Ignite Creation Date:
2025-12-25 @ 2:43 AM
Ignite Modification Date:
2025-12-30 @ 7:50 PM
Study NCT ID:
NCT04092933
Status:
UNKNOWN
Last Update Posted:
2023-11-22
First Post:
2019-07-08
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Perioperative Risk Calculator
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D011183', 'term': 'Postoperative Complications'}], 'ancestors': [{'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'CASE_ONLY'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 175559}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2014-05-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-10', 'completionDateStruct': {'date': '2024-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2023-11-21', 'studyFirstSubmitDate': '2019-07-08', 'studyFirstSubmitQcDate': '2019-09-16', 'lastUpdatePostDateStruct': {'date': '2023-11-22', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2019-09-17', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-09-30', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'postoperative complications', 'timeFrame': '30 days', 'description': 'Postoperative complications are classified by means of the Clavien-Dindo-Score.\n\nThe Clavien-Dindo-Score describes classes of severity of postoperative complications:\n\nGrade I: any deviation from the normal postoperative course without the need for pharmacological treatment or surgical, endoscopic and radiological interventions Grade II: requiring pharmacological treatment Grade IIIa: requiring surgical, endoscopic or radiological intervention not under general anesthesia Grade IIIb: requiring surgical, endoscopic or radiological intervention under general anesthesia Grade IVa: single organ dysfunction Grade IVb: multiorgandysfunction Grade V: death of a patient'}], 'secondaryOutcomes': [{'measure': 'in-hospital mortality', 'timeFrame': '30 days', 'description': 'mortality within hospital stay'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['machine learning', 'decision support', 'risk calculator', 'anesthesia'], 'conditions': ['Perioperative/Postoperative Complications']}, 'referencesModule': {'references': [{'pmid': '37046259', 'type': 'DERIVED', 'citation': 'Andonov DI, Ulm B, Graessner M, Podtschaske A, Blobner M, Jungwirth B, Kagerbauer SM. Impact of the Covid-19 pandemic on the performance of machine learning algorithms for predicting perioperative mortality. BMC Med Inform Decis Mak. 2023 Apr 12;23(1):67. doi: 10.1186/s12911-023-02151-1.'}]}, 'descriptionModule': {'briefSummary': 'The aim of this project is to develop a machine-learning model for calculating the risk of postoperative complications. In addition to the data collected during the premedication, the model will include all intraoperative values recorded in the Patient Data Management System (PDMS), which include not only vital and respiratory parameters, but also medication and doses, intraoperative events and times. Postoperative complications are defined according to their severity according to the Clavien-Dindo score (Dindo, Demartines et al., 2004) and are collected from the data available in the health information system (HIS).\n\nThe machine-learning model is created using an extreme-gradient boosting algorithm which has been updated with new data from the year 2021 to ensure accuracy of the model.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients who underwent surgical interventions with general or regional anesthesia at Klinikum rechts der Isar, Munich after the implementation of an electronic patient data management system in May 2014', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* all patients who underwent surgery with anesthesia\n\nExclusion Criteria:\n\n* none'}, 'identificationModule': {'nctId': 'NCT04092933', 'acronym': 'PROTECT', 'briefTitle': 'Perioperative Risk Calculator', 'organization': {'class': 'OTHER', 'fullName': 'Technical University of Munich'}, 'officialTitle': 'Machine-learning Model for Perioperative Risk Calculation', 'orgStudyIdInfo': {'id': '253/19'}}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Technical University of Munich', 'class': 'OTHER'}, 'collaborators': [{'name': 'Health Information Management, Belgium', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}