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Construction and comparison of predictive models for length of stay after total hip arthroplasty: regression model and machine learning analysis

Construction and comparison of predictive models for length of stay after total hip arthroplasty: regression model and machine learning analysis

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2100049061
Enrollment
Unknown
Registered
2021-07-20
Start date
2021-07-12
Completion date
Unknown
Last updated
2022-04-04

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

Conditions

Total Hip Arthroplasty

Interventions

LOS = 12 d group:No

Sponsors

Zhongda Hospital Southeast University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.Elective unilateral total hip arthroplasty; 2.Over 18 years old; 3.American Society of Anesthesiologists (ASA) ? ~ III.

Exclusion criteria

Exclusion criteria: 1. Hip fractures caused by multiple injuries; 2. Perform total hip arthroplasty and femoral internal fixation removal; 3. Patients with severe complications requiring further treatment before surgery (such as heart failure or severe pulmonary infection with hypoxemia before surgery); 4. Incomplete clinical data.

Design outcomes

Primary

MeasureTime frame
Length of stay;—

Countries

China

Contacts

Public ContactQiu Xiaodong

Zhongda Hospital Southeast University

qxdong@hotmail.com+86 13815880193

Outcome results

None listed

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