Derivation of a prototype asthma attack risk scale centred on blood eosinophils and exhaled nitric oxide.

Reduction of the risk of asthma attacks is a major goal of current asthma management. We propose to derive a risk scale predicting asthma attacks based on the blood eosinophil count and exhaled nitric oxide (FeNO). Biomarker-stratified trial-level attack rates were extracted and pooled from the control arms of the Novel START, CAPTAIN, QUEST, Benralizumab Phase 2b, PATHWAY, STRATOS 1-2 and DREAM trials (n=3051). These were used to derive rate ratios and the predicted asthma attack rate for different patient groups. The resultant prototype risk scale shows potential to predict asthma attacks, which may be prevented by anti-inflammatory treatment.

Longitudinal changes in sputum and blood inflammatory mediators during FeNO suppression testing.

To explore whether fractional exhaled nitric oxide (FeNO) non-suppression identifies corticosteroid resistance, we analysed inflammatory mediator changes during a FeNO suppression test with monitored high-intensity corticosteroid therapy. In linear mixed-effects models analysed over time, the 15 clinically distinct 'suppressors' (ie, ≥42% FeNO suppression) normalised Asthma Control Questionnaire scores (mean±SD, start to end of test: 2.8±1.4 to 1.4±0.9, p<0.0001) and sputum eosinophil counts (median (IQR), start to end of test: 29% (6%-41%) to 1% (1%-5%), p=0.0003) while significantly decreasing sputum prostaglandin D2 (254 (89-894) to 93 (49-209) pg/mL, p=0.004) and numerically decreasing other type-2 cytokine, chemokine and alarmin levels. In comparison, the 19 non-suppressors had persistent sputum eosinophilia (10% (1%-67%) despite high-intensity therapy) with raised end-test inflammatory mediator levels (1.9 (0.9-2.8)-fold greater than suppressors). FeNO non-suppression during monitored treatment implies biological corticosteroid resistance.

Airway remodelling rather than cellular infiltration characterizes both type2 cytokine biomarker-high and -low severe asthma.

BACKGROUND: The most recognizable phenotype of severe asthma comprises people who are blood eosinophil and FeNO-high, driven by type 2 (T2) cytokine biology, which responds to targeted biological therapies. However, in many people with severe asthma, these T2 biomarkers are suppressed but poorly controlled asthma persists. The mechanisms driving asthma in the absence of T2 biology are poorly understood. OBJECTIVES: To explore airway pathology in T2 biomarker-high and -low severe asthma. METHODS: T2 biomarker-high severe asthma (T2-high, n = 17) was compared with biomarker-intermediate (T2-intermediate, n = 21) and biomarker-low (T2-low, n = 20) severe asthma and healthy controls (n = 28). Bronchoscopy samples were processed for immunohistochemistry, and sputum for cytokines, PGD2 and LTE4 measurements. RESULTS: Tissue eosinophil, neutrophil and mast cell counts were similar across severe asthma phenotypes and not increased when compared to healthy controls. In contrast, the remodelling features of airway smooth muscle mass and MUC5AC expression were increased in all asthma groups compared with health, but similar across asthma subgroups. Submucosal glands were increased in T2-intermediate and T2-low asthma. In spite of similar tissue cellular inflammation, sputum IL-4, IL-5 and CCL26 were increased in T2-high versus T2-low asthma, and several further T2-associated cytokines, PGD2 and LTE4 , were increased in T2-high and T2-intermediate asthma compared with healthy controls. CONCLUSIONS: Eosinophilic tissue inflammation within proximal airways is suppressed in T2 biomarker-high and T2-low severe asthma, but inflammatory and structural cell activation is present, with sputum T2-associated cytokines highest in T2 biomarker-high patients. Airway remodelling persists and may be important for residual disease expression beyond eosinophilic exacerbations. Registered at ClincialTrials.gov: NCT02883530.

Healthcare utilization after a first hospitalization for COPD: a new approach of State Sequence Analysis based on the '6W' multidimensional model of care trajectories.

