A simple algorithm for the identification of clinical COPD phenotypes

Joint clinical and molecular subtyping of COPD with variational autoencoders

Chronic Obstructive Pulmonary Disease (COPD) is a complex, heterogeneous disease. Traditional subtyping methods generally focus on either the clinical manifestations or the molecular endotypes of the disease, resulting in classifications that do not fully capture the disease's complexity. Here, we bridge this gap by introducing a subtyping pipeline that integrates clinical and gene expression data with variational autoencoders. We apply this methodology to the COPDGene study, a large study of current and former smoking individuals with and without COPD. Our approach generates a set of vector embeddings, called Personalized Integrated Profiles (PIPs), that recapitulate the joint clinical and molecular state of the subjects in the study. Prediction experiments show that the PIPs have a predictive accuracy comparable to or better than other embedding approaches. Using trajectory learning approaches, we analyze the main trajectories of variation in the PIP space and identify five well-separated subtypes with distinct clinical phenotypes, expression signatures, and disease outcomes. Notably, these subtypes are more robust to data resampling compared to those identified using traditional clustering approaches. Overall, our findings provide new avenues to establish fine-grained associations between the clinical characteristics, molecular processes, and disease outcomes of COPD.

Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases

INTRODUCTION

Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment.

AREAS COVERED

This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation.

EXPERT OPINION

Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.

RETRO-POPE: A Retrospective, Multicenter, Real-World Study of All-Cause Mortality in COPD

Purpose

The Phenotypes of COPD in Central and Eastern Europe (POPE) study assessed the prevalence and clinical characteristics of four clinical COPD phenotypes, but not mortality. This retrospective analysis of the POPE study (RETRO-POPE) investigated the relationship between all-cause mortality and patient characteristics using two grouping methods: clinical phenotyping (as in POPE) and Burgel clustering, to better identify high-risk patients.

Patients and Methods

The two largest POPE study patient cohorts (Czech Republic and Serbia) were categorized into one of four clinical phenotypes (acute exacerbators [with/without chronic bronchitis], non-exacerbators, asthma-COPD overlap), and one of five Burgel clusters based on comorbidities, lung function, age, body mass index (BMI) and dyspnea (very severe comorbid, very severe respiratory, moderate-to-severe respiratory, moderate-to-severe comorbid/obese, and mild respiratory). Patients were followed-up for approximately 7 years for survival status.

Results

Overall, 801 of 1,003 screened patients had sufficient data for analysis. Of these, 440 patients (54.9%) were alive and 361 (45.1%) had died at the end of follow-up. Analysis of survival by clinical phenotype showed no significant differences between the phenotypes (P=0.211). However, Burgel clustering demonstrated significant differences in survival between clusters (P<0.001), with patients in the "very severe comorbid" and "very severe respiratory" clusters most likely to die. Overall survival was not significantly different between Serbia and the Czech Republic after adjustment for age, BMI, comorbidities and forced expiratory volume in 1 second (hazard ratio [HR] 0.80, 95% confidence interval [CI] 0.65-0.99; P=0.036 [unadjusted]; HR 0.88, 95% CI 0.7-1.1; P=0.257 [adjusted]). The most common causes of death were respiratory-related (36.8%), followed by cardiovascular (25.2%) then neoplasm (15.2%).

Conclusion

Patient clusters based on comorbidities, lung function, age, BMI and dyspnea were more likely to show differences in COPD mortality risk than phenotypes defined by exacerbation history and presence/absence of chronic bronchitis and/or asthmatic features.

Heterogeneity and Progression of Chronic Obstructive Pulmonary Disease: Emphysema-Predominant and Non-Emphysema-Predominant Disease

While variation in emphysema severity between patients with chronic obstructive pulmonary disease (COPD) is well-recognized, clinically applicable definitions of the emphysema-predominant disease (EPD) and non-emphysema-predominant disease (NEPD) subtypes have not been established. To study the clinical relevance of the EPD and NEPD subtypes, we tested the association of these subtypes with prospective decline in forced expiratory volume in 1 second (FEV1) and mortality among 3,427 subjects with Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric grade 2-4 COPD at baseline in the Genetic Epidemiology of COPD (COPDGene) Study, an ongoing national multicenter study that started in 2007. NEPD was defined as airflow obstruction with less than 5% computed tomography (CT) quantitative densitometric emphysema at -950 Hounsfield units, and EPD was defined as airflow obstruction with 10% or greater CT emphysema. Mixed-effects models for FEV1 demonstrated larger average annual FEV1 loss in EPD subjects than in NEPD subjects (-10.2 mL/year; P < 0.001), and subtype-specific associations with FEV1 decline were identified. Cox proportional hazards models showed higher risk of mortality among EPD patients versus NEPD patients (hazard ratio = 1.46, 95% confidence interval: 1.34, 1.60; P < 0.001). To determine whether the NEPD/EPD dichotomy is captured by previously described COPDGene subtypes, we used logistic regression and receiver operating characteristic (ROC) curve analysis to predict NEPD/EPD membership using these previous subtype definitions. The analysis generally showed excellent discrimination, with areas under the ROC curve greater than 0.9. The NEPD and EPD COPD subtypes capture important aspects of COPD heterogeneity and are associated with different rates of disease progression and mortality.

Fractional Dynamics Foster Deep Learning of COPD Stage Prediction

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends on an adequate effort from the tester and testee. Moreover, the early diagnosis of COPD is challenging. The authors address COPD detection by constructing two novel physiological signals datasets (4432 records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset). The authors demonstrate their complex coupled fractal dynamical characteristics and perform a fractional-order dynamics deep learning analysis to diagnose COPD. The authors found that the fractional-order dynamical modeling can extract distinguishing signatures from the physiological signals across patients with all COPD stages-from stage 0 (healthy) to stage 4 (very severe). They use the fractional signatures to develop and train a deep neural network that predicts COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation). The authors show that the fractional dynamic deep learning model (FDDLM) achieves a COPD prediction accuracy of 98.66% and can serve as a robust alternative to spirometry. The FDDLM also has high accuracy when validated on a dataset with different physiological signals.

Validation of Clinical COPD Phenotypes for Prognosis of Long-Term Mortality in Swedish and Dutch Cohorts

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease with variable mortality risk. The aim of our investigation was to validate a simple clinical algorithm for long-term mortality previously proposed by Burgel et al. in 2017. Subjects with COPD from two cohorts, the Swedish PRAXIS study (n = 784, mean age (standard deviation (SD)) 64.0 years (7.5), 42% males) and the Rotterdam Study (n = 735, mean age (SD) 72 years (9.2), 57% males), were included. Five clinical clusters were derived from baseline data on age, body mass index, dyspnoea grade, pulmonary function and comorbidity (cardiovascular disease/diabetes). Cox models were used to study associations with 9-year mortality. The distribution of clinical clusters (1-5) was 29%/45%/8%/6%/12% in the PRAXIS study and 23%/26%/36%/0%/15% in the Rotterdam Study. The cumulative proportion of deaths at the 9-year follow-up was highest in clusters 1 (65%) and 4 (72%), and lowest in cluster 5 (10%) in the PRAXIS study. In the Rotterdam Study, cluster 1 (44%) had the highest cumulative mortality and cluster 5 (5%) the lowest. Compared with cluster 5, the meta-analysed age- and sex-adjusted hazard ratio (95% confidence interval) for cluster 1 was 6.37 (3.94-10.32) and those for clusters 2 and 3 were 2.61 (1.58-4.32) and 3.06 (1.82-5.13), respectively. Burgel's clinical clusters can be used to predict long-term mortality risk. Clusters 1 and 4 are associated with the poorest prognosis, cluster 5 with the best prognosis and clusters 2 and 3 with intermediate prognosis in two independent cohorts from Sweden and the Netherlands.

Unsupervised Learning Identifies Computed Tomographic Measurements as Primary Drivers of Progression, Exacerbation, and Mortality in Chronic Obstructive Pulmonary Disease

Rationale: Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome with phenotypic manifestations that tend to be distributed along a continuum. Unsupervised machine learning based on broad selection of imaging and clinical phenotypes may be used to identify primary variables that define disease axes and stratify patients with COPD. Objectives: To identify primary variables driving COPD heterogeneity using principal component analysis and to define disease axes and assess the prognostic value of these axes across three outcomes: progression, exacerbation, and mortality. Methods: We included 7,331 patients between 39 and 85 years old, of whom 40.3% were Black and 45.8% were female smokers with a mean of 44.6 pack-years, from the COPDGene (Genetic Epidemiology of COPD) phase I cohort (2008-2011) in our analysis. Out of a total of 916 phenotypes, 147 continuous clinical, spirometric, and computed tomography (CT) features were selected. For each principal component (PC), we computed a PC score based on feature weights. We used PC score distributions to define disease axes along which we divided the patients into quartiles. To assess the prognostic value of these axes, we applied logistic regression analyses to estimate 5-year (n = 4,159) and 10-year (n = 1,487) odds of progression. Cox regression and Kaplan-Meier analyses were performed to estimate 5-year and 10-year risk of exacerbation (n = 6,532) and all-cause mortality (n = 7,331). Results: The first PC, accounting for 43.7% of variance, was defined by CT measures of air trapping and emphysema. The second PC, accounting for 13.7% of variance, was defined by spirometric and CT measures of vital capacity and lung volume. The third PC, accounting for 7.9% of the variance, was defined by CT measures of lung mass, airway thickening, and body habitus. Stratification of patients across each disease axis revealed up to 3.2-fold (95% confidence interval [CI] 2.4, 4.3) greater odds of 5-year progression, 5.4-fold (95% CI 4.6, 6.3) greater risk of 5-year exacerbation, and 5.0-fold (95% CI 4.2, 6.0) greater risk of 10-year mortality between the highest and lowest quartiles. Conclusions: Unsupervised learning analysis of the COPDGene cohort reveals that CT measurements may bolster patient stratification along the continuum of COPD phenotypes. Each of the disease axes also individually demonstrate prognostic potential, predictive of future forced expiratory volume in 1 second decline, exacerbation, and mortality.

