Recommendations for asthma monitoring in children: A PeARL document endorsed by APAPARI, EAACI, INTERASMA, REG, and WAO.

Monitoring is a major component of asthma management in children. Regular monitoring allows for diagnosis confirmation, treatment optimization, and natural history review. Numerous factors that may affect disease activity and patient well-being need to be monitored: response and adherence to treatment, disease control, disease progression, comorbidities, quality of life, medication side-effects, allergen and irritant exposures, diet and more. However, the prioritization of such factors and the selection of relevant assessment tools is an unmet need. Furthermore, rapidly developing technologies promise new opportunities for closer, or even "real-time," monitoring between visits. Following an approach that included needs assessment, evidence appraisal, and Delphi consensus, the PeARL Think Tank, in collaboration with major international professional and patient organizations, has developed a set of 24 recommendations on pediatric asthma monitoring, to support healthcare professionals in decision-making and care pathway design.

Exploring Perceptions of Biologic Therapies: A Qualitative Study Among Canadians Living with Severe Asthma.

INTRODUCTION: Biologic therapies have demonstrated benefits for individuals with severe asthma, including reduced daily symptoms and severe exacerbations. However, data describing patient perspectives on these treatments are limited. This study sought to understand the preferences and priorities of Canadians with severe asthma in the context of novel biologic treatment options. METHODS: Semi-structured, qualitative interviews were conducted among Canadians with severe asthma from July to August 2022. Purposeful sampling included individuals with and without biologic therapy experience. All participants described daily life with severe asthma, experiences and priorities related to asthma treatment and their impressions of biologics. Reflexive thematic analysis was used to explore patterns in the data. RESULTS: Among 18 individuals included, 10 were currently taking or had prior experience with biologic treatment for asthma. Those who had never been treated with biologics were unfamiliar with them, considering treatment, or believed that they may not be eligible. Four themes were developed to convey the perspectives of participants on biologics: (1) life-changing benefits, but not for all; (2) navigating barriers to being prescribed and remaining adherent to biologic treatments; (3) treatment administration preferences are not only about convenience; (4) concerns about safety and the unknown as a source of treatment hesitancy. CONCLUSIONS: Findings suggest that the clinical benefits of biologics align with patient perceptions of achieving good asthma control. However, treatment gaps persist among individuals who do not experience a meaningful improvement in their asthma symptoms and those who face barriers accessing biologics. People with severe asthma attributed importance to greater availability of at-home treatment options, improved access to financial support to cover treatment costs and support to address safety concerns. This research provides insight into patient-based treatment priorities and preferences for biologics, which may help inform decision-making related to emerging therapies for severe asthma.

For people with severe asthma, biologics are a treatment option that can be taken in addition to their regular medication. In this study, we asked 18 Canadians with severe asthma about how having severe asthma affects their lives, their current and previous asthma treatments, and their views on biologics. Ten people in this study were currently taking or had previously taken biologics for severe asthma. We found that biologics can be life changing. Also, people with severe asthma can find it difficult to get on and stay on biologics. They would like financial and educational support when considering biologics and prefer to take biologics at home, if possible. This study helps us understand the priorities and preferences related to biologics of patients with severe asthma.

The emerging role of digital health in the management of asthma.

The most common reasons seen for lack of asthma control include misconceptions about disease control, low controller treatment adherence, poor inhaler technique, and the resulting underuse of controllers and overuse of short-acting beta2 agonists (SABAs). Narrowing these care gaps may be achieved through well-designed patient education that considers the patient's motivation, beliefs, and capabilities regarding their asthma and its management and empowers the patient to become an active participant in treatment decisions. Digital health technologies (DHTs) and digital therapeutic (DT) devices provide new opportunities to monitor treatment behaviors, improve communication between healthcare providers and patients, and generate data that inform educational interactions. DHT and DT have been proven effective in enhancing patient self-management in other chronic conditions, particularly diabetes. Accelerated integration of DHT and DT into the management of asthma patients is facilitated by the use of digital inhalers that employ sensor technology ("smart" inhalers). These devices efficiently provide real-time feedback on controller adherence, SABA use, and inhaler technique that have the strong potential to optimize asthma control.

