Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Department of Psychiatry, Rob Giel Research Center, University Medical Center Groningen, University Center for Psychiatry, University of Groningen, Groningen, The Netherlands.
Schizophr Bull. 2023 Nov 29;49(6):1447-1459. doi: 10.1093/schbul/sbad046.
Current rates of poor social functioning (SF) in people with psychosis history reach 80% worldwide. We aimed to identify a core set of lifelong predictors and build prediction models of SF after psychosis onset.
We utilized data of 1119 patients from the Genetic Risk and Outcome in Psychosis (GROUP) longitudinal Dutch cohort. First, we applied group-based trajectory modeling to identify premorbid adjustment trajectories. We further investigated the association between the premorbid adjustment trajectories, six-year-long cognitive deficits, positive, and negative symptoms trajectories, and SF at 3-year and 6-year follow-ups. Next, we checked associations between demographics, clinical, and environmental factors measured at the baseline and SF at follow-up. Finally, we built and internally validated 2 predictive models of SF.
We found all trajectories were significantly associated with SF (P < .01), explaining up to 16% of SF variation (R2 0.15 for 3- and 0.16 for 6-year follow-up). Demographics (sex, ethnicity, age, education), clinical parameters (genetic predisposition, illness duration, psychotic episodes, cannabis use), and environment (childhood trauma, number of moves, marriage, employment, urbanicity, unmet needs of social support) were also significantly associated with SF. After validation, final prediction models explained a variance up to 27% (95% CI: 0.23, 0.30) at 3-year and 26% (95% CI: 0.22, 0.31) at 6-year follow-up.
We found a core set of lifelong predictors of SF. Yet, the performance of our prediction models was moderate.
目前全球范围内,有精神病史的人群中,社会功能不良(SF)的发生率高达 80%。我们旨在确定一组与精神分裂症相关的终生预测因素,并建立精神分裂症发病后的 SF 预测模型。
我们利用了来自荷兰 GROUP 纵向队列的 1119 名患者的数据。首先,我们应用基于群组的轨迹建模来识别潜在的调整轨迹。然后,我们进一步研究了潜在调整轨迹、六年认知缺陷、阳性和阴性症状轨迹与发病后 3 年和 6 年的 SF 之间的关系。接着,我们检查了基线时测量的人口统计学、临床和环境因素与随访时的 SF 之间的关联。最后,我们建立并内部验证了 SF 的 2 个预测模型。
我们发现所有轨迹都与 SF 显著相关(P <.01),解释了 SF 变化的 16%(3 年随访时 R2 为 0.15,6 年随访时 R2 为 0.16)。人口统计学因素(性别、种族、年龄、教育)、临床参数(遗传易感性、发病时间、精神病发作、大麻使用)和环境因素(儿童期创伤、迁居次数、婚姻、就业、城市化程度、社会支持需求未满足)也与 SF 显著相关。验证后,最终预测模型在 3 年随访时解释了高达 27%的方差(95% CI:0.23,0.30),在 6 年随访时解释了 26%的方差(95% CI:0.22,0.31)。
我们确定了一组与 SF 相关的终生预测因素。然而,我们的预测模型的性能仅为中等。