Pranab Haldar

Cluster Analysis and Clinical Asthma Phenotypes

RATIONALE Heterogeneity in asthma expression is multidimensional, including variability in clinical, physiologic, and pathologic parameters. Classification requires consideration of these disparate domains in a unified model. OBJECTIVES To explore the application of a multivariate mathematical technique, k-means cluster analysis, for identifying distinct phenotypic groups. METHODS We performed k-means cluster analysis in three independent asthma populations. Clusters of a population managed in primary care (n = 184) with predominantly mild to moderate disease, were compared with a refractory asthma population managed in secondary care (n = 187). We then compared differences in asthma outcomes (exacerbation frequency and change in corticosteroid dose at 12 mo) between clusters in a third population of 68 subjects with predominantly refractory asthma, clustered at entry into a randomized trial comparing a strategy of minimizing eosinophilic inflammation (inflammation-guided strategy) with standard care. MEASUREMENTS AND MAIN RESULTS Two clusters (early-onset atopic and obese, noneosinophilic) were common to both asthma populations. Two clusters characterized by marked discordance between symptom expression and eosinophilic airway inflammation (early-onset symptom predominant and late-onset inflammation predominant) were specific to refractory asthma. Inflammation-guided management was superior for both discordant subgroups leading to a reduction in exacerbation frequency in the inflammation-predominant cluster (3.53 [SD, 1.18] vs. 0.38 [SD, 0.13] exacerbation/patient/yr, P = 0.002) and a dose reduction of inhaled corticosteroid in the symptom-predominant cluster (mean difference, 1,829 mug beclomethasone equivalent/d [95% confidence interval, 307-3,349 mug]; P = 0.02). CONCLUSIONS Cluster analysis offers a novel multidimensional approach for identifying asthma phenotypes that exhibit differences in clinical response to treatment algorithms.

Qualitative Analysis of High-Resolution CT Scans in Severe Asthma

BACKGROUND High-resolution CT (HRCT) scanning is part of the management of severe asthma, but its application varies between centers. We sought to describe the HRCT scan abnormalities of a large severe asthma cohort and to determine the utility of clinical features to direct the use of HRCT scanning in this group of patients. METHODS Subjects attending our Difficult Asthma Clinic (DAC) between February 2000 and November 2006 (n = 463) were extensively re-characterized and 185 underwent HRCT scan. The HRCT scans were analyzed qualitatively and the interobserver variability was assessed. Using logistic regression we defined clinical parameters that were associated with bronchiectasis (BE) and bronchial wall thickening (BWT) alone or in combination. RESULTS HRCT scan abnormalities were present in 80% of subjects and often coexisted with BWT (62%), BE (40%), and emphysema (8%). The interobserver agreement for BE (kappa = 0.76) and BWT (kappa = 0.63) was substantial. DAC patients who underwent HRCT scanning compared with those who did not were older, had longer disease duration, had poorer lung function, were receiving higher doses of corticosteroids, and had increased neutrophilic airway inflammation. The sensitivity and specificity of detecting BE clinically were 74% and 45%, respectively. FEV(1)/FVC ratio emerged as an important predictor for both BE and BWT but had poor discriminatory utility for subjects who did not have airway structural changes (FEV(1)/FVC ratio, >or= 75%; sensitivity, 67%; specificity, 65%). CONCLUSION HRCT scan abnormalities are common in patients with severe asthma. Nonradiologic assessments fail to reliably predict important bronchial wall changes; therefore, CT scan acquisition may be required in all patients with severe asthma.

Quantitative analysis of high-resolution computed tomography scans in severe asthma subphenotypes

Background Severe asthma is a heterogeneous condition. Airway remodelling is a feature of severe asthma and can be determined by the assessment of high-resolution computed tomography (HRCT) scans. The aim of this study was to assess whether airway remodelling is restricted to specific subphenotypes of severe asthma. Methods A retrospective analysis was performed of HRCT scans from subjects who had attended a single-centre severe asthma clinic between 2003 and 2008. The right upper lobe apical segmental bronchus (RB1) dimensions were measured and the clinical and sputum inflammatory characteristics associated with RB1 geometry were assessed by univariate and multivariate regression analyses. Longitudinal sputum data were available and were described as area under the time curve (AUC). Comparisons were made in RB1 geometry across subjects in four subphenotypes determined by cluster analysis, smokers and non-smokers, and subjects with and without persistent airflow obstruction. Results Ninety-nine subjects with severe asthma and 16 healthy controls were recruited. In the subjects with severe asthma the RB1 percentage wall area (%WA) was increased (p=0.009) and lumen area (LA)/body surface area (BSA) was decreased (p=0.008) compared with controls but was not different across the four subphenotypes. Airway geometry was not different between smokers and non-smokers and RB1 %WA was increased in those with persistent airflow obstruction. RB1 %WA in severe asthma was best associated with airflow limitation and persistent neutrophilic airway inflammation (model R2=0.27, p=0.001). Conclusions Airway remodelling of proximal airways occurs in severe asthma and is associated with impaired lung function and neutrophilic airway inflammation.