Tangent functional connectomes uncover more unique phenotypic traits.

Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the "fingerprint gradient" (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).

Patterns of care in the management of high-risk COPD in the US (2011-2019): an observational study for the CONQUEST quality improvement program.

Background: In this study, we compare management of patients with high-risk chronic obstructive pulmonary disease (COPD) in the United States to national and international guidelines and quality standards, including the COllaboratioN on QUality improvement initiative for achieving Excellence in STandards of COPD care (CONQUEST). Methods: Patients were identified from the DARTNet Practice Performance Registry and categorized into three high-risk cohorts in each year from 2011 to 2019: newly diagnosed (≤12 months after diagnosis), already diagnosed, and patients with potential undiagnosed COPD. Patients were considered high-risk if they had a history of exacerbations or likely exacerbations (respiratory consult with prescribed medication). Descriptive statistics for 2019 are reported, along with annual trends. Findings: In 2019, 10% (n = 16,610/167,197) of patients met high-risk criteria. Evidence of spirometry for diagnosis was low; in 2019, 81% (n = 1228/1523) of patients newly diagnosed at high-risk had no record of spirometry/peak expiratory flow in the 12 months pre- or post-diagnosis and 43% (n = 651/1523) had no record of COPD symptom review. Among those newly and already diagnosed at high-risk, 52% (n = 4830/9350) had no evidence of COPD medication. Interpretation: Findings suggest inconsistent adherence to evidence-based guidelines, and opportunities to improve identification, documentation of services, assessment, therapeutic intervention, and follow-up of patients with COPD. Funding: This study was conducted by the Observational and Pragmatic Research Institute (OPRI) Pte Ltd and was partially funded by Optimum Patient Care Global and AstraZeneca Ltd. No funding was received by the Observational & Pragmatic Research Institute Pte Ltd (OPRI) for its contribution.

For UW Health, Health Plan Ownership Provides Financial Stability.

For health systems seeking a path forward and upward in a value-based environment, ownership of a health plan can yield many positives, including a chance to drive value-based care, financial margin improvement, and opportunities for rewarding partnerships. However, being both a payer and a provider-a "payvider"-can make extraordinary demands on both the health system and health plan. Developing this hybrid business has been a learning experience for UW Health, an academic medical center that, like others in academic healthcare, was built on a fee-for-service model. Today, UW Health is a majority owner of what has become the state's largest provider-owned health plan. As illustrated here, health plan ownership is not for every system. The burdens are heavy. But for UW Health, it is an important component of both mission and margin.

Canadian Health Care Professionals' Familiarity with Chronic Cough Guidelines and Experiences with Diagnosis and Management: A Cross-Sectional Survey.

INTRODUCTION: Educational programs on chronic cough may improve patient care, but little is known about how Canadian physicians manage this common debilitating condition. We aimed to investigate Canadian physicians' perceptions, attitudes, and knowledge of chronic cough. METHODS: We administered a 10-min anonymous, online, cross-sectional survey to 3321 Canadian physicians in the Leger Opinion Panel who managed adult patients with chronic cough and had been in practice for > 2 years. RESULTS: Between July 30 and September 22, 2021, 179 physicians (101 general practitioners [GPs] and 78 specialists [25 allergists, 28 respirologists, and 25 ear/nose/throat specialists]) completed the survey (response rate: 5.4%). In a month, GPs saw a mean of 27 patients with chronic cough, whereas specialists saw 46. About one-third of physicians appropriately identified a duration of > 8 weeks as the definition for chronic cough. Many physicians reported not using international chronic cough management guidelines. Patient referrals and care pathways varied considerably, and patients frequently experienced lost to follow-up. While physicians endorsed nasal and inhaled corticosteroids as common treatments for chronic cough, they rarely used other guideline-recommended treatments. Both GPs and specialists expressed high interest in education on chronic cough. CONCLUSION: This survey of Canadian physicians demonstrates low uptake of recent advances in chronic cough diagnosis, disease categorization, and pharmacologic management. Canadian physicians also report unfamiliarity with guideline-recommended therapies, including centrally acting neuromodulators for refractory or unexplained chronic cough. This data highlights the need for educational programs and collaborative care models on chronic cough in primary and specialist care.

