Relapse Predicting Model for First Episode Depression

The Study of Relapse Predicting Model for First Episode Depression: Big Data Analysis Based on Clinical Features and Immunochemistry

Status

UNKNOWN

Phases

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT02942251

Acronym

RPM-FED

Enrollment

300

Registered

2016-10-24

Start date

2016-12-31

Completion date

2019-12-31

Last updated

2016-10-26

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Major Depressive Disorders

Keywords

MDD, big data analytics

Brief summary

Detailed description

Major depressive depression(MDD) is an severe public mental disorders. The purpose of current study is using big data analysis based on clinical features and immunochemistry to investigate and establish an relapse predict model for patients with first episode MDD. This study includes two steps. Step 1: Big data analysis based on the clinical features and immunochemical figures of 30000 patients with first episode MDD will be conducted to construct a relapse predict model. Step 2: 300 patients with first episode MDD will be recruited in this step. Physicians prefer to give corresponding treatment recommendation based on the predictive factors to verify this relapse model.

Interventions

OTHERClinical features and medication

This group will be suggested to take optimize treatment according to Chinese treatment guidelines.

OTHERPsycho-social

This group will be suggested to add on psychotherapy on medical treatment.

OTHERimmunology

This group will be suggested to add on Omega-3 polyunsaturated fatty acid(PUFAs) on medical treatment.

OTHERLaboratory abnormality

This group will be suggested to more safety antidepressants.

OTHERComorbidity

This group will be suggested to treat their comorbidities as well as treat MDD.

OTHERTreatment as usual

Patients will accept routine treatments based on psychiatrist experience, not based on the relapse predict model.

Study design

Allocation

NON_RANDOMIZED

Intervention model

PARALLEL

Primary purpose

PREVENTION

Masking

NONE

Eligibility

Sex/Gender

ALL

Age

18 Years to 65 Years

Healthy volunteers

Yes

Inclusion criteria

* Age from 18 to 65 years old; * Han Chinese; * Outpatient and inpatient patients; * Patients met Diagnostic and Statistical Manual of Mental Disorders(DSM-IV-TR) criteria of bipolar I or II disorder, currently depressed, ascertained with the Mini International Neuropsychiatric Interview (MINI) * Hamilton Depression Scale(HAM-D) total score≥17, HAM-D item 1 (depressed mood) score≥2, and Young Mania Rating Scale(YMRS) total score≤10 at baseline; * Written informed consent was given; * Junior high school education and above, with enough audio-visual ability to accomplish the visits; * Normally resident in one country and had a residential address, able to follow-up.

Exclusion criteria

* Bipolar disorder rapid cycling or mixed episode; * Experienced DSM-IV-TR axis I disorders in the 6 months prior to randomization; * Severe personality disorder, metal retardation, anorexia/bulimia nervosa; * Actively suicide ascertained by research psychiatrist or HAM-D item 3 score≥3(suicidality); * Female patients who were pregnant, planning to be pregnant or breast feeding; * Severe medical or neurological problems.

Design outcomes

Primary

Measure	Time frame	Description
HAM-D total score	12 weeks	The change from baseline to end of study (EOS) in HAM-D total score

Secondary

Measure	Time frame	Description
Time to relapse	12 months	The time to new intervention for an emerging mood episode

Contacts

Primary ContactJun Chen, M.D., Ph.D

doctorcj2010@gmail.com021-34773367

Backup ContactJia Huang, Master

kittyhj_2000@aliyun.com13750892815

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026