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Bipolar Disorder and Oxidative Stress Injury Mechanism - Clinical Big Data Analysis Based on Machine Learning

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT03949218
Recruitment Status : Completed
First Posted : May 14, 2019
Last Update Posted : May 14, 2019
Sponsor:
Information provided by (Responsible Party):
Yiru FANG M.D., Ph.D., Shanghai Mental Health Center

Brief Summary:
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.

Condition or disease
Bipolar Disorder

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Study Type : Observational
Actual Enrollment : 3702 participants
Observational Model: Other
Time Perspective: Retrospective
Official Title: Bipolar Disorder and Oxidative Stress Injury Mechanism - Clinical Big Data Analysis Based on Machine Learning
Actual Study Start Date : November 20, 2018
Actual Primary Completion Date : November 20, 2018
Actual Study Completion Date : November 20, 2018

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Bipolar Disorder

Group/Cohort
bipolar disorder(BD)
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
major depressive disorder(MDD)
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



Primary Outcome Measures :
  1. Early prediction model of bipolar disorder with oxidative stress index as the core [ Time Frame: at August 2019 ]
    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.



Information from the National Library of Medicine

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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Sampling Method:   Non-Probability Sample
Study Population
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
Criteria

Inclusion Criteria:

  • age is not limited
  • gender is not limited
  • meets the diagnostic criteria for bipolar disorder of ICD-10 F31,F32 and its sub-categories
  • has relevant HIS system data that can be utilized.

Exclusion Criteria:

  • patients who did not meet the appeal diagnosis after three-level rounds of ward
  • patients who met the above three diagnoses but had severe data loss (missing value ≥ estimated data value of 30%)

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03949218


Locations
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China, Shanghai
Shanghai Mental Health Center
Shanghai, Shanghai, China, 200030
Sponsors and Collaborators
Shanghai Mental Health Center
Investigators
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Principal Investigator: Yiru Fang Shanghai Mental Health Center
Publications of Results:
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Responsible Party: Yiru FANG M.D., Ph.D., director of clinical research center & division of mood disorders, Shanghai Mental Health Center
ClinicalTrials.gov Identifier: NCT03949218    
Other Study ID Numbers: CRC2018DSJ01-1
First Posted: May 14, 2019    Key Record Dates
Last Update Posted: May 14, 2019
Last Verified: May 2019

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Yiru FANG M.D., Ph.D., Shanghai Mental Health Center:
bipolar disorder
machine learning
Additional relevant MeSH terms:
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Disease
Bipolar Disorder
Pathologic Processes
Bipolar and Related Disorders
Mental Disorders