Evidence map›Paper›PMID 39298629›Full record

ArticleMedicine2024

Antipsychotic prescription patterns among schizophrenia patients in Guangdong Province, China's 686 program: A retrospective study.

Yingji Hu, Jinghua Su, Xuanyu Xu, Junyu Li, Shujia Zhang, Xiaodong Chen

Abstract read
In one paragraph

Article in Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yingji HuGuangzhou Medical University, Guangzhou, China.
Jinghua SuKey Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China.
Xuanyu XuGuangzhou Medical University, Guangzhou, China.
Junyu LiGuangzhou Medical University, Guangzhou, China.
Shujia ZhangGuangzhou Medical University, Guangzhou, China.
Xiaodong ChenDepartment of Chronic Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China.ORCID 0000-0001-5621-0902

Funding

Guangdong Medical Science and Technology Research Foundation, Guangzhou Research-oriented Hospital Grant no. B2021185
6 · The paper itself

Abstract

Schizophrenia is a severe mental disorder. However, there is limited data on the prescribing patterns of patients under China's Central Government Support for the Local Management and Treatment of Severe Mental Illnesses Program, known as the "686 program." This study aimed to investigate the use of antipsychotic medications and associated factors among discharged schizophrenia patients in Guangdong Province, within the 686 Program. This study encompassed schizophrenia patients who were discharged from the Affiliated Brain Hospital, Guangzhou Medical University and enrolled in the 686 Program between January 2019 and December 2019. A total of 1645 hospitalized schizophrenia patients were included in the analysis. Clinical and sociodemographic data were acquired from medical records upon discharge. A total of 15 unique antipsychotic medications were utilized, comprising 4 first-generation (FGAs) and 11 second-generation (SGAs) options. FGAs were prescribed at a rate of 8.3%, while SGAs dominated at 98.8%. Risperidone (40.8%), olanzapine (30.2%), clozapine (24.6%), and amisulpride (15.4%) emerged as the top 4 prescribed medications. Additionally, mood stabilizers were used by 20.4%, antidepressants by 14.8%, sedative-hypnotics by 33.6%, anticholinergics by 26.9%, and other internal medicine drugs by 46.4%. Notably, 60.5% received antipsychotic monotherapy (AMT), while 39.5% underwent antipsychotic polypharmacy (APP). Predictors of polypharmacy included multiple hospital admissions, longer hospital stays, and undergoing modified Electroconvulsive Therapy (mECT) during hospitalization. In Guangdong Province, China's 686 Program, hospitalized patients commonly receive multiple antipsychotic medications simultaneously. Due to the varying outcomes in current studies on the benefits and risks of polypharmacy, it's vital to educate psychiatrists about the importance of AMT to reduce APP. Additionally, randomized, controlled trials are essential to identify the safest and most effective antipsychotic combinations, as well as to understand which patient profiles may benefit from these combinations.

Indexed as

Antipsychotic AgentsPractice Patterns, Physicians'SchizophreniaAdultChinaFemaleHumansMaleMiddle AgedRetrospective StudiesYoung AdultAntipsychotic Agents

Identifiers

PMID39298629
PMCPMC11404898

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.