Predicting Psychotic Relapse Using Speech-Based Early Detection
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 250
- 试验地点
- 3
- 主要终点
- Likelihood of relapse estimated using Speech-NLP Metrics
研究概览
简要总结
Psychotic disorders, including schizophrenia and affective psychosis, are severe mental health conditions marked by recurrent episodes that contribute to long-term disability. Relapses, characterized by the re-emergence of psychotic symptoms after remission, are a critical factor in the progression of these disorders, increasing risks such as suicide, cognitive impairment, and unemployment. This study aims to develop a novel, speech-based digital model to predict relapses in individuals with psychosis. Building on previous research into language abnormalities in schizophrenia, the study will employ a longitudinal design across Early Psychosis Intervention (EPI) clinics in Ontario and Quebec to advance relapse prediction
详细描述
OBJECTIVES: The primary goal of this study is to develop and validate a speech-based digital model to predict psychotic relapses in individuals with early psychosis. The study specifically aims to:
Test the hypothesis that within-subject changes in speech coherence, connectedness, and complexity, as measured by natural language processing (NLP) tools, will accurately identify imminent relapse, up to four weeks before clinical relapse in individuals receiving care in Early Psychosis Intervention (EPI) programs.
Investigate whether these speech-based relapse prediction models generalize across different languages (English and French) and are equally predictive in both males and females, addressing potential sociodemographic and linguistic influences on model performance.
Explore whether combining acoustic and prosodic features with core NLP-based speech measures improves the model's sensitivity and specificity for relapse prediction.
METHODS:
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Prospective
入排标准
- 年龄范围
- 16 Years 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age must be 16 years and older
- •Diagnosis must meet DSM-5 criteria for psychotic disorders, including schizophrenia, schizoaffective disorder, or related conditions
- •Fluency in English or French
- •Must be currently receiving treatment through an EPI program
排除标准
- •Severe comorbid speech or language disorders (e.g., aphasia)
- •Primary diagnosis of non-psychotic disorders
- •Inability to provide consent or complete assessments
结局指标
主要结局
Likelihood of relapse estimated using Speech-NLP Metrics
时间窗: Monthly, up to 24 months
This primary outcome will assess the ability of speech-based NLP metrics (coherence, connectedness, and complexity) to predict impending relapses in psychosis. Monthly speech samples will be analyzed to determine if changes in these metrics can distinguish timepoints preceding relapses from those not followed by relapse, with the aim of predicting relapses up to four weeks in advance. The likelihood of relapse is a numerical probabilistic estimate without any units. Outcome definition: Occurrence of relapse (i.e., psychiatric hospitalization, an increase in the level of psychiatric care, or substantial clinical deterioration \>1wk that requires \>25% increase in Defined Daily Dose equivalents of antipsychotics)
Generalization of Speech-Based Relapse Prediction Models Across Languages and Genders
时间窗: Monthly, up to 24 months
This outcome will assess whether the speech-based relapse prediction models are valid and perform equally well across different languages (English and French) and genders (male and female). The study will evaluate how sociodemographic factors such as language and sex impact the predictive accuracy of the models. NLP metrics (coherence, connectedness, complexity) will be correlated with clinical outcomes, and model performance will be compared across linguistic and gender subgroups to ensure generalizability.
次要结局
- Likelihood of relapse estimated using multi-level speech features(Monthly, up to 24 months)
研究者
Lena Palaniyappan
Director, Centre of Excellence in Youth Mental Health
Douglas Mental Health University Institute