Hepatitis B virus (HBV) infection remains a global threat and causes a substantial disease burden. The World Health Organization has set a goal to eliminate viral hepatitis as a public health threat by 2030. Mother-to-child transmission (MTCT) is the main route of HBV transmission. Most MTCT cases can be prevented by timely active and passive immunisation at birth, but failed immunoprophylaxis in infants continues to occur among women with a high viral load during pregnancy. Hepatitis B virus disease activity in infected mothers should be assessed during early pregnancy, and multidisciplinary management with antiviral medication should be offered to women with a high viral load. In these guidelines, we present management strategies for HBV-infected pregnant women that are intended to reduce the risk of MTCT in Hong Kong.

This study compared the performance of the artificial neural network (ANN) model with the Acute Physiologic and Chronic Health Evaluation (APACHE) II and IV models for predicting hospital mortality among critically ill patients in Hong Kong. This retrospective analysis included all patients admitted to the intensive care unit of Pamela Youde Nethersole Eastern Hospital from January 2010 to December 2019. The ANN model was constructed using parameters identical to the APACHE IV model. Discrimination performance was assessed using area under the receiver operating characteristic curve (AUROC); calibration performance was evaluated using the Brier score and Hosmer-Lemeshow statistic. In total, 14 503 patients were included, with 10% in the validation set and 90% in the ANN model development set. The ANN model (AUROC=0.88, 95% confidence interval [CI]=0.86-0.90, Brier score=0.10; P in Hosmer-Lemeshow test=0.37) outperformed the APACHE II model (AUROC=0.85, 95% CI=0.80-0.85, Brier score=0.14; P<0.001 for both comparisons of AUROCs and Brier scores) but showed performance similar to the APACHE IV model (AUROC=0.87, 95% CI=0.85-0.89, Brier score=0.11; P=0.34 for comparison of AUROCs, and P=0.05 for comparison of Brier scores). The ANN model demonstrated better calibration than the APACHE II and APACHE IV models. Our ANN model outperformed the APACHE II model but was similar to the APACHE IV model in terms of predicting hospital mortality in Hong Kong. Artificial neural networks are valuable tools that can enhance real-time prognostic prediction.

Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) [hereafter, SJS/TEN] are uncommon but severe mucocutaneous reactions. Although they have been described in many populations worldwide, data from Hong Kong are limited. Here, we explored the epidemiology, disease characteristics, aetiology, morbidity, and mortality of SJS/TEN in Hong Kong. This retrospective cohort study included all hospitalised patients who had been diagnosed with SJS/TEN in Prince of Wales Hospital from 1 January 2004 to 31 December 2020. There were 125 cases of SJS/TEN during the 17-year study period. The annual incidence was 5.07 cases per million. The mean age at onset was 51.4 years. The mean maximal body surface area of epidermal detachment was 23%. Overall, patients in 32% of cases required burns unit or intensive care unit admission. Half of the cases involved concomitant sepsis, and 23.2% of cases resulted in multiorgan failure or disseminated intravascular coagulation. The mean length of stay was 23.9 days. The cause of SJS/TEN was attributed to a drug in 91.9% of cases, including 84.2% that involved anticonvulsants, allopurinol, antibiotics, or analgesics. In most cases, patients received treatment comprising either best supportive care alone (35.2%) or combined with intravenous immunoglobulin (43.2%). The in-hospital mortality rate was 21.6%. Major causes of death were multiorgan failure and/or fulminant sepsis (81.5%). This study showed that SJS/TEN are uncommon in Hong Kong but can cause substantial morbidity and mortality. Early recognition, prompt withdrawal of offending agents, and multidisciplinary supportive management are essential for improving clinical outcomes.