Predicting SARS-CoV-2 infection among hemodialysis patients using deep neural network methods.

COVID-19 has a higher rate of morbidity and mortality among dialysis patients than the general population. Identifying infected patients early with the support of predictive models helps dialysis centers implement concerted procedures (e.g.

Murray law-based quantitative flow ratio for assessment of nonculprit lesions in patients with ST-segment elevation myocardial infarction.

Introduction: Revascularization of nonculprit arteries in patients with ST-Segment Elevation Myocardial Infarction (STEMI) is now recommended based on several trials. However, the optimal therapeutic strategy of nonculprit lesions remains unknown. Murray law-based Quantitative Flow Ratio (μQFR) is a novel, non-invasive, vasodilator-free method for evaluating the functional severity of coronary artery stenosis, which has potential applications for nonculprit lesion assessment in STEMI patients.

Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data.

Background: The coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective.