Predicting Platelet Count from Viscoelastic Testing

March 8, 2025 updated by: Kepler University Hospital

Machine Learning Based Prediction of Platelet Concentration from ROTEM Measurements

Viscoelastic testing is a highly recommended cornerstone of modern coagulation medicine, reducing transfusion needs. A disadvantage of viscoelastic tests is the impossibility of making a definitive statement about the platelet count.

Therefore, the aim of this retrospective observational study is, on the one hand, to predict the platelet count based on standard ROTEM parameters with the help of several machine learning methods and, on the other hand, to detect a low platelet count ( <100000 ml-1 and < 50000 ml-1).

Study Overview

Status

Active, not recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

2500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Austria
      • Linz, Austria
        • Universitätsklinik für Anästhesie und Intensivmedizin

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

surgical patients in the operating room and the intensive care of participating centers

Description

Inclusion Criteria:

  • ROTEM measurement and platelet count measurement within 3 hours.

Exclusion Criteria:

  • under 18 Years
  • more than 3 hours between ROTEM and platelet count measurement

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Predicition of platelet conentration from ROTEM measurements using machine learning
Time Frame: Obtained ROTEM analyses are the baseline at all four centres and patients will be included if platelets were determined concomitantly within three hours on the same day.
Several machine learning techniques for the prediction of the platelet concentration from ROTEM parameters (regression approach), namely linear regression, Random Forest, neural network, gradient boosting machine (GBM) and adaptive boosting (ADA) will be assessed. Describing the quality of these prediction models, the mean square error (MSE), the root of the mean of the square of errors(RMSE), the mean absolute error (MAE), and the root mean squared logarithmic error (RMSLE), and the coefficient of determination (R2) will be used.
Obtained ROTEM analyses are the baseline at all four centres and patients will be included if platelets were determined concomitantly within three hours on the same day.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Kepler University Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 1, 2024

Primary Completion (Estimated)

April 1, 2025

Study Completion (Estimated)

December 1, 2025

Study Registration Dates

First Submitted

March 3, 2025

First Submitted That Met QC Criteria

March 8, 2025

First Posted (Actual)

March 25, 2025

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

March 8, 2025

Last Verified

March 1, 2025

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • plateletprediction

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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