Predicting Platelet Count From Viscoelastic Testing

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).

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Thrombocytopenia Blood Platelet Disorders Cytopenia Hematologic Diseases Hemic and Lymphatic Diseases

Age range

18 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Universitätsklinik für Anästhesie und Intensivmedizin

Linz, Austria

Who can participate

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

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.

Sponsors and collaborators

Lead sponsor

Kepler University Hospital

Other

Registry information

Official study title

Machine Learning Based Prediction of Platelet Concentration From ROTEM Measurements

Important dates

Study start: 2024
Primary completion: 2025
Study completion: 2025
First posted: Mar 11, 2025
Registry last updated: Mar 11, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Active, Not Recruiting