Correlates of severity in a clinical staging model of schizophrenia: a cross-sectional study among 158 subjects.
Antipsychotics
Mood
Psychosis
Schizophrenia
Staging
Journal
BMC psychiatry
ISSN: 1471-244X
Titre abrégé: BMC Psychiatry
Pays: England
ID NLM: 100968559
Informations de publication
Date de publication:
04 09 2023
04 09 2023
Historique:
received:
15
03
2023
accepted:
26
08
2023
medline:
6
9
2023
pubmed:
5
9
2023
entrez:
4
9
2023
Statut:
epublish
Résumé
Clinical staging has been widely used to predict and optimize the treatment of medical disorders. Different models have been proposed to map the development, progression, and extension of psychiatric disorders over time, mainly for schizophrenia. The primary objective of this study was to classify patients with psychosis according to the McGorry staging model and compare factors between the different stages. This was a cross-sectional study, collecting data from 158 patients hospitalized for schizophrenia/psychosis. The survey included the Mini International Neuropsychiatric Interview (MINI), Positive and Negative Symptom Scale (PANSS), Montgomery-Asberg Depression Rating Scale (MADRS), Yong Mania Rating Scale (YMRS), Clinical Global Impression (CGI) scale, and the McGorry staging model. Patients have been classified into three clinical stages: relapse of psychotic disorder (43%), multiple relapses (47.5%), and persistent and severe illness (9.5%). A higher mean duration of hospitalization, psychotic symptoms (PANSS total scale and subscales), chlorpromazine equivalent dose, and number of antipsychotic treatments were found among participants in Stage 4 as compared to the other groups. However, a significantly higher mean GAF scale was found among participants in stage 3b as compared to the other groups. Each stage in the McGorry staging model of schizophrenia is associated with well-defined clinical presentations, which help decide the appropriate treatment. Using such models in psychiatry can improve the diagnostic process and potential therapeutic interventions for patients suffering from mental disorders.
Sections du résumé
BACKGROUND
Clinical staging has been widely used to predict and optimize the treatment of medical disorders. Different models have been proposed to map the development, progression, and extension of psychiatric disorders over time, mainly for schizophrenia. The primary objective of this study was to classify patients with psychosis according to the McGorry staging model and compare factors between the different stages.
METHODS
This was a cross-sectional study, collecting data from 158 patients hospitalized for schizophrenia/psychosis. The survey included the Mini International Neuropsychiatric Interview (MINI), Positive and Negative Symptom Scale (PANSS), Montgomery-Asberg Depression Rating Scale (MADRS), Yong Mania Rating Scale (YMRS), Clinical Global Impression (CGI) scale, and the McGorry staging model.
RESULTS
Patients have been classified into three clinical stages: relapse of psychotic disorder (43%), multiple relapses (47.5%), and persistent and severe illness (9.5%). A higher mean duration of hospitalization, psychotic symptoms (PANSS total scale and subscales), chlorpromazine equivalent dose, and number of antipsychotic treatments were found among participants in Stage 4 as compared to the other groups. However, a significantly higher mean GAF scale was found among participants in stage 3b as compared to the other groups.
CONCLUSION
Each stage in the McGorry staging model of schizophrenia is associated with well-defined clinical presentations, which help decide the appropriate treatment. Using such models in psychiatry can improve the diagnostic process and potential therapeutic interventions for patients suffering from mental disorders.
Identifiants
pubmed: 37667266
doi: 10.1186/s12888-023-05144-6
pii: 10.1186/s12888-023-05144-6
pmc: PMC10478431
doi:
Substances chimiques
Antipsychotic Agents
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
648Informations de copyright
© 2023. BioMed Central Ltd., part of Springer Nature.
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