Prediction of Violent Behavior in Patients With Schizophrenia by Multimodal Machine Learning

NCT ID: NCT04520399

Last Updated: 2020-08-20

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-10-31

Study Completion Date

2022-09-30

Brief Summary

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Patients with schizophrenia have a higher risk of committing violent crimes than the general population, and the relative risk of violence against others is four times higher than the general population. Violence is a major public health problem because it often leads to poor prognosis, readmission and stigma in patients with schizophrenia.

MRI studies on violent behavior in schizophrenia are relatively few. These studies have found that violence is primarily associated with dysfunction in the ventral prefrontal and temporal limbic systems. Structural MRI found that violent behavior in schizophrenia was associated with increased volume of white matter in caudate nucleus, left orbitofrontal gyrus and right orbitofrontal gyrus. However, the current research results in this field are uneven, the methods are not consistent, and there is a lack of breakthrough progress, which needs to be integrated and deepened urgently.

If the violent behavior of the patients with schizophrenia could be predicted by magnetic resonance imaging, it would be a revolutionary try. By doing so, the investigators can strengthen the treatment of these patients and reduce the occurrence of violence. Based on previous studies, the investigators believe that violent schizophrenics exhibit recognizable imaging characteristics under structural phase, resting state, negative emotional images and natural stimuli models. Anomalies in a particular mode may be subtle and difficult to identify, but when multiple different modes are integrated, a significant and characteristic set of imaging markers will be present. This study will use the multivariate model of machine learning method, detection brain activation patterns under different situations among patients with violence. The investigators are going to study imaging biomarkers, and try to predict the possibility of onset of violence among schizophrenia patients, thus reduce the risks of violence.

Detailed Description

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Conditions

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Schizophrenia Violence

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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violence

none intervention

Intervention Type OTHER

there is no intervention in this study

non-violence

none intervention

Intervention Type OTHER

there is no intervention in this study

Interventions

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none intervention

there is no intervention in this study

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

for violent group

1. Inpatient or out-patient treatment;
2. Junior high school education or above, eliminate mental retardation;
3. 18-60 years old;
4. Han, right-handed;
5. Meet the diagnostic criteria of ICD-10 schizophrenia;
6. Self-report and confirmed by family members that violence occurred at least 2 times in the recent six months;
7. In the recent half year, the revised Version of the weighted total score of The Explicit Attack Scale (MOAS) ≥4;
8. Exclude severe heart, liver, kidney, blood, digestive and nervous system diseases;

1\) Schizophrenic patients treated in hospital or outpatient; 2) Junior high school education or above, eliminate mental retardation; 3) 18-60 years old; 4) Han, right-handed; 5) Meet the diagnostic criteria of ICD-10 schizophrenia; 6) Self-reported and confirmed by family members that no violence has occurred in the past six months; 7) within the past revisions explicit scale (MOAS) weighted total score ˂ 4 points; 8) Exclude severe heart, liver, kidney, blood, digestive and nervous system diseases; 9) Willing to participate in this study and sign the informed consent.

\-
Minimum Eligible Age

18 Years

Maximum Eligible Age

60 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Shanghai Mental Health Center

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Shanghai Mental Health Center

Shanghai, Shanghai Municipality, China

Site Status

Countries

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China

Central Contacts

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Yi Qiao

Role: CONTACT

13817615936 ext. 13817615936

Other Identifiers

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20200498

Identifier Type: -

Identifier Source: org_study_id

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