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Using machine learning models to advance prediction of massive transfusion risk in liver transplantation: a multicenter cohort study

Using machine learning models to advance prediction of massive transfusion risk in liver transplantation: a multicenter cohort study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2100054457
Enrollment
Unknown
Registered
2021-12-17
Start date
2019-03-01
Completion date
Unknown
Last updated
2022-11-20

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

Conditions

None listed

Interventions

massive transfusion group and non-massive transfusion group:none

Sponsors

The Third Xiangya Hospital of Central South University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: From March 2014 to November 2021, all patients who received liver transplants at the Second Xiangya Hospital of Central South University, the Third Xiangya Hospital, and the Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine.

Exclusion criteria

Exclusion criteria: 1. Receive preventive interventions (such as preventive platelet transfusions) before surgery; 2. Living donor liver transplantation; 3. Retransplantation patients; 4. Less than 18 years old; 5. The data loss rate exceeds 20%.

Design outcomes

Primary

MeasureTime frame
massive blood transfusion;area under the receiver operating characteristic curve;—

Countries

China

Contacts

Public ContactGui Rong
guirong@csu.edu.cn+86 13975199279

Outcome results

None listed

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