Relationships between symptoms and lung function in asthma and/or chronic obstructive pulmonary disease in a real-life setting: the NOVEL observational longiTudinal studY.

BACKGROUND: The relationships between spirometric assessment of lung function and symptoms (including exacerbations) in patients with asthma and/or chronic obstructive pulmonary disease (COPD) in a real-life setting are uncertain. OBJECTIVES: To assess the relationships between baseline post-bronchodilator (post-BD) spirometry measures of lung function and symptoms and exacerbations in patients with a physician-assigned diagnosis of asthma and/or COPD. DESIGN: The NOVEL observational longiTudinal studY (NOVELTY) is a global, prospective, 3-year observational study. METHODS: Logistic regression analysis was used to evaluate relationships. Spirometry measures were assessed as percent predicted (%pred). Symptoms were assessed at baseline, and exacerbations were assessed at baseline and Year 1. RESULTS: A total of 11,181 patients in NOVELTY had spirometry data (asthma, n = 5903; COPD, n = 3881; asthma + COPD, n = 1397). A 10% lower post-BD %pred forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) - adjusted for age and sex - were significantly associated with dyspnea (modified Medical Research Council ⩾ grade 2), frequent breathlessness [St George's Respiratory Questionnaire (SGRQ)], frequent wheeze attacks (SGRQ), nocturnal awakening (Respiratory Symptoms Questionnaire; ⩾1 night/week), and frequent productive cough (SGRQ). Lower post-BD %pred FEV1 and, to a lesser extent, lower post-BD %pred FVC were significantly associated with ⩾1 physician-reported exacerbation at baseline or Year 1. This association was stronger in patients with COPD than in those with asthma. CONCLUSION: In a real-life setting, reduced lung function is consistently associated with symptoms in patients with asthma, COPD, or asthma + COPD. The relationship with exacerbations is stronger in COPD only than in asthma. TRAIL REGISTRATION: clinicaltrials.gov identifier: NCT02760329 (www.clinicaltrials.gov).

Relationships between symptoms and lung function in asthma and/or chronic obstructive pulmonary disease in a study performed in a real-life setting: the NOVELTY studyBackground: Asthma and chronic obstructive pulmonary disease (COPD) have many symptoms in common. To confirm diagnosis, doctors use spirometry, a test to measure the amount of air that can be breathed out from the lungs and how fast it can be blown out. The relationship between these measurements and symptoms in asthma and COPD is not well understood.Objectives: The aim of this research is to describe the characteristics, treatment, and impact of asthma and/or COPD in patients who are receiving their usual medical care.Methods: NOVELTY is a large study of around 12,000 patients across 19 countries. This analysis of NOVELTY looked at the relationships between two spirometry measurements and the symptoms of asthma and/or COPD experienced by patients. The spirometry measurements were: - forced expiratory volume in 1 second (FEV₁) ­ the amount of air that can be blown out of the lungs in 1 second- forced vital capacity (FVC) ­ the amount of air that can be forcibly breathed out from the lungs after taking the deepest breath possibleResults: The lower the FEV₁ and FVC, the more common the symptoms of breathlessness, wheeze attacks, night-time awakening, and coughing up of phlegm or mucus. These relationships were similar for FEV₁ and FVC. Lower FEV₁ was more strongly associated with worse symptoms in COPD than in asthma.Conclusion: These findings help to improve our understanding of the relationships between spirometry measures and symptoms in patients with asthma and/or COPD.

Symptom Burden, Health Status, and Productivity in Patients with Uncontrolled and Controlled Severe Asthma in NOVELTY.

Background: Few studies have quantified symptom burden, health status, and productivity in patients with uncontrolled and controlled severe asthma. Up-to-date, real-world, global evidence is needed. Objective: To quantify symptom burden, health status, and productivity in patients with uncontrolled and controlled severe asthma using baseline data from the NOVEL observational longiTudinal studY (NOVELTY; NCT02760329). Methods: NOVELTY included patients aged ≥18 years (or ≥12 years in some countries) from primary care and specialist centres in 19 countries, with a physician-assigned diagnosis of asthma, asthma+chronic obstructive pulmonary disease (COPD), or COPD. Disease severity was physician-assessed. Uncontrolled severe asthma was defined by an Asthma Control Test (ACT) score <20 and/or severe physician-reported exacerbations in the previous year; controlled severe asthma required an ACT score ≥20 and no severe exacerbations. Assessment of symptom burden included Respiratory Symptoms Questionnaire (RSQ) and ACT score. Assessment of health status included St George's Respiratory Questionnaire (SGRQ), EuroQoL 5 Dimensions 5 Levels Health Questionnaire (EQ-5D-5L) index value, and EQ-5D-5L Visual Analog Score (EQ-VAS). Assessment of productivity loss included absenteeism, presenteeism, overall work impairment, and activity impairment. Results: Of 1652 patients with severe asthma, asthma was uncontrolled in 1078 (65.3%; mean age 52.6 years, 65.8% female) and controlled in 315 (19.1%; mean age 55.2 years, 56.5% female). With uncontrolled versus controlled severe asthma, symptom burden was higher (mean RSQ score 7.7 vs 2.5), health status more impaired (mean SGRQ total score 47.5 vs 22.4; mean EQ-5D-5L index value 0.68 vs 0.90; mean EQ-VAS score 64.1 vs 78.1), and productivity lower (presenteeism 29.3% vs 10.5%). Conclusion: Our findings highlight the symptom burden of uncontrolled severe asthma compared with controlled severe asthma and its impact on patient health status and productivity, and support the need for interventions to improve control of severe asthma.

A Machine Learning Pipeline for Measurement of Arterial Stiffness in A-Mode Ultrasound.

Arterial stiffness (AS) of the carotid artery is an early marker of stratifying cardiovascular disease risk. This article aims to improve the performance of ARTSENS, a noninvasive A-mode ultrasound-based device for measuring AS. The primary objective of ARTSENS is to enable the measurement of elastic modulus using A-Mode ultrasound and blood pressure. As this device is image-free, there is a need to automate: 1) carotid detection; 2) wall localization; and 3) inner lumen diameter measurement. This has been performed using conventional signal processing methods in some of the earlier works in this domain. In this article, deep neural network (DNN) models are employed to perform the above three tasks. The DNNs were trained over data acquired from 82 subjects at two different medical centers. Ground-truth labeling was performed by a trained operator using corresponding measurements from the state-of-the-art Aloka e-Tracking system. All three DNN models had significantly lower errors compared to earlier signal processing methods and could perform their measurements using a single A-Mode frame. Using the DNNs, two different machine learning pipelines have been proposed here to measure the elastic modulus; the best among them could achieve an error of 9.3% with the Pearson correlation coefficient of 0.94 ( ). The models were tested on Raspberry Pi and Jetson Nano single board computers to demonstrate real-time processing on low computational resources.