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      The characteristics of the frequent exacerbator with chronic bronchitis phenotype and non-exacerbator phenotype in patients with chronic obstructive pulmonary disease: a meta-analysis and system review

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          Abstract

          Background

          Chronic obstructive pulmonary disease (COPD) patients with different phenotypes show different clinical characteristics. Therefore, we conducted a meta-analysis to explore the clinical characteristics between the non-exacerbator (NE) phenotype and the frequent exacerbator with chronic bronchitis (FE-CB) phenotype among patients with COPD.

          Methods

          CNKI, Wan fang, Chongqing VIP, China Biology Medicine disc, PubMed, Cochrane Library, and EMBASE databases were searched from the times of their inception to April 30, 2019. All studies that reported the clinical characteristics of the COPD phenotypes and which met the inclusion criteria were included. The quality assessment was analyzed by Cross-Sectional/Prevalence Study Quality recommendations. The meta-analysis was carried out using RevMan5.3.

          Results

          Ten cross-sectional observation studies ( n = 8848) were included. Compared with the NE phenotype, patients with the FE-CB phenotype showed significantly lower forced expiratory volume in 1 s percent predicted (FEV 1%pred) (mean difference (MD) -8.50, 95% CI -11.36–-5.65, P < 0.001, I 2  = 91%), forced vital capacity percent predicted (FVC%pred) [MD − 6.69, 95% confidence interval (CI) -7.73–-5.65, P < 0.001, I 2  = 5%], and forced expiratory volume in 1 s/forced vital capacity (FEV 1/FVC) (MD -3.76, 95% CI -4.58–-2.95, P < 0.001, I 2  = 0%); in contrast, Charlson comorbidity index (MD 0.47, 95% CI 0.37–0.58, P < 0.001, I 2  = 0], COPD assessment test (CAT) score (MD 5.61, 95% CI 4.62–6.60, P < 0.001, I 2  = 80%), the quantity of cigarettes smoked (pack-years) (MD 3.09, 95% CI 1.60–4.58, P < 0.001, I 2  = 41%), exacerbations in previous year (2.65, 95% CI 2.32–2.97, P < 0.001, I 2  = 91%), modified Medical British Research Council (mMRC) score (MD 0.72, 95% CI 0.63–0.82, P < 0.001, I 2  = 57%), and body mass index (BMI), obstruction, dyspnea, exacerbations (BODEx) (MD 1.78, 95% CI 1.28–2.28, P < 0.001, I 2  = 91%), I 2  = 34%) were significantly higher in patients with FE-CB phenotype. No significant between-group difference was observed with respect to BMI (MD-0.14, 95% CI -0.70–0.42, P = 0.62, I 2  = 75%).

          Conclusion

          COPD patients with the FE-CB phenotype had worse pulmonary function and higher CAT score, mMRC scores, frequency of acute exacerbations, and the quantity of cigarettes smoked (pack-years) than those with the NE phenotype.

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          Most cited references25

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          Phenotypes of COPD patients with a smoking history in Central and Eastern Europe: the POPE Study

          Chronic obstructive pulmonary disease (COPD) represents a major health problem in Central and Eastern European (CEE) countries; however, there are no data regarding clinical phenotypes of these patients in this region. Participation in the Phenotypes of COPD in Central and Eastern Europe (POPE) study was offered to stable patients with COPD in a real-life setting. The primary aim of this study was to assess the prevalence of phenotypes according to predefined criteria. Secondary aims included analysis of differences in symptom load, comorbidities and pharmacological treatment. 3362 patients with COPD were recruited in 10 CEE countries. 63% of the population were nonexacerbators, 20.4% frequent exacerbators with chronic bronchitis, 9.5% frequent exacerbators without chronic bronchitis and 6.9% were classified as asthma–COPD overlap. Differences in the distribution of phenotypes between countries were observed, with the highest heterogeneity observed in the nonexacerbator cohort and the lowest heterogeneity observed in the asthma–COPD cohort. There were statistically significant differences in symptom load, lung function, comorbidities and treatment between these phenotypes. The majority of patients with stable COPD in CEE are nonexacerbators; however, there are distinct differences in surrogates of disease severity and therapy between predefined COPD phenotypes.
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            Precision medicine in COPD: where are we and where do we need to go?

