IMPORTANCE End-of-life decisions occur daily in intensive care units (ICUs) around the world, and these practices could change over time.OBJECTIVE To determine the changes in end-of-life practices in European ICUs after 16 years. DESIGN, SETTING, AND PARTICIPANTS Ethicus-2 was a prospective observational study of 22 European ICUs previously included in the Ethicus-1 study (1999)(2000). During a self-selected continuous 6-month period at each ICU, consecutive patients who died or had any limitation of life-sustaining therapy from September 2015 until October 2016 were included. Patients were followed up until death or until 2 months after the first treatment limitation decision.

No abstract

No abstract

No abstract

No abstract

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. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results The database included a total of 2022 patients (mean age 64 [IQR 5–71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10–17]) and SOFA score (5 [IQR 3–7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. Conclusion The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

PCT has a high negative predictive value (94%) and lower PCT levels seems to be a good tool for excluding coinfection, particularly for patients without shock.

No abstract