Published by Elsevier Inc.BACKGROUND Peak inspiratory flow (PIF) has been proposed as a measure to assess a patient's ability to use dry powder inhalers (DPI). However, robust quality criteria to determine a repeatability limit for measuring PIF are lacking. RESEARCH QUESTIONS What are the repeatability limits for measuring PIF? What is the relationship between PIF measured using the In-Check™ DIAL device at Diskus® (PIFD) and HandiHaler® (PIFHH) resistances? STUDY DESIGN AND METHODS Data from a randomized, controlled, phase 3 trial (study 0149) was used to define repeatability limits for PIF. Additionally, a model to characterize the relationship between PIF measured using the In-Check DIAL device at PIFD and PIFHH was defined using data from two randomized, controlled, phase 3 trials (studies 0128 and 0149). RESULTS In study 0128, the mean values (standard deviation [SD]) for PIF at zero resistance and PIFHH were 84.6 (33.4) and 57.3 (26.1) L/min, respectively. In study 0149, the mean values (SD) for PIFD and PIFHH were 42.4 (11.2) and 29.0 (8.3) L/min, respectively. At the mean level, the mean difference between measurement attempts for PIFD and PIFHH was small, less then 5 L/min and less then 3 L/min, respectively. The repeatability limit was determined as 10 and 5 L/min for PIFD and PIFHH, respectively. Modeling the relationship between PIFD and PIFHH, after controlling for significant covariates demonstrated that a PIFD value of 60 L/min was approximately equivalent to PIFHH of 40 L/min.  https://www.selleckchem.com/products/valproic-acid.html  INTERPRETATIONS This analysis demonstrated that the two highest values of PIF using the In-Check DIAL device among three inspiratory efforts met the repeatability limit. Altogether, these data provide guidance for measuring PIF against the simulated resistance of a specific DPI in clinical practice and research studies. BACKGROUND Obstructive sleep apnea (OSA) conveys worse clinical outcomes in coronary artery disease patients. The STOP-BANG score is a simple tool that evaluates the risk of OSA and can be added to the large number of clinical variables and scores obtained during the management of myocardial infarction (MI) patients. Currently, machine learning (ML) is able to select and integrate numerous variables to optimize prediction tasks. RESEARCH QUESTION Can the integration of STOP-BANG score with clinical data and scores through ML better identify patients who suffered an in-hospital cardiovascular event after acute MI? STUDY DESIGN AND METHODS This is a prospective observational cohort study of 124 acute MI patients in which the STOP-BANG score classified 34 low-(27.4%), 30 intermediate-(24.2%), and 60 high-(48.4%) OSA-risk patients who were followed during hospitalization. ML implemented feature selection and integration across 47 variables (including STOP-BANG score, Killip-class, GRACE score and LVEF) to identift. BACKGROUND Pulmonary gas exchange efficiency, determined by the alveolar-to-arterial PO2 difference (A-aDO2), progressively worsens during exercise at sea-level; this response is further elevated during exercise in hypoxia. Traditionally, pulmonary gas exchange efficiency is assessed through measurements of ventilation and end-tidal gases paired with direct arterial blood gas (ABG) sampling. Since these measures have a number of caveats, particularly invasive blood sampling, the development of new approaches for the non-invasive assessment of pulmonary gas exchange is needed. RESEARCH QUESTION Is a non-invasive method of assessing pulmonary gas exchange valid during rest and exercise in acute hypoxia? STUDY DESIGN and Methods Twenty-five healthy participants (10 female) completed a staged maximal exercise test on a cycle ergometer in a hypoxic chamber (FIO2=0.11). Simultaneous ABGs via a radial arterial catheter and non-invasive gas-exchange measurements (AGM100) were obtained in two-minute intervals. Non-invbe used clinically as a tool during normoxic exercise in patients with pre-existing impairments in gas exchange efficiency. BACKGROUND Competence in Point-of-Care Ultrasound (PoCUS) is widely recommended by several critical care societies. Despite numerous introductory short-courses, very few doctors attain PoCUS competence, due to the challenges in establishing longitudinal competence programs. RESEARCH QUESTION To evaluate the methodological quality of the literature on basic PoCUS competence processes in critical care. STUDY DESIGN and Methods A systematic review to identify manuscripts meeting predefined inclusion criteria was performed using three medical databases (PubMed, OVID Embase, Web of Science), extra references from original articles, review articles and expert panel guidelines, and directly contacting authors for further information if required. The objectives, domains, and inclusion and exclusion criteria of the review were determined during discussions between experienced PoCUS educators. Data extraction and analyses were performed independently by 3 reviewers. RESULTS Of the 5408 abstracts extracted, 42 met the inclusion criteria for longitudinal PoCUS competence. Each study was described along four broad categories general information, study design and trainee characteristics; description of introductory course; description of longitudinal competence program; grading of overall methodological quality on a 4-point Likert scale. Thirty-nine studies (92.9%) were from a single center. Most studies lacked important details on study methodology such as prior ultrasound experience, pre-and post-course tests, models for hands-on sessions, ratio of instructor to trainee, competence assessment criteria, number of scans performed by individual trainees, and formative and summative assessments. The studies were rated as follows Poor = 19 (45.2%), Average = 15 (35.7%), Good = 4 (9.5%) and Excellent = 4 (9.5%). INTERPRETATION There is very little high-quality evidence on PoCUS competence. To help frame policy guidelines to improve PoCUS education, there is a need for well-designed longitudinal studies on PoCUS competence. BACKGROUND Noninvasive ventilation (NIV) is an effective form of treatment in obesity hypoventilation syndrome (OHS) with severe obstructive sleep apnea (OSA). However, there is paucity of evidence in OHS patients without severe OSA phenotype. RESEARCH QUESTIONS Is NIV effective in OHS without severe OSA phenotype? STUDY DESIGN AND METHODS In this multicenter, open-label parallel group clinical trial performed at 16 sites in Spain, we randomly assigned 98 stable ambulatory patients with untreated OHS and apnea-hypopnea index less then 30 events/hour (i.e., no severe OSA) to NIV or lifestyle modification (control group) using simple randomization through an electronic database. The primary end point was hospitalization days/year. Secondary endpoints included other hospital resource utilization, incident cardiovascular events, mortality, respiratory functional tests, blood pressure, quality of life, sleepiness and other clinical symptoms. Both investigators and patients were aware of the treatment allocation; however, treating clinicians from the routine care team were not aware of patients' enrollment in the clinical trial.