Ignite Creation Date:
2025-12-24 @ 11:43 PM
Ignite Modification Date:
2025-12-25 @ 9:34 PM
Study NCT ID:
NCT06870851
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-03-11
First Post:
2025-03-03
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Predicting Platelet Count From Viscoelastic Testing
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D013921', 'term': 'Thrombocytopenia'}], 'ancestors': [{'id': 'D001791', 'term': 'Blood Platelet Disorders'}, {'id': 'D006402', 'term': 'Hematologic Diseases'}, {'id': 'D006425', 'term': 'Hemic and Lymphatic Diseases'}, {'id': 'D000095542', 'term': 'Cytopenia'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2024-10-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-03', 'completionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-03-08', 'studyFirstSubmitDate': '2025-03-03', 'studyFirstSubmitQcDate': '2025-03-08', 'lastUpdatePostDateStruct': {'date': '2025-03-11', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-03-11', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-04', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Predicition of platelet conentration from ROTEM measurements using machine learning', 'timeFrame': '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.', 'description': '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.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Thrombocytopenia']}, 'descriptionModule': {'briefSummary': '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.\n\nTherefore, 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).'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '100 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'surgical patients in the operating room and the intensive care of participating centers', 'eligibilityCriteria': 'Inclusion Criteria:\n\n* ROTEM measurement and platelet count measurement within 3 hours.\n\nExclusion Criteria:\n\n* under 18 Years\n* more than 3 hours between ROTEM and platelet count measurement'}, 'identificationModule': {'nctId': 'NCT06870851', 'briefTitle': 'Predicting Platelet Count From Viscoelastic Testing', 'organization': {'class': 'OTHER', 'fullName': 'Kepler University Hospital'}, 'officialTitle': 'Machine Learning Based Prediction of Platelet Concentration From ROTEM Measurements', 'orgStudyIdInfo': {'id': 'plateletprediction'}}, 'contactsLocationsModule': {'locations': [{'city': 'Linz', 'country': 'Austria', 'facility': 'Universitätsklinik für Anästhesie und Intensivmedizin', 'geoPoint': {'lat': 48.30639, 'lon': 14.28611}}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Kepler University Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}