BackgroundThe ultimate goal of successful schizophrenia treatment is not just to alleviate psychotic symptoms, but also to reduce distress and achieve subjective well-being (SWB). We aimed to identify the determinants of SWB and their interrelationships in schizophrenia.MethodsData were obtained from 637 patients with schizophrenia enrolled in multicenter, open-label, non-comparative clinical trials. The SWB under the Neuroleptic Treatment Scale (SWN) was utilized; a cut-off score of 80 indicated a high level of SWB at baseline and 6 months. Various machine learning (ML) algorithms were employed to identify the determinants of SWB. Furthermore, network analysis and structural equation modeling (SEM) were conducted to explore detailed relationship patterns.ResultsThe random forest (RF) model had the highest area under the curve (AUC) of 0.794 at baseline. Obsessive-compulsive symptoms (OCS) had the most significant impact on high levels of SWB, followed by somatization, cognitive deficits, and depression. The network analysis demonstrated robust connections among the SWB, OCS, and somatization. SEM analysis revealed that OCS exerted the strongest direct effect on SWB, and also an indirect effect via the mediation of depression. Furthermore, the contribution of OCS at baseline to SWB was maintained 6 months later.ConclusionsOCS, somatization, cognition, and depression, rather than psychotic symptoms, exerted significant impacts on SWB in schizophrenia. Notably, OCS exhibited the most significant contribution not only to the current state of well-being but also to follow-up SWB, implying that OCS was predictive of SWB. The findings demonstrated that OCS management is critical for the treatment of schizophrenia.

中文翻译：

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对精神分裂症主观幸福感的决定因素进行建模


背景精神分裂症治疗成功的最终目标不仅是缓解精神病症状，而且还要减少痛苦并实现主观幸福感 （SWB）。我们旨在确定精神分裂症中 SWB 的决定因素及其相互关系。方法数据来自参加多中心、开放标签、非对照临床试验的 637 例精神分裂症患者。使用神经阻滞剂治疗量表 （SWN） 下的 SWB;临界分数 80 表示基线和 6 个月时 SWB 水平高。采用了各种机器学习 （ML） 算法来识别 SWB 的决定因素。此外，还进行了网络分析和结构方程建模 （SEM） 以探索详细的关系模式。结果随机森林 （RF） 模型基线时曲线下面积 （AUC） 最高，为 0.794。强迫症状 （OCS） 对高水平的 SWB 影响最显着，其次是躯体化、认知缺陷和抑郁。网络分析表明 SWB 、 OCS 和躯体化之间的强大联系。SEM 分析显示，OCS 对 SWB 的直接作用最强，也通过抑制的介导产生间接作用。此外，基线时 OCS 对 SWB 的贡献在 6 个月后保持不变。结论OCS 、躯体化、认知和抑郁，而不是精神病症状，对精神分裂症的 SWB 产生显着影响。值得注意的是，OCS 不仅对当前的健康状况做出了最显着的贡献，而且对后续 SWB 也表现出最显着的贡献，这意味着 OCS 可以预测 SWB。研究结果表明，OCS 管理对于精神分裂症的治疗至关重要。