Ignite Creation Date:
2025-12-25 @ 1:26 AM
Ignite Modification Date:
2026-02-28 @ 9:31 AM
Study NCT ID:
NCT03235193
Status:
COMPLETED
Last Update Posted:
2021-09-21
First Post:
2017-07-27
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Predictive algoRithm for EValuation and Intervention in SEpsis
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D018805', 'term': 'Sepsis'}, {'id': 'D012772', 'term': 'Shock, Septic'}], 'ancestors': [{'id': 'D007239', 'term': 'Infections'}, {'id': 'D018746', 'term': 'Systemic Inflammatory Response Syndrome'}, {'id': 'D007249', 'term': 'Inflammation'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D012769', 'term': 'Shock'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NON_RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'FACTORIAL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 2296}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2017-07-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-09', 'completionDateStruct': {'date': '2017-08-30', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2021-09-17', 'studyFirstSubmitDate': '2017-07-27', 'studyFirstSubmitQcDate': '2017-07-27', 'lastUpdatePostDateStruct': {'date': '2021-09-21', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2017-08-01', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2017-08-30', 'type': 'ACTUAL'}}, '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': {'conditions': ['Sepsis', 'Septic Shock', 'Severe Sepsis']}, 'referencesModule': {'references': [{'pmid': '27489621', 'type': 'BACKGROUND', 'citation': 'Calvert J, Desautels T, Chettipally U, Barton C, Hoffman J, Jay M, Mao Q, Mohamadlou H, Das R. High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Ann Med Surg (Lond). 2016 May 10;8:50-5. doi: 10.1016/j.amsu.2016.04.023. eCollection 2016 Jun.'}, {'pmid': '27208704', 'type': 'BACKGROUND', 'citation': 'Calvert JS, Price DA, Chettipally UK, Barton CW, Feldman MD, Hoffman JL, Jay M, Das R. A computational approach to early sepsis detection. Comput Biol Med. 2016 Jul 1;74:69-73. doi: 10.1016/j.compbiomed.2016.05.003. Epub 2016 May 12.'}, {'pmid': '27694098', 'type': 'BACKGROUND', 'citation': 'Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, Shimabukuro D, Chettipally U, Feldman MD, Barton C, Wales DJ, Das R. Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Med Inform. 2016 Sep 30;4(3):e28. doi: 10.2196/medinform.5909.'}]}, 'descriptionModule': {'briefSummary': 'In this prospective study, the ability of a machine learning algorithm to predict sepsis and influence clinical outcomes, will be investigated at Cabell Huntington Hospital (CHH).'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* All adult patients visiting the emergency department, or admitted to the participating intensive care unit (ICU) wards of Cabell Huntington Hospital will be eligible.\n\nExclusion Criteria:\n\n* All patients younger than 18 years of age will be excluded.'}, 'identificationModule': {'nctId': 'NCT03235193', 'acronym': 'PREVISE', 'briefTitle': 'Predictive algoRithm for EValuation and Intervention in SEpsis', 'organization': {'class': 'INDUSTRY', 'fullName': 'Dascena'}, 'officialTitle': 'Prediction of Severe Sepsis Using a Machine Learning Algorithm', 'orgStudyIdInfo': {'id': '1097090-1'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'With InSight', 'description': 'Healthcare provider receives an alert from InSight for patients trending towards severe sepsis. Healthcare provider also receives information from the severe sepsis detector in the CHH electronic health record.', 'interventionNames': ['Other: Severe Sepsis Prediction', 'Other: Severe Sepsis Detection']}, {'type': 'ACTIVE_COMPARATOR', 'label': 'Without Insight', 'description': 'Healthcare provider does not receive any alerts from InSight. Healthcare provider receives information from the severe sepsis detector in the CHH electronic health record.', 'interventionNames': ['Other: Severe Sepsis Detection']}], 'interventions': [{'name': 'Severe Sepsis Prediction', 'type': 'OTHER', 'description': 'Upon receiving an InSight alert, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.', 'armGroupLabels': ['With InSight']}, {'name': 'Severe Sepsis Detection', 'type': 'OTHER', 'description': 'Upon receiving information from the severe sepsis detector in the CHH electronic health record, healthcare provider follows standard practices in assessing possible (severe) sepsis and intervening accordingly.', 'armGroupLabels': ['With InSight', 'Without Insight']}]}, 'contactsLocationsModule': {'locations': [{'zip': '25701', 'city': 'Huntington', 'state': 'West Virginia', 'country': 'United States', 'facility': 'Cabell Huntington Hospital', 'geoPoint': {'lat': 38.41925, 'lon': -82.44515}}], 'overallOfficials': [{'name': 'Hoyt Burdick', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Cabell Huntington Hospital'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Dascena', 'class': 'INDUSTRY'}, 'collaborators': [{'name': 'Cabell Huntington Hospital', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}