BACKGROUND: Published methods to describe and visualize Care Trajectories (CTs) as patterns of healthcare use are very sparse, often incomplete, and not intuitive for non-experts. Our objectives are to propose a typology of CTs one year after a first hospitalization for Chronic Obstructive Pulmonary Disease (COPD), and describe CT types and compare patients' characteristics for each CT type. METHODS: This is an observational cohort study extracted from Quebec's medico-administrative data of patients aged 40 to 84 years hospitalized for COPD in 2013 (index date). The cohort included patients hospitalized for the first time over a 3-year period before the index date and who survived over the follow-up period. The CTs consisted of sequences of healthcare use (e.g. ED-hospital-home-GP-respiratory therapists, etc.) over a one-year period. The main variable was a CT typology, which was generated by a 'tailored' multidimensional State Sequence Analysis, based on the "6W" model of Care Trajectories. Three dimensions were considered: the care setting ("where"), the reason for consultation ("why"), and the speciality of care providers ("which"). Patients were grouped into specific CT types, which were compared in terms of care use attributes and patients' characteristics using the usual descriptive statistics. RESULTS: The 2581 patients were grouped into five distinct and homogeneous CT types: Type 1 (n = 1351, 52.3%) and Type 2 (n = 748, 29.0%) with low healthcare and moderate healthcare use respectively; Type 3 (n = 216, 8.4%) with high healthcare use, mainly for respiratory reasons, with the highest number of urgent in-hospital days, seen by pulmonologists and respiratory therapists at primary care settings; Type 4 (n = 100, 3.9%) with high healthcare use, mainly cardiovascular, high ED visits, and mostly seen by nurses in community-based primary care; Type 5 (n = 166, 6.4%) with high healthcare use, high ED visits and non-urgent hospitalisations, and with consultations at outpatient clinics and primary care settings, mainly for other reasons than respiratory or cardiovascular. Patients in the 3 highest utilization CT types were older, and had more comorbidities and more severe condition at index hospitalization. CONCLUSIONS: The proposed method allows for a better representation of the sequences of healthcare use in the real world, supporting data-driven decision making.

Predicting One-year Mortality After a "First" Hospitalization for Chronic Obstructive Pulmonary Disease: An Eight-Variable Assessment Score Tool.

Several authors have studied predictors of outcomes following a hospitalization for chronic obstructive pulmonary disease (COPD); however, few have reported outcomes following a first hospitalization for COPD. The objective is to develop a predictive mortality risk model in patients surviving a first hospitalization for COPD. This is a retrospective cohort study using linked administrative and clinical data. The cohort included 1129 patients of 40-84 years, discharged alive from a hospitalization for COPD in a regional hospital (Sherbrooke, Canada) between 04/2006 and 03/2013 and to whom were prescribed at least two COPD drugs during their hospitalization. One-year mortality was analysed using logistic regression on a derivation sample and validated on a testing sample. In total, 141 (12.5%) patients died within one year from discharge of their first hospitalization for COPD. Predictors were: older age (OR (95% CI): 1.055 (1.026-1.085)), male sex (OR (95% CI): 1.474 (0.921-2.358)), having a severe COPD exacerbation (OR (95% CI): 2.548 (1.571-4.132)), higher hospital length of stay (OR (95% CI): 1.024 (0.996-1.053)), higher Charlson co-morbidity index (OR (95% CI): 1.262 (1.099-1.449)), being diagnosed of cancer (OR (95% CI): 2.928 (1.456-5.885)), the number of prior all-cause hospitalizations (OR (95% CI): 1.323 (1.097-1.595)), and a COPD duration exceeding 3 years (OR (95% CI): 1.710 (1.058-2.763)). A simple clinical prognosis tool is proposed and shows good discrimination in both the derivation and validation cohorts (c-statistic >0.78). One over eight patients discharged alive from a first COPD hospitalization will die the following year. It is thus important to identify higher-risk patients in order to plan and manage appropriate treatment.

How I do it. Choosing the right biologic for the right patient with severe asthma.

