhaoalei@sina.com
    • Contact: Lixia Li, Master's; Phone Number: +86818810616341; Email: 935496838@qq.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Patients 65~100 years of age who have undergone surgical anesthesia

Description

Inclusion Criteria:

• Patients ≥65 years of age who have undergone surgical anesthesia; Sign informed consent

Exclusion Criteria:

• Inability to complete cognitive function assessment; Illiteracy, hearing impairment or visual impairment; He has a history of epilepsy, depression, schizophrenia, Alzheimer's disease and other psychiatric and neurological diseases

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

postoperative delirium(POD) and postoperative neurocognitive disorder(pNCD); Delirium (CAM scale ) was assessed 7 days after surgery and divided into POD and non-POD groups; one of the above scenarios indicated postoperative delirium;The patients in the POD group were evaluated for cognitive function at 1 month and 12 months after surgery to determine whether pNCD occurred. The patients in the POD group were further divided into pNCD subgroup and non-PNCD subgroup, and EEG data collection and fMRI scanning were performed

Other: no intervention; this is an observation study,no intervention

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Screening for risk factors of perioperative cognitive dysfunction	The feature selection technique in artificial intelligence was used to screen and analyze data from a large dataset of clinical care after fusion The risk factors with the highest probability of PND occurrence can be screened from a large number of characteristics,By screening the risk factors that have the highest correlation with the probability of POD occurrence, combined with the comparison of fMRI imaging data of different groups of large sample size POD patients with long-term conversion to pNCD group and non-PNCD group, the brain network mechanism and perioperative high risk factors of POD conversion to long-term cognitive dysfunction were further explored.	2024.4.1-2027.12.31
Establish a prediction system for adverse brain function events	The monitoring data of surgical patients contains a large amount of medical information, and the analysis and modeling of the data can provide effective early warning and intervention. The project intends to adopt EEG time-frequency feature extraction and analysis, EEG micro-state analysis, and brain network analysis, and adopt feature fusion technology to fuse various features into unified features of patients. On this basis, a prediction model of adverse brain function events based on domain adaptation algorithm was constructed to realize real-time tracking, early diagnosis and early warning of postoperative delirium and long-term cognitive dysfunction in elderly patients	2025.1.1-2027.12.31

Collaborators and Investigators

• Sponsor: Xuanwu Hospital, Beijing
• Collaborators: Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences
• Investigators: Study Chair: lei zhao, xuanwu hospital of capital medical university,Beijing; Principal Investigator: yong yang, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences; Principal Investigator: yi an, xuanwu hospital of capital medical university,Beijing; Principal Investigator: xia li li, xuanwu hospital of capital medical university,Beijing; Principal Investigator: yang liu, xuanwu hospital of capital medical university,Beijing; Principal Investigator: yi shu yang, xuanwu hospital of capital medical university,Beijing

Publications and helpful links

General Publications

• Patel A, Zhang M, Liao G, Karkache W, Montroy J, Fergusson DA, Khadaroo RG, Tran DTT, McIsaac DI, Lalu MM. A Systematic Review and Meta-analysis Examining the Impact of Age on Perioperative Inflammatory Biomarkers. Anesth Analg. 2022 Apr 1;134(4):751-764. doi: 10.1213/ANE.0000000000005832.
• An Y, Zhao L, Wang T, Huang J, Xiao W, Wang P, Li L, Li Z, Chen X. Preemptive oxycodone is superior to equal dose of sufentanil to reduce visceral pain and inflammatory markers after surgery: a randomized controlled trail. BMC Anesthesiol. 2019 Jun 11;19(1):96. doi: 10.1186/s12871-019-0775-x.

Study record dates

Study Major Dates

• Study Start (Actual): July 30, 2024
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027

Study Registration Dates

• First Submitted: May 15, 2024
• First Submitted That Met QC Criteria: May 15, 2024
• First Posted (Actual): May 21, 2024

Study Record Updates

• Last Update Posted (Actual): April 3, 2025
• Last Update Submitted That Met QC Criteria: March 30, 2025
• Last Verified: March 1, 2025

More Information

Terms related to this study

• Keywords: postoperative delirium;; postoperative neurocognitive disorder;; risk prediction model;; artificial intelligence;; evolutionary neural network
• Additional Relevant MeSH Terms: Neurologic Manifestations; Nervous System Diseases; Mental Disorders; Postoperative Complications; Pathologic Processes; Confusion; Neurobehavioral Manifestations; Cognition Disorders; Emergence Delirium; Delirium; Cognitive Dysfunction; Neurocognitive Disorders
• Other Study ID Numbers: 62376168

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No