Diagnostic Performance of a Machine Learning Algorithm (Asthma/Chronic Obstructive Pulmonary Disease [COPD] Differentiation Classification) Tool Versus Primary Care Physicians and Pulmonologists in Asthma, COPD, and Asthma/COPD Overlap.

BACKGROUND: The differential diagnosis of asthma and chronic obstructive pulmonary disease (COPD) poses a challenge in clinical practice and its misdiagnosis results in inappropriate treatment, increased exacerbations, and potentially death. OBJECTIVE: To investigate the diagnostic accuracy of the Asthma/COPD Differentiation Classification (AC/DC) tool compared with primary care physicians and pulmonologists in asthma, COPD, and asthma-COPD overlap. METHODS: The AC/DC machine learning-based diagnostic tool was developed using 12 parameters from electronic health records of more than 400,000 patients aged 35 years and older. An expert panel of three pulmonologists and four general practitioners from five countries evaluated 119 patient cases from a prospective observational study and provided a confirmed diagnosis (n = 116) of asthma (n = 53), COPD (n = 43), asthma-COPD overlap (n = 7), or other (n = 13). Cases were then reviewed by 180 primary care physicians and 180 pulmonologists from nine countries and by the AC/DC tool, and diagnostic accuracies were compared with reference to the expert panel diagnoses. RESULTS: Average diagnostic accuracy of the AC/DC tool was superior to that of primary care physicians (median difference, 24%; 95% posterior credible interval: 17% to 29%; P < .0001) and was noninferior and superior (median difference, 12%; 95% posterior credible interval: 6% to 17%; P < .0001 for noninferiority and P = .0006 for superiority) to that of pulmonologists. Average diagnostic accuracies were 73%, 50%, and 61% by AC/DC tool, primary care physicians, and pulmonologists versus expert panel diagnosis, respectively. CONCLUSION: The AC/DC tool demonstrated superior diagnostic accuracy compared with primary care physicians and pulmonologists in the diagnosis of asthma and COPD in patients aged 35 years and greater and has the potential to support physicians in the diagnosis of these conditions in clinical practice.

Homological landscape of human brain functional sub-circuits.

Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. Nonetheless, the non-local properties of topological strata induced by local/quasilocal functional sub-circuits have yet to be addressed. To that end, we proposed a homological formalism that enables the quantification of higher-order characteristics of human brain functional sub-circuits. Our results indicated that each homological order uniquely unravels diverse, complementary properties of human brain functional sub-circuits. Noticeably, the H1 homological distance between rest and motor task were observed at both whole-brain and sub-circuit consolidated level which suggested the self-similarity property of human brain functional connectivity unraveled by homological kernel. Furthermore, at the whole-brain level, the rest-task differentiation was found to be most prominent between rest and different tasks at different homological orders: i) Emotion task H0, ii) Motor task H1, and iii) Working memory task H2. At the functional sub-circuit level, the rest-task functional dichotomy of default mode network is found to be mostly prominent at the first and second homological scaffolds. Also at such scale, we found that the limbic network plays a significant role in homological reconfiguration across both task- and subject- domain which sheds light to subsequent Investigations on the complex neuro-physiological role of such network. From a wider perspective, our formalism can be applied, beyond brain connectomics, to study non-localized coordination patterns of localized structures stretching across complex network fibers.

Systemic corticosteroids in asthma: A call to action from World Allergy Organization and Respiratory Effectiveness Group.

