Outcome of severe obsessive-compulsive disorder with schizotypal features: a pilot study.

BACKGROUND/PURPOSE: Long-term outcome of patients with severe obsessive-compulsive disorder (OCD) and schizotypal features has been rarely studied. We investigated this issue in this retrospective pilot study. METHODS: Twenty-two patients with severe OCD and schizotypal features were identified by chart review. Another 22 OCD patients without schizotypal features (OCD-NS) served as the comparison group. Those with schizotypal features must not fulfill a diagnosis of schizophrenia or schizotypal disorder. After an average follow-up of 6.6 years, each patient received a re-diagnosis clinical interview. Relevant demographic and clinical data were collected. Patients with schizotypal features were classified into two groups after re-diagnosis: those with schizophrenia or schizotypal disorder (OCD-SS group, n = 9) and those with only schizotypal traits (OCD-ST group, n = 13) that did not fulfill a well-formed schizophrenia-spectrum disorder. Demographic data, family history, clinical symptoms, and OCD course were compared among the three patient groups. RESULTS: Compared with the OCD-NS group, the OCD-SS group was significantly less educated, less likely to be married or female, and had earlier onset of illness and poorer OCD course (p<0.05). There was no significant difference in any demographic and clinical variables between the OCD-SS and OCD-ST groups except that the OCD-ST group had a significantly better OCD course (p < 0.01). CONCLUSION: The findings suggest that a substantial proportion of the patients with severe OCD and schizotypal features evolve into schizophrenia spectrum disorder and are associated with a poor long-term outcome, whereas the OCD-NS group might stay with limited manifestations of schizotypal features and have a better outcome.

A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data.

BACKGROUND: In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms (SNPs). METHODS: We employed the dataset that was original to the previous study by the CDC Chronic Fatigue Syndrome Research Group. To uncover relationships between CFS and SNPs, we applied three classification algorithms including naive Bayes, the support vector machine algorithm, and the C4.5 decision tree algorithm. Furthermore, we utilized feature selection methods to identify a subset of influential SNPs. One was the hybrid feature selection approach combining the chi-squared and information-gain methods. The other was the wrapper-based feature selection method. RESULTS: The naive Bayes model with the wrapper-based approach performed maximally among predictive models to infer the disease susceptibility dealing with the complex relationship between CFS and SNPs. CONCLUSION: We demonstrated that our approach is a promising method to assess the associations between CFS and SNPs.

Identification of significant genes in genomics using Bayesian variable selection methods.

In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for research ranging from candidate gene studies to genome-wide association studies. In this study, we proposed a Bayesian method for identifying the promising candidate genes that are significantly more influential than the others. We employed the framework of variable selection and a Gibbs sampling based technique to identify significant genes. The proposed approach was applied to a genomics study for persons with chronic fatigue syndrome. Our studies show that the proposed Bayesian methodology is effective for deriving models for genomic studies and for providing information on significant genes.

Association study of a brain-derived neurotrophic-factor polymorphism and short-term antidepressant response in major depressive disorders.

Major depressive disorder (MDD) is one of the most common mental disorders worldwide. Single nucleotide polymorphisms (SNPs) can be used in clinical association studies to determine the contribution of genes to drug efficacy. A common SNP in the brain-derived neurotrophic factor (BDNF) gene, a methionine (Met) substitution for valine (Val) at codon 66 (Val66Met), is a candidate SNP for influencing antidepressant treatment outcome. In this study, our goal was to determine the relationship between the Val66Met polymorphism in the BDNF gene and the rapid antidepressant response to venlafaxine in a Taiwanese population with MDD. Overall, the BDNF Val66Met polymorphism was found not to be associated with short-term venlafaxine treatment outcome. However, the BDNF Val66Met polymorphism showed a trend to be associated with rapid venlafaxine treatment response in female patients. Future research with independent replication in large sample sizes is needed to confirm the role of the BDNF Val66Met polymorphism identified in this study.

Outcome of short-term hospitalization in a psychosomatic ward.

BACKGROUND AND PURPOSE: This study investigated the short-term outcome of patients with common mental disorders (CMDs) admitted to a psychosomatic ward. Multidimensional outcome measurements were used, including psychological symptoms, global functioning, and service satisfaction. METHODS: A total of 56 consecutive patients with CMDs admitted to a 33-bed psychosomatic ward for crisis intervention or due to refractory conditions unresponsive to treatment at outpatient clinics were enrolled. Structured measurements including psychological symptoms, personality traits, family functioning and global functioning, at admission and discharge, were used to assess outcome. Baseline social functioning was measured at admission. Perspective on life satisfaction and satisfaction with therapy were assessed at discharge. Univariate analysis and multiple regression models that employed the stepwise method were used to determine the predictors of outcome. RESULTS: Psychological symptoms, global functioning and family function demonstrated significant improvement after hospitalization (p<0.05). Personality traits remained stable during hospitalization. Baseline social functioning, educational level, marital status, comorbid mental disorder, length of hospital stay, and neuroticism were significantly associated with psychological stress at discharge (adjusted R2=0.51). Higher educational level and male gender were significantly correlated with better global functioning at discharge (adjusted R2=0.18). Perspective on life satisfaction and improvement of family functioning predicted satisfaction with therapy during the index hospitalization (adjusted R2=0.19). CONCLUSIONS: This study demonstrated a significant improvement after short-term hospitalization in a psychosomatic ward. Demographic and clinical variables were able to predict the outcome measurement of symptoms, functioning and service satisfaction. This study also suggested that individual psychotherapy is a useful approach to the treatment of hospitalized patients with CMDs.