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In COPD, emphysematous patients present worse pulmonary function and greater dyspnea, although there were no differences in the use of hospital health care resources. The greater comorbidity in Group 2 patients may require specific strategies in this subgroup of patients.

Background Many factors involved in the onset and clinical course of the ongoing COVID-19 pandemic are still unknown. Although big data analytics and artificial intelligence are widely used in the realms of health and medicine, researchers are only beginning to use these tools to explore the clinical characteristics and predictive factors of patients with COVID-19. Objective Our primary objectives are to describe the clinical characteristics and determine the factors that predict intensive care unit (ICU) admission of patients with COVID-19. Determining these factors using a well-defined population can increase our understanding of the real-world epidemiology of the disease. Methods We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling) to analyze the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the Servicio de Salud de Castilla-La Mancha (SESCAM) Health Care Network (Castilla-La Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1 to March 29, 2020. We extracted related clinical information regarding diagnosis, progression, and outcome for all COVID-19 cases. Results A total of 10,504 patients with a clinical or polymerase chain reaction–confirmed diagnosis of COVID-19 were identified; 5519 (52.5%) were male, with a mean age of 58.2 years (SD 19.7). Upon admission, the most common symptoms were cough, fever, and dyspnea; however, all three symptoms occurred in fewer than half of the cases. Overall, 6.1% (83/1353) of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm, we identified that a combination of age, fever, and tachypnea was the most parsimonious predictor of ICU admission; patients younger than 56 years, without tachypnea, and temperature <39 degrees Celsius (or >39 ºC without respiratory crackles) were not admitted to the ICU. In contrast, patients with COVID-19 aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnea and delayed their visit to the emergency department after being seen in primary care. Conclusions Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnea with or without respiratory crackles) predicts whether patients with COVID-19 will require ICU admission.

Inhalable medications for patients with asthma and chronic obstructive pulmonary disease (COPD) can be confusing even for health care professionals because of the multitude of available devices each with different operating principles. Dry powder inhalers (DPI) are a valuable option for almost all of the patients with asthma or COPD. Based on recorded patient inspiratory profiles, the peak inspiratory flow requirement of 30 L min−1 of high-resistance devices does not usually pose any practical limitations for the patients. Suboptimal adherence and errors in device handling are common and require continuous checking and patient education in order to avoid these pitfalls of all inhalation therapy. The aim of this opinion paper is to describe the working principles of DPIs and to summarise their key properties in order to help prescribing the correct inhaler for each patient.Funding: Orion Pharma.

The present results confirm that non-invasive home mechanical ventilation is an effective approach for long-term treatment of OHS.