Antimicrobial therapies for prevention of recurrent acute exacerbations of COPD (AECOPD): beyond the guidelines

Background

Unfortunately, many COPD patients continue to exacerbate despite good adherence to GOLD Class D recommended therapy. Acute exacerbations lead to an increase in symptoms, decline in lung function and increased mortality rate. The purpose of this review is to do a literature search for any prophylactic anti-microbial treatment trials in GOLD class D patients who ‘failed’ recommended therapy and discuss the role of COPD phenotypes, lung and gut microbiota and co-morbidities in developing a tailored approach to anti-microbial therapies for high frequency exacerbators.

Main text

There is a paucity of large, well-conducted studies in the published literature to date. Factors such as single-centre, study design, lack of well-defined controls, insufficient patient numbers enrolled and short follow-up periods were significant limiting factors in numerous studies. One placebo-controlled study involving more than 1000 patients, who had 2 or more moderate exacerbations in the previous year, demonstrated a non-significant reduction in exacerbations of 19% with 5 day course of moxifloxacillin repeated at 8 week intervals. In Pseudomonas aeruginosa (Pa) colonised COPD patients, inhaled antimicrobial therapy using tobramycin, colistin and gentamicin resulted in significant reductions in exacerbation frequency. Viruses were found to frequently cause acute exacerbations in COPD (AECOPD), either as the primary infecting agent or as a co-factor. However, other, than the influenza vaccination, there were no trials of anti-viral therapies that resulted in a positive effect on reducing AECOPD. Identifying clinical phenotypes and co-existing conditions that impact on exacerbation frequency and severity is essential to provide individualised treatment with targeted therapies. The role of the lung and gut microbiome is increasingly recognised and identification of pathogenic bacteria will likely play an important role in personalised antimicrobial therapies.

Conclusion

Antimicrobial therapeutic options in patients who continue to exacerbate despite adherence to guidelines-directed therapy are limited. Phenotyping patients, identification of co-existing conditions and assessment of the microbiome is key to individualising antimicrobial therapy. Given the impact of viruses on AECOPD, anti-viral therapeutic agents and targeted anti-viral vaccinations should be the focus of future research studies.

COPD profiles and treatable traits using minimal resources: identification, decision tree and stability over time

Background and objective

Profiles of people with chronic obstructive pulmonary disease (COPD) often do not describe treatable traits, lack validation and/or their stability over time is unknown. We aimed to identify COPD profiles and their treatable traits based on simple and meaningful measures; to develop and validate a decision tree and to explore profile stability over time.

Methods

An observational, prospective study was conducted. Clinical characteristics, lung function, symptoms, impact of the disease (COPD Assessment Test—CAT), health-related quality of life, physical activity, lower-limb muscle strength and functional status were collected cross-sectionally and a subsample was followed-up monthly over six months. A principal component analysis and a clustering procedure with k-medoids were applied to identify profiles. A decision tree was developed and validated cross-sectionally. Stability was explored over time with the ratio between the number of timepoints that a participant was classified in the same profile and the total number of timepoints (i.e., 6).

Results

352 people with COPD (67.4 ± 9.9 years; 78.1% male; FEV1 = 56.2 ± 20.6% predicted) participated and 90 (67.6 ± 8.9 years; 85.6% male; FEV1 = 52.1 ± 19.9% predicted) were followed-up. Four profiles were identified with distinct treatable traits. The decision tree included CAT (< 18 or ≥ 18 points); age (< 65 or ≥ 65 years) and FEV1 (< 48 or ≥ 48% predicted) and had an agreement of 71.7% (Cohen’s Kappa = 0.62, p < 0.001) with the actual profiles. 48.9% of participants remained in the same profile whilst 51.1% moved between two (47.8%) or three (3.3%) profiles over time. Overall stability was 86.8 ± 15%.

Conclusion

Four profiles and treatable traits were identified with simple and meaningful measures possibly available in low-resource settings. A decision tree with three commonly used variables in the routine assessment of people with COPD is now available for quick allocation to the identified profiles in clinical practice. Profiles and treatable traits may change over time in people with COPD hence, regular assessments to deliver goal-targeted personalised treatments are needed.

