OBJECTIVES: Considering large diversity of clinical presentation of schizophrenia, it is important to identify valid clinical subtypes or dimensions that might have homogeneous biological underpinning. The current study aimed to explore lifetime symptom-based dimensional phenotypes in patients with chronic schizophrenia, and to investigate their correlation with cognitive functions and other clinical characteristics. METHODS: Lifetime-based symptoms and additional clinical variables were measured using the Diagnostic Interview for Genetic Studies and the Schedule for the Deficit Syndrome in 315 clinically stable patients with chronic schizophrenia. Through principal components factor analysis, eight dimensional phenotypes were obtained. Comprehensive neuropsychological tests were administered for 103 out of 315 patients, and domain scores were calculated for cognitive domains defined in the MATRICS consensus battery. RESULTS: 'Non-paranoid delusion factor' including delusions of grandiose or religious nature, showed significant negative correlation with processing speed, working memory, attention/vigilance, and general cognitive ability, and positive correlation with intra-individual variability. 'Negative symptom factor' showed significant negative correlation only with general cognitive ability. Those two factors were also negatively correlated with function levels measured by Global Assessment Scale (GAS), and associated with poor treatment responses. CONCLUSION: Symptom-based dimensional phenotypes of schizophrenia measured on a lifetime basis showed discriminative correlation with cognitive function domains, global functioning level, and overall treatment responses, indicating their possibility as valid phenotype axes of schizophrenia having homogeneous biologic basis.
Appointments and Schedules ; Cognition ; Consensus ; Delusions ; Humans ; Memory, Short-Term ; Neuropsychological Tests ; Phenotype ; Schizophrenia*

Objectives: In this study, we conducted an exploratory analysis of the current media trends on schizophrenia using text-mining methods. Methods: First, web-crawling techniques extracted text data from 575 news articles in10 major newspapers between 2018 and 2019, which were selected by searching “schizophrenia” in the Naver News. We had developed document-term matrix (DTM) and/or term-document matrix (TDM) through pre-processing techniques. Through the use of DTM and TDM, frequency analysis, cooccurrence network analysis, and topic model analysis were conducted. Results: Frequency analysis showed that keywords such as “police,” “mental illness,” “admission,” “patient,” “crime,” “apartment,” “lethal weapon,” “treatment,” “Jinju,” and “residents” were frequently mentioned in news articles on schizophrenia. Within the article text, many of these keywords were highly correlated with the term “schizophrenia” and were also interconnected with each other in the co-occurrence network. The latent Dirichlet allocation model presented 10 topics comprising a combination of keywords: “police-Jinju,” “hospital-admission,” “research-finding,” “care-center,” “schizophrenia-symptom,” “society-issue,” “family-mind,” “woman-school,” and “disabled-facilities.” Conclusion The results of the present study highlight that in recent years, the media has been reporting violence in patients with schizophrenia, thereby raising an important issue of hospitalization and community management of patients with schizophrenia.

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.

This study aimed to investigate cognitive impairment and psychological distress of critically ill patients at discharge from intensive care unit (ICU). This study included 30 critically ill patients who had neither pre-existing dementia nor ongoing delirium. At ICU discharge, they performed a screening test for cognitive impairment (Mini-Cog test) and completed questionnaires for depression (Patient Health Questionnaire-2, PHQ-2) and for 4 stressful experiences during ICU stay including nightmares, severe anxiety or panic, severe pain, and trouble to breathe or feeling of suffocation (Post-Traumatic Stress Syndrome 14-Question Inventory, PTSS-14 Part A). Thirteen patients (43.3%) screened positive for cognitive impairment and 18 patients (60.0%) exhibited depressive symptoms. Twenty three patients (76.7%) recollected one or more stressful in-ICU experiences. Female patients (88.9%) was more likely to feel depressed at ICU discharge, compared to male patients (47.6%) (χ2=4.47, p=0.03). To the best of our knowledge, this is the first report on cognitive and psychological outcomes of ICU survivors in Korea. In this study, we observed that a considerable number of critically ill patients had experienced cognitive impairment or psychological distress at ICU discharge.
Anxiety ; Asphyxia ; Cognition Disorders* ; Critical Care* ; Critical Illness ; Delirium ; Dementia ; Depression ; Dreams ; Female ; Humans ; Intensive Care Units* ; Korea ; Male ; Mass Screening ; Panic ; Survivors

OBJECTIVES: Intra-individual variability of cognitive performance across different tests or domains has been reported as an important index of cognitive function. The aim of the current study is to examine the intra-individual variability across different cognitive domains and tests in patients with schizophrenia, their unaffected siblings, and normal controls. We also compared the variability among three patient sub-groups divided according to the duration of illness. METHODS: Comprehensive neurocognitive tests were administered in order to stabilize patients with schizophrenia (n=129), healthy siblings (n=38) of the patients, and normal controls (n=110). Intra-individual variability was computed from the variance of the scores of six cognitive domains of the Measurement and Treatment Research to Improve Cognition in Schizophrenia consensus battery. We examined intra-individual variability across six factor-based cognitive scores and individual test scores of each cognitive domain. RESULTS: Compared to the normal control and sibling groups, patients showed significantly increased intra-individual variability across six cognitive domains and individual cognitive tests of each domain. Compared to the normal control, siblings showed significantly increased intra-individual variability only across individual tests of the processing of speed domain. Among patient sub-groups, those with the longest duration of illness (> or =11 years) showed significantly higher intra- individual variability across six cognitive domains and across individual tests of the processing of speed domain compared to the other two groups. CONCLUSION: This study identified cognitive dissonances across six cognitive domain schizophrenia patients. These cognitive characteristics were not observed in the sibling groups and seemed to progress during the course of illness.
Cognition ; Cognitive Dissonance ; Consensus ; Humans ; Neuropsychological Tests* ; Schizophrenia* ; Siblings*

