Ignite Creation Date:
2025-12-25 @ 4:46 AM
Ignite Modification Date:
2025-12-26 @ 3:48 AM
Study NCT ID:
NCT03949218
Status:
COMPLETED
Last Update Posted:
2019-05-14
First Post:
2019-05-13
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Bipolar Disorder and Oxidative Stress Injury Mechanism - Clinical Big Data Analysis Based on Machine Learning
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001714', 'term': 'Bipolar Disorder'}], 'ancestors': [{'id': 'D000068105', 'term': 'Bipolar and Related Disorders'}, {'id': 'D019964', 'term': 'Mood Disorders'}, {'id': 'D001523', 'term': 'Mental Disorders'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 3702}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2018-11-20', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2019-05', 'completionDateStruct': {'date': '2018-11-20', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2019-05-13', 'studyFirstSubmitDate': '2019-05-13', 'studyFirstSubmitQcDate': '2019-05-13', 'lastUpdatePostDateStruct': {'date': '2019-05-14', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2019-05-14', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2018-11-20', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Early prediction model of bipolar disorder with oxidative stress index as the core', 'timeFrame': 'at August 2019', 'description': 'Based on the oxidative stress data, the study will analysis related indicators of oxidative stress injury in patients with bipolar disorder. Then use the method of machine learning to build up the early prediction model of bipolar disorder.'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['bipolar disorder', 'machine learning'], 'conditions': ['Bipolar Disorder']}, 'referencesModule': {'references': [{'pmid': '21869688', 'type': 'RESULT', 'citation': 'Fang Y, Yuan C, Xu Y, Chen J, Wu Z, Cao L, Yi Z, Hong W, Wang Y, Jiang K, Cui X, Calabrese JR, Gao K; OPERATION Study Team. A pilot study of the efficacy and safety of paroxetine augmented with risperidone, valproate, buspirone, trazodone, or thyroid hormone in adult Chinese patients with treatment-resistant major depression. J Clin Psychopharmacol. 2011 Oct;31(5):638-42. doi: 10.1097/JCP.0b013e31822bb1d9.'}, {'pmid': '26731438', 'type': 'RESULT', 'citation': 'Zhang C, Lu W, Wang Z, Ni J, Zhang J, Tang W, Fang Y. A comprehensive analysis of NDST3 for schizophrenia and bipolar disorder in Han Chinese. Transl Psychiatry. 2016 Jan 5;6(1):e701. doi: 10.1038/tp.2015.199.'}, {'pmid': '26343587', 'type': 'RESULT', 'citation': 'Guo X, Li Z, Zhang C, Yi Z, Li H, Cao L, Yuan C, Hong W, Wu Z, Peng D, Chen J, Xia W, Zhao G, Wang F, Yu S, Cui D, Xu Y, Golam CM, Smith AK, Wang T, Fang Y. Down-regulation of PRKCB1 expression in Han Chinese patients with subsyndromal symptomatic depression. J Psychiatr Res. 2015 Oct;69:1-6. doi: 10.1016/j.jpsychires.2015.07.011. Epub 2015 Jul 17.'}, {'pmid': '40211177', 'type': 'DERIVED', 'citation': 'Wu X, Wang S, Niu Z, Zhu Y, Sun P, Sun W, Chen J, Fang Y. Bipolar disorder at mixed states and major depressive disorder with mixed features differ in peripheral biochemical parameters. BMC Psychiatry. 2025 Apr 10;25(1):362. doi: 10.1186/s12888-025-06800-9.'}, {'pmid': '35795028', 'type': 'DERIVED', 'citation': 'Zhu Y, Ji H, Niu Z, Liu H, Wu X, Yang L, Wang Z, Chen J, Fang Y. Biochemical and Endocrine Parameters for the Discrimination and Calibration of Bipolar Disorder or Major Depressive Disorder. Front Psychiatry. 2022 Jun 20;13:875141. doi: 10.3389/fpsyt.2022.875141. eCollection 2022.'}]}, 'descriptionModule': {'briefSummary': 'This study is a single-center, retrospective, cross-sectional study. We plan to work with our network information center to analysis the related indicators of oxidative stress injury in patients with bipolar disorder based on oxidative stress data. During the study, machine learning was used as a data analysis method to screen out the biomarker risk factors with sensitivity and specificity for early recognition of bipolar disorder from major depression disorder with oxidative stress injury as the core. And then build up effective clinical predictive models for early identification of bipolar disorder, which can predict the early quantitative probabilistic of the onset of bipolar disorder.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Subjects are hospitalized patients in SMHC from 2009 to 2018; meet ICD-10 diagnosis of F31 bipolar disorder, F32 depressive disorder criteria and its subtype (mental examination was conducted by three levels of doctors including at least one attending physician and one chief physician in psychiatric); available relevant HIS system biochemical data; hospitalized patients need to the first admission; age and gender is not limited', 'eligibilityCriteria': 'Inclusion Criteria:\n\n* age is not limited\n* gender is not limited\n* meets the diagnostic criteria for bipolar disorder of ICD-10 F31,F32 and its sub-categories\n* has relevant HIS system data that can be utilized.\n\nExclusion Criteria:\n\n* patients who did not meet the appeal diagnosis after three-level rounds of ward\n* patients who met the above three diagnoses but had severe data loss (missing value ≥ estimated data value of 30%)'}, 'identificationModule': {'nctId': 'NCT03949218', 'briefTitle': 'Bipolar Disorder and Oxidative Stress Injury Mechanism - Clinical Big Data Analysis Based on Machine Learning', 'organization': {'class': 'OTHER', 'fullName': 'Shanghai Mental Health Center'}, 'officialTitle': 'Bipolar Disorder and Oxidative Stress Injury Mechanism - Clinical Big Data Analysis Based on Machine Learning', 'orgStudyIdInfo': {'id': 'CRC2018DSJ01-1'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'bipolar disorder(BD)', 'description': 'hospitalized patients BD patients in SMHC from 2009 to 2018; meet ICD-10 diagnosis of F31 bipolar disorder criteria and its subtype (mental examination was conducted by three levels of doctors including at least one attending physician and one chief physician in psychiatric); available relevant HIS system biochemical data; hospitalized patients need to the first admission; age and gender is not limited'}, {'label': 'major depressive disorder（MDD）', 'description': 'hospitalized patients MDD patients in SMHC from 2009 to 2018; meet ICD-10 diagnosis of F32 depressive disorder criteria and its subtype (mental examination was conducted by three levels of doctors including at least one attending physician and one chief physician in psychiatric); available relevant HIS system biochemical data; hospitalized patients need to the first admission; age and gender is not limited'}]}, 'contactsLocationsModule': {'locations': [{'zip': '200030', 'city': 'Shanghai', 'state': 'Shanghai Municipality', 'country': 'China', 'facility': 'Shanghai Mental Health Center', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}], 'overallOfficials': [{'name': 'Yiru Fang', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Shanghai Mental Health Center'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Shanghai Mental Health Center', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'director of clinical research center & division of mood disorders', 'investigatorFullName': 'Yiru FANG M.D., Ph.D.', 'investigatorAffiliation': 'Shanghai Mental Health Center'}}}}