Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain.

Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes.

Methods: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient's factors associated with mortality risk.

[Neuro-Behçet: clinicopathological findings in an autopsy case].

Introduction: Behçet's disease (BD) was first described by Hulusi Behçet in 1937 as a triad of oral aphthae, genital ulcers and uveitis. It affects most of tissues and organs without exception and has a prevalence of neurologic involvement between 5 and 30%. Histopathological research of autopsy cases is limited, even though the cause of death is confirmed in this study.

[Discrepancies between clinical and pathological diagnosis in a polyvalent intensive care service].

Objectives: Analyze the frequency and spectrum of the most relevant diseases found in the necropsic study. Assess the association between stay in Intensive Care Unit (ICU) less than 24 hours and rate of diagnostic errors.

Material And Methods: Retrospective, observational study during a 46 month period in a polyvalent ICU.