Internal and emergency medicine
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Cognitive biases are systematic cognitive distortions, which can affect clinical reasoning. The aim of this study was to unravel the most common cognitive biases encountered in in the peculiar context of the COVID-19 pandemic. Case study research design. ⋯ The pandemic context is a breeding ground for the emergence of cognitive biases, which can influence clinical reasoning and lead to errors. Awareness of these cognitive mechanisms could potentially reduce biases and improve clinical reasoning. Moreover, the analysis of cognitive biases can offer an insight on the functioning of the clinical reasoning process in the midst of the pandemic crisis.
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The 4S-AF scheme [Stroke risk, Symptom severity, Severity of atrial fibrillation (AF) burden, Substrate severity] was recently proposed to characterize AF patients. In this post hoc analysis we evaluated the agreement between the therapeutic strategy (rate or rhythm control, respectively), as suggested by the 4S-AF scheme, and the actual strategy followed in a patients cohort. Outcomes of interest were as follows: all-cause death, a composite of all-cause death/any thromboembolism/acute coronary syndrome, and a composite of all-cause death, any thrombotic/ischemic event, and major bleeding (net clinical outcome). ⋯ When 4S-AF indicated rhythm control, disagreement was associated with a higher risk of all-cause death (HR 7.59; 95% CI 1.65-35.01), and of the composite outcome (HR 2.69; 95% CI 1.19-6.06). The 4S-AF scheme is a useful tool to comprehensively evaluate AF patients and aid the decision-making process. Disagreement with the rhythm control suggestion of the 4S-AF scheme was associated with adverse clinical outcomes.
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The rapid worldwide spread of the Coronavirus disease (COVID-19) crisis has put health systems under pressure to a level never experienced before, putting intensive care units in a position to fail to meet an exponentially growing demand. The main clinical feature of the disease is a progressive arterial hypoxemia which rapidly leads to ARDS which makes the use of intensive care and mechanical ventilation almost inevitable. The difficulty of health systems to guarantee a corresponding supply of resources in intensive care, together with the uncertain results reported in the literature with respect to patients who undergo early conventional ventilation, make the search for alternative methods of oxygenation and ventilation and potentially preventive of the need for tracheal intubation, such as non-invasive respiratory support techniques particularly valuable. ⋯ This position paper describes the indications for the use of non-invasive respiratory support techniques in respiratory failure secondary to COVID-19-related pneumonia, formulated by the Non-invasive Ventilation Faculty of the Italian Society of Emergency Medicine (SIMEU) on the base of what is available in the literature and on the authors' direct experience. Rationale, literature, tips & tricks, resources, risks and expected results, and patient interaction will be discussed for each one of the escalating non-invasive respiratory techniques: standard oxygen, HFNCO, CPAP, NIPPV, and awake self-repositioning. The final chapter describes our suggested approach to the failing patient.
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Previous studies have found that fat mass and lean body mass may act differently on the prognosis in patients type 2 diabetes mellitus (T2DM). However, the change of fat mass and lean body mass on prognosis in T2DM patients has not yet been investigated. We performed a Post hoc analysis of data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. ⋯ Further study is needed to determine whether increased FMI or LBMI increases the risk of MACEs. Trial registration: clinicaltrials.gov., No. NCT00000620.
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Observational Study
Aging underlies heterogeneity between comorbidity and multimorbidity frameworks.
Studies exploring differences between comorbidity (i.e., the co-existence of additional diseases with reference to an index condition) and multimorbidity (i.e., the presence of multiple diseases in which no one holds priority) are lacking. In this single-center, observational study conducted in an academic, internal medicine ward, we aimed to evaluate the prevalence of patients with two or more multiple chronic conditions (MCC), comorbidity, or multimorbidity, correlating them with other patients' characteristics. The three categories were compared to the Cumulative Illness Rating Scale (CIRS) comorbidity index, age, gender, polytherapy, 30-day readmission, in-hospital and 30-day mortalities. ⋯ The CIRS comorbidity index was always higher in multimorbid patients, but only in the subgroups 75-84 years and ≥ 85 years was a significant (p < 0.001) difference (1.24 and 1.36, respectively) noticed. At multivariable analysis, age was always independently associated with in-hospital mortality (p = 0.002), 30-day mortality (p < 0.001), and 30-day readmission (p = 0.037), while comorbidity and multimorbidity were not. We conclude that age determines the most important differences between comorbid and multimorbid patients, as well as major outcomes, in a hospital setting.