基于使用机器学习的纵向症状轨迹对首发精神病进行分型。

Yanan Liu, Sara Jalali, Ridha Joober, Martin Lepage, Srividya Iyer, Jai Shah, David Benrimoh
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引用次数: 0

摘要

首发精神病(FEP)后的临床病程具有异质性。亚分组和预测FEP后的纵向症状轨迹可能有助于开发个性化的治疗方法。基于纵向阳性和阴性症状,我们采用k-均值聚类来识别411例FEP患者的聚类。确定了三个集群。集群1表现出较低的阳性和阴性症状(LS)、较低的抗精神病药物剂量和相对较高的情感性精神病;集群2表现为较低的阳性症状、持续的阴性症状(LPPN)和中等抗精神病药物剂量;集群3表现出持续高水平的阳性和阴性症状(PPNS)和较高的抗精神病药物剂量。我们对基线数据使用岭型逻辑回归预测聚类隶属度(AUC为0.74)。与LPPN组相比,LS组的关键预测指标包括较低的冷漠、情感扁平化和快感缺乏/社会性。幻觉严重程度、积极思维障碍和躁狂敌意预测PPNS。这些结果有助于分析FEP轨迹的异质性,并可能促进个性化治疗的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning.

Clinical course after first episode psychosis (FEP) is heterogeneous. Subgrouping and predicting longitudinal symptom trajectories after FEP may help develop personalized treatment approaches. We utilized k-means clustering to identify clusters of 411 FEP patients based on longitudinal positive and negative symptoms. Three clusters were identified. Cluster 1 exhibits lower positive and negative symptoms (LS), lower antipsychotic dose, and relatively higher affective psychosis; Cluster 2 shows lower positive symptoms, persistent negative symptoms (LPPN), and intermediate antipsychotic doses; Cluster 3 presents persistently high levels of both positive and negative symptoms (PPNS), and higher antipsychotic doses. We predicted cluster membership (AUC of 0.74) using ridge logistic regression on baseline data. Key predictors included lower levels of apathy, affective flattening, and anhedonia/asociality in the LS cluster, compared to the LPPN cluster. Hallucination severity, positive thought disorder and manic hostility predicted PPNS. These results help parse the FEP trajectory heterogeneity and may facilitate the development of personalized treatments.

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