ABSTRACT
PURPOSE: Acute kidney injury (AKI) is a frequent complication among COVID-19 patients in the intensive care unit, but it is less frequently investigated in general internal medicine wards. We aimed to examine the incidence, the predictors of AKI, and AKI-associated mortality in a prospective cohort of non-ventilated COVID-19 patients. We aimed to describe the natural history of AKI by describing trajectories of urinary markers of hemodynamic, glomerular, and tubular injury. METHODS: 141 COVID-19 patients were enrolled to the study. AKI was defined according to KDIGO guidelines. Urine and renal function parameters were followed twice a week. Multivariate logistic regression was used to determine the predictors of AKI and mortality. Trajectories of urinary markers were described by unadjusted linear mixed models. RESULTS: 19.7% patients developed AKI. According to multiple logistic regression, higher urinary albumin-to-creatinine ratio (OR 1.48, 95% CI 1.04-2.12/1 mg/mmol) and lower serum albumin (OR 0.86, 95% CI 0.77-0.94/1 g/L) were independent predictors of AKI. Mortality was 42.8% in the AKI and 8.8% in the group free from AKI (p < 0.0001). According to multiple logistic regression, older age, lower albumin, and AKI (OR 3.9, 95% CI 1.24-12.21) remained independent predictors of mortality. Urinary protein-to-creatinine trajectories were diverging with decreasing values in those without incident AKI. CONCLUSION: We found high incidence of AKI and mortality among moderately severe, non-ventilated COVID-19 patients. Its development is predicted by higher albuminuria suggesting that the originally damaged renal structure may be more susceptible for virus-associated effects. No clear relationship was found with a prerenal mechanism, and the higher proteinuria during follow-up may point toward tubular damage.
ABSTRACT
The rapid development of technology has drastically changed the way consumers do their shopping. The volume of global online commerce has significantly been increasing partly due to the recent COVID-19 crisis that has accelerated the expansion of e-commerce. A growing number of webshops integrate Artificial Intelligence (AI), state-of-the-art technology into their stores to improve customer experience, satisfaction and loyalty. However, little research has been done to verify the process of how consumers adopt and use AI-powered webshops. Using the technology acceptance model (TAM) as a theoretical background, this study addresses the question of trust and consumer acceptance of Artificial Intelligence in online retail. An online survey in Hungary was conducted to build a database of 439 respondents for this study. To analyse data, structural equation modelling (SEM) was used. After the respecification of the initial theoretical model, a nested model, which was also based on TAM, was developed and tested. The widely used TAM was found to be a suitable theoretical model for investigating consumer acceptance of the use of Artificial Intelligence in online shopping. Trust was found to be one of the key factors influencing consumer attitudes towards Artificial Intelligence. Perceived usefulness as the other key factor in attitudes and behavioural intention was found to be more important than the perceived ease of use. These findings offer valuable implications for webshop owners to increase customer acceptance.
ABSTRACT
The distinction between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related and community-acquired pneumonias poses significant difficulties, as both frequently involve the elderly. This study aimed to predict the risk of SARS-CoV-2-related pneumonia based on clinical characteristics at hospital presentation. Case-control study of all patients admitted for pneumonia at Semmelweis University Emergency Department. Cases (n = 30) were patients diagnosed with SARS-CoV-2-related pneumonia (based on polymerase chain reaction test) between 26 March 2020 and 30 April 2020; controls (n = 82) were historical pneumonia cases between 1 January 2019 and 30 April 2019. Logistic models were built with SARS-CoV-2 infection as outcome using clinical characteristics at presentation. Patients with SARS-CoV-2-related pneumonia were younger (mean difference, 95% CI: 9.3, 3.2-15.5 years) and had a higher lymphocyte count, lower C-reactive protein, presented more frequently with bilateral infiltrate, less frequently with abdominal pain, diarrhoea, and nausea in age- and sex-adjusted models. A logistic model using age, sex, abdominal pain, C-reactive protein, and the presence of bilateral infiltrate as predictors had an excellent discrimination (AUC 0.88, 95% CI: 0.81-0.96) and calibration (p = 0.27-Hosmer-Lemeshow test). The clinical use of our screening prediction model could improve the discrimination of SARS-CoV-2 related from other community-acquired pneumonias and thus help patient triage based on commonly used diagnostic approaches. However, external validation in independent datasets is required before its clinical use.