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运用机器学习方法剖析精神病中的认知障碍

Deconstructing Cognitive Impairment in Psychosis With a Machine Learning Approach.

作者信息

McCutcheon Robert A, Keefe Richard S E, McGuire Philip M, Marquand Andre

机构信息

Department of Psychiatry, University of Oxford, Oxford, United Kingdom.

Oxford Health NHS Foundation Trust, Oxford, United Kingdom.

出版信息

JAMA Psychiatry. 2025 Jan 1;82(1):57-65. doi: 10.1001/jamapsychiatry.2024.3062.

Abstract

IMPORTANCE

Cognitive functioning is associated with various factors, such as age, sex, education, and childhood adversity, and is impaired in people with psychosis. In addition to specific effects of the disorder, cognitive impairments may reflect a greater exposure to general risk factors for poor cognition.

OBJECTIVE

To determine the extent that impairments in cognition in psychosis reflect risk factor exposures.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study examined the relationship between exposures and cognitive function using data from the Bipolar-Schizophrenia Network on Intermediate Phenotypes studies 1 and 2 across 6 sites. Participants included healthy controls; patients with schizophrenia, schizoaffective disorder, or bipolar I disorder with psychosis; and relatives of patients. Predictive modeling was performed using extreme gradient boosting regression to train a composite cognitive score prediction model with nested cross-validation. Shapley additive explanations values were used to examine the relationship between exposures and cognitive function.

EXPOSURE

Exposures were chosen based on associations with cognition previously identified: age, sex, race and ethnicity, childhood adversity, education, parental education, parental socioeconomic status, parental age at birth, substance use, antipsychotic dose, and diagnosis.

MAIN OUTCOMES AND MEASURES

Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia.

RESULTS

A total of 3370 participants were included: 840 healthy controls, 709 patients with schizophrenia, 541 with schizoaffective disorder, 457 with bipolar I disorder with psychosis, and 823 relatives of patients. The mean (SD) age was 37.9 (13.3) years; 1887 were female (56%) and 1483 male (44%). The model predicted cognitive scores with high accuracy: out-of-sample Pearson correlation between predicted and observed cognitive composite score was r = 0.72 (SD = 0.03). Individuals with schizophrenia (z = -1.4), schizoaffective disorder (z = -1.2), and bipolar I disorder with psychosis (z = -0.5) all had significantly worse cognitive composite scores than controls. Factors other than diagnosis and medication accounted for much of this impairment (schizophrenia z = -0.73, schizoaffective disorder z = -0.64, bipolar I disorder with psychosis z = -0.13). Diagnosis accounted for a lesser proportion of this deficit (schizophrenia z = -0.29, schizoaffective disorder z = -0.15, bipolar I disorder with psychosis z = -0.13), and antipsychotic use accounted for a similar deficit across diagnostic groups (schizophrenia z = -0.37, schizoaffective disorder z = -0.33, bipolar I disorder with psychosis z = -0.26).

CONCLUSIONS AND RELEVANCE

This study found that transdiagnostic factors accounted for a meaningful share of the variance in cognitive functioning in psychosis. A significant proportion of the cognitive impairment in psychosis may reflect factors relevant to cognitive functioning in the general population. When considering interventions, a diagnosis-agnostic, symptom-targeted approach may therefore be appropriate.

摘要

重要性

认知功能与多种因素相关,如年龄、性别、教育程度和童年逆境,且在精神病患者中受损。除了该疾病的特定影响外,认知障碍可能反映出更易暴露于认知功能差的一般风险因素中。

目的

确定精神病患者的认知障碍在多大程度上反映了风险因素暴露情况。

设计、背景和参与者:这项横断面研究利用来自跨6个地点的双相情感障碍 - 精神分裂症中间表型网络研究1和2的数据,研究了暴露因素与认知功能之间的关系。参与者包括健康对照者;精神分裂症、分裂情感性障碍或伴有精神病性症状的双相I型障碍患者;以及患者的亲属。使用极端梯度提升回归进行预测建模,以训练具有嵌套交叉验证的综合认知评分预测模型。使用Shapley加性解释值来检验暴露因素与认知功能之间的关系。

暴露因素

根据先前确定的与认知的关联选择暴露因素:年龄、性别、种族和民族、童年逆境、教育程度、父母教育程度、父母社会经济地位、父母生育年龄、物质使用情况、抗精神病药物剂量和诊断。

主要结局和测量指标

使用《精神分裂症认知简要评估》评估认知功能。

结果

共纳入3370名参与者:840名健康对照者、709名精神分裂症患者、541名分裂情感性障碍患者、457名伴有精神病性症状的双相I型障碍患者以及823名患者亲属。平均(标准差)年龄为37.9(13.3)岁;1887名女性(56%),1483名男性(44%)。该模型对认知评分的预测准确率很高:预测的和观察到的认知综合评分之间的样本外Pearson相关系数为r = 0.72(标准差 = 0.03)。精神分裂症患者(z = -1.4)、分裂情感性障碍患者(z = -1.2)和伴有精神病性症状的双相I型障碍患者(z = -0.5)的认知综合评分均显著低于对照者。除诊断和药物治疗外的因素占这种损害的大部分(精神分裂症z = -0.73,分裂情感性障碍z = -0.64,伴有精神病性症状的双相I型障碍z = -0.13)。诊断占这种缺陷的比例较小(精神分裂症z = -0.29,分裂情感性障碍z = -0.15,伴有精神病性症状的双相I型障碍z = -0.13),并且抗精神病药物的使用在各诊断组中占类似的缺陷比例(精神分裂症z = -0.37,分裂情感性障碍z = -0.33,伴有精神病性症状的双相I型障碍z = -0.26)。

结论和意义

本研究发现,跨诊断因素在精神病患者认知功能差异中占相当大的比例。精神病患者认知障碍的很大一部分可能反映了与一般人群认知功能相关的因素。因此,在考虑干预措施时,采用不依赖诊断、以症状为靶点的方法可能是合适的。

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