4.6 Article

Developing prediction models for symptom severity around the time of discharge from a tertiary-care program for treatment-resistant psychosis

期刊

FRONTIERS IN PSYCHIATRY
卷 14, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fpsyt.2023.1181740

关键词

schizophrenia; psychosis; prediction model; tertiary care; treatment resistance

向作者/读者索取更多资源

Antipsychotics are the primary treatment for psychotic disorders, but may not always benefit all patients with schizophrenia or schizoaffective disorder. Refractoriness to conventional treatment can be challenging to address. This study aimed to predict symptom severity using clinicodemographic information and prescription drug data at admission. A boosted beta regression model was trained using a dataset of 320 hospital admissions, and internal validation was conducted. The results demonstrated the potential for predicting symptom severity using this approach.
Antipsychotics are the only therapeutic class indicated in the symptomatic management of psychotic disorders. However, individuals diagnosed with schizophrenia or schizoaffective disorder may not always benefit from these first-line agents. This refractoriness to conventional treatment can be difficult to address in most clinical settings. Therefore, a referral to a tertiary-care program that is better able to deliver specialized care in excess of the needs of most individuals may be necessary. The average outcome following a period of treatment at these programs tends to be one of improvement. Nonetheless, accurate prognostication of individual-level responses may be useful in identifying those who are unlikely to improve despite receiving specialized care. Thus, the main objective of this study was to predict symptom severity around the time of discharge from the Refractory Psychosis Program in British Columbia, Canada using only clinicodemographic information and prescription drug data available at the time of admission. To this end, a different boosted beta regression model was trained to predict the total score on each of the five factors of the Positive and Negative Syndrome Scale (PANSS) using a data set composed of 320 hospital admissions. Internal validation of these prediction models was then accomplished by nested cross-validation. Insofar as it is possible to make comparisons of model performance across different outcomes, the correlation between predictions and observations tended to be higher for the negative and disorganized factors than the positive, excited, and depressed factors on internal validation. Past scores had the greatest effect on the prediction of future scores across all 5 factors. The results of this study serve as a proof of concept for the prediction of symptom severity using this specific approach.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据