The purpose of this study is to investigate the reliability generalization of 2 forms of the Supportive Care Needs Survey (SCNS), the questionnaires commonly used to assess the unmet needs of cancer patients.

Reviewed articles were retrieved through databases including PubMed, Ovid, Embase, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, Scopus, and ProQuest. The inclusion criteria were quantitative studies that assessed the unmet needs of cancer patients using the SCNS and presented reliability coefficients with sample size. Two independent reviewers examined the studies according to inclusion criteria and quality. The final studies included in the meta-analysis were determined by consensus. A random effects model was adopted for the analysis. To estimate reliability coefficients, the alpha coefficients for each study were transformed into the Z statistic for normalization and back to alpha. The values were weighted by the inverse of the studies’ variance. The Higgins I2 statistic was used to test for heterogeneity, and the Egger’s test and funnel plot were performed to evaluate publication bias.

Out of 12,522 studies, 26 studies were included in the meta-analysis. The overall mean weighted effect size of the SCNS long-form (LF) was 0.90 and the subdomains ranged from 0.90 to 0.97. The overall alpha for the SCNS short-form (SF) was 0.92, and the alphas for the subdomains were between 0.81 and 0.92. The estimated reliability coefficients in both LF and SF were highest in psychological and health information needs and lowest in sexuality. No publication bias was indicated in this study.

In this study, the overall reliability of SCNS was presented and the factors affecting the reliability of SCNS were identified. The results of this study may help clinicians or researchers make decisions about selecting tools to measure unmet needs of cancer patients.

Predicting the course of depression is necessary for personalized treatment. Impaired glucose metabolism (IGM) was introduced as a promising depression biomarker, but no consensus was made. This study aimed to predict IGM at the time of depression diagnosis and examine the relationship between long-term prognosis and predicted results.

Clinical data were extracted from four electronic health records in South Korea. The study population included patients with depression, and the outcome was IGM within 1 year. One database was used to develop the model using three algorithms. External validation was performed using the best algorithm across the three databases. The area under the curve (AUC) was calculated to determine the model’s performance. Kaplan–Meier and Cox survival analyses of the risk of hospitalization for depression as the long-term outcome were performed. A meta-analysis of the long-term outcome was performed across the four databases.

A prediction model was developed using the data of 3,668 people, with an AUC of 0.781 with least absolute shrinkage and selection operator (LASSO) logistic regression. In the external validation, the AUCs were 0.643, 0.610, and 0.515. Through the predicted results, survival analysis and meta-analysis were performed; the hazard ratios of risk of hospitalization for depression in patients predicted to have IGM was 1.20 (95% confidence interval [CI] 1.02–1.41, p = 0.027) at a 3-year follow-up.

We developed prediction models for IGM occurrence within a year. The predicted results were related to the long-term prognosis of depression, presenting as a promising IGM biomarker related to the prognosis of depression.

The purpose of this study was to analyze the cost-effectiveness of helicopter emergency medical services (HEMS) for its economic operations in South Korea.

This study targeted trauma patients that were transported by either HEMS or ground emergency medical services (GEMS) from the scene of an accident to a regional emergency medical center. From this patient population, severe trauma patients (injury severity score ISS ≥ 16 points) with a distance travelled from the scene of the injury to the hospital that was 30 km or longer and with analyzable outcome data were extracted and included in this study. Cost-effectiveness was analyzed from survival and efficiency based on medical costs incurred from the pre-hospital setting to hospital discharge. This study included a total of 34 HEMS and 105 GEMS patients with an Injury Severity Score (ISS) ≥ 16 points from a pool of 357 potential patients.

The survival-to-discharge rate of HEMS was 29 of 34 patients (85.3%) and was significantly higher than that of GEMS, where only 66 of 105 patients (62.8%) survived to discharge (P = 0.024). The expected and the actual mortality was higher in HEMS than it was in GEMS. Statistical significant difference in cost was found between the 2 groups (P = 0.002).

The results of the present study indicate the increased discharge rate, survival rate and reduced in hospital mortality of HEMS with reduced admission time. This result association leads to reasonable cost effectiveness and efficient estimates overall.