Stratification of COPD patients towards personalized medicine: reproduction and formation of clusters

BACKGROUND

The global initiative for chronic obstructive lung disease (GOLD) 2020 emphasizes that there is only a weak correlation between FEV₁, symptoms and impairment of the health status of patients with chronic obstructive pulmonary disease (COPD). Various studies aimed to identify COPD phenotypes by cluster analyses, but behavioral aspects besides smoking were rarely included.

METHODS

The aims of the study were to investigate whether (i) clustering analyses are in line with the classification into GOLD ABCD groups; (ii) clustering according to Burgel et al. (Eur Respir J. 36(3):531-9, 2010) can be reproduced in a real-world COPD cohort; and (iii) addition of new behavioral variables alters the clustering outcome. Principal component and hierarchical cluster analyses were applied to real-world clinical data of COPD patients newly referred to secondary care (n = 155). We investigated if the obtained clusters paralleled GOLD ABCD subgroups and determined the impact of adding several variables, including quality of life (QOL), fatigue, satisfaction relationship, air trapping, steps per day and activities of daily living, on clustering.

RESULTS

Using the appropriate corresponding variables, we identified clusters that largely reflected the GOLD ABCD groups, but we could not reproduce Burgel's clinical phenotypes. Adding six new variables resulted in the formation of four new clusters that mainly differed from each other in the following parameters: number of steps per day, activities of daily living and QOL.

CONCLUSIONS

We could not reproduce previously identified clinical COPD phenotypes in an independent population of COPD patients. Our findings therefore indicate that COPD phenotypes based on cluster analysis may not be a suitable basis for treatment strategies for individual patients.

Individual trajectory-based care for COPD: getting closer, but not there yet

Chronic obstructive pulmonary disease (COPD) is a main cause of death due to interplaying factors, including comorbidities that interfere with symptoms and response to therapy. It is now admitted that COPD management should be based on clinical symptoms and health status and should consider the heterogeneity of patients' phenotypes and treatable traits. This precision medicine approach involves a regular assessment of the patient's status and of the expected benefits and risks of therapy. The cornerstone of COPD pharmacological therapy is inhaled long-acting bronchodilation. In patients with persistent or worsened symptoms, factors likely to interfere with treatment efficacy include the patient's non-adherence to therapy, treatment preference, inhaler misuse and/or comorbidities, which should be systematically investigated before escalation is considered. Several comorbidities are known to impact symptoms, physical and social activity and lung function. The possible long-term side-effects of inhaled corticosteroids contrasting with their over-prescription in COPD patients justify the regular assessment of their benefits and risks, and de-escalation under close monitoring after a sufficient period of stability is to be considered. While commonly used in clinical trials, the relevance of routine blood eosinophil counts to guide therapy adjustment is not fully clear. Patients' characteristics, which define phenotypes and treatable traits and thus guide therapy, often change during life, forming the basis of the concept of clinical trajectory. The application of individual trajectory-based management of COPD in clinical practice therefore implies that the benefit:risk ratio is regularly reviewed according to the evolution of the patient's traits over time to allow optimised therapy adjustments.

Unravelling the mechanisms driving multimorbidity in COPD to develop holistic approaches to patient-centred care

COPD is a major cause of morbidity and mortality worldwide. Multimorbidity is common in COPD patients and a key modifiable factor, which requires timely identification and targeted holistic management strategies to improve outcomes and reduce the burden of disease.We discuss the use of integrative approaches, such as cluster analysis and network-based theory, to understand the common and novel pathobiological mechanisms underlying COPD and comorbid disease, which are likely to be key to informing new management strategies.Furthermore, we discuss the current understanding of mechanistic drivers to multimorbidity in COPD, including hypotheses such as multimorbidity as a result of shared common exposure to noxious stimuli (e.g. tobacco smoke), or as a consequence of loss of function following the development of pulmonary disease. In addition, we explore the links to pulmonary disease processes such as systemic overspill of pulmonary inflammation, immune cell priming within the inflamed COPD lung and targeted messengers such as extracellular vesicles as a result of local damage as a cause for multimorbidity in COPD.Finally, we focus on current and new management strategies which may target these underlying mechanisms, with the aim of holistic, patient-centred treatment rather than single disease management.

Burden and Characteristics of Severe Chronic Hypoxemia in a Real-World Cohort of Subjects with COPD

Background

Chronic respiratory failure may occur as a consequence of chronic obstructive pulmonary disease (COPD) and is associated with significant morbidity and mortality. Hypoxemia is determined by underlying disease characteristics and comorbidities. Severe hypoxemia is typically only found in subjects with severe airflow obstruction (FEV₁<50% predicted). However, how hypoxemia relates to disease characteristics is not fully understood.

