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.
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.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
PREVENTION
Masking
NONE
Enrollment
300
This group will be suggested to take optimize treatment according to Chinese treatment guidelines.
This group will be suggested to add on psychotherapy on medical treatment.
This group will be suggested to add on Omega-3 polyunsaturated fatty acid(PUFAs) on medical treatment.
HAM-D total score
The change from baseline to end of study (EOS) in HAM-D total score
Time frame: 12 weeks
Time to relapse
The time to new intervention for an emerging mood episode
Time frame: 12 months
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This group will be suggested to more safety antidepressants.
This group will be suggested to treat their comorbidities as well as treat MDD.
Patients will accept routine treatments based on psychiatrist experience, not based on the relapse predict model.