We present a case of 34-year-old female patient with major depressive disorder who had decreased metabolic activity of CYP2D6. Low dosage regimen of mirtazapine & paroxetine led to unexpectedly severe adverse effects and noncompliance in this case with genotype CYP2D6 *1/*5. Antidepressant change to imipramine with consideration of the genotyping resulted with tolerable adverse effects and remission of depression. This case suggests the clinical usefulness of pharmacogenetic testing in individualized antidepressant treatments.
Adult ; Cytochrome P-450 CYP2D6 ; Cytochrome P-450 Enzyme System* ; Cytochromes* ; Depression ; Depressive Disorder, Major* ; Female ; Genotype* ; Humans ; Imipramine ; Paroxetine ; Pharmacogenetics

OBJECTIVES: In the current study, we quantitatively estimated changes in appetite and eating behavior of bipolar disorder patients during the pharmacotherapy. We also investigated their contribution to the weight gain and their association with specific food-craving characteristics of the patients. METHODS: Subjects included forty-one bipolar disorder patients and fifty-six controls. Currently sustained natures of food craving were assessed using the General-Food Craving Questionnaire-Trait (G-FCQ-T) and changes in appetite and eating behavior were measured using the Drug-Related Eating Behavior Questionnaire (DR-EBQ). RESULTS: Compared to the control group, the patients' group showed significantly higher body mass index (t=2.028, p=0.045). The patients' group had significantly higher 'Preoccupation with food' factor score of G-FCQ-T (p=0.016) than that of the control group. Hierarchical multiple regression analysis showed that only 'preoccupation with food' factor independently predicted psychotropic medication-induced appetite change. CONCLUSIONS: Appetite change while receiving psychotropic medication seems to be related to the weight-gain and associated with craving natures of 'preoccupation with food' in bipolar disorder. Appetite and/or eating behavioral changes measured by G-FCQ-T and DR-EBQ could be regarded as an important mediating factor in future studies exploring biological mechanisms of weight gain related with pharmacotherapy for bipolar disorder.
Appetite ; Bipolar Disorder ; Body Mass Index ; Eating ; Feeding Behavior ; Humans ; Negotiating ; Weight Gain ; Surveys and Questionnaires

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.
Forests ; Korea ; Machine Learning* ; Mass Screening ; ROC Curve ; Sensitivity and Specificity ; Suicide*

OBJECTIVE: This study aimed to investigate clinical factors contributing to the low physical activity (PA) of patients with chronic schizophrenia. METHODS: PA was measured in 50 outpatients with chronic schizophrenia using the International Physical Activity Questionnaire Short Form (IPAQ-SF). Psychopathology, psychosocial functioning, and extrapyramidal symptoms were assessed using the 18 item-Brief Psychiatric Rating Scale (BPRS-18), Global Assessment of Functioning (GAF), and Drug-Induced Extrapyramidal Symptom Scale (DIEPSS), respectively. We examined differences in these clinical variables between “inactive,”“minimally active,” and “health enhancing physical activity” groups. Linear regression analysis was used to examine the clinical factors explaining low PA levels in patients with schizophrenia. RESULTS: Subjects spent an average of 130.18±238.89 min/wk on moderate/vigorous-intensity PA and only 26% of them met the recommended PA guideline of 150 minutes of at least moderate PA per week. The inactive group showed significantly higher BPRS-18 and DIEPSS scores, and a lower GAF score than the other groups. Linear regression analysis showed that DIEPSS scores independently explained the amount of total PA (p=0.001) and time spent being sedentary (p=0.028). CONCLUSION: This study provides preliminary evidence that extrapyramidal symptoms could be a major impediment to the PA of patients with schizophrenia.
Humans ; Linear Models ; Motor Activity* ; Outpatients ; Psychopathology ; Schizophrenia* ; Sedentary Lifestyle

OBJECTIVE: We aimed to develop predictive models to identify suicide attempters among individuals with suicide ideation using a machine learning algorithm. METHODS: Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 5,773 subjects who reported experiencing suicide ideation and had answered a survey question about suicide attempts. Then, we performed resampling with the Synthetic Minority Over-sampling TEchnique (SMOTE) to obtain data corresponding to 1,324 suicide attempters and 1,330 non-suicide attempters. We randomly assigned the samples to a training set (n=1,858) and a test set (n=796). In the training set, random forest models were trained with features selected through recursive feature elimination with 10-fold cross validation. Subsequently, the fitted model was used to predict suicide attempters in the test set. RESULTS: In the test set, the prediction model achieved very good performance [area under receiver operating characteristic curve (AUC)=0.947] with an accuracy of 88.9%. CONCLUSION: Our results suggest that a machine learning approach can enable the prediction of individuals at high risk of suicide through the integrated analysis of various suicide risk factors.
Forests ; Korea ; Machine Learning ; Risk Factors ; ROC Curve ; Suicide