Airway wall geometry in asthma and nonasthmatic eosinophilic bronchitis

Background:  Variable airflow obstruction and airway hyperresponsiveness (AHR) are features of asthma, which are absent in nonasthmatic eosinophilic bronchitis (EB). Airway remodelling is characteristic of both conditions suggesting that remodelling and airway dysfunction are disassociated, but whether the airway geometry differs between asthma and nonasthmatic EB is uncertain.

Eosinophil protein in airway macrophages: A novel biomarker of eosinophilic inflammation in patients with asthma

BACKGROUND Noneosinophilic asthma is common across asthma severities. However, in patients with moderate-to-severe disease, the absence of sputum eosinophilia cannot distinguish between asthmatic subjects with eosinophilic inflammation controlled by corticosteroids versus those in whom eosinophilic inflammation is not a component of the disease. OBJECTIVES We sought to develop a method to quantify eosinophil proteins in airway macrophages as a novel biomarker of eosinophilic airway inflammation. METHODS Eosinophil proteins in airway macrophages were assessed by means of flow cytometry, immunofluorescence, and cytoplasmic hue change after ingestion of apoptotic eosinophils. Airway macrophage median percentage of red-hued area in stained sputum cytospin preparations was assessed by means of image analysis from (1) subjects with mild-to-severe asthma, subjects with nonasthmatic eosinophilic bronchitis, and healthy control subjects; (2) subjects with eosinophilic severe asthma after treatment with prednisolone; and (3) subject with noneosinophilic asthma before corticosteroid withdrawal. RESULTS Eosinophil proteins were detected in airway macrophages, and cytoplasmic red hue increased after ingestion of apoptotic eosinophils. Airway macrophage percentage redhued area was increased in subjects with moderate-to-severe asthma compared with that seen in subjects with mild asthma and healthy control subjects, was similar in those with or without a sputum eosinophilia, and was increased after corticosteroid therapy. In asthmatic subjects without sputum eosinophilia, the airway macrophage percentage red-hued area was increased in subjects who did versus those who did not have sputum eosinophilia after corticosteroid withdrawal. CONCLUSIONS Eosinophil proteins can be reliably measured in airway macrophages. In combination with sputum eosinophilia, the macrophage eosinophil protein content might further define the asthma phenotype and provide an additional tool to direct therapy.

Mepolizumab in refractory eosinophilic asthma

In a recent Lung Alert reviewing our study of mepolizumab in severe eosinophilic asthma,1 Barratt states that the study population represented a minority of patients with asthma and that they had corticosteroid-resistant disease.2 These comments require clarification. While we accept that the population studied by us represents about 3% of the total asthma population, it was 30% of patients with refractory asthma, a population with the greatest unmet need for …

Cluster analysis and clinical asthma phenotypes

RATIONALE Heterogeneity in asthma expression is multidimensional, including variability in clinical, physiologic, and pathologic parameters. Classification requires consideration of these disparate domains in a unified model. OBJECTIVES To explore the application of a multivariate mathematical technique, k-means cluster analysis, for identifying distinct phenotypic groups. METHODS We performed k-means cluster analysis in three independent asthma populations. Clusters of a population managed in primary care (n = 184) with predominantly mild to moderate disease, were compared with a refractory asthma population managed in secondary care (n = 187). We then compared differences in asthma outcomes (exacerbation frequency and change in corticosteroid dose at 12 mo) between clusters in a third population of 68 subjects with predominantly refractory asthma, clustered at entry into a randomized trial comparing a strategy of minimizing eosinophilic inflammation (inflammation-guided strategy) with standard care. MEASUREMENTS AND MAIN RESULTS Two clusters (early-onset atopic and obese, noneosinophilic) were common to both asthma populations. Two clusters characterized by marked discordance between symptom expression and eosinophilic airway inflammation (early-onset symptom predominant and late-onset inflammation predominant) were specific to refractory asthma. Inflammation-guided management was superior for both discordant subgroups leading to a reduction in exacerbation frequency in the inflammation-predominant cluster (3.53 [SD, 1.18] vs. 0.38 [SD, 0.13] exacerbation/patient/yr, P = 0.002) and a dose reduction of inhaled corticosteroid in the symptom-predominant cluster (mean difference, 1,829 mug beclomethasone equivalent/d [95% confidence interval, 307-3,349 mug]; P = 0.02). CONCLUSIONS Cluster analysis offers a novel multidimensional approach for identifying asthma phenotypes that exhibit differences in clinical response to treatment algorithms.

Responsiveness to oral prednisolone in severe asthma is related to the degree of eosinophilic airway inflammation

Patients with severe asthma appear relatively corticosteroid resistant. Corticosteroid responsiveness is closely related to the degree of eosinophilic airway inflammation. The extent to which eosinophilic airway inflammation in severe asthma responds to treatment with systemic corticosteroids is not clear.

