The sensitivity of the ICD-11 trait model to the symptoms of clinical disorders in young adults.

Hierarchical psychopathology contributes to providing a broader picture of the links between emerging personality structures such as the DSM-5/ICD-11 trait models and clinical disorders. The present study aimed to predict the specific and general clinical symptoms by the less studied constructs of the ICD-11 model (negative affectivity, detachment, dissociality, disinhibition, and anankastia). Data from 642 young adults from Iran (63% female, 18-34 years) were collected by three mental symptom scales and the Personality Inventory for DSM-5 (PID-5), which was recently used to harmonize the constructs of the DSM-5 and ICD-11 trait models. Multiple linear regressions showed that the ICD-11 model significantly predicted both the specific clinical symptoms (ranging from R2 = 0.15 to 0.40) and the general factor of clinical symptoms extracted by exploratory factor analysis (R2 = 0.40, all p < 0.001). Negative affectivity was the strongest construct correlated with both the specific symptoms (ranging from ß = 0.36 to 0.69) and the general symptom factor (ß = 0.59, all p < 0.001). Because the ICD-11 trait model is a practical structure related to the clinical psychopathology in young adults, screening for maladaptive traits can help clinicians in case formulation for diagnosis and treatment.

Clinical cut scores for the Persian version of the personality inventory for DSM-5.

BACKGROUND: The cut points of psychological tools to diagnose clinical conditions are not universal and depend on the region and prevalence of the disorder. Thus, we aimed to identify the cutoff points of the Persian original version of the personality inventory for DSM-5 (PID-5; 220 items) that would optimally distinguish nonclinical from clinical groups. METHODS: Both nonclinical (N = 634, 73% female, 34.0 ± 10.8 years) and clinical (N = 454, 29% female, 29.5 ± 7.4 years) samples from the West of Iran participated in the study. Data were analyzed using receiver operating characteristic (ROC) and Youden's index was used to determine the cutoff scores across the PID-5 domains and facets. The means and standard deviations of both the clinical male and female were compared with the nonclinical group using Cohen's d and independent t-tests. RESULTS: All the PID-5 algorithms and facets significantly distinguished clinical from nonclinical samples with some unique findings for male and female samples. The mean score of all the PID-5 algorithms and facets in the clinical male and female samples were respectively 1.0-2.0 SD and 0.5-1.0 SD above the mean for the nonclinical counterparts. A score higher than 1.5 on ranging from 0 to 3 in each domain or facet indicated clinical status. CONCLUSION: Raw cutting scores throughout the PID-5 algorithms can be well used to diagnose any pathology of personality and the severity of the disorder in clinical patients. The cut scores provide a useful tool for the clinical use of the original version of PID-5 in Iran.

Risk Factors and Psychosocial Correlates of Emotionally Negative Dreams in Patients Referred to a Cardiac Rehabilitation Centre.

INTRODUCTION: Dream, as a kind of mental activity, includes various functions such as mood regulation, adjustment and integration of new information with the available memory system. The study was done for assessing the relationship between physiological and psychological components of cardiac diseases with emotionally negative dreams in cardiac rehabilitation. METHODS: At the baseline of this cross-sectional study, 156 patients from Western Iran participated during April-November 2016. People 20 years-80 years able to recall the emotional content of dreams after cardiac surgery entered the study. The Beck depression inventory (BDI), Beck anxiety inventory (BAI), Buss and Perry's aggression questionnaire (BPAQ) and Schredl's dream emotions manual were used for collecting data. A binary logistic regression analysis used for the study of the relationship between risk factors and emotionally negative dreams. RESULTS: The mean age of participants was 59 (SD = 9) years (men: 64.1%). The results showed that 25% of patients have negative emotional content. After adjustment for demographic variables, the results showed that increased anxiety [adjusted odds ratio (adj OR) = 1.08 [1.01-1.16], P = 0.020] and anger (adj OR = 1.03 [1.00-1.06], P = 0.024) and hypertension (adj OR = 2.71 [1.10-6.68], P = 0.030) can predict the dreams with negative content significantly. CONCLUSION: The increasing rates of anxiety and anger and history of hypertension are related to increasing dreams with the negative emotional load. The control of risk factors of dreams with negative emotional load can be the target of future interventions.