Predicting Platelet Count From Viscoelastic Testing

Machine Learning Based Prediction of Platelet Concentration From ROTEM Measurements

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06870851

Enrollment

2500

Registered

2025-03-11

Start date

2024-10-01

Completion date

2025-12-31

Last updated

2025-03-11

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

Conditions

Thrombocytopenia

Brief summary

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).

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 100 Years

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

Design outcomes

Primary

Measure	Time frame	Description
Predicition of platelet conentration from ROTEM measurements using machine learning	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.

Countries

Austria

Outcome results