Risk Prediction Model of Cardiovascular Events in Patients With Schizophrenia

NCT ID: NCT05498571

Last Updated: 2022-08-12

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 1500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-12-01
• Study Completion Date: 2022-12-31

Brief Summary

Schizophrenia is a serious mental illness that has a great impact on social function. Studies have evidenced that schizophrenia patients live 10-20 years less than general population.It mainly dues to high cardiovascular risk. How to improve patients' survival rates? At present, there is an objective model to assess cardiovascular risk among schizoprenia patients in England - PRIMROSE.But there is a lack of model for schizophrenia patients in china. In order to better guide clinical practice, we are now exploring a domestic cardiovascular risk prediction model to raise people's awareness.

Detailed Description

Schizophrenia is a common severe mental disorder with high rate of disability, which seriously affects patients' life.It is necessary to take long-term therapy to control symptoms or prevent recurrence. Studies have found that schizophrenia patients live on average ten to twenty-five years less than others. The main reason for high rates of mortality and disability is the high risk of cardiovascular events.There are few domestic large-scale studys on the risk of cardiovascular events in schizophrenia, thus they have not formed objective tools to assess cardiovascular risk. If this risk can be objectively assessed and predicted at an early stage, it is expected to reduce the risk of cardiovascular events and thus reduce mortality.At present, there are mature tools for assessing the risk of cardiovascular events in patients, such as Framingham, PRIMROSE, etc.The latter is the new tool for assessing cardiovascular events in patients with severe mental disorders. This study used a retrospective cohort design to collect the history of hospitalized schizophrenia patients within 2 years, collect relevant informations about cardiovascular events. The investigators validate the PRIMROSE model in China and explore the risk prediction model of cardiovascular events in schizophrenia patients.

Conditions

Schizophrenia

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Schizophrenia Patients

We collect relevant information, review cardiovascular events in schizophrenia patients.

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• Patients hospitalized before July 1, 2020；and hospitalized between July 1 2020-June 30, 2022；
• Meeting the ICD-10 diagnostic criteria for "Schizophrenia, Schizotypy, delusional disorder"or ICD-11 diagnostic criteria for "Schizophrenia and other Primary Mental Disorders";
• At least 2 doctors in charge of ward make definitive diagnoses;
• Age ≥18 years old, male and female are not limited.

Exclusion Criteria

• Researchers consider patients unsuitable for the study;
• past history of cardiovascular disease (fatal and nonfatal cardiovascular events defined as myocardial infarction, Angina, coronary heart disease, coronary artery surgery, cerebrovascular accident and transient ischemic attack, peripheral arterial disease or revascularization) prior to antipsychotic therapy ;
• use of lipid lowering drugs or antiplatelet drugs, such as aspirin、statins ，prior to antipsychotic therapy.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Shanghai Mental Health Center

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Shengke Pan, Master

Role: STUDY_DIRECTOR

Shanghai Mental Health Center

Locations

Shanghai Mental Health Center

Shanghai, Shanghai Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

Shengke Pan, Master

Role: CONTACT

psk2012@126.com

8618817314216

Facility Contacts

Shengke Pan, Master

Role: primary

psk2012@126.com

8618817314216

Other Identifiers

2020-YJ13

Identifier Type: -

Identifier Source: org_study_id