Internal and emergency medicine
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This study aimed to evaluate the effectiveness of various scoring systems in predicting in-hospital mortality for COVID-19 patients admitted to the internal medicine ward. We conducted a prospective collection of clinical data from patients admitted to the Internal Medicine Unit at Santa Maria Nuova Hospital in Florence, Italy, with confirmed pneumonia caused by SARS-CoV-2. We calculated three scoring systems: the CALL score, the PREDI-CO score, and the COVID-19 in-hospital Mortality Risk Score (COVID-19 MRS). ⋯ The mortality rate increased significantly across increasing quartiles (p<0.001). In conclusion the COVID-19 in-hospital Mortality Risk Score (MRS) demonstrated reasonable prognostic stratification for patients admitted to the internal medicine ward with SARS-CoV-2-induced pneumonia. The inclusion of Delirium and IL6 as additional prognostic indicators in the scoring systems enhanced their predictive performance, specifically in determining in-hospital mortality among COVID-19 patients.
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Advanced heart failure (HF) with congestive symptoms refractory to diuretic treatment worsens the patient's prognosis and quality of life. Peritoneal ultrafiltration (PUF) attempts to improve symptoms and reduce HF-related events. This study analyzes the impact of PUF on older adult patients with significant comorbidity and advanced HF. ⋯ There was no significant deterioration in renal function during the first year of follow-up or major complications associated with the technique. Survival was 72% at 1 year. In older adult patients with comorbidity, advanced HF, and refractory congestive symptoms, PUF reduced hospital admissions and the use of intravenous diuretic rescue treatment, without major complications.
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Age-related cognitive impairment can occur many years before the onset of the clinical symptoms of dementia. Uric acid (UA), a metabolite of purine-rich foods, has been shown to be positively associated with improved cognitive function, but such association remains controversial. Moreover, most of the previous studies investigating the association included elderly participants with memory-related diseases. ⋯ Adjusted multivariable linear regression analyses showed that higher sUA is associated with poorer performance on the visual memory domain of cognitive function (β = - 6.87, 95% CI - 11.65 to - 2.10, P = 0.005), but not on the speed of reaction domain (β = - 55.16, 95% CI - 190.63 to 80.30, P = 0.424). Our findings support previous studies suggesting an inverse association between high sUA levels and cognitive function in elderly and extend the evidence for such a role to middle-aged participants. Further prospective studies are warranted to investigate the relationship between UA and cognition.
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Observational Study
DOACs use in extreme body-weighted patients: results from the prospective START-register.
Direct oral anticoagulants (DOACs) are widely used for the treatment of venous thromboembolism (VTE) and for stroke prevention in atrial fibrillation (AF). However, evidence in obese and underweight patients is limited. We assessed the safety and effectiveness of DOACs and vitamin K antagonists (VKAs) in patients ≥ 120 kg or ≤ 50 kg enrolled in an observational prospective cohort study, the START-Register. ⋯ DOACs seem to be effective and safe also for the treatment of patients with extreme body weights, both underweight and overweight. Further prospective studies are needed to support these findings.
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COVID-19 is responsible for high mortality, but robust machine learning-based predictors of mortality are lacking. To generate a model for predicting mortality in patients hospitalized with COVID-19 using Gradient Boosting Decision Trees (GBDT). The Spanish SEMI-COVID-19 registry includes 24,514 pseudo-anonymized cases of patients hospitalized with COVID-19 from 1 February 2020 to 5 December 2021. ⋯ Clinical and laboratory data from 23,983 patients were analyzed. CatBoost mortality prediction models achieved an AUC performance of 84.76 (standard deviation 0.45) for patients in the test group (potentially vaccinated patients not included in model training) using 16 features. The performance of the 16-parameter GBDT model for predicting COVID-19 hospital mortality, although requiring a relatively large number of predictors, shows a high predictive capacity.