Methods

In the French Initiatives BPCO real-life cohort, arterial blood gases were routinely collected in most patients. Relationships between severe hypoxemia, defined by a Pa0₂<60 mmHg (8 kPa) and clinical/lung function features, comorbidities and mortality were assessed. In subjects with severe hypoxemia, clinical characteristics and comorbidities were compared between those with non-severe versus severe airflow limitation. Classification and regression trees (CART) were used to define clinically relevant subgroups (phenotypes).

Results

Arterial blood gases were available from 887 subjects, of which 146 (16%) exhibited severe hypoxemia. Compared to subjects with a PaO2≥60 mmHg, the severe hypoxemia group exhibited higher mMRC dyspnea score, lower FEV₁, higher RV and RV/TLC, more impaired quality of life, lower 6-minute walking distance, less frequent history of asthma, more frequent diabetes and higher 3-year mortality rate (14% versus 8%, p=0.026). Compared to subjects with Pa0₂<60 mmHg and FEV₁<50% (n=115, 13%), those with severe hypoxemia but FEV1≥50% predicted (n=31) were older, had higher BMI, less hyperinflation, better quality of life and a higher rate of diabetes (29% versus 13%, p=0.02). Severe hypoxemia was better related to CART-defined phenotypes than to GOLD ABCD classification.

Conclusion

In this cohort of stable COPD subjects, severe hypoxemia was associated with worse prognosis and more severe symptoms, airflow limitation and hyperinflation. Compared to subjects with severe hypoxemia and severe airflow limitation, subjects with severe hypoxemia despite non-severe airflow limitation were older, had higher BMI and more diagnosed diabetes.

Trial Registration

04–479.

Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease

Image segmentation is an essential pre-processing step and is an indispensable part of image analysis. This paper proposes Renyi's entropy multi-threshold image segmentation based on an improved Slime Mould Algorithm (DASMA). First, we introduce the diffusion mechanism (DM) into the original SMA to increase the population's diversity so that the variants can better avoid falling into local optima. The association strategy (AS) is then added to help the algorithm find the optimal solution faster. Finally, the proposed algorithm is applied to Renyi's entropy multilevel threshold image segmentation based on non-local means 2D histogram. The proposed method's effectiveness is demonstrated on the Berkeley segmentation dataset and benchmark (BSD) by comparing it with some well-known algorithms. The DASMA-based multilevel threshold segmentation technique is also successfully applied to the CT image segmentation of chronic obstructive pulmonary disease (COPD). The experimental results are evaluated by image quality metrics, which show the proposed algorithm's extraordinary performance. This means that it can help doctors analyze the lesion tissue qualitatively and quantitatively, improve its diagnostic accuracy and make the right treatment plan. The supplementary material and info about this article will be available at https://aliasgharheidari.com.

The Relation Between Clinical Phenotypes, GOLD Groups/Stages and Mortality in COPD Patients - A Prospective Multicenter Study

Introduction

The concept of phenotyping emerged, reflecting specific clinical, pulmonary and extrapulmonary features of each particular chronic obstructive pulmonary disease (COPD) case. Our aim was to analyze prognostic utility of: “Czech“ COPD phenotypes and their most frequent combinations, ”Spanish” phenotypes and Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages + groups in relation to long-term mortality risk.

Methods

Data were extracted from the Czech Multicenter Research Database (CMRD) of COPD. Kaplan-Meier (KM) estimates (at 60 months from inclusion) were used for mortality assessment. Survival rates were calculated for the six elementary “Czech” phenotypes and their most frequent and relevant combinations, “Spanish” phenotypes, GOLD grades and groups. Statistically significant differences were tested by Log Rank test. An analysis of factors underlying mortality risk (the role of confounders) has been assessed with the use of classification and regression tree (CART) analysis. Basic factors showing significant differences between deceased and living patients were entered into the CART model. This showed six different risk groups, the differences in risk were tested by a Log Rank test.

Results

The cohort (n=720) was 73.1% men, with a mean age of 66.6 years and mean FEV₁ 44.4% pred. KM estimates showed bronchiectases/COPD overlap (HR 1.425, p=0.045), frequent exacerbator (HR 1.58, p<0.001), cachexia (HR 2.262, p<0.001) and emphysematous (HR 1.786, p=0.015) phenotypes associated with higher mortality risk. Co-presence of multiple phenotypes in a single patient had additive effect on risk; combination of emphysema, cachexia and frequent exacerbations translated into poorest prognosis (HR 3.075; p<0.001). Of the “Spanish” phenotypes, AE CB and AE non-CB were associated with greater risk of mortality (HR 1.787 and 2.001; both p=0.001). FEV₁% pred., cachexia and chronic heart failure in patient history were the major underlying factors determining mortality risk in our cohort.