In this new instalment of the How I Do It: Severe Asthma series, we tackle the clinical conundrum of choosing the right biologic for the right patient with severe asthma. With 6 biologics now approved for use in this area comprising 4 different targeting strategies (anti-immunoglobulin E, omalizumab; anti-interleukin (IL)-5/5receptor, mepolizumab, reslizumab, and benralizumab; anti-IL-4receptor, dupilumab; anti-thymic stromal lymphopoietin, tezepelumab), this question is increasingly complex. Recognising that there is no head-to-head trial comparing biologics, we base our review on the expected effects of inhibiting different aspects of type-2 airway inflammation, supported whenever possible by clinical trial and real-world data. We use four variations of a case of severe uncontrolled asthma to develop concepts and considerations introduced in the previous Work-up of severe asthma installment and discuss pregnancy-, biomarker-, comorbidity-, and corticosteroid-dependency-related considerations when choosing a biologic. The related questions of deciding when, why, and how to switch from one biologic to another are also discussed. Overall, we consider that the choice between biologics should be based on the available clinical trial data for the desired efficacy outcomes; the biomarker profile of the patient; safety profiles (e.g., when pregnancy is considered); and opportunities to target two comorbidities with one biologic. Using systemic and airway biomarkers (blood eosinophils and exhaled nitric oxide (FeNO)) and other phenotypic characteristics, we suggest a framework to facilitate therapeutic decision-making. Post hoc studies and new comparative studies are urgently needed to test this framework and determine whether it allows us to make other clinically useful predictions.

Type 2 severe asthma: pathophysiology and treatment with biologics.

INTRODUCTION: The hallmark of most patients with severe asthma is type 2 inflammation, driven by innate and adaptive immune responses leading to either allergic or non-allergic eosinophilic infiltration of airways. The cellular and molecular pathways underlying severe type 2 asthma can be successfully targeted by specific monoclonal antibodies. AREAS COVERED: This review article provides a concise overview of the pathophysiology of type 2 asthma, followed by an updated appraisal of the mechanisms of action and therapeutic efficacy of currently available biologic treatments used for management of severe type 2 asthma. Therefore, all reported information arises from a wide literature search performed on PubMed. EXPERT OPINION: The main result of the recent advances in the field of anti-asthma biologic therapies is the implementation of a personalized medicine approach, aimed to achieve clinical remission of severe asthma. Today this accomplishment is made possible by the right choice of the most beneficial biologic drug for the pathologic traits characterizing each patient, including type 2 severe asthma and its comorbidities.

Phenotyping of Severe Asthma in the Era of Broad-Acting Anti-Asthma Biologics.

Severe asthma is associated with significant morbidity and mortality despite the maximal use of inhaled corticosteroids and additional controller medications, and has a high economic burden. Biologic therapies are recommended for the management of severe, uncontrolled asthma to help to prevent exacerbations and to improve symptoms and health-related quality of life. The effective management of severe asthma requires consideration of clinical heterogeneity that is driven by varying clinical and inflammatory phenotypes, which are reflective of distinct underlying disease mechanisms. Phenotyping patients using a combination of clinical characteristics such as the age of onset or comorbidities and biomarker profiles, including blood eosinophil counts and levels of fractional exhaled nitric oxide and serum total immunoglobulin E, is important for the differential diagnosis of asthma. In addition, phenotyping is beneficial for risk assessment, selection of treatment, and monitoring of the treatment response in patients with asthma. This review describes the clinical and inflammatory phenotypes of asthma, provides an overview of biomarkers routinely used in clinical practice and those that have recently been explored for phenotyping, and aims to assess the value of phenotyping in severe asthma management in the current era of biologics.

Toward a Predict and Prevent Approach in Obstructive Airway Diseases.

Asthma and chronic obstructive pulmonary disease are currently diagnosed and treated after the demonstration of variable airflow limitation and symptoms. Under this framework, undiagnosed and unchecked airway inflammation is associated with recurrent acute attacks, airway remodeling, airflow limitation, adverse effects of corticosteroids, and impaired quality of life, ultimately leading to the collection of side effects termed "people remodeling." This one-size-fits-all damage control approach aims to control symptoms and treat exacerbations rather than modify the underlying disease process. The advent of highly effective therapies targeting proximal drivers of airway inflammation calls for a paradigm shift; upstream-acting therapies offer potential to alter the disease course and achieve clinical remission. We propose moving away from downstream firefighting and toward a "predict and prevent" model, measuring inflammation and providing anti-inflammatory therapy early, without waiting for further clinical deterioration. Much in the same way that high blood pressure and cholesterol are used to predict and prevent heart attacks, in asthma, elevated blood eosinophils and/or exhaled nitric oxide can be used to predict and prevent asthma attacks. We also advocate moving research further upstream by identifying patients with subclinical airway inflammation or disease who may be at risk of progressing to airflow limitation and associated morbidities and intervening early to prevent them. In summary, we call for a predict and prevent approach in obstructive airway disease.