Distinct Phenotypes of Cigarette Smokers Identified by Cluster Analysis of Patients with Severe Asthma

Konno, Satoshi; Taniguchi, Natsuko; Makita, Hironi; Nakamaru, Yuji; Shimizu, Kaoruko; Shijubo, Noriharu; Fuke, Satoshi; Takeyabu, Kimihiro; Oguri, Mitsuru; Kimura, Hirokazu; Maeda, Yukiko; Suzuki, Masaru; Nagai, Katsura; Ito, Yoichi M.; Wenzel, Sally E.; Nishimura, Masaharu

doi:10.1513/annalsats.201507-407oc

Cited by 15 publications

(13 citation statements)

References 33 publications

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“…Clusters 4 comprised male‐predominant elderly asthma with a high frequency of comorbid COPD. Many studies have identified asthma–COPD overlap as a distinct asthma phenotype , and a high frequency of asthma exacerbation/hospitalization among patients with asthma–COPD overlap has been documented . Thus, the results of the present study are compatible with previous studies.…”

Section: Discussionsupporting

confidence: 92%

Severe or life‐threatening asthma exacerbation: patient heterogeneity identified by cluster analysis

Sekiya

Nakatani

Fukutomi

et al. 2016

Clin Experimental Allergy

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SummaryBackground Severe or life-threatening asthma exacerbation is one of the worst outcomes of asthma because of the risk of death. To date, few studies have explored the potential heterogeneity of this condition.Objectives To examine the clinical characteristics and heterogeneity of patients with severe or life-threatening asthma exacerbation.Methods This was a multicentre, prospective study of patients with severe or life-threatening asthma exacerbation and pulse oxygen saturation < 90% who were admitted to 17 institutions across Japan. Cluster analysis was performed using variables from patient-and physicianorientated structured questionnaires.Results Analysis of data from 175 patients with severe or life-threatening asthma exacerbation revealed five distinct clusters. Cluster 1 (n = 27) was younger-onset asthma with severe symptoms at baseline, including limitation of activities, a higher frequency of treatment with oral corticosteroids and short-acting beta-agonists, and a higher frequency of asthma hospitalizations in the past year. Cluster 2 (n = 35) was predominantly composed of elderly females, with the highest frequency of comorbid, chronic hyperplastic rhinosinusitis/nasal polyposis, and a long disease duration. Cluster 3 (n = 40) was allergic asthma without inhaled corticosteroid use at baseline. Patients in this cluster had a higher frequency of atopy, including allergic rhinitis and furred pet hypersensitivity, and a better prognosis during hospitalization compared with the other clusters. Cluster 4 (n = 34) was characterized by elderly males with concomitant chronic obstructive pulmonary disease (COPD). Although cluster 5 (n = 39) had very mild symptoms at baseline according to the patient questionnaires, 41% had previously been hospitalized for asthma. Conclusions & Clinical RelevanceThis study demonstrated that significant heterogeneity exists among patients with severe or life-threatening asthma exacerbation. Differences were observed in the severity of asthma symptoms and use of inhaled corticosteroids at baseline, and the presence of comorbid COPD. These findings may contribute to a deeper understanding and better management of this patient population.

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Section: Discussionsupporting

confidence: 92%

Severe or life‐threatening asthma exacerbation: patient heterogeneity identified by cluster analysis

Sekiya

Nakatani

Fukutomi

et al. 2016

Clin Experimental Allergy

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“…Second, not surprisingly, a gene set associated with eosinophilic airway inflammation was identified in the sputum of patients with PAL. Eosinophilic airway inflammation has been reported in patients with PAL by multiple studies in severe asthma and was also present in this study population [9,14,15]. Third, IL-13 was identified to be involved in PAL in severe asthma, which was in concordance with a study by KAMINSKA et al [32] who identified IL-13 in bronchial biopsies of severe asthma patients with PAL.…”

Section: Discussionsupporting

confidence: 91%

“…Even though PAL and exacerbations are different phenotypic characteristics, there is evidence of an association of PAL with more frequent exacerbations and more hospitalisations in subgroups of severe asthma patients [10,11]. Clinical clustering studies showed that PAL is an important constituent for severe asthma phenotypes that are characterised by eosinophilic airway inflammation, poor quality of life and smoking history [11][12][13][14]. To understand this clinical trait, several studies have been conducted to identify risk factors for PAL in smoking and nonsmoking severe asthmatic subjects.…”

