Bidirectional Association between First-Episode Panic Disorder and Major Depressive Disorder in a Nationwide General Population Survey in Korea

Could Dispositional Optimism Impair Panic Disorder and Depression Outcomes?

ABSTRACT

The objective of this study was to investigate potential correlates of dispositional optimism and quality of life in patients with depression and panic disorder. The study used a cross-sectional design. The analyzed sample consisted of 77 participants with panic disorder and 75 participants with depression attending two outpatient clinics at the Psychiatry Institute of the Federal University of Rio de Janeiro. Both groups presented similar impairments in optimism and quality of life. In the panic disorder group, optimism scores were significantly correlated with a decrease in anxiety and depression scores (r = 0.26 and r = 0.37, respectively); in the depression group, increases in optimism scores were significantly correlated with decreases in anxiety and depression scores (r = 0.23 and r = 0.3, respectively). The present study showed that high anxiety and depression are correlated with poor optimism and quality of life scores in panic disorder and depression groups. Thus, psychological treatments that can address these topics, besides acute symptoms, are crucial to the absolute recovery of patients.

Prediction of depression among medical check-ups of 433,190 patients: A nationwide population-based study

Depression is a mental illness that causes significant disturbances in daily life. Depression is commonly associated with low mood, severe health problems, and substantial socioeconomic burden; hence, it is necessary to be able to detect depression earlier. We utilized the medical check-up cohort database of the National Health Insurance Sharing Service in Korea. We split the total dataset into training (70%) and test (30%) sets. Subsequently, five-fold cross validation was performed in the training set. The holdout test set was only used in the last step to evaluate the performance of the predictive model. Random forest algorithm was used for the predictive model. The analysis included 433,190 individuals who had a national medical check-up from 2009-2015, which included 10,824 (2.56%) patients in the depression group. The area under the receiver-operating curve was 0.849. Other performance metrics included a sensitivity of 0.737, specificity of 0.824, positive predictive value of 0.097, negative predictive value of 0.992, and accuracy of 0.780. Our predictive model could contribute to proactively reducing depression prevalence by administering interventions to prevent depression in patients receiving medical check-up. Future studies are needed to prospectively validate the predictability of this model.