Optimization of Diagnosis and Treatment of Depression Based on Multidimensional Clinical Assessment Classification
Optimization of Diagnosis and Treatment of Depression Based on Multidimensional Clinical Assessment Classification
Major depressive disorder (MDD) characterized by high prevalence, high recurrence rate and
 high disability rate is a mental illness with the heaviest burden and has become a major
 public health issue in China and the world. Great challenges in diagnosis and treatment of
 depression consist of the complicated pathogenesis, a lack of objective diagnostic criteria,
 unsatisfactory treatment outcomes and poor treatment compliance. The previous studies of our
 research team showed that depression is affected by multiple factors. We could explore
 important markers for the diagnosis, treatment and prediction of treatment efficacy in
 depressed patients' data collected from different dimensions including immunometabolism,
 brain electrophysiology, brain structure and functional neural circuits, neuropsychology and
 psychophysiology. Our completed studies in the National Science and Technology Support
 Program and National Key Research and Development (R&D) Program of the 12th and 13th
 Five-Year Plan in China found that treatment designed for specific clinical subtypes can
 improve the treatment effect, and meanwhile, the application of electronic-measurement based
 care (e-MBC) combined with smart mobile terminals can effectively provide whole-course
 medical management for patients, improve patient compliance and increase the efficacy of
 clinical diagnosis and treatment. However, due to disease diagnosis based on clinical
 symptomatology without subtype distinction and lack of multi-scale biological data mining,
 multidimensional assessment and deep integration, the results of most previous studies can
 hardly be used in clinical practice. Therefore, there is a strong urge to carry out a
 systematic research in which multidimensional evaluation of clinical characteristics and a
 large scale of data collection and mining are needed to form clinical diagnosis and optimal
 treatment regimens for depression subtypes. To achieve the goal, patients with depression
 will be our research subject in this study. First, on the basis of the previous cohort study
 and the whole-course e-MBC, patients' data of movement, respiratory rate, heart rate and
 sleep will be further collected. With the help of artificial intelligence (AI) technology
 such as deep machine learning, the data integrated with EEG imaging and specific
 immunometabolic markers in blood will be analyzed with clinical characteristics. The model of
 diagnosis and classification will be established based on multidimensional clinical
 assessment and verified. Second, through a prospective multicenter randomized controlled
 trial, optimal treatment regimens for different depression subtypes and individualized
 magnetic stimulation physical intervention technology navigated by AI will be explored so as
 to establish a predictive model of curative effect. Finally, long-term follow-up and its
 regular data collection can be completed on the patient diagnosis and treatment platform
 which is linked to the e-MBC. Thus, a stable clinical cohort and an advanced database
 containing multidimensional information of depression will be set up. The whole course e-MBC
 management platform will be optimized and promoted to improve patient compliance, treatment
 efficiency and prognosis. This study can provide evidence for precise diagnosis and
 classification of depression and optimal treatment regimens for different subtypes.
Inclusion Criteria:

 1. 18-60 years old;

 2. Meeting with the criteria of major depressive disorder in the Diagnostic and
 Statistical Manual of Mental Disorders (DSM)-5;

 3. Scored 20 or higher on the Hamilton's Depression Scale with 24 items (HAMD-24);

 4. With enough audio-visual ability and comprehensive ability to accomplish the visits;

 5. No medication or washout period of at least 2 weeks

 6. Scored less than 14 on the Hypomania Symptom Checklist-32 (HCL-32);

 Exclusion Criteria:

 1. Existing serious and active physical diseases that may interfere with treatment
 (abnormal indicators> 2 times the normal value), or there is a pharmacological
 conflict between the current medical medication and the study drug;

 2. Previous mania or hypomania episodes;

 3. Female patients who are pregnant, planning to be pregnant or breastfeeding;

 4. Current high suicide risk (e.g. 3rd item of HAMD-24 scored≥3(suicidality));

 5. Had ECT, MECT or rTMS in the past 6 months;

 6. Experienced a history of dependence on psychoactive substances, organic mental
 disorders, personality disorders, mental retardation, neurodegenerative diseases,
 brain trauma and cerebrovascular diseases.