The American journal of medicine
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Falanga is a widespread form of torture, but details of the chronic skin sequelae on physical examination are unreported. ⋯ Plantar hyperpigmentation was present in all cases 9 months to 10 years after suffering falanga. This physical sign can support victims' legal requests for political asylum, and its recognition can aid physicians who care for torture victims.
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The role of the electrocardiogram for risk stratification in patients with severe aortic stenosis is not established. We assessed the hemodynamic correlates and the prognostic value of the corrected QT interval (QTc) in patients with severe aortic stenosis undergoing aortic valve replacement. ⋯ In patients with severe aortic stenosis, prolonged QTc is a marker of an advanced disease stage associated with an adverse hemodynamic profile and increased long-term mortality after aortic valve replacement.
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Malnutrition is a major determinant of health outcomes among the older adult population. Our goal was to evaluate the impact of malnutrition on hospitalization outcomes for older adults who were admitted with a diagnosis of sepsis. ⋯ Among the geriatric population diagnosed with sepsis, malnutrition is an independent predictor for poor hospitalization outcomes.
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The Centers for Disease Control and Prevention and New York State Department of Health recently identified the Capital District of New York (CDNY) as an emerging endemic area for blastomycosis. However, no clinical or epidemiological description of blastomycosis in the CDNY has been published. ⋯ Our data support recent reports that blastomycosis is an emerging disease in the CDNY. Most patients were misdiagnosed as malignancy or non-mycotic infection, which led to treatment delays.
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General medical wards admit high-risk patients. Artificial intelligence algorithms can use big data for developing models to assess patients' risk stratification. The aim of this study was to develop a mortality prediction machine learning model using data available at the time of admission to the medical ward. ⋯ A machine learning model outperforms single variables predictions of in-hospital mortality at the time of admission to the medical ward. Such a decision support tool has the potential to augment clinical decision-making regarding level of care needed for admitted patients.