Clinical Trial Details
— Status: Not yet recruiting
Administrative data
NCT number |
NCT06128408 |
Other study ID # |
2023-6-002 |
Secondary ID |
|
Status |
Not yet recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
December 1, 2023 |
Est. completion date |
October 13, 2024 |
Study information
Verified date |
October 2023 |
Source |
Peking University |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial 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.
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%).