Affiliations: For a full list of the authors' affiliations please refer to the Acknowledgements. ABSTRACT This European Respiratory Society (ERS) statement provides a comprehensive overview on physical activity in patients with chronic obstructive pulmonary disease (COPD). A multidisciplinary Task Force of experts representing the ERS Scientific Group 01.02 ''Rehabilitation and Chronic Care'' determined the overall scope of this statement through consensus. Focused literature reviews were conducted in key topic areas and the final content of this Statement was agreed upon by all members.The current knowledge regarding physical activity in COPD is presented, including the definition of physical activity, the consequences of physical inactivity on lung function decline and COPD incidence, physical activity assessment, prevalence of physical inactivity in COPD, clinical correlates of physical activity, effects of physical inactivity on hospitalisations and mortality, and treatment strategies to improve physical activity in patients with COPD.This Task Force identified multiple major areas of research that need to be addressed further in the coming years. These include, but are not limited to, the disease-modifying potential of increased physical activity, and to further understand how improvements in exercise capacity, dyspnoea and self-efficacy following interventions may translate into increased physical activity.The Task Force recommends that this ERS statement should be reviewed periodically (e.g. every 5-8 years).@ERSpublications An official ERS statement providing a comprehensive overview on physical activity in patients with COPD http://ow.ly/C6v78

Background: We used a validated inpatient satisfaction questionnaire to evaluate the health care received by patients admitted to several hospitals. This questionnaire was factored into distinct domains, creating a score for each to assist in the analysis.

A simple score using clinical data available at the time of the emergency department visit provides a practical diagnostic decision aid, and predicts the development of severe community-acquired pneumonia.

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The Spanish version of the HADS demonstrated good reliability and validity when used in medical patients.

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patientyears of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision recall curve of 63.0%. The model was externally validated in a large independent patient cohort.

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