ABSTRACT
BACKGROUND AND AIMS: Recommendations for stopping nucleoside analogue (NA) therapy in hepatitis B e antigen-negative chronic hepatitis B (CHB) are unclear. End-of-treatment quantitative hepatitis B serum antigen (EOTqHBsAg) thresholds <100 IU/mL or <1000 IU/mL have been proposed as stopping criteria, which we assessed by meta-analysis and meta-regression. METHODS: We searched PubMed, EMBASE, and conference abstracts for studies of hepatitis B e antigen-negative CHB NA discontinuation. Extracted studies were analyzed for risk of bias, pooled risk of hepatitis B serum antigen (HBsAg) loss, virological relapse (VR), and biochemical relapse (BR). Significant heterogeneity (I2) was addressed by subgroup analysis and random-effects meta-regression with known important covariates, including EOTqHBsAg thresholds, ethnicity, duration of therapy, and follow-up. RESULTS: We found 24 articles (3732 subjects); 16 had low and 8 had moderate risk of bias. The pooled risks of HBsAg loss, VR, and BR for stopping therapy at EOTqHBsAg <100 IU/mL were 41.8%, 33.4%, and 17.3%, respectively, vs 4.6%, 72.1%, and 34.6%, respectively, for EOTqHBsAg ≥100 IU/mL. The pooled risks of HBsAg loss, VR, and BR for stopping therapy at EOTqHBsAg <1000 IU/mL were 22.0%, 52.7%, and 15.9%, respectively, vs 3.4%, 63.8%, and 26.4%, respectively, for EOTqHBsAg ≥1000 IU/mL. Multivariable analysis for HBsAg loss showed that ethnicity, follow-up duration, and EOTqHBsAg <100 IU/mL and ≥100 IU/mL explained 85% of the variance in heterogeneity; Asians with EOTqHBsAg <100 IU/mL had 28.2%, while non-Asians with EOTqHBsAg <1000 IU/mL had 38.4% HBsAg loss. Multivariable analysis showed EOTqHBsAg <100 IU/mL and ≥100 IU/mL and other covariates only explained 43% and 63% of the variance in heterogeneity for VR and BR, respectively, suggesting that other factors are also important for relapse. CONCLUSIONS: While EOTqHBsAg thresholds, ethnicity, and follow-up duration strongly predict HBsAg loss, this is not true for VR and BR, hence stopping NA therapy should be considered cautiously.
ABSTRACT
OBJECTIVE: The current evidence regarding how different predictor domains contributes to predicting incident dementia remains unclear. This study aims to assess the incremental value of five predictor domains when added to a simple dementia risk prediction model (DRPM) for predicting incident dementia in older adults. DESIGN: Population-based, prospective cohort study. SETTING: UK Biobank study. PARTICIPANTS: Individuals aged 60 or older without dementia. MEASUREMENTS: Fifty-five dementia-related predictors were gathered and categorized into clinical and medical history, questionnaire, cognition, polygenetic risk, and neuroimaging domains. Incident dementia (all-cause) and the subtypes, Alzheimer's disease (AD) and vascular dementia (VaD), were determined through hospital and death registries. Ensemble machine learning (ML) DRPMs were employed for prediction. The incremental values of risk predictors were assessed using the percent change in Area Under the Curve (∆AUC%) and the net reclassification index (NRI). RESULTS: The simple DRPM which included age, body mass index, sex, education, diabetes, hyperlipidaemia, hypertension, depression, smoking, and alcohol consumption yielded an AUC of 0.711 (± 0.008 SD). The five predictor domains exhibited varying levels of incremental value over the basic model when predicting all-cause dementia and the two subtypes. Neuroimaging markers provided the highest incremental value in predicting all-cause dementia (∆AUC% +9.6%) and AD (∆AUC% +16.5%) while clinical and medical history data performed the best at predicting VaD (∆AUC% +12.2%). Combining clinical and medical history, and questionnaire data synergistically enhanced ML DRPM performance. CONCLUSION: Combining predictors from different domains generally results in better predictive performance. Selecting predictors involves trade-offs, and while neuroimaging markers can significantly enhance predictive accuracy, they may pose challenges in terms of cost or accessibility.
ABSTRACT
BACKGROUND: Creation and maintenance of dialysis vascular access (VA) is a major component of healthcare resource utilization and cost for patients newly started on hemodialysis (HD). Different VA format arises due to patient acceptance of anticipatory care versus late preparation, and clinical characteristics. This study reviews the clinical journey and resource utilization required for different VA formats in the first year of HD. METHOD: Data of patients newly commenced on HD between July 2015 and June 2016 were reviewed. Patients were grouped by their VA format: (A) pre-emptive surgically created VA (SCVA), (B) tunneled central venous catheter (CVC) followed by SCVA creation, (C) long-term tunneled CVC only. Clinical events, number of investigations and procedures, hospital admissions, and incurred costs of the three groups were compared. RESULTS: In the multivariable analysis, the cost incurred by the group A patients had no significant difference to that incurred in the group B patients (p = 0.08), while the cost of group C is significantly lower (p < 0.001). Both the 62.7% of group A with successful SCVA who avoided tunneled CVC usage, and those with a functionally matured SCVA in group B (66.1%), used fewer healthcare resources and incurred less cost for their access compared to those did not (p = 0.01, p = 0.02, respectively) during the first year of HD. CONCLUSION: With comparable cost, a pre-emptive approach enables avoidance of tunneled CVC. Tunneled CVC only access format incurred lower cost and is suitable for carefully selected patients. Successful maturation of SCVA greatly affects patients' clinical journey and healthcare cost.
ABSTRACT
The maritime industry plays a key role in reducing greenhouse gas (GHG) emissions, as an effort to combat the global issue of climate change. The International Maritime Organization (IMO) is targeting a 50% reduction in GHG emissions by 2050 compared to 2008. To measure Singapore's progress towards this target, we have conducted a comprehensive analysis of carbon dioxide (CO2) emissions from the Western Singapore Straits based on the voyage data from Automatic Identification System (AIS) and static information from Singapore Maritime Data Hub (SG-MDH). Two methodologies, the MEET and TRENDS frameworks were applied to estimate the emission volume per vessel per hour. The data analysis results were next aggregated and visualised to answer key questions such as: How did the carbon emission level change from 2019 to 2020, in general, and for specific vessel types? What are the top vessel types and flags that had the highest carbon emissions? Did the traffic volume and emission level decrease during the Circuit Breaker period in 2020? The results of this study can be used to review Singapore's emission control measures and will be of value to the Maritime and Port Authority (MPA) of Singapore responsible for managing CO2 emissions at the Singapore Port.