Prognostic value of interferon-γ release assays and tuberculin skin test in predicting the development of active tuberculosis (UK PREDICT TB): a prospective cohort study

Summary Background Tackling tuberculosis requires testing and treatment of latent tuberculosis in high-risk groups. The aim of this study was to estimate the predictive values of the tuberculin skin test (TST) and two interferon-γ release assays (IGRAs) for the development of active tuberculosis in high-risk groups—ie, people in recent contact with active tuberculosis cases and from high-burden countries. Method In this prospective cohort study, we recruited participants from 54 centres (eg, clinics, community settings) in London, Birmingham, and Leicester in the UK. Participants were eligible if they were aged 16 years or older and at high risk for latent tuberculosis infection (ie, recent contact with someone with active tuberculosis [contacts] or a migrant who had arrived in the UK in the past 5 years from—or who frequently travelled to—a country with a high burden of tuberculosis [migrants]). Exclusion criteria included prevalent cases of tuberculosis, and participants who were treated for latent tuberculosis after a positive test result in this study. Each participant received three tests (QuantiFERON-TB Gold-In Tube, T-SPOT.TB, and a Mantoux TST). A positive TST result was reported using three thresholds: 5 mm (TST-5), 10 mm (TST-10), and greater than 5 mm in BCG-naive or 15 mm in BCG-vaccinated (TST-15) participants. Participants were followed up from recruitment to development of tuberculosis or censoring. Incident tuberculosis cases were identified by national tuberculosis databases, telephone interview, and review of medical notes. Our primary objective was to estimate the prognostic value of IGRAs compared with TST, assessed by the ratio of incidence rate ratios and predictive values for tuberculosis development. The study was registered with ClinicalTrials.gov, NCT01162265, and is now complete. Findings Between May 4, 2010, and June 1, 2015, 10 045 people were recruited, of whom 9610 were eligible for inclusion. Of this cohort, 4861 (50·6%) were contacts and 4749 (49·4%) were migrants. Participants were followed up for a median of 2·9 years (range 21 days to 5·9 years). 97 (1·0%) of 9610 participants developed active tuberculosis (77 [1·2%] of 6380 with results for all three tests). In all tests, annual incidence of tuberculosis was very low in those who tested negatively (ranging from 1·2 per 1000 person-years, 95% CI 0·6–2·0 for TST-5 to 1·9 per 1000 person-years, 95% CI 1·3–2·7, for QuantiFERON-TB Gold In-Tube). Annual incidence in participants who tested positively were highest for T-SPOT.TB (13·2 per 1000 person-years, 95% CI 9·9–17·4), TST-15 (11·1 per 1000 person-years, 8·3–14·6), and QuantiFERON-TB Gold In-Tube (10·1 per 1000 person-years, 7·4–13·4). Positive results for these tests were significantly better predictors of progression than TST-10 and TST-5 (eg, ratio of test positivity rates in those progressing to tuberculosis compared with those not progressing T-SPOT.TB vs TST-5: 1·99, 95% CI 1·68–2·34; p<0·0001). However, TST-5 identified a higher proportion of participants who progressed to active tuberculosis (64 [83%] of 77 tested) than all other tests and TST thresholds (≤75%). Interpretation IGRA-based or BCG-stratified TST strategies appear most suited to screening for potential disease progression among high-risk groups. Further work will be needed to assess country-specific cost-effectiveness of each screening test, and in the absence of highly specific diagnostic tests, cheap non-toxic treatments need to be developed that could be given to larger groups of people at potential risk. Funding National Institute for Health Research Health Technology Assessment Programme 08-68-01.

Cluster analysis and clinical asthma phenotypes.

RATIONALE Heterogeneity in asthma expression is multidimensional, including variability in clinical, physiologic, and pathologic parameters. Classification requires consideration of these disparate domains in a unified model. OBJECTIVES To explore the application of a multivariate mathematical technique, k-means cluster analysis, for identifying distinct phenotypic groups. METHODS We performed k-means cluster analysis in three independent asthma populations. Clusters of a population managed in primary care (n = 184) with predominantly mild to moderate disease, were compared with a refractory asthma population managed in secondary care (n = 187). We then compared differences in asthma outcomes (exacerbation frequency and change in corticosteroid dose at 12 mo) between clusters in a third population of 68 subjects with predominantly refractory asthma, clustered at entry into a randomized trial comparing a strategy of minimizing eosinophilic inflammation (inflammation-guided strategy) with standard care. MEASUREMENTS AND MAIN RESULTS Two clusters (early-onset atopic and obese, noneosinophilic) were common to both asthma populations. Two clusters characterized by marked discordance between symptom expression and eosinophilic airway inflammation (early-onset symptom predominant and late-onset inflammation predominant) were specific to refractory asthma. Inflammation-guided management was superior for both discordant subgroups leading to a reduction in exacerbation frequency in the inflammation-predominant cluster (3.53 [SD, 1.18] vs. 0.38 [SD, 0.13] exacerbation/patient/yr, P = 0.002) and a dose reduction of inhaled corticosteroid in the symptom-predominant cluster (mean difference, 1,829 mug beclomethasone equivalent/d [95% confidence interval, 307-3,349 mug]; P = 0.02). CONCLUSIONS Cluster analysis offers a novel multidimensional approach for identifying asthma phenotypes that exhibit differences in clinical response to treatment algorithms.