Mood disorders require consistent management of symptoms to prevent recurrences of mood episodes. Circadian rhythm (CR) disruption is a key symptom of mood disorders to be proactively managed to prevent mood episode recurrences. This study aims to predict impending mood episodes recurrences using digital phenotypes related to CR obtained from wearable devices and smartphones.

The study is a multicenter, nationwide, prospective, observational study with major depressive disorder, bipolar disorder I, and bipolar II disorder. A total of 495 patients were recruited from eight hospitals in South Korea. Patients were followed up for an average of 279.7 days (a total sample of 75 506 days) with wearable devices and smartphones and with clinical interviews conducted every 3 months. Algorithms predicting impending mood episodes were developed with machine learning. Algorithm-predicted mood episodes were then compared to those identified through face-to-face clinical interviews incorporating ecological momentary assessments of daily mood and energy.

Two hundred seventy mood episodes recurred in 135 subjects during the follow-up period. The prediction accuracies for impending major depressive episodes, manic episodes, and hypomanic episodes for the next 3 days were 90.1, 92.6, and 93.0%, with the area under the curve values of 0.937, 0.957, and 0.963, respectively.

We predicted the onset of mood episode recurrences exclusively using digital phenotypes. Specifically, phenotypes indicating CR misalignment contributed the most to the prediction of episodes recurrences. Our findings suggest that monitoring of CR using digital devices can be useful in preventing and treating mood disorders.

There are growing concerns about the impact of the COVID-19 pandemic on the mental health of older adults. We examined the effect of the pandemic on the risk of depression in older adults.

We analyzed data from the prospective cohort study of Korean older adults, which has been followed every 2 years. Among the 2308 participants who completed both the third and the fourth follow-up assessments, 58.4% completed their fourth follow-up before the outbreak of COVID-19 and the rest completed it during the pandemic. We conducted face-to-face diagnostic interviews using Mini International Neuropsychiatric Interview and used Geriatric Depression Scale. We performed generalized estimating equations and logistic regression analyses.

The COVID-19 pandemic was associated with increased depressive symptoms in older adults [b (standard error) = 0.42 (0.20), p = 0.040] and a doubling of the risk for incident depressive disorder even in euthymic older adults without a history of depression (odds ratio = 2.44, 95% confidence interval 1.18–5.02, p = 0.016). Less social activities, which was associated with the risk of depressive disorder before the pandemic, was not associated with the risk of depressive disorder during the pandemic. However, less family gatherings, which was not associated with the risk of depressive disorder before the pandemic, was associated with the doubled risk of depressive disorder during the pandemic.

The COVID-19 pandemic significantly influences the risk of late-life depression in the community. Older adults with a lack of family gatherings may be particularly vulnerable.

This study aims to identify factors associated with divorce following breast cancer diagnosis and measures the impact of divorce on the quality of life (QoL) of patients.

We used cross-sectional survey data collected at breast cancer outpatient clinics in South Korea from November 2018 to April 2019. Adult breast cancer survivors who completed active treatment without any cancer recurrence at the time of the survey (N = 4,366) were included. The participants were classified into two groups: “maintaining marriage” and “being divorced,” between at the survey and at the cancer diagnosis. We performed logistic regression and linear regression to identify the factors associated with divorce after cancer diagnosis and to compare the QoL of divorced and nondivorced survivors.

Approximately 11.1/1,000 of married breast cancer survivors experienced divorce after cancer diagnosis. Younger age, lower education, and being employed at diagnosis were associated with divorce. Being divorced survivors had significantly lower QoL (Coefficient [Coef] = −7.50; 95% CI = −13.63, −1.36), social functioning (Coef = −9.47; 95% CI = −16.36, −2.57), and body image (Coef = −8.34; 95% CI = −6.29, −0.39) than survivors who remained married. They also experienced more symptoms including pain, insomnia, financial difficulties, and distress due to hair loss.

Identifying risk factors of divorce will ultimately help ascertain the resources necessary for early intervention.

Prognostic heterogeneity in early psychosis patients yields significant difficulties in determining the degree and duration of early intervention; this heterogeneity highlights the need for prognostic biomarkers. Although mismatch negativity (MMN) has been widely studied across early phases of psychotic disorders, its potential as a common prognostic biomarker in early periods, such as clinical high risk (CHR) for psychosis and first-episode psychosis (FEP), has not been fully studied.

