Background and Objectives: The impact of off-hours admission (such as weekends, nighttime, and non-working hours) vs. regular hours (weekdays and daytime working hours) on the mortality risk of patients undergoing surgery for type A aortic dissection (TAAD) repair is still uncertain. To address this uncertainty, we undertook a comprehensive systematic review and meta-analysis. We aimed to assess the potential link between off-hours admission and the risk of mortality in patients undergoing TAAD repair surgery. Methods: We conducted a thorough search of the PubMed, Embase, and Cochrane Library databases, covering the period from their inception to May 20, 2023. Our inclusion criteria encompassed all studies that examined the potential relationship between off-hour admission and mortality in individuals who had undergone surgery for TAAD repair. The odds ratios (ORs) were extracted and combined utilizing a random effects model for our synthesis. Results: Nine studies with 16,501 patients undergoing TAAD repair surgery were included in the meta-analysis. Overall, patients who underwent surgery during the weekend had higher in-hospital mortality (pooled OR, 1.41; 95% confidence interval [CI], 1.14–1.75; p=0.002) than those treated on weekdays. However, the mortality risks among patients who underwent TAAD surgery during nighttime and non-working hours were not significantly elevated compared to daytime and working hours admission. Conclusions Weekend surgery for TAAD was associated with a higher in-hospital mortality risk than weekday surgery. However, further studies are warranted to identify and develop strategies to improve the quality of round-the-clock care for patients with TAAD.

Objective: Healthcare workers (HCWs) suffered from a heavy mental health burden during the coronavirus disease-2019 (COVID-19) pandemic. We aimed to explore the differences in sleep disturbance in three waves of the COVID-19 pandemic in Taiwan among HCWs. Moreover, factors associated with sleep disturbances in the third wave were investigated. Methods: This study, with three waves of cross-sectional surveys, recruited first-line and second-line HCWs. The level of sleep disturbance and related demographic variables were collected through self-report questionnaires. Differences in sleep disturbance across the three waves were compared with analysis of variance. Factors associated with the level of sleep disturbance were identified using univariate linear regression and further used for multivariate stepwise and bootstrap linear regression to identify the independent predictors. Results: In total, 711, 560, and 747 HCWs were included in the first, second, and third waves, respectively. For first-line HCWs, sleep disturbance was significantly higher in the third wave than in the first wave. The level of sleep disturbance gradually increased across the three waves for all HCWs. In addition, sleep disturbance was associated with depression, posttraumatic stress disorder (PTSD) symptoms, anxiety about COVID-19, vaccine mistrust, and poorer physical and mental health among first-line HCWs. Among second-line HCWs, sleep disturbance was associated with younger age, depression, PTSD symptoms, lower preference for natural immunity, and poorer physical health. Conclusion The current study identified an increase in sleep disturbance and several predictors among HCWs. Further investigation is warranted to extend the application and generalizability of the current study.

OBJECTIVE: The study investigated to compare health care utilization and expenditures between diabetic patients with and without depression in Taiwan. METHODS: Health care utilization and expenditure among diabetic patients with and without depression disorder during 2000 and 2004 were examined using Taiwan's population-based National Health Insurance claims database. Health care utilization included outpatient visits and the use of inpatient services, and health expenditures were outpatient, inpatient, and total medical expenditures. Moreover, general estimation equation models were used for analyzing the factors associated with outpatient visits and expenditures. Multiple logistic regression analysis was applied for identifying the factors associated with hospitalization. RESULTS: The average annual outpatient visits and annual total medical expenditures in the study period were 44.23–52.20; NT$87,496–133,077 and 30.75–32.92; NT$64,411–80,955 for diabetic patients with and without depression. After adjustment for covariates, our results revealed that gender and complication were associated with out-patient visits. Moreover, the time factor was associated with the total medical expenditure, and residential urbanization and complication factors were associated with hospitalization. CONCLUSION: Health care utilization and expenditures for diabetic patients with depression were significantly higher than those without depression. Sex, complications, time, and urbanization are the factors associated with health care utilization and expenditures.
Cohort Studies* ; Delivery of Health Care* ; Depression* ; Health Expenditures* ; Hospitalization ; Humans ; Inpatients ; Logistic Models ; National Health Programs ; Outpatients ; Patient Acceptance of Health Care* ; Taiwan ; Time Factors ; Urbanization

OBJECTIVE: The therapeutic effect of methylphenidate (MPH) in treating attention-deficit/hyperactivity disorder (ADHD) has been related to the alpha-2A adrenergic receptor (ADRA2A) gene -1291C/G single nucleotide polymorphism (SNP). We investigated the effect of MPH in treating Taiwanese children and adolescent with ADHD and its relation to the ADRA2A gene -1291C/G SNP. METHODS: The subjects with DSM-IV ADHD diagnosis underwent a titration period to find out the dose of MPH for maintenance treatment. After 4 weeks maintenance treatment, the effect of MPH was evaluated by the Swanson, Nolan and Pelham version IV total scores. The subjects with more than 25% score reduction were referred to responders and those with ≥50% improvement were considered as better responders. The -1291C/G variant of the ADRA2A gene was identified by DNA sequencing and what relevance it has to the MPH response was examined by binary logistic regression analysis. RESULTS: Of the 59 subjects, 44 (74.6%) were responsive to MPH treatment and the responsiveness was not shown to be associated with the ADRA2A gene -1291C/G SNP. As the responsive subjects were categorized as moderate responders and better responders and subjected to statistical analysis, the GG homozygotes showed a greater chance to have a better response to MPH treatment than CC homozygotes (p=0.02), with an odds ratio of 32.14 (95% CI=1.64–627.80). CONCLUSION: The ADRA2A gene -1291C/G SNP is associated with the efficacy of MPH for the treatment of ADHD in Taiwanese children and adolescents. The responsive subjects bearing homozygous -1291G allele are more likely to have a better response to MPH treatment.
Adolescent* ; Alleles ; Child* ; Diagnosis ; Diagnostic and Statistical Manual of Mental Disorders ; Homozygote ; Humans ; Logistic Models ; Methylphenidate* ; Odds Ratio ; Pharmacogenetics ; Polymorphism, Single Nucleotide* ; Receptors, Adrenergic, alpha-2* ; Sequence Analysis, DNA

Background/Aims: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. Methods: The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. Results: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Conclusions Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.