Systemic corticosteroids (SCS) are a highly effective treatment for acute exacerbations and long-term symptom control in asthma. Long-term SCS use is highly prevalent across all asthma severities, occurring in over 20% of patients with severe or uncontrolled disease globally. It is now well known that exposure to both long-term and repeated acute courses of SCS is associated with a high risk of serious adverse effects (AEs), such as osteoporosis, and metabolic and cardiovascular complications, especially when prescribed onto a background of other corticosteroids. The aim of this call-to-action article, endorsed by the World Allergy Organization and the Respiratory Effectiveness Group, is to review the accumulating evidence on the burden of SCS on patients with asthma and provide an overview of potential strategies for implementing SCS Stewardship. Primary prevention of exacerbations and improvement of asthma control is a key first step in achieving SCS Stewardship, by optimizing maintenance asthma medications and addressing modifiable risk factors, such as adherence and inhaler technique. Other key elements of SCS Stewardship include increasing appropriate specialist referrals for multidisciplinary review, assessment of biomarkers, and consideration of oral corticosteroid-sparing add-on therapies (eg, biologics). In cases where SCS use is deemed clinically justified, it should be tapered to the lowest possible dose. In addition, patients receiving long-term SCS or frequent acute courses should be closely monitored for emergence of SCS-related AEs. Because of the extensive data available on the costly and burdensome AEs associated with SCS use, as well as the range of treatment options now available, there is a need for healthcare providers (HCPs) to carefully evaluate whether the benefits of SCS outweigh the potential harms, to adopt SCS-sparing and Stewardship strategies, and to consider alternative therapies where possible. Development of a structured and collaborative SCS Stewardship approach is urgently required to protect patients from the potential harm of SCS use.

Addressing sex and gender to improve asthma management.

Sex (whether one is 'male' or 'female', based on biological characteristics) and gender (defined by socially constructed roles and behaviors) influence asthma diagnosis and management. For example, women generally report more severe asthma symptoms than men; men and women are exposed to different asthma-causing triggers; men tend to be more physically active than women. Furthermore, implicit, often unintended gender bias by healthcare professionals (HCPs) is widespread, and may result in delayed asthma diagnosis, which can be greater in women than men. The sex and gender of the HCP can also impact asthma management. Pregnancy, menstruation, and menopause can all affect asthma in several ways and may be associated with poor asthma control. This review provides guidance for considering sex- and gender-associated impacts on asthma diagnosis and management and offers possible approaches to support HCPs in providing personalized asthma care for all patients, regardless of their sex or gender.

Asthma Exacerbations and Glucagon-Like Peptide-1 Receptor Agonists: a Review of the Current Evidence.

Asthma is a chronic inflammatory disease involving multiple mediators and cytokines. While our current treatments have shown significant therapeutic benefits, there still appear to be some patients who, despite aggressive therapy, good adherence, and inhaler technique, continue to have exacerbations. Exacerbations lead to loss of lung function, exposure to systemic corticosteroids, effects on quality of life, and even mortality. There is a large number of glucagon-like peptide-1 (GLP-1) receptors in the lung even compared with other organs, and studies have shown evidence of reduced exacerbations in asthmatics treated with GLP-1 receptor agonists (GLP-1 RA). While weight loss may affect lung mechanics, evidence of inflammatory changes has been revealed that could explain this relationship. This article will review the data behind these conjectures and outline potential clinical utility and the need for future studies to truly understand the role of GLP-1 receptors in the lung.

Obesity is a common issue and a comorbidity that negatively impacts asthma outcomes. Weight loss can improve asthma outcomes, and evidence shows that a particular type of therapy currently indicated for diabetes that assists in weight loss and targets receptors that are abundant in the lungs will outperform other therapies. GLP-1-receptor agonists may particularly help overweight patients who have asthma to control the disease as best as possible and prevent exacerbations. Video Abstract.

Unsupervised probabilistic models for sequential Electronic Health Records.

We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences of arbitrary length, such as medications and laboratory results. This allows for subgrouping and incorporation of the dynamics underlying heterogeneous data types. The model consists of a layered set of latent variables that encode underlying structure in the data. These variables represent subject subgroups at the top layer, and unobserved states for sequences in the second layer. We train this model on episodic data from subjects receiving medical care in the Kaiser Permanente Northern California integrated healthcare delivery system. The resulting properties of the trained model generate novel insight from these complex and multifaceted data. In addition, we show how the model can be used to analyze sequences that contribute to assessment of mortality likelihood.

