Clinical trial NCT06423547: Risk Warning Model of Postoperative Delirium and Long-term Cognitive Dysfunction in Elderly Patients
Official title: Risk Warning Model of Postoperative Delirium and Long-term Cognitive Dysfunction in Elderly Patients Based on Autonomous Evolutionary Neural Network Algorithm
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Postoperative Delirium
Postoperative Neurocognitive Disorder
Surgery
Keywords: postoperative delirium;
postoperative neurocognitive disorder;
risk prediction model;
artificial intelligence;
evolutionary neural network
Lead sponsor: Xuanwu Hospital, Beijing
Sponsor class: OTHER
Collaborators: name: Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences
First posted: 2024-05-21
Enrollment: 10000
Sex: ALL
Minimum age (years): 65
Maximum age (years): 100
Standard ages: OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients 65\~100 years of age who have undergone surgical anesthesia

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 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Screening for risk factors of perioperative cognitive dysfunction
timeFrame: 2024.4.1-2027.12.31
measure: Establish a prediction system for adverse brain function events
timeFrame: 2025.1.1-2027.12.31
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Xuanwu Hospital, Capital Medical University
Study officials:
affiliation: xuanwu hospital of capital medical university,Beijing
name: lei zhao
role: STUDY_CHAIR
affiliation: Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences
name: yong yang
role: PRINCIPAL_INVESTIGATOR
affiliation: xuanwu hospital of capital medical university,Beijing
name: yi an
role: PRINCIPAL_INVESTIGATOR
affiliation: xuanwu hospital of capital medical university,Beijing
name: xia li li
role: PRINCIPAL_INVESTIGATOR
affiliation: xuanwu hospital of capital medical university,Beijing
name: yang liu
role: PRINCIPAL_INVESTIGATOR
affiliation: xuanwu hospital of capital medical university,Beijing
name: yi shu yang
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.