Machine Learning Model for Perioperative Transfusion Prediction
Sponsor
Diskapi Teaching and Research Hospital (Other)
Overall Status
Completed
CT.gov ID
NCT05228548
Collaborator
Hacettepe University (Other), Dokuz Eylul University (Other), Saglik Bilimleri Universitesi (Other), Bulent Ecevit University (Other), Erzincan University (Other), Kahramanmaras Sutcu Imam University (Other), Ufuk University (Other), Istanbul Medeniyet University (Other), Marmara University (Other), Eskisehir Osmangazi University (Other), Inonu University (Other), Mersin University (Other), Istanbul University (Other), Selcuk University (Other), Balikesir University (Other), Trakya University (Other), Necmettin Erbakan University (Other), Ankara University (Other), Suleyman Demirel University (Other), Tobb University of Economics and Technology (Other), Akdeniz University (Other), Uludag University (Other), T.C. ORDU ÜNİVERSİTESİ (Other), Gazi University (Other), TC Erciyes University (Other), Hitit University (Other), Firat University (Other), Karadeniz Technical University (Other), Ondokuz Mayıs University (Other), Yuzuncu Yıl University (Other), Namik Kemal University (Other), Baskent University (Other), Celal Bayar University (Other), Osmaniye Government Hospital (Other)
6,121
1
19
9805.7
Study Details
Study Description
Brief Summary
This study aimed to develop and interpret a machine learning model to predict red blood cell (RBC) transfusion.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
Detailed Description
A dataset from a multicenter study involving 6121 patients underwent elective major surgery was analysed. Data concerning patients who received inappropriate RBC transfusion were excluded. Twenty one perioperative features were used to predict RBC transfusion. The data set was randomly split into train and validation sets (70-30). Decision tree, random forest, k-nearest neighbors, logistic regression, and eXtreme garadient boosting (XGBoost) methods were used for prediction. The area under the curves (AUC) of the receiver operating characteristics curves for the machine learning models used for RBC transfusion prediction were compared.
Study Design
Study Type:
Observational
Actual Enrollment
:
6121 participants
Observational Model:
Other
Time Perspective:
Prospective
Official Title:
Development and Interpretation of a Machine Learning Model for Perioperative Transfusion Prediction
Actual Study Start Date
:
Jan 13, 2022
Actual Primary Completion Date
:
Feb 1, 2022
Actual Study Completion Date
:
Feb 1, 2022
Outcome Measures
Primary Outcome Measures
- Number of patients received Red blood cell transfusion [Perioperative period]
Number of patients received Red blood cell transfusion
- The area under the curve [Perioperative period]
The the area under the curve of the receiver operating characteristics curves
Eligibility Criteria
Criteria
Ages Eligible for Study:
18 Years
to 100 Years
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
No
Inclusion Criteria:
-
Adult
-
Underwent major elective surgery
Exclusion Criteria:
-
Pediatric patients
-
Emergency cases
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Dilek D Unal | Ankara | Turkey | 06110 |
Sponsors and Collaborators
- Diskapi Teaching and Research Hospital
- Hacettepe University
- Dokuz Eylul University
- Saglik Bilimleri Universitesi
- Bulent Ecevit University
- Erzincan University
- Kahramanmaras Sutcu Imam University
- Ufuk University
- Istanbul Medeniyet University
- Marmara University
- Eskisehir Osmangazi University
- Inonu University
- Mersin University
- Istanbul University
- Selcuk University
- Balikesir University
- Trakya University
- Necmettin Erbakan University
- Ankara University
- Suleyman Demirel University
- Tobb University of Economics and Technology
- Akdeniz University
- Uludag University
- T.C. ORDU ÜNİVERSİTESİ
- Gazi University
- TC Erciyes University
- Hitit University
- Firat University
- Karadeniz Technical University
- Ondokuz Mayıs University
- Yuzuncu Yıl University
- Namik Kemal University
- Baskent University
- Celal Bayar University
- Osmaniye Government Hospital
Investigators
- Principal Investigator: Dilek D Unal, Prof, UNIVERSITY OF HEALTH SCIENCES TURKEY DISKAPI YILDIRIM BEYAZIT TRAINING RESEARCH HOSPITAL ANKARA
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.Responsible Party:
DILEK YAZICIOGLU,
Prof. Dr. Dilek Unal,
Diskapi Teaching and Research Hospital
ClinicalTrials.gov Identifier:
NCT05228548
Other Study ID Numbers:
- Machine learning DiskapiTRH
First Posted:
Feb 8, 2022
Last Update Posted:
Mar 8, 2022
Last Verified:
Jan 1, 2022
Individual Participant Data (IPD) Sharing Statement:
No
Plan to Share IPD:
No
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Keywords provided by DILEK YAZICIOGLU,
Prof. Dr. Dilek Unal,
Diskapi Teaching and Research Hospital