Establishing a Clinical Database and Biobank for Schizophrenia：A Cohort Study

Participants screened through inclusion and exclusion criteria will be recruited and will undergo follow-up for a minimum of one year, with regular clinical data collection at baseline, half a month, 1st month, 3rd month, 6th month, 9th month, and 12th month.The information of demographic data, medical history and previous medication regimen will be collected at baseline. Current medication regimen, physical examination, anthropometry, electrocardiogram, electroencephalogram, psychiatry scales(Positive And Negative Syndrome Scale, Clinical Global Impression, Global Assessment Function and Personal and Social Performance Scale), the Measurement and Treatment Research to Improve Cognition in Schizophrenia(MATRICS) Consensus Cognitive Battery and blood test(blood routine, liver function, renal function, blood lipids, fasting blood glucose, fasting serum insulin, fasting blood glycosylated hemoglobin) will be performed at every follow-up timepoint, as well as functional MRI, eye movement and functional near-infrared spectroscope. If antipsychotic medication is already being used at baseline, the Simpson-Angus Scale (SAS), the Treatment Emergent Symptom Scale (TESS), and the Morisky Medication Adherence Scale (MMAS-8) are administered. Blood samples for exploring underlying mechanisms will be collected and stored at the same time for blood tests. Data quality control is a crucial step in ensuring the quality and credibility of research data. This project has established a rigorous data quality control system to ensure the rigor and effectiveness of its research work: 1. Establishing management specifications and systems for raw data. Emphasis is placed on protecting the authenticity, integrity, and traceability of raw data, and arbitrary changes are not allowed. In the process of data processing, if there are errors, omissions, or modifications needed, it should follow certain procedures and rules for revision (such as documenting all changes, including detailed information such as time, reasons, values before and after modification, and retaining the original erroneous data as a backup). Trace the entire data processing process to ensure that any changes have clear reasons and evidence support, avoiding the risk of tampering or false reporting. 2. Conduct regular consistency training. This project conducts consistency training for team members on scales and MCCB once every three months, including theoretical explanations, practical operation demonstrations, and practice discussions in various forms. This ensures that all scale assessors understand the design principles, applicability, assessment methods, and scoring criteria of the above scales, thereby reducing errors caused by improper operation. 3. Random inspections. This project also invites senior clinical physicians and researchers to conduct quality control of scale assessments and data collection processes through random inspections. This includes, but is not limited to, reviewing the operating procedures of participating researchers, data recording forms, equipment usage, and other aspects to ensure that all links are within a controllable range and comply with predetermined standards. If any problems or deviations are found, they should be corrected and relevant operating procedures adjusted in a timely manner to ensure continuous improvement in data quality, thereby reducing the error rate and the proportion of invalid data.