Conclusion

Certain phenotypes (“Czech” or “Spanish”) of COPD are associated with higher risk of death. Co-presence of multiple phenotypes (emphysematous plus cachectic plus frequent exacerbator) in a single individual was associated with amplified risk of mortality.

Identifying Behavioral Phenotypes in Chronic Illness: Self-Management of COPD and Comorbid Hypertension

OBJECTIVES

To identify and characterize the constellation, or clusters, of self-management behaviors in patients with chronic obstructive pulmonary disease (COPD) and comorbid hypertension.

METHODS

Cluster analysis (n = 204) was performed with standardized scores for medication adherence to COPD and hypertension medications, inhaler technique, and diet as well as self-reported information on physical activity, appointment keeping, smoking status, and yearly influenza vaccination for a total of eight variables. Classification and regression tree analysis (CART) was performed to further characterize the resulting clusters.

RESULTS

Patients were divided into three clusters based on eight self-management behaviors, which included 95 patients in cluster 1, 42 in cluster 2, and 67 in cluster 3. All behaviors except for inhaler technique differed significantly among the three clusters (P's<0.005). CART indicated physical activity was the first differentiating variable.

CONCLUSIONS

Patients with COPD and hypertensioncan be separated into those with adequate and inadequate adherence. The group with inadequate adherence can further be divided into those with poor adherence to medical behaviors compared to those with poor adherence to lifestyle behaviors.

PRACTICE IMPLICATIONS

Once validated in other populations, the identification of patient clusters using patient self-management behaviors could be used to inform interventions for patients with multimorbidity.

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.

Multimorbidity in Patients with Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a complex disease manifested primarily as airflow limitation that is partially reversible as confirmed by spirometry. COPD patients frequently develop systemic manifestations, such as skeletal muscle wasting and cachexia. COPD patients often develop other comorbid diseases, such as ischemic heart disease, heart failure, osteoporosis, anemia, lung cancer, and depression. Comorbidities complicate management of COPD and need to be evaluated because detection and treatment have important consequences. Novel approaches aimed at integrating the multiple morbidities seen in COPD and other chronic diseases will provide new avenues of research and allow developing more comprehensive and effective therapeutic approaches.

Different Clusters in Patients with Chronic Obstructive Pulmonary Disease (COPD): A Two-Center Study in Brazil

Background

Chronic obstructive pulmonary disease (COPD) has a functional definition. However, differences in clinical characteristics and systemic manifestations make COPD a heterogeneous disease and some manifestations have been associated with different risks of acute exacerbations, hospitalizations, and death.

Objective

Therefore, the objective of the study was to evaluate possible clinical clusters in COPD at two study centers in Brazil and identify the associated exacerbation and mortality rate during 1 year of follow-up.

Methods

We included patients with COPD and all underwent an evaluation composed of the Charlson Index, body mass index (BMI), current pharmacological treatment, smoking history (packs-year), history of exacerbations/hospitalizations in the last year, spirometry, six-minute walking test (6MWT), quality of life questionnaires, dyspnea, and hospital anxiety and depression scale. Blood samples were also collected for measurements of C-reactive protein (CRP), blood gases, laboratory analysis, and blood count. For the construction of the clusters, 13 continuous variables of clinical importance were considered: hematocrit, CRP, triglycerides, low density lipoprotein, absolute number of peripheral eosinophils, age, pulse oximetry, BMI, forced expiratory volume in the first second, dyspnea, 6MWD, total score of the Saint George Respiratory Questionnaire and packs-year of smoking. We used the Ward and K-means methods and determined the best silhouette value to identify similarities of individuals within the cluster (cohesion) in relation to the other clusters (separation). The number of clusters was determined by the heterogeneity values of the cluster, which in this case was determined as four clusters.

Results

We evaluated 301 COPD patients and identified four different groups of COPD patients. The first cluster (203 patients) was characterized by fewer symptoms and lower functional severity of the disease, the second cluster by higher values of peripheral eosinophils, the third cluster by more systemic inflammation and the fourth cluster by greater obstructive severity and worse gas exchange. Cluster 2 had an average of 959±3 peripheral eosinophils, cluster 3 had a higher prevalence of nutritional depletion (46.1%), and cluster 4 had a higher BODE index. Regarding the associated comorbidities, we found that only obstructive sleep apnea syndrome and pulmonary thromboembolism were more prevalent in cluster 4. Almost 50% of all patients presented an exacerbation during 1 year of follow-up. However, it was higher in cluster 4, with 65% of all patients having at least one exacerbation. The mortality rate was statistically higher in cluster 4, with 26.9%, vs 9.6% in cluster 1.

Conclusion

We could identify four clinical different clusters in these COPD populations, that were related to different clinical manifestations, comorbidities, exacerbation, and mortality rate. We also identified a specific cluster with higher values of peripheral eosinophils.

