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Machine learning models of emergence delirium prediction for elderly patients with general anesthesia

Machine learning models of emergence delirium prediction for elderly patients with general anesthesia

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300078314
Enrollment
Unknown
Registered
2023-12-05
Start date
2023-12-05
Completion date
Unknown
Last updated
2023-12-10

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

emergence delirium

Interventions

delirium group vs. non- delirium group:N/A

Sponsors

Taizhou Central Hospital (Taizhou University Hospital)
Lead Sponsor

Eligibility

Sex/Gender
All
Age
65 Years to 105 Years

Inclusion criteria

Inclusion criteria: Age =65 years, planned to undergo elective non-cardiac surgery under general anesthesia and expected operation time =1 hour. For patients who have multiple operations, only the medical records of the first operation will be included.

Exclusion criteria

Exclusion criteria: Dementia, central nervous system disease or mental illness; Long-term use of sedatives or antidepressants; Craniocerebral injury or neurosurgery; Severe hearing or speech impairment and inability to communicate; Have a history of surgery within the last 3 months; Transferred to the intensive care unit after surgery; Refusing to sign informed consent; Incomplete medical records or failed to complete delirium assessment.

Design outcomes

Primary

MeasureTime frame
Incidence of emergence delirium;—

Secondary

MeasureTime frame
The area under the ROC curve;sensitiveness;specificity;accuracy;—

Countries

China

Contacts

Public ContactLu Yufan

Taizhou Central Hospital (Taizhou University Hospital)

864949608@qq.com+86 157 1269 6869

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026