NCT06870851|Active Not Recruiting
Predicting Platelet Count From Viscoelastic Testing
Machine Learning Based Prediction of Platelet Concentration From ROTEM Measurements
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Machine Learning Based Prediction of Platelet Concentration From ROTEM Measurements
Sponsor study identifier
observational
2,500
1 country
1
Study Start
October 1, 2024
Actual (reported)Primary Completion
April 2025
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
Study Completion
December 2025
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
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).
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
December 2025
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
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Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
October 1, 2024
Actual (reported)March 3, 2025
Actual (reported)March 11, 2025
Actual (reported)April 2025
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
December 2025
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
Predicition of platelet conentration from ROTEM measurements using machine learning
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.
surgical patients in the operating room and the intensive care of participating centers
Universitätsklinik für Anästhesie und Intensivmedizin
Linz, Austria
First Submitted
March 3, 2025
First Posted
March 11, 2025
Study Start
October 1, 2024
Actual (reported)Primary Completion
April 2025
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
The Study of Different Cycles of High-dose Dexamethasone in the Treatment of ITP
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Study Completion
December 2025
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
Last Updated
March 11, 2025
Record last verified: 2025-03
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