Internal and emergency medicine
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Bacterial infections may complicate the course of COVID-19 patients. The rate and predictors of bacterial infections were examined in patients consecutively admitted with COVID-19 at one tertiary hospital in Madrid between March 1st and April 30th, 2020. Among 1594 hospitalized patients with COVID-19, 135 (8.5%) experienced bacterial infectious events, distributed as follows: urinary tract infections (32.6%), bacteremia (31.9%), pneumonia (31.8%), intra-abdominal infections (6.7%) and skin and soft tissue infections (6.7%). ⋯ They were not independently associated with increased mortality rates. Baseline COVID-19 severity rather than the incidence of bacterial infections seems to contribute to mortality. When indicated, the use of steroids or steroids plus tocilizumab might improve survival in this population.
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Since the publication of the RECOVERY trial, the use of glucocorticoid drugs (GC) has spread for the treatment of severe COVID-19 worldwide. However, the benefit of dexamethasone was largest in patients who received mechanical ventilation or supplemental oxygen therapy, while no benefit was found among patients without hypoxemia. In addition, a positive outcome was found in patients who received dexamethasone after several days of symptoms, while possible harm could exist if administered early. ⋯ Previous studies showed that an early GC use during the first phase of the disease, when viral replication peaks, may negatively affect the innate immune response through several mechanisms, such as the inhibition of pro-inflammatory and antiviral cytokine production and signaling pathway, including type I interferon, that is fundamental to counteract the virus and that was found to be impaired in several patients with life-threatening COVID-19. The GC misuse can lead to a more severe disease even in patients who do not have the established risk factors, such as obesity and cardiovascular diseases. In our focused review, we describe the role of immune response in viral infections, especially SARS-CoV-2, and discuss the potential harms of GC misuse in COVID-19.
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Review
Type 2 myocardial infarction: a diagnostic and therapeutic challenge in contemporary cardiology.
In the expanding world of cardiovascular diseases, rapidly reaching pandemic proportions, the main focus is still on coronary atherosclerosis and its clinical consequences. However, at least in the Western world, middle-aged male patients with acute myocardial infarction are no more the rule. Due to a higher life expectancy and major medical advances, physicians are to treat older and frailer individuals, usually with multiple comorbidities. ⋯ Perhaps more importantly, T2MI is also victim of undertreatment, as drugs that constitute the cornerstone of therapy in most cardiovascular diseases are much more unlikely to be prescribed in T2MI than in coronary atherothrombosis. In this paper, we review the recent literature on the classification, pathophysiology, epidemiology, and management of T2MI, trying to summarise the state-of-the-art knowledge about this increasingly important pathologic condition. Finally, based on the current scientific evidence, we also propose an algorithm that may be easily utilised in clinical practice, in order to improve T2MI diagnosis and risk stratification.
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Conflicting results can be found in the literature on the frequency of hepatitis B virus (HBV) reactivation (HBVr) on rituximab (RTX) in rheumatic patients with previously resolved HBV (prHBV) infection. Here, we report the frequency of HBVr in a large historical cohort of caucasian rheumatic patients with prHBV receiving RTX. Registry data of rheumatic patients treated with RTX were retrospectively analysed. ⋯ Kaplan-Meier functions were similar in patients with or without prHBV infection which was not associated with RTX discontinuation neither at univariate nor at multivariate analysis. These data are in favor of the concept that patients with rheumatologic diseases have a very low risk of reactivation of the HBV infection under RTX treatment. However, future prospective studies, including a larger number of patients, are still necessary to draw definitive conclusions.
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Machine learning, in particular deep learning, may be able to assist in the prediction of the length of stay and timing of discharge for individual patients. Artificial neural networks applied to medical text have previously shown promise in this area. In this study, a previously derived artificial neural network was applied to prospective and external validation datasets. ⋯ The performance in the prediction of discharge within the next 7 days was more limited (area under the receiver operator curve 0.68 and 0.67). This study has shown that in prospective and external validation datasets the previously derived deep learning algorithms have demonstrated moderate performance in the prediction of which patients will be discharged within the next 2 days. Future studies may seek to further refine or evaluate the effect of the implementation of such algorithms.