认知
心理学
精神分裂症(面向对象编程)
睡眠剥夺对认知功能的影响
纵向研究
临床心理学
精神分裂症谱
损耗
社会认知
认知功能衰退
发展心理学
神经认知
认知矫正疗法
听力学
精神科
认知测验
辍学(神经网络)
作者
Maria Lee,Granville James Matheson,Ellen E Lee,Sara Holmqvist,Håkan Nyman,Ingrid Agartz,Alison R. Hwong,Erik G. Jönsson
标识
DOI:10.1093/schbul/sbag162
摘要
BACKGROUND AND HYPOTHESIS: The longitudinal course of cognitive functioning in schizophrenia is debated. Previous longitudinal studies have reported selective attrition. We aimed to analyze cognitive trajectories over 13 years among patients with schizophrenia spectrum disorders (SSDs) in comparison with controls, while accounting for dropout bias, and explored baseline predictors of cognitive outcome. STUDY DESIGN: Cognition was assessed at study start, 5-year, and 13-year follow-up in 108 participants with schizophrenia spectrum disorder and 85 controls of similar age. Linear Mixed Effects models were used to model trajectories over time, with inverse probability weights to account for non-random dropout in patients. Predictors of cognitive function at 13 years were explored using linear regression. STUDY RESULTS: Retention was 51% for patients and 65% for controls. At study start, patients who dropped out performed worse on most cognitive tasks and were significantly older (4.5 years) than completers. In primary inverse probability of attrition weight-weighted analyses, patients scored lower than controls across nearly all cognitive domains (cognitive composite: β = -0.91 SD [95% CI -1.42, -0.39]) but their trajectories did not significantly differ from those of controls. Higher age predicted worse cognitive function. Illness duration was an independent negative predictor in patients. Study start cognition predicted 13-year follow-up cognition in patients. CONCLUSIONS: Although cognitive performance of patients was lower than that of controls at study start, their change in performance over 13 years did not differ from the controls. Patients who dropped out were more cognitively impaired, highlighting risk of attrition bias. The robustness of findings across weighted and unweighted analyses strengthens confidence in the observed trajectories.
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