            Chronic obstructive pulmonary disease (COPD) was the fourth leading cause of death worldwide in 2015. Current treatments for patients ease discomfort and help decrease disease progression; however, none improve lung function or change mortality. COPD is heterogeneous in its molecular and clinical presentation, making it difficult to understand disease aetiology and define robust therapeutic strategies. Given the complexity of the disease we propose a precision medicine approach to understanding and better treating COPD. It is possible that multiOMICs can be used as a tool to integrate data from multiple fields. Moreover, analysis of electronic medical records could aid in the treatment of patients and in the predictions of outcomes. The Precision Medicine Initiative created in 2015 has made precision medicine approaches to treat disease a reality; one of these diseases being COPD.
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              Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records

              Background COPD is a highly heterogeneous disease composed of different phenotypes with different aetiological and prognostic profiles and current classification systems do not fully capture this heterogeneity. In this study we sought to discover, describe and validate COPD subtypes using cluster analysis on data derived from electronic health records. Methods We applied two unsupervised learning algorithms (k-means and hierarchical clustering) in 30,961 current and former smokers diagnosed with COPD, using linked national structured electronic health records in England available through the CALIBER resource. We used 15 clinical features, including risk factors and comorbidities and performed dimensionality reduction using multiple correspondence analysis. We compared the association between cluster membership and COPD exacerbations and respiratory and cardiovascular death with 10,736 deaths recorded over 146,466 person-years of follow-up. We also implemented and tested a process to assign unseen patients into clusters using a decision tree classifier. Results We identified and characterized five COPD patient clusters with distinct patient characteristics with respect to demographics, comorbidities, risk of death and exacerbations. The four subgroups were associated with 1) anxiety/depression; 2) severe airflow obstruction and frailty; 3) cardiovascular disease and diabetes and 4) obesity/atopy. A fifth cluster was associated with low prevalence of most comorbid conditions. Conclusions COPD patients can be sub-classified into groups with differing risk factors, comorbidities, and prognosis, based on data included in their primary care records. The identified clusters confirm findings of previous clustering studies and draw attention to anxiety and depression as important drivers of the disease in young, female patients. Electronic supplementary material The online version of this article (10.1186/s12911-019-0805-0) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                c498@bucm.edu.cn
                2935856242@qq.com
                1240617645@qq.com
                1105598949@qq.com
                yhy63@126.com
                liangduojiang@163.com
                wang601@vip.sina.com
                hanmeizoujin@163.com
                Journal
                BMC Pulm Med
                BMC Pulm Med
                BMC Pulmonary Medicine
                BioMed Central (London )
                1471-2466
                23 April 2020
                23 April 2020
                2020
                : 20
                : 103
                Affiliations
                [1 ]ISNI 0000 0001 1431 9176, GRID grid.24695.3c, The Third Affiliated Hospital of Beijing University of Chinese Medicine, ; No. 51, Xiaoguan Street outside Anding Men, Chaoyang, Beijing, 100029 People’s Republic of China
                [2 ]ISNI 0000 0001 1431 9176, GRID grid.24695.3c, Dongzhimen Hospital, Beijing University of Chinese Medicine, ; No.5 Haiyuncang, Dongcheng District, Beijing, 100700 People’s Republic of China
                [3 ]ISNI 0000 0001 1431 9176, GRID grid.24695.3c, Centre for Evidence-Based Chinese Medicine, , Beijing University of Chinese Medicine, ; 11 East Road North 3rd Ring Road, Beijing, 100029 People’s Republic of China
                Article
                1126
                10.1186/s12890-020-1126-x
                7181594
                32326924
                2878236f-d617-49d3-be72-2e2aa1f06e1a
                © The Author(s). 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 16 June 2019
                : 27 March 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100005089, Beijing Municipal Natural Science Foundation;
                Award ID: 7182100
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Respiratory medicine
                fe-cb,ne,phenotype,copd,pulmonary function,meta-analysis
                Respiratory medicine
                fe-cb, ne, phenotype, copd, pulmonary function, meta-analysis

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