## Protocol Section
### Identification Module
**NCT ID:** NCT06423547

**Brief Title:** 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

#### Organization Study ID Info
**ID:** 62376168
#### Organization
**Class:** OTHER
**Full Name:** Xuanwu Hospital, Beijing
### Status Module
#### Completion Date
**Date:** 2027-12-31

**Type:** ESTIMATED

#### Expanded Access Info
#### Last Update Post Date
**Date:** 2024-05-30

**Type:** ACTUAL

**Last Update Submit Date:** 2024-05-29

**Overall Status:** NOT_YET_RECRUITING

#### Primary Completion Date
**Date:** 2027-12-31

**Type:** ESTIMATED

#### Start Date
**Date:** 2024-06-15

**Type:** ESTIMATED

**Status Verified Date:** 2024-03

#### Study First Post Date
**Date:** 2024-05-21
**Type:** ACTUAL
**Study First Submit Date:** 2024-05-15
**Study First Submit QC Date:** 2024-05-15
### Sponsor Collaborators Module

#### Collaborators

**Class:** UNKNOWN

**Name:** Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences

#### Lead Sponsor

**Class:** OTHER

**Name:** Xuanwu Hospital, Beijing

#### Responsible Party

**Type:** SPONSOR

### Oversight Module

**Is FDA Regulated Device:** False

**Is FDA Regulated Drug:** False

**Is US Export:** False

**Oversight Has DMC:** False

### Description Module

**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.

### Conditions Module

**Conditions:**

- Postoperative Delirium

- Postoperative Neurocognitive Disorder

- Surgery

**Keywords:**

- postoperative delirium;

- postoperative neurocognitive disorder;

- risk prediction model;

- artificial intelligence;

- evolutionary neural network

### Design Module

#### Design Info

**Observational Model:** COHORT

**Time Perspective:** PROSPECTIVE

#### Enrollment Info

**Count:** 10000

**Type:** ESTIMATED

**Patient Registry:** True

**Study Type:** OBSERVATIONAL

**Target Duration:** 1 Year

### Arms Interventions Module

#### Arm Group 1

**Description:** 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

**Intervention Names:**

- Other: no intervention

**Label:** postoperative delirium(POD) and postoperative neurocognitive disorder(pNCD)

### Interventions

#### Intervention 1

**Arm Group Labels:**

- postoperative delirium(POD) and postoperative neurocognitive disorder(pNCD)

**Description:** this is an observation study,no intervention

**Name:** no intervention

**Type:** OTHER

### Outcomes Module

#### Primary Outcomes

**Description:** 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.

**Measure:** Screening for risk factors of perioperative cognitive dysfunction

**Time Frame:** 2024.4.1-2027.12.31

**Description:** 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

**Measure:** Establish a prediction system for adverse brain function events

**Time Frame:** 2025.1.1-2027.12.31

### Eligibility Module

**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

**Maximum Age:** 100 Years

**Minimum Age:** 65 Years

**Sampling Method:** PROBABILITY_SAMPLE

**Sex:** ALL

**Standard Ages:**

- OLDER_ADULT

**Study Population:** Patients 65\~100 years of age who have undergone surgical anesthesia

### Contacts Locations Module

#### Central Contacts

**Contact 1:**

**Email:** zhaoalei@sina.com

**Name:** lei zhao

**Phone:** +8613811035886

**Role:** CONTACT

**Contact 2:**

**Email:** 935496838@qq.com

**Name:** xia li li

**Phone:** +86818810616341

**Role:** CONTACT

#### Overall Officials

**Official 1:**

**Affiliation:** xuanwu hospital of capital medical university,Beijing

**Name:** lei zhao

**Role:** STUDY_CHAIR

**Official 2:**

**Affiliation:** Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences

**Name:** yong yang

**Role:** PRINCIPAL_INVESTIGATOR

**Official 3:**

**Affiliation:** xuanwu hospital of capital medical university,Beijing

**Name:** yi an

**Role:** PRINCIPAL_INVESTIGATOR

**Official 4:**

**Affiliation:** xuanwu hospital of capital medical university,Beijing

**Name:** xia li li

**Role:** PRINCIPAL_INVESTIGATOR

**Official 5:**

**Affiliation:** xuanwu hospital of capital medical university,Beijing

**Name:** yang liu

**Role:** PRINCIPAL_INVESTIGATOR

**Official 6:**

**Affiliation:** xuanwu hospital of capital medical university,Beijing

**Name:** yi shu yang

**Role:** PRINCIPAL_INVESTIGATOR

### IPD Sharing Statement Module

**IPD Sharing:** NO

### References Module

#### 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.

**PMID:** 34962902

**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.

**PMID:** 31185942

## Derived Section

### Condition Browse Module - Ancestors

- ID: D000003221

- Term: Confusion

- ID: D000019954

- Term: Neurobehavioral Manifestations

- ID: D000009461

- Term: Neurologic Manifestations

- ID: D000009422

- Term: Nervous System Diseases

- ID: D000001523

- Term: Mental Disorders

- ID: D000003072

- Term: Cognition Disorders

- ID: D000011183

- Term: Postoperative Complications

- ID: D000010335

- Term: Pathologic Processes

### Condition Browse Module - Browse Branches

- Abbrev: BXM

- Name: Behaviors and Mental Disorders

- Abbrev: All

- Name: All Conditions

- Abbrev: BC10

- Name: Nervous System Diseases

- Abbrev: BC23

- Name: Symptoms and General Pathology

### Condition Browse Module - Browse Leaves

- ID: M29705

- Name: Cognitive Dysfunction

- Relevance: HIGH

- As Found: Cognitive Dysfunction

- ID: M6894

- Name: Delirium

- Relevance: HIGH

- As Found: Delirium

- ID: M21836

- Name: Neurocognitive Disorders

- Relevance: HIGH

- As Found: Neurocognitive Disorders

- ID: M772

- Name: Emergence Delirium

- Relevance: HIGH

- As Found: Postoperative Delirium

- ID: M6446

- Name: Confusion

- Relevance: LOW

- As Found: Unknown

- ID: M21826

- Name: Neurobehavioral Manifestations

- Relevance: LOW

- As Found: Unknown

- ID: M12404

- Name: Neurologic Manifestations

- Relevance: LOW

- As Found: Unknown

- ID: M4815

- Name: Mental Disorders

- Relevance: LOW

- As Found: Unknown

- ID: M14473

- Name: Psychotic Disorders

- Relevance: LOW

- As Found: Unknown

- ID: M6301

- Name: Cognition Disorders

- Relevance: LOW

- As Found: Unknown

- ID: M14065

- Name: Postoperative Complications

- Relevance: LOW

- As Found: Unknown

### Condition Browse Module - Meshes

- ID: D000003693

- Term: Delirium

- ID: D000071257

- Term: Emergence Delirium

- ID: D000060825

- Term: Cognitive Dysfunction

- ID: D000019965

- Term: Neurocognitive Disorders

### Misc Info Module

- Version Holder: 2024-05-31
