Suicidality and Related Psychopathology across Different Stages of Schizophrenia / 대한정신분열병학회지

Objectives@#This study aimed to investigate suicidal behaviors and the related psychopathology across the different stages of schizophrenia. @*Methods@#We recruited 131 patients with schizophrenia and categorized them into two groups, according to the duration of illness (DI) as follows: ≤10 years (n=39) and >10 years (n=92). Psychopathology and suicidality were assessed using the 18-item Brief Psychiatric Rating Scale (BPRS-18) and the suicidality module from the Mini-International Neuropsychiatric Interview, respectively. @*Results@#One-quarter of the patients with a DI ≤10 years and nearly one-sixth of the patients with a DI >10 years experienced suicidal behaviors in the previous month. Suicidality scores were significantly associated with the “affect” factor scores of the BPRS-18 in patients with a DI ≤10 years (β=0.55, p=0.003) and with the “resistance” factor scores in patients with a DI of >10 years (β=0.29, p=0.006). @*Conclusion@#The present study demonstrated that psychopathological factors were differentially associated with suicidality in patients with schizophrenia according to the illness stage. Our findings suggest that for effective suicide prevention, different approaches are required for the management of each stage of schizophrenia.

Functional Disabilities Evaluated using World Health Organization Disability Assessment Schedule 2.0 in Patients with Chronic Schizophrenia and Its Related Factors / 신경정신의학

OBJECTIVES: This study examined the functional disabilities of patients with chronic schizophrenia using WHO Disability Assessment Schedule 2.0 (WHODAS 2.0) and its related factors. METHODS: The subjects consisted of 86 patients with schizophrenia with more than 10 years' duration of illness and 40 healthy volunteers. The functional disabilities and psychopathology were evaluated using the WHODAS 2.0 and 18-items Brief Psychiatric Rating Scale (BPRS-18), respectively. This study analyzed the six sub-domains ('cognition', 'mobility', 'self-care', 'getting along', 'life activities', and 'participation') of WHODAS 2.0 and the four sub-scales ('positive symptoms', 'negative symptoms', 'affect', and 'resistance') of BPRS-18. RESULTS: Patients with chronic schizophrenia experienced severe functional disabilities across all six sub-domains of WHODAS 2.0 compared to healthy people. Hierarchical regression showed that 'negative symptoms' explained the disabilities in the WHODAS 2.0 sub-domains of 'cognition' (p<0.05), 'self-care' (p<0.05), 'getting along' (p<0.01), and 'life activities' (p<0.05). 'Positive symptoms' and 'affect' explained the disabilities in 'cognition' (p<0.01 and p<0.05, respectively) and 'participation' (p<0.05 and p<0.01, respectively). 'Resistance' was found to be a predictor of 'getting along' disabilities (p<0.01). CONCLUSION: Negative symptoms mainly accounted for the multiple domains of functional disabilities in the WHODAS 2.0 but residual positive and affective symptoms could also deteriorate the cognition and social participation of patients with chronic schizophrenia.

Use of a Machine Learning Algorithm to Predict Individuals with Suicide Ideation in the General Population

OBJECTIVE: In this study, we aimed to develop a model predicting individuals with suicide ideation within a general population using a machine learning algorithm. METHODS: Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 11,628 individuals via random down-sampling. This included 5,814 suicide ideators and the same number of non-suicide ideators. We randomly assigned the subjects to a training set (n=10,466) and a test set (n=1,162). In the training set, a random forest model was trained with 15 features selected with recursive feature elimination via 10-fold cross validation. Subsequently, the fitted model was used to predict suicide ideators in the test set and among the total of 35,116 subjects. All analyses were conducted in R. RESULTS: The prediction model achieved a good performance [area under receiver operating characteristic curve (AUC)=0.85] in the test set and predicted suicide ideators among the total samples with an accuracy of 0.821, sensitivity of 0.836, and specificity of 0.807. CONCLUSION: This study shows the possibility that a machine learning approach can enable screening for suicide risk in the general population. Further work is warranted to increase the accuracy of prediction.