COPD Population in US Primary Care: Data From the Optimum Patient Care DARTNet Research Database and the Advancing the Patient Experience in COPD Registry.

PURPOSE: To describe demographic and clinical characteristics of chronic obstructive pulmonary disease patients managed in US primary care. METHODS: This was an observational registry study using data from the Chronic Obstructive Pulmonary Disease (COPD) Optimum Patient Care DARTNet Research Database from which the Advancing the Patient Experience COPD registry is derived. Registry patients were aged ≥35 years at diagnosis. Electronic health record data were collected from both registries, supplemented with patient-reported information/outcomes from the Advancing the Patient Experience registry from 5 primary care groups in Texas, Ohio, Colorado, New York, and North Carolina (June 2019 through November 2020). RESULTS: Of 17,192 patients included, 1,354 were also in the Advancing the Patient Experience registry. Patients were predominantly female (56%; 9,689/17,192), White (64%; 9,732/15,225), current/ex-smokers (80%; 13,784/17,192), and overweight/obese (69%; 11,628/16,849). The most commonly prescribed maintenance treatments were inhaled corticosteroid with a long-acting ß2-agonist (30%) and inhaled corticosteroid with a long-acting muscarinic antagonist (27%). Although 3% (565/17,192) of patitents were untreated, 9% (1,587/17,192) were on short-acting bronchodilator monotherapy, and 4% (756/17,192) were on inhaled corticosteroid monotherapy. Despite treatment, 38% (6,579/17,192) of patients experienced 1 or more exacerbations in the last 12 months. These findings were mirrored in the Advancing Patient Experience registry with many patients reporting high or very high impact of disease on their health (43%; 580/1,322), a breathlessness score 2 or more (45%; 588/1,315), and 1 or more exacerbation in the last 12 months (50%; 646/1,294). CONCLUSIONS: Our findings highlight the high exacerbation, symptom, and treatment burdens experienced by COPD patients managed in US primary care, and the need for more real-life effectiveness trials to support decision making at the primary care level.

Clarifying SABA overuse: Translating Canadian Thoracic Society guidelines into clinical practice.

Patients with asthma frequently over rely on short-acting beta-agonists (SABA) to treat acute symptoms. This can adversely impact quality of life and increase the risk of exacerbations. SABA overuse is also associated with an increased risk of mortality. In their 2021 update on the diagnosis and management of mild asthma, the Canadian Thoracic Society (CTS) newly recommended that a combination inhaled corticosteroid (ICS) and long-acting beta-agonist, specifically budesonide/formoterol, may be used as-needed (PRN) as an alternative reliever to SABA. The CTS developed an algorithm as a guide for deciding for whom PRN budesonide/formoterol versus PRN SABA is appropriate as a reliever. While the CTS algorithm provides necessary and precise guidance, the somewhat complicated requirements for determining control and exacerbation risk may still end up leaving some patients at-risk of SABA overreliance. This communication simplifies the reliever decision algorithm developed by the CTS for application in daily practice. A 30-s evaluation of 2 simple questions related to reliever use can usually accurately assess if a patient's asthma is controlled: How many SABA canisters do you use a year AND how many times do you use SABA a week? If the patient indicates use of > 2 SABA canisters per year or > 2 administrations of SABA per week for any reason, the patient does not have controlled asthma and PRN SABA is not an appropriate treatment regimen. Similarly, for patients using PRN ICS/formoterol, more than 2 administrations per week indicates a clinical review and reevaluation of their management, including augmentation. An education process is essential to inform patients, caregivers, and healthcare providers that overuse of any reliever is not acceptable and is potentially harmful.

Continuous-time probabilistic models for longitudinal electronic health records.

Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular sampling of EHR data. We present an unsupervised probabilistic model that captures nonlinear relationships between variables over continuous-time. This method works with arbitrary sampling patterns and captures the joint probability distribution between variable measurements and the time intervals between them. Inference algorithms are derived that can be used to evaluate the likelihood of future using under a trained model. As an example, we consider data from the United States Veterans Health Administration (VHA) in the areas of diabetes and depression. Likelihood ratio maps are produced showing the likelihood of risk for moderate-severe vs minimal depression as measured by the Patient Health Questionnaire-9 (PHQ-9).

Variation in Demographic and Clinical Characteristics of Patients with COPD Receiving Care in US Primary Care: Data from the Advancing the Patient EXperience (APEX) in COPD Registry.

Introduction: Little is known about the variability in chronic obstructive pulmonary disease (COPD) management and how it may be affected by patient characteristics across different healthcare systems in the US. This study aims to describe demographic and clinical characteristics of people with COPD and compare management across five primary care medical groups in the US. Methods: This is a retrospective observational registry study utilizing electronic health records stored in the Advancing the Patient Experience (APEX) COPD registry. The APEX registry contains data from five US healthcare organizations located in Texas, Ohio, Colorado, New York, and North Carolina. Data on demographic and clinical characteristics of primary care patients with COPD between December 2019 and January 2020 were extracted and compared. Results: A total of 17,192 patients with COPD were included in analysis: Texas (n = 811), Ohio (n = 8722), Colorado (n = 472), New York (n = 1149) and North Carolina (n = 6038). The majority of patients at each location were female (>54%) and overweight/obese (>60%). Inter-location variabilities were noted in terms of age, race/ethnicity, exacerbation frequency, treatment pattern, and prevalence of comorbid conditions. Patients from the Colorado site experienced the lowest number of exacerbations per year while those from the New York site reported the highest number. Hypertension was the most common co-morbidity at 4 of 5 sites with the highest prevalence in New York. Depression was the most common co-morbidity in Ohio. Treatment patterns also varied by site; Colorado had the highest proportion of patients not on any treatment. ICS/LABA was the most commonly prescribed treatment except in Ohio, where ICS/LABA/LAMA was most common. Conclusions and Relevance: Our data show heterogeneity in demographic, clinical, and treatment characteristics of patients diagnosed with COPD who are managed in primary care across different healthcare organizations in the US.

Access Denied? The Unintended Consequences of Pending Drug Pricing Rules.

The government of Canada now plans to bring into force new federal drug pricing regulations on 1 July 2022. We do not take issue with the goal of medication affordability, which is vital in healthcare the world over. Our concern is that the new guidelines are being implemented without due consideration for three major unintended consequences: regulatory changes will lower the number of clinical trials for new medications in Canada, fewer clinical trials will mean lower research and development investments, and changes will reduce patients' access to new medications. Access to effective medications is a cornerstone of healthcare for Canadian patients. As physicians, our duty to patient care demands that we tell the government to protect the right of Canadians to timely access to life-changing medicines.

AutoAtlas: Neural Network for 3D Unsupervised Partitioning and Representation Learning.

We present a novel neural network architecture called AutoAtlas for fully unsupervised partitioning and representation learning of 3D brain Magnetic Resonance Imaging (MRI) volumes. AutoAtlas consists of two neural network components: one neural network to perform multi-label partitioning based on local texture in the volume, and a second neural network to compress the information contained within each partition. We train both of these components simultaneously by optimizing a loss function that is designed to promote accurate reconstruction of each partition, while encouraging spatially smooth and contiguous partitioning, and discouraging relatively small partitions. We show that the partitions adapt to the subject specific structural variations of brain tissue while consistently appearing at similar spatial locations across subjects. AutoAtlas also produces very low dimensional features that represent local texture of each partition. We demonstrate prediction of metadata associated with each subject using the derived feature representations and compare the results to prediction using features derived from FreeSurfer anatomical parcellation. Since our features are intrinsically linked to distinct partitions, we can then map values of interest, such as partition-specific feature importance scores onto the brain for visualization.