Ignite Creation Date:
2025-12-26 @ 11:00 AM
Ignite Modification Date:
2025-12-31 @ 11:29 AM
Study NCT ID:
NCT06574906
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-09-03
First Post:
2024-08-14
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Machine Learning Prediction of Parameters of Early Warning Scores in General Wards
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 3000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2025-08-15', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-08', 'completionDateStruct': {'date': '2026-10-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-08-26', 'studyFirstSubmitDate': '2024-08-14', 'studyFirstSubmitQcDate': '2024-08-26', 'lastUpdatePostDateStruct': {'date': '2025-09-03', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2024-08-28', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-10-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) for Prediction of Parameters of Early Warning Scores', 'timeFrame': '2024-10-01 to 2026-10-31', 'description': 'Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) for Prediction of Parameters of Early Warning Scores'}, {'measure': 'Area Under the Curve of the Precision-Recall Curve (AUC-PRC) for Prediction of Parameters of Early Warning Scores', 'timeFrame': '2024-10-01 to 2026-10-31', 'description': 'Area Under the Curve of the Precision-Recall Curve (AUC-PRC) for Prediction of Parameters of Early Warning Scores'}, {'measure': 'F-Beta Score with Beta = 1 (F1-Score) for Prediction of Parameters of Early Warning Scores', 'timeFrame': '2024-10-01 to 2026-10-31', 'description': 'F-Beta Score with Beta = 1 (F1-Score) for Prediction of Parameters of Early Warning Scores'}, {'measure': 'Confusion Matrix for Prediction of Parameters of Early Warning Scores', 'timeFrame': '2024-10-01 to 2026-10-31', 'description': 'Confusion Matrix for Prediction of Parameters of Early Warning Scores'}], 'secondaryOutcomes': [{'measure': "SHapley's Additive exPlanations (SHAP) Values for Prediction Models", 'timeFrame': '2024-10-01 to 2026-10-31', 'description': "SHapley's Additive exPlanations (SHAP) Values for Prediction Models"}, {'measure': 'Prediction of Routine Laboratory Values', 'timeFrame': '2024-10-01 to 2026-10-31', 'description': 'Routine laboratory values measurements collected in routine care; this comprises organ function measures such as liver function tests, and in turn more specifically aspartate-aminotransferase (ASAT), alanine transaminase (ALAT), cholinesterase (CHE). Predictions are made on their future values in the respective units they are measured in.'}, {'measure': 'Prediction of Parameters Measured by Photophlethysmogram (PPG)', 'timeFrame': '2024-10-01 to 2026-10-31', 'description': 'Future values of the photophlethysmogram (PPG) are predicted; this comprises heart rate, respiration rate, peripheral oxygen saturation Predictions are made on their future values in the respective units they are measured in.'}, {'measure': 'Prediction of Medical Emergency Team or Emergency Critical Care Treatment', 'timeFrame': '2024-10-01 to 2026-10-31', 'description': 'In case of deterioration, patients are examined by a medical emergency team (MET) at the ward they are admitted to, if necessary stabilized there or referred to a specialized emergency critical care (ECC) area for further examination and treatment. Both, MET and ECC activation are made on locally established protocols and defined threshold criteria such as the patient at risk-score. Predictions are made as categorical variable, either MET and/or ECC activation happen, or not (yes/no).'}, {'measure': 'Prediction of Unplanned Intensive Care Unit (ICU) Admission', 'timeFrame': '2024-10-01 to 2026-10-31', 'description': 'Intensive care unit (ICU) admission may happen in direct referral from a general ward, via MET referral or after ECC treatment. In either case, this variable will be predicted as a category, i. e. yes/no on ICU admission. An admission in an emergency due to deterioration is always considered unplanned.'}, {'measure': 'Prediction of Electrocardiogram (ECG) Waveform', 'timeFrame': '2024-10-01 to 2026-10-31', 'description': 'Future values of the electrocardiogram (ECG) waveform (millivolts at specific point in time) are predicted.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Patient Safety']}, 'descriptionModule': {'briefSummary': "In the event of illness or injury, patients are medically evaluated and initially treated in acute medical outpatient clinics, emergency rooms and surgeries. If medically indicated, care and treatment can also be provided in hospital. Depending on the severity of the illness and the main medical problem, this care is provided on hospital wards, which are primarily looked after by specific specialist disciplines and assigned to them in the form of clinical departments, for example.\n\nAs part of the inpatient stay, treatment and care is usually provided through ward rounds by the medical staff. However, ward rounds are spot checks of individual measured values at predefined times.\n\nQualified nursing staff carry out the agreed treatment plans and check the patient's general condition several times a day. In contrast to intensive medical monitoring, however, there is no continuous monitoring and therefore an aggravation of a patient's condition is not always immediately apparent. Furthermore, in addition to known complications of existing conditions, new or unexpected complications can also occur.\n\nAlthough non-intensive care monitoring is based on discontinuous monitoring, incidents and complications can sometimes be life-threatening, especially if there is no immediate response to a deterioration in the patient's condition. Even if there are early warning systems such as scores, their ability to react is limited, partly due to the frequency with which they are collected.\n\nIn addition to patient-specific limitations of inpatient monitoring, such as patient cooperation in the sense of self-monitoring, medical limitations, such as the frequency of the survey, there are also economic limitations, such as the availability of staff who can be deployed for more frequent monitoring.\n\nAlthough there are telemedical approaches to monitoring, setting these up is often limited both economically and by the additional training required, for example.\n\nEven if threshold values are (or can be) defined for the measured data (vital signs, laboratory parameters, clinical impression and others), if these are exceeded or not reached, a consequence, e.g. a therapy step, can only be initiated retrospectively. In this situation, a pathophysiological change is already so far advanced that in many cases a compensation mechanism no longer functions adequately and turns into a decompensation situation. In this situation, the affected patients in a hospital ward are potentially in mortal danger.\n\nOne way of averting the dangers described above could be to use a reduced combination of monitoring methods compared to intensive care monitoring. At the same time, the use of artificial intelligence enables the automated evaluation of the collected data and can thus lead to the prediction of changes in parameters, which enables early alerting, i.e. before the occurrence of pathophysiological decompensation."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients treated in general wards.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Treated in general ward between 2024-10-01 and 2026-10-31 at the study center.\n\nExclusion Criteria:\n\n* None.'}, 'identificationModule': {'nctId': 'NCT06574906', 'briefTitle': 'Machine Learning Prediction of Parameters of Early Warning Scores in General Wards', 'organization': {'class': 'OTHER', 'fullName': 'Kepler University Hospital'}, 'officialTitle': 'Machine Learning Prediction of Parameters of Early Warning Scores in General Wards', 'orgStudyIdInfo': {'id': 'AIM-PEW-WAR'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Parameters of Early Warning Scores', 'type': 'OTHER', 'description': 'Parameters of Early Warning Scores'}]}, 'contactsLocationsModule': {'locations': [{'zip': '4020', 'city': 'Linz', 'state': 'Upper Austria', 'country': 'Austria', 'facility': 'Johannes Kepler University, Kepler University Hospital', 'geoPoint': {'lat': 48.30639, 'lon': 14.28611}}], 'overallOfficials': [{'name': 'Thomas Tschoellitsch, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Johannes Kepler University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Kepler University Hospital', 'class': 'OTHER'}, 'collaborators': [{'name': 'RISC Software GmbH', 'class': 'UNKNOWN'}, {'name': 'innovethic eU', 'class': 'UNKNOWN'}, {'name': 'FiveSquare GmbH', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}