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An Algorithm for Predicting Blood Loss and Transfusion Risk in Fast Track Total Hip Arthroplasty

An Algorithm for Predicting Blood Loss and Transfusion Risk in Fast Track Total Hip Arthroplasty

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT02750852
Enrollment
124
Registered
2016-04-26
Start date
2016-04-30
Completion date
2016-05-31
Last updated
2016-04-26

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

Conditions

Blood Loss, Total Hip Replacement

Brief summary

Aim of these study is to create an algorithm, using data from patients that undergoing total hip arthroplasty, to predict blood loss after surgery and permit a safe domestic discharge.

Detailed description

Two different cohorts of patients undergoing total hip arthroplasty will be analyzed to obtain data about hemoglobin trends. The first group will be used to obtain the algorithm, the second one to validate results of the first group, determining sensitivity and specificity.

Interventions

PROCEDURETotal hip arthroplasty, blood loss analysis

Data analysis about blood loss in total hip replacement

Sponsors

Azienda Ospedaliera Bolognini di Seriate Bergamo
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
CROSS_SECTIONAL

Eligibility

Sex/Gender
ALL
Healthy volunteers
No

Inclusion criteria

* Patients with hip arthritis

Exclusion criteria

* ASA 4 - 5 * Active treatment with warfarin * Coagulation disorders

Design outcomes

Primary

MeasureTime frameDescription
Create an algorithm to predict blood loss after total hip arthroplasty3 monthsData from the fist cohort of patients have been collected and analyzed to obtain an algorithm describing the trends of hemoglobin values after total hip arthroplasty. These algorithm was after applied to patients of a second cohort of consecutive patients undergoing total hip arthroplasty to determine specificity and sensitivity.

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026