Background: Results on the association between posttraumatic stress symptoms (PTSS) and posttraumatic growth (PTG) are inconsistent, and there may be unknown factors mediating or moderating this relationship. Identifying these factors could help in developing an intervention strategy for promoting PTG. However, few studies have examined relationships among PTSS, resilience, and PTG concurrently, and no study has investigated the effect of childhood trauma on these relationships in adulthood. Objective: The aim of this study was to examine the moderated mediating effect of childhood trauma on resilience and its associations with PTSS and PTG in adult victims of traumatic accidents or crimes. We hypothesized that resilience would mediate relationships between PTSS and PTG and that its mediating effects would differ depending on childhood trauma. Methods: We included adult victims of accidents or crimes referred to a university hospital or specialized support centre (n = 143). PTSS, resilience, childhood trauma, and PTG were measured with the following questionnaires: PTSD Checklist for DSM-5, Connor-Davidson Resilience Scale, Adverse Childhood Experiences Questionnaire, and the Short Form of the Posttraumatic Growth Inventory, respectively. Results: The effect of PTSS on PTG was found to be fully mediated by resilience, and this mediating effect was moderated according to childhood trauma: the more childhood traumatic experiences, the greater the mediating effect of resilience was between PTSS and PTG. The effect of resilience on PTG was highest in the high childhood trauma group. Conclusion: Therapists treating individuals with psychological trauma should attempt to identify a history of childhood trauma and to evaluate resilience. Therapeutic approaches tailored according thereto may improve PTG among individuals with PTSS symptoms, especially those with high levels of childhood trauma.

Background: Machine-learning techniques are useful for creating prediction models in clinical practice. This study aimed to construct a prediction model of postoperative 30-day mortality based on an automatically extracted electronic preoperative evaluation sheet. Methods: We used data from 276,341 consecutive adult patients who underwent non-cardiac surgery between January 2011 and December 2020 at a tertiary center for model development and internal validation, and another dataset from 63,384 patients between January 2011 and October 2021 at another center for external validation. Postoperative 30-day mortality was 0.16%. We developed an extreme gradient boosting (XGB) prediction model using only variables from preoperative evaluation sheets. Results: The model yielded an area under the curve of 0.960 and an area under the precision and recall curve of 0.216, which were 0.932 and 0.122, respectively, in the external validation set. The optimal threshold calculated by Youden’s J statistic had a sensitivity of 0.885 and specificity of 0.914. In an additional analysis with balanced distribution, the model showed a similar predictive value. Conclusion: We presented a machine-learning prediction model for 30-day mortality after non-cardiac surgery using preoperative variables automatically extracted from electronic medical records and validated the model in a multi-center setting. Our model may help clinicians predict postoperative outcomes.

BackgroundChildren today are exposed to various media devices, and their usage of these is increasing. Prior studies have outlined forms of harm this can potentially cause. However, there has been little empirical research on the use of media devices among preschool children in Asia. The aim of this study was to examine and analyze longitudinal trends in media device use among Korean preschool children, focusing on the frequency of engagement, time spent with, and ownership of media devices, delineated by sex.MethodsFour hundred parents of children aged 2–5 years were invited to enroll. The baseline assessment, Wave 1, was conducted between December 2015 and June 2016, and follow-up assessments, Wave 2 and Wave 3, were conducted annually for the following 2 years. Time of media use, frequency of media use, and ownership of media devices (TV, tablet PCs, and smartphones) were investigated.ResultsOwnership of tablet PCs increased significantly between Wave 1 and Wave 3 for boys and girls (corrected P < 0.001). Frequency of media use increased significantly between Wave 1 and Wave 3 only in boys' use of tablet PCs (mean difference 0.8 day/wk). Time of media use increased significantly between Wave 1 and Wave 3 for both sexes in all devices, measured by mean difference on weekdays and weekends (TV by 0.6 and 0.7 hr/day, tablet PCs by 0.6 and 0.8 hr/day, and smartphones by 0.4 and 0.4 hr/day). Children spent more time using media devices during weekends than on weekdays.ConclusionThis study observed an increase in the tendency of media device use among preschool children in Korea. The patterns of use indicate that paying attention to the types of devices children use is needed, as well as vigilance on weekends.

Incident depression has been reported to be associated with poor prognosis in patients with cardiovascular disease (CVD), which might be associated with beta-blocker therapy. Because early detection and intervention can alleviate the severity of depression, we aimed to develop a machine learning (ML) model predicting the onset of major depressive disorder (MDD). A model based on L1 regularized logistic regression was trained against the South Korean nationwide administrative claims database to identify risk factors for the incident MDD after beta-blocker therapy in patients with CVD. We identified 50,397 patients initiating beta-blockers for CVD, with 774 patients developing MDD within 365 days after initiating beta-blocker therapy. An area under the receiver operating characteristic curve (AUC) of 0.74 was achieved. A history of non-selective beta-blockers and factors related to anxiety disorder, sleeping problems, and other chronic diseases were the most strong predictors. AUCs of 0.62–0.71 were achieved in the external validation conducted on six independent electronic health records and claims databases in the USA and South Korea. In conclusion, an ML model that identifies patients at high-risk for incident MDD was developed. Application of ML to identify susceptible patients for adverse events of treatment may serve as an important approach for personalized medicine.

Background Although electronic health records (EHRs) have been widely used in secondary assessments, clinical documents are relatively less utilized owing to the lack of standardized clinical text frameworks across different institutions. Objective This study aimed to develop a framework for processing unstructured clinical documents of EHRs and integration with standardized structured data. Methods We developed a framework known as Staged Optimization of Curation, Regularization, and Annotation of clinical text (SOCRATex). SOCRATex has the following four aspects: (1) extracting clinical notes for the target population and preprocessing the data, (2) defining the annotation schema with a hierarchical structure, (3) performing document-level hierarchical annotation using the annotation schema, and (4) indexing annotations for a search engine system. To test the usability of the proposed framework, proof-of-concept studies were performed on EHRs. We defined three distinctive patient groups and extracted their clinical documents (ie, pathology reports, radiology reports, and admission notes). The documents were annotated and integrated into the Observational Medical Outcomes Partnership (OMOP)-common data model (CDM) database. The annotations were used for creating Cox proportional hazard models with different settings of clinical analyses to measure (1) all-cause mortality, (2) thyroid cancer recurrence, and (3) 30-day hospital readmission. Results Overall, 1055 clinical documents of 953 patients were extracted and annotated using the defined annotation schemas. The generated annotations were indexed into an unstructured textual data repository. Using the annotations of pathology reports, we identified that node metastasis and lymphovascular tumor invasion were associated with all-cause mortality among colon and rectum cancer patients (both P=.02). The other analyses involving measuring thyroid cancer recurrence using radiology reports and 30-day hospital readmission using admission notes in depressive disorder patients also showed results consistent with previous findings. Conclusions We propose a framework for hierarchical annotation of textual data and integration into a standardized OMOP-CDM medical database. The proof-of-concept studies demonstrated that our framework can effectively process and integrate diverse clinical documents with standardized structured data for clinical research.