A total of 104 FEP patients, 102 CHR individuals, and 107 healthy controls (HCs) participated in baseline MMN recording. Clinical outcomes were assessed; 17 FEP patients were treatment resistant, 73 FEP patients were nonresistant, 56 CHR individuals were nonremitters (15 transitioned to a psychotic disorder), and 22 CHR subjects were remitters. Baseline MMN amplitudes were compared across clinical outcome groups and tested for utility prognostic biomarkers using binary logistic regression.

MMN amplitudes were greatest in HCs, intermediate in CHR subjects, and smallest in FEP patients. In the clinical outcome groups, MMN amplitudes were reduced from the baseline in both FEP and CHR patients with poor prognostic trajectories. Reduced baseline MMN amplitudes were a significant predictor of later treatment resistance in FEP patients [Exp(β) = 2.100, 95% confidence interval (CI) 1.104–3.993, p = 0.024] and nonremission in CHR individuals [Exp(β) = 1.898, 95% CI 1.065–3.374, p = 0.030].

These findings suggest that MMN could be used as a common prognostic biomarker across early psychosis periods, which will aid clinical decisions for early intervention.

Over the past two decades, early detection and early intervention in psychosis have become essential goals of psychiatry. However, clinical impressions are insufficient for predicting psychosis outcomes in clinical high-risk (CHR) individuals; a more rigorous and objective model is needed. This study aims to develop and internally validate a model for predicting the transition to psychosis within 10 years.

Two hundred and eight help-seeking individuals who fulfilled the CHR criteria were enrolled from the prospective, naturalistic cohort program for CHR at the Seoul Youth Clinic (SYC). The least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was used to develop a predictive model for a psychotic transition. We performed k-means clustering and survival analysis to stratify the risk of psychosis.

The predictive model, which includes clinical and cognitive variables, identified the following six baseline variables as important predictors: 1-year percentage decrease in the Global Assessment of Functioning score, IQ, California Verbal Learning Test score, Strange Stories test score, and scores in two domains of the Social Functioning Scale. The predictive model showed a cross-validated Harrell's C-index of 0.78 and identified three subclusters with significantly different risk levels.

Overall, our predictive model showed a predictive ability and could facilitate a personalized therapeutic approach to different risks in high-risk individuals.

We report our experience with an emergency room (ER) shutdown related to an accidental exposure to a patient with coronavirus disease 2019 (COVID-19) who had not been isolated.

A 635-bed, tertiary-care hospital in Daegu, South Korea.

To prevent nosocomial transmission of the disease, we subsequently isolated patients with suspected symptoms, relevant radiographic findings, or epidemiology. Severe acute respiratory coronavirus 2 (SARS-CoV-2) reverse-transcriptase polymerase chain reaction assays (RT-PCR) were performed for most patients requiring hospitalization. A universal mask policy and comprehensive use of personal protective equipment (PPE) were implemented. We analyzed effects of these interventions.

From the pre-shutdown period (February 10–25, 2020) to the post-shutdown period (February 28 to March 16, 2020), the mean hourly turnaround time decreased from 23:31 ±6:43 hours to 9:27 ±3:41 hours (P < .001). As a result, the proportion of the patients tested increased from 5.8% (N=1,037) to 64.6% (N=690) (P < .001) and the average number of tests per day increased from 3.8±4.3 to 24.7±5.0 (P < .001). All 23 patients with COVID-19 in the post-shutdown period were isolated in the ER without any problematic accidental exposure or nosocomial transmission. After the shutdown, several metrics increased. The median duration of stay in the ER among hospitalized patients increased from 4:30 hours (interquartile range [IQR], 2:17–9:48) to 14:33 hours (IQR, 6:55–24:50) (P < .001). Rates of intensive care unit admissions increased from 1.4% to 2.9% (P = .023), and mortality increased from 0.9% to 3.0% (P = .001).

Problematic accidental exposure and nosocomial transmission of COVID-19 can be successfully prevented through active isolation and surveillance policies and comprehensive PPE use despite longer ER stays and the presence of more severely ill patients during a severe COVID-19 outbreak.