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Prediction model of postoperative complications after total joint arthroplasty based on machine learning

Prediction model of postoperative complications after total joint arthroplasty based on machine learning

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000033951
Enrollment
Unknown
Registered
2020-06-18
Start date
2020-07-01
Completion date
Unknown
Last updated
2020-07-20

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

Conditions

Total joint arthroplasty

Interventions

Gold Standard:Clinical outcomes
Index test:Prediction&#32
model&#32
of&#32
postoperative&#32
complications&#32
after&#32
total&#32
joint&#32
arthroplasty&#32
based&#32
on&#32
machine&#32
learning.

Sponsors

Shandong Provincial Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Patients who underwent total hip or knee arthroplasty in the Department of Joint of Shandong Provincial Hospital from 2010 to 2020.

Exclusion criteria

Exclusion criteria: Patients with inaccessible preoperative assessment data.

Design outcomes

Primary

MeasureTime frame
Blood transfusion;extended postoperative hospital stay;infection;Postoperative complications;SEN, SPE, ACC, AUC of ROC,+PV,-PV;—

Countries

China

Contacts

Public ContactShui Sun

Shandong Provincial Hospital

sunshui1965@foxmail.com+86 13808933938

Outcome results

None listed

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