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2020–2025: Machine learning models based on real-world data predict impaired oxygenation in elderly patients after hip arthroplasty during the anesthesia recovery period

2020–2025: Machine learning models based on real-world data predict impaired oxygenation in elderly patients after hip arthroplasty during the anesthesia recovery period

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500113707
Enrollment
Unknown
Registered
2025-12-02
Start date
2026-01-01
Completion date
Unknown
Last updated
2025-12-08

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

Conditions

Impaired oxygenation

Interventions

Observation group:None

Sponsors

Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School
Lead Sponsor

Eligibility

Sex/Gender
All
Age
65 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.Patients underwent unilateral hip arthroplasty; 2.ASA II–III patients. 3.>=65 years old. 4.Patients were under general anesthesia. 5.Clinical data were complete.

Exclusion criteria

Exclusion criteria: 1.Patients who had uncontrolled or acutely exacerbated pulmonary disease(e.g, asthma or COPD); 2.Polytrauma with associated fractures or injuries to other body regions or organs; 3.Neoplastic and pathologic fractures; 4.Clinical data were imcomplete.

Design outcomes

Primary

MeasureTime frame
Impaired oxygenation;—

Countries

China

Contacts

Public ContactSun Yu’e

Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School

sunyue@nju.edu.cn+86 139 1384 6977

Outcome results

None listed

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