Ignite Creation Date:
2025-12-24 @ 5:14 PM
Ignite Modification Date:
2025-12-24 @ 5:14 PM
Study NCT ID:
NCT04200950
Status:
WITHDRAWN
Last Update Posted:
2021-09-24
First Post:
2019-12-11
Is Possible Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Trial for the Early Identification of Acute Kidney Injury
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D058186', 'term': 'Acute Kidney Injury'}], 'ancestors': [{'id': 'D051437', 'term': 'Renal Insufficiency'}, {'id': 'D007674', 'term': 'Kidney Diseases'}, {'id': 'D014570', 'term': 'Urologic Diseases'}, {'id': 'D052776', 'term': 'Female Urogenital Diseases'}, {'id': 'D005261', 'term': 'Female Urogenital Diseases and Pregnancy Complications'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D052801', 'term': 'Male Urogenital Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['PHASE2'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 0}}, 'statusModule': {'whyStopped': 'No longer conducting this retrospective research', 'overallStatus': 'WITHDRAWN', 'startDateStruct': {'date': '2020-07', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-09', 'completionDateStruct': {'date': '2021-06', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2021-09-20', 'studyFirstSubmitDate': '2019-12-11', 'studyFirstSubmitQcDate': '2019-12-13', 'lastUpdatePostDateStruct': {'date': '2021-09-24', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2019-12-16', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2021-06', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Incidence of adverse kidney events as assessed by Major Adverse Kidney Event within 30 days (MAKE30) criteria', 'timeFrame': 'Through study completion, an average of twelve months', 'description': 'The proportion of patients meeting one or more criteria for the Major Adverse Kidney Events within 30 days (MAKE30) composite of death, new renal replacement therapy, or persistent creatinine elevation ≥ 200% of baseline, all censored at the first of hospital discharge or 30 days'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Acute Kidney Injury']}, 'referencesModule': {'references': [{'pmid': '30094049', 'type': 'BACKGROUND', 'citation': 'Mohamadlou H, Lynn-Palevsky A, Barton C, Chettipally U, Shieh L, Calvert J, Saber NR, Das R. Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data. Can J Kidney Health Dis. 2018 Jun 8;5:2054358118776326. doi: 10.1177/2054358118776326. eCollection 2018.'}]}, 'descriptionModule': {'briefSummary': 'Previse is a novel, software-based clinical decision support (CDS) system that predicts acute kidney injury (AKI). Previse uses machine learning methods and information drawn from the electronic health record (EHR) to identify the early signs of acute kidney injury; by doing so before the clinical syndrome of AKI is fully developed, Previse can give clinicians the time to intervene with the goals of preventing further kidney damage, and decreasing the sequelae of AKI. It has been demonstrated in retrospective work that Previse can predict AKI with high accuracy at long prediction horizons, but the tool has yet to be validated in prospective settings; therefore, in this project, the clinical utility of Previse will be assessed through an individually randomized controlled multicenter trial.', 'detailedDescription': 'The trial is designed as an individually randomized, controlled, and non-blinded multicenter prevention trial with a baseline period and a primary endpoint of proportion of patients meeting one or more criteria for the Major Adverse Kidney Events within 30 days (MAKE30) composite of death, new renal replacement therapy, or persistent creatinine elevation ≥ 200% of baseline, all censored at the first of hospital discharge or 30 days. The trial will evaluate the efficacy of a machine learning algorithm for AKI prediction, in approximately 8,574 patients aged ≥ 18 years admitted to one of three participating study hospitals. Individual patient randomization will be performed at the time of the alert with a 1:1 allocation ratio. Patients will be evaluated for inclusion in the trial upon admission, and will be automatically enrolled upon meeting inclusion criteria. Because data collection will be conducted through noninvasive procedures that are routinely employed in clinical practice, it will require a waiver of informed consent. Trial efficacy will be assessed at regularly scheduled study visits, and safety will be monitored on an ongoing basis for all patients. Safety will be assessed through the collection of adverse events, laboratory tests, vital signs, and physical examinations throughout the study. An independent Data Monitoring Committee (DMC) will be formed to assist in the periodic monitoring of safety, data quality, and integrity of study conduct. In addition, the DMC will review the interim efficacy analysis performed to determine whether the primary endpoint has been met. Total trial duration is expected to be approximately 12 months.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Adult ≥ 18 years admitted to a participating study hospital\n\nExclusion Criteria:\n\n* ﹤18 years of age\n* ESRD diagnosis code\n* Stage 4 or Stage 5 CKD diagnosis code\n* Initial creatinine ≥4.0mg/dl\n* Nephrectomy during admission\n* Admission to hospice service\n* Admission to observation status\n* Any organ transplant (including kidney transplant) within 6 months\n* Dialysis order prior to AKI onset\n* Dialysis order within 24 hours of admission\n* Prior admission in which patient was randomized'}, 'identificationModule': {'nctId': 'NCT04200950', 'briefTitle': 'Trial for the Early Identification of Acute Kidney Injury', 'organization': {'class': 'INDUSTRY', 'fullName': 'Dascena'}, 'officialTitle': 'Randomized Controlled Trial for the Early Identification of Acute Kidney Injury Using Deep Recurrent Neural Nets', 'orgStudyIdInfo': {'id': '07012020'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Intervention', 'description': 'Previse alert arm', 'interventionNames': ['Device: Previse']}, {'type': 'NO_INTERVENTION', 'label': 'Control', 'description': 'No alert'}], 'interventions': [{'name': 'Previse', 'type': 'DEVICE', 'description': 'Machine learning algorithm for early acute kidney injury (AKI) prediction.', 'armGroupLabels': ['Intervention']}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Dascena', 'class': 'INDUSTRY'}, 'responsibleParty': {'type': 'SPONSOR'}}}}