Background: The 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.
Methods: Data 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.
Results: The 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.
Conclusions: OCS, 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.
Keywords: machine learning; network analysis; obsessive-compulsive symptom; schizophrenia; structural equation modeling; subjective well-being.
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