Mortality prediction in chronic obstructive pulmonary disease comparing the GOLD 2015 and GOLD 2019 staging: a pooled analysis of individual patient data

In 2019, The Global Initiative for Chronic Obstructive Lung Disease (GOLD) modified the grading system for patients with COPD, creating 16 subgroups (1A–4D). As part of the COPD Cohorts Collaborative International Assessment (3CIA) initiative, we aim to compare the mortality prediction of the 2015 and 2019 COPD GOLD staging systems.

We studied 17 139 COPD patients from the 3CIA study, selecting those with complete data. Patients were classified by the 2015 and 2019 GOLD ABCD systems, and we compared the predictive ability for 5-year mortality of both classifications.

In total, 17 139 patients with COPD were enrolled in 22 cohorts from 11 countries between 2003 and 2017; 8823 of them had complete data and were analysed. Mean±sd age was 63.9±9.8 years and 62.9% were male. GOLD 2019 classified the patients in milder degrees of COPD. For both classifications, group D had higher mortality. 5-year mortality did not differ between groups B and C in GOLD 2015; in GOLD 2019, mortality was greater for group B than C. Patients classified as group A and B had better sensitivity and positive predictive value with the GOLD 2019 classification than GOLD 2015. GOLD 2015 had better sensitivity for group C and D than GOLD 2019. The area under the curve values for 5-year mortality were only 0.67 (95% CI 0.66–0.68) for GOLD 2015 and 0.65 (95% CI 0.63–0.66) for GOLD 2019.

The new GOLD 2019 classification does not predict mortality better than the previous GOLD 2015 system.

GOLD 2019 staging system created 16 subgroups. GOLD 2015 and GOLD 2019 are not strong predictors of mortality, and do not have sufficient discriminatory power to be used as a tool for risk classification of mortality in patients with COPD.https://bit.ly/3idBuaN

Comprehensive Cluster Analysis for COPD Including Systemic and Airway Inflammatory Markers

Chronic obstructive pulmonary disease (COPD) is a complex, multidimensional and heterogeneous disease. The main purpose of the present study was to identify clinical phenotypes through cluster analysis in adults suffering from COPD. A retrospective study was conducted on 178 COPD patients in stable state recruited from ambulatory care at University hospital of Liege. All patients were above 40 years, had a smoking history of more than 20 pack years, post bronchodilator FEV1/FVC <70% and denied any history of asthma before 40 years. In this study, the patients were described by a total of 84 mixed sets of variables with some missing values. Hierarchical clustering on principal components (HCPC) was applied on multiple imputation. In the final step, patients were classified into homogeneous distinct groups by consensus clustering. Three different clusters, which shared similar smoking history were found. Cluster 1 included men with moderate airway obstruction (n = 67) while cluster 2 comprised men who were exacerbation-prone, with severe airflow limitation and intense granulocytic airway and neutrophilic systemic inflammation (n = 56). Cluster 3 essentially included women with moderate airway obstruction (n = 55). All clusters had a low rate of bacterial colonization (5%), a low median FeNO value (<20 ppb) and a very low sensitization rate toward common aeroallergens (0-5%). CAT score did not differ between clusters. Including markers of systemic airway inflammation and atopy and applying a comprehensive cluster analysis we provide here evidence for 3 clusters markedly shaped by sex, airway obstruction and neutrophilic inflammation but not by symptoms and T2 biomarkers.

COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda

Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research.

Somatotypes trajectories during adulthood and their association with COPD phenotypes

RATIONALE

Chronic obstructive pulmonary disease (COPD) comprises distinct phenotypes, all characterised by airflow limitation.

OBJECTIVES

We hypothesised that somatotype changes - as a surrogate of adiposity - from early adulthood follow different trajectories to reach distinct phenotypes.

METHODS

Using the validated Stunkard's Pictogram, 356 COPD patients chose the somatotype that best reflects their current body build and those at ages 18, 30, 40 and 50 years. An unbiased group-based trajectory modelling was used to determine somatotype trajectories. We then compared the current COPD-related clinical and phenotypic characteristics of subjects belonging to each trajectory.

MEASUREMENTS AND MAIN RESULTS

At 18 years of age, 88% of the participants described having a lean or medium somatotype (estimated body mass index (BMI) between 19 and 23 kg·m-2) while the other 12% a heavier somatotype (estimated BMI between 25 and 27 kg·m-2). From age 18 onwards, five distinct trajectories were observed. Four of them demonstrating a continuous increase in adiposity throughout adulthood with the exception of one, where the initial increase was followed by loss of adiposity after age 40. Patients with this trajectory were primarily females with low BMI and D LCO (diffusing capacity of the lung for carbon monoxide). A persistently lean trajectory was seen in 14% of the cohort. This group had significantly lower forced expiratory volume in 1 s (FEV1), D LCO, more emphysema and a worse BODE (BMI, airflow obstruction, dyspnoea and exercise capacity) score thus resembling the multiple organ loss of tissue (MOLT) phenotype.

