The Characteristics of Treatment Resistant Schizophrenia From the Illness Onset
Study Details
Study Description
Brief Summary
Previous long-term follow-up studies on patients with first-episode schizophrenia have shown that up to 30% of patients who have never received antipsychotic medication treatment do not experience symptom relief or have poor treatment response after standard antipsychotic medication treatment, becoming treatment-resistant schizophrenia (TRS). Moreover, in long-term follow-up, patients with treatment-resistant schizophrenia from the illness onset (TRO) account for 80% of all TRS patients. Preliminary studies abroad have found that TRO patients have characteristics such as early age of onset, male predominance, prominent negative symptoms, high proportion of positive family history, and long duration of untreated psychosis, but there is still no consistent conclusion on the pathological mechanisms. There is currently no research on this type of patient in China, and there are difficulties in early diagnosis of TRO patients in clinical practice. This study aims to establish a TRO prediction model by integrating data on demographics, disease characteristics, psychopathology, social function, and neurocognition from a cohort of patients with first-episode schizophrenia. Mathematical modeling methods such as K-Means/SVM and convolutional neural networks will be used. Therefore, in patients with untreated first-episode schizophrenia, early and accurate identification of TRO patients at the initial diagnosis stage and treatment with clozapine is particularly important for potentially shortening the treatment period and reducing the personal and societal burden of TRO patients. Based on the progress of existing research and the previous work of the research team, we speculate that TRO patients have unique clinical features. This project will establish a TRO prediction model based on multidimensional clinical data using mathematical modeling methods. From a clinical application perspective, the study selects TRO model prediction factors based on existing clinical assessment methods, making the model highly clinically applicable and generalizable. By establishing a TRO prediction model, not only can high-risk TRO patients be identified early in the initial diagnosis stage, enabling appropriate clinical treatment interventions, but it can also provide new insights into the future clinical treatment of TRO, promote the development of early and personalized precision identification and treatment of TRO, and improve long-term prognosis and reduce the burden of the disease for patients.
| Condition or Disease | Intervention/Treatment | Phase |
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Detailed Description
Research steps: (1) First, based on the applicant's project funded by the National Natural Science Foundation of China (National Natural Science Foundation: Precise Identification and Treatment of Treatment-Resistant First-Episode Schizophrenia), treatment-resistant first-episode schizophrenia patients (TRO) and non-treatment-resistant first-episode schizophrenia patients (None-TRO) will be included. The demographic information, disease characteristics, psychopathology, social functioning, and neurocognition data of the patients will be collected. This includes scores from the Positive and Negative Syndrome Scale (PANSS), Bech-Rafaelsen Mania Scale (BRMS), Calgary Depression Scale for Schizophrenia (CDSS), Insight into Treatment Adherence Questionnaire (ITAQ), Perceived Stress Scale- Chinese Version (PSS-CV), Childhood Trauma Questionnaire (CTQ), Abnormal Involuntary Movement Scale (AIMS), Rating Scale for Extrapyramidal Side Effects (RSESE), Clinical Global Impressions Scale (CGI), Medication Adherence Rating Scale (MARS), UKU Side Effect Rating Scale, Personal and Social Performance Scale (PSP), Premorbid Adjustment Scale (PAS), and Computerized Battery of Cognitive Tests (CBCT). All data will be anonymized and will not involve the personal privacy of the participants. (2) For TRO patients, demographic information, disease characteristics, psychopathology, social functioning, and neurocognition data will be integrated. Mathematical modeling methods such as K-Means/SVM and Convolutional Neural Networks will be used to try to establish a TRO prediction model, with predictive factors obtained through literature review, expert consultation, and data analysis.
Treatment-resistant schizophrenia: After 6 weeks of treatment with two second-generation antipsychotic drugs at an adequate dose (minimum dose for acute treatment of schizophrenia as stated in the antipsychotic drug instructions or equivalent dose of 600mg chlorpromazine), no clinical improvement is observed (CGI-S ≥ 4 or PANSS reduction rate < 50%).
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
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| schizophrenia
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Drug: risperidone, olanzapine, aripiprazole
Participants from the retrospective cohort were randomly assigned to one of the three drug groups of risperidone, olanzapine and aripiprazole for a period of 1 years of treatment.
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Outcome Measures
Primary Outcome Measures
- treatment-resistant schizophrenia [From December 1, 2023 to October 12, 2024]
Treated with sufficient dosage of two types of second-generation antipsychotic medications (at least the minimum dose for acute phase treatment of schizophrenia or an equivalent dose of 600mg chlorpromazine) for 6 weeks, but did not achieve clinical improvement (CGI-S≥4 or PANSS reduction rate <50%).
Eligibility Criteria
Criteria
Inclusion Criteria:
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Having a diagnosis of schizophrenics based on the patient edition of the Structured Clinical Interview for Axis I Diagnostic and Statistical Manual-IV Axis I Disorders (SCID).
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Age 18-45 years.
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First episode of schizophrenia.
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Course of disease ≤ 3 years.
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Previous continuous medication ≤ 4 weeks, cumulative intermittent medication ≤ 12 weeks.
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Be able to understand the interview content and sign written informed consent.
Exclusion Criteria:
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Previous history of major physical diseases.
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Previous substance abuse or dependence.
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Contraindications to olanzapine, risperidone or aripiprazole.
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- Peking University
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 2023-6-002