Internal and emergency medicine
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Observational Study
Clinical characteristics and respiratory support of 310 COVID-19 patients, diagnosed at the emergency room: a single-center retrospective study.
An ongoing outbreak of pneumonia associated with severe acute respiratory coronavirus 2 (SARS-CoV-2) occurred at the end of February 2020 in Lombardy, Italy. We analyzed data from a retrospective, single-center case series of 310 consecutive patients, with confirmed SARS-CoV-2 infection, admitted to the emergency room. We aimed to describe the clinical course, treatment and outcome of a cohort of patients with COVID-19 pneumonia, with special attention to oxygen delivery and ventilator support. ⋯ Among the 63 patients treated with CPAP/NIV without DNI, NIV failure occurred in 36 patients (57.1%), with mortality rate of 47.2%. Twenty-seven (27) patients were treated with CPAP/NIV without needing mechanical ventilation and 26 were discharged alive (96.3%). The study documents the poor prognosis of patients with severe respiratory failure, although a considerable minority of patients treated with CPAP/NIV had a positive outcome.
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Clinical profile and outcome of patients with chronic inflammatory arthritis and metabolic syndrome.
Systemic chronic inflammation may favor the onset of metabolic syndrome (MetS) which represents a risk factor for CV events. Rheumatoid arthritis (RA), ankylosing spondylitis (AS) and psoriatic arthritis (PsA) are disorders with high prevalence of MetS. We assessed the factors associated with MetS and its prognostic role in non-selected RA/AS/PsA patients. ⋯ At multivariate Cox regression analysis, MetS was related to primary end point (HR 1.52 [CI 1.01-2.47], p = 0.04) together with higher LVM, disease duration and higher prevalence of biologic DMARDs refractoriness, and to co-primary end point (HR 2.05 [CI 1.16-3.60], p = 0.01) together with older age and higher LVM. The RA/AS/PsA phenotype MetS + is a subject with moderate/high disease activity, LV structural and functional abnormalities at increased risk for cancer. MetS + identifies RA/AS/PsA patients at higher risk for CV and non-CV events, independently of traditional CV risk factors analyzed individually and traditional indexes of inflammation.
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As a tool to support clinical decision-making, Mortality Prediction Models (MPM) can help clinicians stratify and predict patient risk. There are numerous scoring systems for patients with sepsis that predict sepsis-related mortality and the severity of sepsis. ⋯ Machine learning applied to minimal medical records of patients diagnosed with sepsis can be a useful tool. Progress is needed in the development and validation of clinical decision support tools that can assist in patient risk stratification, prognosis, discussion of patient outcomes, and shared decision making.