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The utility of machine learning algorithms for the prediction of postoperative complications following hip and knee total joint arthroplasty

Research on perioperative dynamic risk assessment and prediction system based on machine learning

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300069673
Enrollment
Unknown
Registered
2023-03-23
Start date
2023-03-23
Completion date
Unknown
Last updated
2023-05-29

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

Conditions

postoperative mortality and morbidity following surgery

Interventions

Observation group:None

Sponsors

Sichuan University west china hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: This study will collect data from patients who underwent hip and knee arthroplasty in three hospitals, including West China Hospital of Sichuan University, Chengdu Public Health Clinical Medical Center, and Sichuan Provincial Orthopedic Hospital. The inclusion criteria for the cases are as follows: (1) age >= 18 years; (2) received hip and knee arthroplasty surgery; (3) have complete surgical and medical records and postoperative follow-up data.

Exclusion criteria

Exclusion criteria: Exclusion criteria: Patients who cannot comply with postoperative follow-up.

Design outcomes

Secondary

MeasureTime frame
Postoperative acute kidney injury;postoperative pulmonary complications;Moderate to severe postoperative pain;postoperative mortality;ICU admission;length of hospital;cost;postoperative mortality and morbidity;—

Countries

China

Contacts

Public ContactTao Zhu

West China Hospital, Sichuan University

739501155@qq.com18980601552

Outcome results

None listed

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