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Background and objective: Decline in hospitalizations for various respiratory diseases has been reported during the COVID‐19 pandemic, but what led to such an observation is uncertain. Methods: This was a territory‐wide, retrospective cohort study involving all public hospital admissions in Hong Kong from 1 January 2017 to 31 December 2020. Hospital admissions for respiratory diseases, including asthma, COPD and non‐COVID pneumonia, were assessed. COVID‐related admissions were excluded from this study. The time of commencement of the pandemic was taken from the fourth week of January 2020. The associations between air pollutant levels, influenza and mask‐wearing rates with hospital admissions were assessed by mediation analyses. Results: There were altogether 19,485, 78,693 and 238,781 admissions for asthma, COPD and non‐COVID pneumonia from January 2017 to December 2020. There was a marked reduction in hospital admissions of asthma, COPD and non‐COVID pneumonia (37%, 36% and 12% decrease in average daily admissions, respectively) during the COVID‐19 pandemic compared to before. Air pollutant levels and influenza rate were decreased while mask‐wearing rate was increased. Collinearity of mask‐wearing rates and pandemic year was observed. For COPD, NO2, SO2, PM10 and influenza rates (4%, 11%, 4% and 4% of the total effect, respectively), while for non‐COVID pneumonia, PM10 and influenza rates (11% and 52%, respectively) had significant mediation effect on changes in hospital admissions before and during the COVID‐19 pandemic. Conclusion: During the COVID‐19 pandemic, a decrease in air pollutant levels and influenza rate had mediation effect on the reduction in hospitalizations of COPD and non‐COVID pneumonia. The reason behind the observation of decline in respiratory hospitalizations during the COVID‐19 pandemic was uncertain. This study found that air pollutants levels and influenza rate had mediation effects on the decrease in hospitalizations for COPD and non‐COVID pneumonia, but not for asthma during the COVID‐19 pandemic. See relatedEditorial [ABSTRACT FROM AUTHOR]

The provision of endoscopic training was significantly disrupted during the coronavirus disease (COVID‐19) pandemic. It is difficult to provide training on endoscopic submucosal dissection (ESD) due to the complexity of the procedure and the shortage of local trainers. We have designed and conducted a simulation training course with telementoring, incorporating the concept of rapid cycle deliberate practice (RCDP). After a short tutorial on basic setup and maneuvering, the participants undergo 2 h of training on a live anesthetized pig to acquire the necessary skills for ESD. Two overseas experts were involved virtually in real time as telementors. A video camera was set up at each station, and both external and endoscopic views were transmitted live between the animal laboratory and telementors via the Zoom application. The overseas experts actively engaged in the simulation by giving live feedback and instructions to participants using visual aids supported by the meeting application. The training model utilized the concept of RCDP, where repeated immediate feedback and discussion would be carried out to enhance the efficiency of learning. Eighteen trainees were invited to participate in the course, six of them instructed by overseas telementors. Four ESD procedures were performed by participants under telementoring and achieved 100% en bloc resection without perforation. The times required for each ESD were similar with telementoring than that of on‐site mentors. The establishment of telementoring has enabled knowledge and skill transfer of complex endoscopic procedures of ESD. With the limitations of international travel, this could be an alternative model allowing remote training, particularly when local experts are not readily available. [ABSTRACT FROM AUTHOR]

Summary: Background: Patients with a history of Helicobacter pylori–negative idiopathic bleeding ulcers have an increased risk of recurring ulcer complications. Aim: To build a machine learning model to identify patients at high risk for recurrent ulcer bleeding. Methods: Data from a retrospective cohort of 22 854 patients (training cohort) diagnosed with peptic ulcer disease in 2007‐2016 were analysed to build a model (IPU‐ML) to predict recurrent ulcer bleeding. We tested the IPU‐ML in all patients with a diagnosis of gastrointestinal bleeding (n = 1265) in 2008‐2015 from a different catchment population (independent validation cohort). Any co‐morbid conditions which had occurred in >1% of study population were eligible as predictors. Results: Recurrent ulcer bleeding developed in 4772 patients (19.5%) in the training cohort, during a median follow‐up period of 2.7 years. IPU‐ML model built on six parameters (age, baseline haemoglobin, and presence of gastric ulcer, gastrointestinal diseases, malignancies, and infections) identified patients with bleeding recurrence within 1 year with an area under the receiver operating characteristic curve (AUROC) of 0.648. When we set the IPU‐ML cutoff value at 0.20, 27.5% of patients were classified as high risk for rebleeding with a sensitivity of 41.4%, specificity of 74.6%, and a negative predictive value of 91.1%. In the validation cohort, the IPU‐ML identified patients with a recurrence ulcer bleeding within 1 year with an AUROC of 0.775, and 84.3% of overall accuracy. Conclusion: We developed a machine‐learning model to identify those patients with a history of idiopathic gastroduodenal ulcer bleeding who are not at high risk for recurrent ulcer bleeding. [ABSTRACT FROM AUTHOR]