Section: Introductionmentioning

confidence: 99%

Transcriptomic gene signatures associated with persistent airflow limitation in patients with severe asthma

et al. 2017

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A proportion of severe asthma patients suffers from persistent airflow limitation (PAL), often associated with more symptoms and exacerbations. Little is known about the underlying mechanisms. Here, our aim was to discover unexplored potential mechanisms using Gene Set Variation Analysis (GSVA), a sensitive technique that can detect underlying pathways in heterogeneous samples.Severe asthma patients from the U-BIOPRED cohort with PAL (post-bronchodilator forced expiratory volume in 1 s/forced vital capacity ratio below the lower limit of normal) were compared with those without PAL. Gene expression was assessed on the total RNA of sputum cells, nasal brushings, and endobronchial brushings and biopsies. GSVA was applied to identify differentially enriched predefined gene signatures based on all available gene expression publications and data on airways disease.Differentially enriched gene signatures were identified in nasal brushings (n=1), sputum (n=9), bronchial brushings (n=1) and bronchial biopsies (n=4) that were associated with response to inhaled steroids, eosinophils, interleukin-13, interferon-α, specific CD4 T-cells and airway remodelling.PAL in severe asthma has distinguishable underlying gene networks that are associated with treatment, inflammatory pathways and airway remodelling. These findings point towards targets for the therapy of PAL in severe asthma.

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“…This data-driven approach generates novel subgroups that are not based on any a priori hypotheses. In other words, unsupervised machine learning methods such as clustering analysis allow data to be grouped in a way that may be useful, without the user specifying how this grouping should occur (13–14). …”

Section: Methodsmentioning

confidence: 99%

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach*

Vranas

Jopling

Sweeney

et al. 2017

Critical Care Medicine

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Objective Identifying subgroups of intensive care unit (ICU) patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients’ shared needs. However, objective methods for identifying these ICU patient subgroups are lacking. We used a machine learning approach to empirically identify ICU patient subgroups through clustering analysis and evaluate whether these groups might represent appropriate targets for care redesign efforts. Design We performed clustering analysis using data from patients’ hospital stays to retrospectively identify patient subgroups from a large, heterogeneous ICU population. Setting Kaiser Permanente Northern California (KPNC), a healthcare delivery system serving 3.9 million members. Patients ICU patients aged ≥ 18 years with an ICU admission between January 1, 2012 and December 31, 2012 at one of 21 KPNC hospitals. Interventions none. Measurements and Main Results We used clustering analysis to identify putative clusters among 5,000 patients randomly selected from 24,884 ICU patients. To assess cluster validity, we evaluated the distribution and frequency of patient characteristics, and the need for invasive therapies. We then applied a classifier built from the sample cohort to the remaining 19,884 patients to compare the derivation and validation clusters. Clustering analysis successfully identified six clinically-recognizable subgroups that differed significantly in all baseline characteristics and clinical trajectories, despite sharing common diagnoses. In the validation cohort, the proportion of patients assigned to each cluster was similar and demonstrated significant differences across clusters for all variables. Conclusions A machine learning approach revealed important differences between empirically-derived subgroups of ICU patients that are not typically revealed by admitting diagnosis or severity of illness alone. Similar data-driven approaches may provide a framework for future organizational innovations in ICU care tailored around patients’ shared needs.

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Distinct Phenotypes of Cigarette Smokers Identified by Cluster Analysis of Patients with Severe Asthma

Abstract: This study reveals two distinct phenotypes of severe asthma in current and former cigarette smokers with potentially different biological pathways contributing to fixed airflow limitation. Clinical trial registered with www.umin.ac.jp (000003254).

Cited by 15 publications

References 33 publications

Severe or life‐threatening asthma exacerbation: patient heterogeneity identified by cluster analysis

Severe or life‐threatening asthma exacerbation: patient heterogeneity identified by cluster analysis

Transcriptomic gene signatures associated with persistent airflow limitation in patients with severe asthma

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach*

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