Ignite Creation Date:
2025-12-25 @ 2:20 AM
Ignite Modification Date:
2026-03-05 @ 7:57 AM
Study NCT ID:
NCT03212534
Status:
WITHDRAWN
Last Update Posted:
2021-09-24
First Post:
2017-07-06
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Inpatient Mortality Prediction Algorithm Clinical Trial (IMPACT)
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D006333', 'term': 'Heart Failure'}, {'id': 'D003643', 'term': 'Death'}, {'id': 'D004194', 'term': 'Disease'}], 'ancestors': [{'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 0}}, 'statusModule': {'whyStopped': 'This trial did not start. No participants enrolled', 'overallStatus': 'WITHDRAWN', 'startDateStruct': {'date': '2017-07', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-09', 'completionDateStruct': {'date': '2017-10', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2021-09-20', 'studyFirstSubmitDate': '2017-07-06', 'studyFirstSubmitQcDate': '2017-07-06', 'lastUpdatePostDateStruct': {'date': '2021-09-24', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2017-07-11', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2017-10', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Hospital readmission', 'timeFrame': 'Through study completion, an average of 30 days'}, {'measure': 'ICU length of stay', 'timeFrame': 'Through study completion, an average of 30 days'}], 'primaryOutcomes': [{'measure': 'In-hospital mortality', 'timeFrame': 'Through study completion, an average of 30 days'}], 'secondaryOutcomes': [{'measure': 'Hospital length of stay', 'timeFrame': 'Through study completion, an average of 30 days'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Dascena', 'patient mortality', 'machine learning', 'algorithm', 'diagnostic'], 'conditions': ['Decompensation, Heart', 'Decompensation; Heart, Congestive', 'Death']}, 'referencesModule': {'references': [{'pmid': '28638239', 'type': 'BACKGROUND', 'citation': 'Desautels T, Calvert J, Hoffman J, Mao Q, Jay M, Fletcher G, Barton C, Chettipally U, Kerem Y, Das R. Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting. Biomed Inform Insights. 2017 Jun 12;9:1178222617712994. doi: 10.1177/1178222617712994. eCollection 2017.'}, {'pmid': '27253619', 'type': 'BACKGROUND', 'citation': 'Calvert J, Mao Q, Rogers AJ, Barton C, Jay M, Desautels T, Mohamadlou H, Jan J, Das R. A computational approach to mortality prediction of alcohol use disorder inpatients. Comput Biol Med. 2016 Aug 1;75:74-9. doi: 10.1016/j.compbiomed.2016.05.015. Epub 2016 May 24.'}, {'pmid': '27026611', 'type': 'BACKGROUND', 'citation': 'Calvert JS, Price DA, Barton CW, Chettipally UK, Das R. Discharge recommendation based on a novel technique of homeostatic analysis. J Am Med Inform Assoc. 2017 Jan;24(1):24-29. doi: 10.1093/jamia/ocw014. Epub 2016 Mar 28.'}, {'pmid': '27699003', 'type': 'BACKGROUND', 'citation': 'Calvert J, Mao Q, Hoffman JL, Jay M, Desautels T, Mohamadlou H, Chettipally U, Das R. Using electronic health record collected clinical variables to predict medical intensive care unit mortality. Ann Med Surg (Lond). 2016 Sep 6;11:52-57. doi: 10.1016/j.amsu.2016.09.002. eCollection 2016 Nov.'}]}, 'descriptionModule': {'briefSummary': 'Through the mapping of retrospective patient data into a discrete multidimensional space, a novel algorithm for homeostatic analysis, was built to make outcome predictions. In this prospective study, the ability of the algorithm to predict patient mortality and influence clinical outcomes, will be investigated.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* All adult patients admitted to the participating units will be eligible.\n\nExclusion Criteria:\n\n* All patients younger than 18 years of age will be excluded.'}, 'identificationModule': {'nctId': 'NCT03212534', 'acronym': 'IMPACT', 'briefTitle': 'Inpatient Mortality Prediction Algorithm Clinical Trial (IMPACT)', 'organization': {'class': 'INDUSTRY', 'fullName': 'Dascena'}, 'officialTitle': 'A Randomized Clinical Trial of a Mortality Prediction Algorithm', 'orgStudyIdInfo': {'id': '17-22319'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Prediction Algorithm', 'interventionNames': ['Other: Patient mortality prediction']}, {'type': 'NO_INTERVENTION', 'label': 'Control'}], 'interventions': [{'name': 'Patient mortality prediction', 'type': 'OTHER', 'description': 'Healthcare provider is notified of patient mortality prediction.', 'armGroupLabels': ['Prediction Algorithm']}]}, 'contactsLocationsModule': {'locations': [{'zip': '94143', 'city': 'San Francisco', 'state': 'California', 'country': 'United States', 'facility': 'UCSF Moffit-Long Hospital', 'geoPoint': {'lat': 37.77493, 'lon': -122.41942}}], 'overallOfficials': [{'name': 'David Shimabukuro', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'University of California, San Francisco'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Dascena', 'class': 'INDUSTRY'}, 'collaborators': [{'name': 'University of California, San Francisco', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}