Ignite Creation Date:
2025-12-24 @ 12:50 PM
Ignite Modification Date:
2025-12-27 @ 10:37 PM
Study NCT ID:
NCT03883061
Status:
UNKNOWN
Last Update Posted:
2021-01-06
First Post:
2019-03-19
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Mathematical Modeling and Risk Factor Analysis for Mortality of Sepsis
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D018805', 'term': 'Sepsis'}], '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'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2000}, 'targetDuration': '1 Month', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2017-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-01', 'completionDateStruct': {'date': '2021-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2021-01-05', 'studyFirstSubmitDate': '2019-03-19', 'studyFirstSubmitQcDate': '2019-03-19', 'lastUpdatePostDateStruct': {'date': '2021-01-06', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2019-03-20', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2019-03-01', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'mortality of sepsis', 'timeFrame': '4 weeks', 'description': 'mortality of sepsis'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Morality', 'Risk Factor, Sepsis', 'Predictive Model']}, 'referencesModule': {'references': [{'pmid': '24238358', 'type': 'BACKGROUND', 'citation': 'Lu Y, Tang ZH, Zeng F, Li Y, Zhou L. The association and predictive value analysis of metabolic syndrome combined with resting heart rate on cardiovascular autonomic neuropathy in the general Chinese population. Diabetol Metab Syndr. 2013 Nov 17;5(1):73. doi: 10.1186/1758-5996-5-73.'}]}, 'descriptionModule': {'briefSummary': 'The purpose of this study was to investigate the risk factors for mortality of sepsis and to create mathematical models to predict the survival rate based on electronic health records that extracted from hospital information system. More than 1000 records should be collected and used to data analysis. Univariate and multivariable logistic regression model were applied to risk factors analysis for the outcome, and machine learn algorithms were employed to generate predictive models for the outcome.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'maximumAge': '99 Years', 'minimumAge': '14 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'all patients with sepsis in emergence department of hospitals', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* all records with sepsis in emergence department of hospitals\n\nExclusion Criteria:\n\n* subjects with major missing data'}, 'identificationModule': {'nctId': 'NCT03883061', 'briefTitle': 'Mathematical Modeling and Risk Factor Analysis for Mortality of Sepsis', 'organization': {'class': 'OTHER', 'fullName': 'Shanghai Tongji Hospital, Tongji University School of Medicine'}, 'officialTitle': 'Mathematical Modeling and Risk Factor Analysis for Mortality of Sepsis Based on Real World Data in China', 'orgStudyIdInfo': {'id': '20190319'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'mortality of sepsis', 'description': 'the study sample would be extracted from electronic health records in emergence departments. risk factor analysis and mathematical modeling would be performed to evaluate the significant and independent risk factors and predictive models.', 'interventionNames': ['Other: regular medical treatment']}], 'interventions': [{'name': 'regular medical treatment', 'type': 'OTHER', 'description': 'regular medical treatment', 'armGroupLabels': ['mortality of sepsis']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Shanghai', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Zihui Tang, Dr', 'role': 'CONTACT', 'email': 'albert.tang@163.com', 'phone': '15821993541', 'phoneExt': '021'}, {'name': 'Yu Lu, Dr', 'role': 'PRINCIPAL_INVESTIGATOR'}, {'name': 'Zihui Tang, Dr', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Zihui Tang', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Shanghai Tongji Hospital, Tongji University School of Medicine', 'class': 'OTHER'}, 'collaborators': [{'name': 'Department of Emergence, The First Hospital Affiliated to South China University, Hengyang, Hunan, China.', 'class': 'UNKNOWN'}, {'name': 'Department of Integrative Medicine, Huashan Hospital of Fudan University, Shanghai, China', 'class': 'UNKNOWN'}, {'name': 'Department of Biomedical Informatics and Statistics, Insitute of Integrative Medicine, Fudan University, Shanghai, China', 'class': 'UNKNOWN'}, {'name': "Department of emergence, Hunan people's hospital, Changhai, Hunan, China", 'class': 'UNKNOWN'}, {'name': 'Department of emergence, The hospital affiliated to Jining medical college, Jining, Shandong, China', 'class': 'UNKNOWN'}, {'name': "Department of emergence, Huaihua people's hospital, Huaihua, Hunan, China", 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Professor', 'investigatorFullName': 'Zihui Tang', 'investigatorAffiliation': 'Huashan Hospital'}}}}