CONCLUSIONS

COPD patients have distinct somatotype trajectories throughout adulthood. Those with the MOLT phenotype maintain a lean trajectory throughout life. Smoking subjects with this lean phenotype in early adulthood deserve particular attention as they seem to develop more severe COPD.

Validation of a method to assess emphysema severity by spirometry in the COPDGene study

Background

Standard spirometry cannot identify the predominant mechanism underlying airflow obstruction in COPD, namely emphysema or airway disease. We aimed at validating a previously developed methodology to detect emphysema by mathematical analysis of the maximal expiratory flow-volume (MEFV) curve in standard spirometry.

Methods

From the COPDGene population we selected those 5930 subjects with MEFV curve and inspiratory-expiratory CT obtained on the same day. The MEFV curve descending limb was fit real-time using forced vital capacity (FVC), peak expiratory flow, and forced expiratory flows at 25, 50 and 75% of FVC to derive an emphysema severity index (ESI), expressed as a continuous positive numeric parameter ranging from 0 to 10. According to inspiratory CT percent lung attenuation area below − 950 HU we defined three emphysema severity subgroups (%LAA_-950insp < 6, 6–14, ≥14). By co-registration of inspiratory-expiratory CT we quantified persistent (%pLDA) and functional (%fLDA) low-density areas as CT metrics of emphysema and airway disease, respectively.

Results

ESI differentiated CT emphysema severity subgroups increasing in parallel with GOLD stages (p < .001), but with high variability within each stage. ESI had significantly higher correlations (p < .001) with emphysema than with airway disease CT metrics, explaining 67% of %pLDA variability. Conversely, standard spirometric variables (FEV₁, FEV₁/FVC) had significantly lower correlations than ESI with emphysema CT metrics and did not differentiate between emphysema and airways CT metrics.

Conclusions

ESI adds to standard spirometry the power to discriminate whether emphysema is the predominant mechanism of airway obstruction. ESI methodology has been validated in the large multiethnic population of smokers of the COPDGene study and therefore it could be applied for clinical and research purposes in the general population of smokers, using a readily available online website.

Multi-perspective clustering of obstructive sleep apnea towards precision therapeutic decision including craniofacial intervention

PURPOSE

Previous studies focusing on phenotyping obstructive sleep apnea (OSA) have outlined its heterogeneity in clinical symptoms, comorbidities, and polysomnographic features. However, the role of anatomical or pathophysiological causality including craniofacial skeletal deformity has not been studied. We aimed to identify and characterize phenotypes of OSA based on multi-perspective clustering by incorporating craniofacial risks with obesity, apnea severity, arousability, symptom, and comorbidity.

METHODS

A total of 421 Korean patients with OSA (apnea-hypopnea index, AHI ≥ 5; age ≥ 20 years old) were recruited. A K-means cluster analysis was performed following principal component analysis with sagittal and vertical skeletal variables (ANB and mandibular plane angle), AHI, body mass index, and Epworth sleepiness scale. Inter-cluster comparison was conducted using demographic, cephalometric, and polysomnographic variables in addition to presence of diabetes and hypertension. Risk factors contributing to OSA severity were evaluated in each cluster using multivariable regression analysis with adjustment for age and gender.

RESULTS

Three phenotypic clusters were identified and characterized as follows: Cluster-1 (noncraniofacial phenotype, 39%), non-obese moderate-to-severe OSA with no skeletal discrepancy representing low arousal threshold (ArTh), little sleepiness, and low comorbidity; Cluster-2 (craniofacial skeletal phenotype, 33%), non-obese moderate OSA with definite skeletal discrepancy showing low ArTh, mild sleepiness, and low comorbidity; and Cluster-3 (complicated phenotype, 28%), obese severe OSA with skeletal discrepancy exhibiting high ArTh, excessive daytime sleepiness, and high incidence of hypertension.

CONCLUSIONS

The three OSA phenotypes from multi-perspective clustering may provide a basis for precise therapeutic decision-making including craniofacial skeletal intervention beyond usual characterization of OSA subgroups.

Presence or Absence of Skeletal Muscle Dysfunction in Chronic Obstructive Pulmonary Disease is Associated With Distinct Phenotypes

INTRODUCTION

Reduced skeletal muscle function and cognitive performance are common extrapulmonary features in Chronic Obstructive Pulmonary Disease (COPD) but their connection remains unclear. Whether presence or absence of skeletal muscle dysfunction in COPD patients is linked to a specific phenotype consisting of reduced cognitive performance, comorbidities and nutritional and metabolic disturbances needs further investigation.

METHODS

Thirty-seven patients with COPD (grade II-IV) were divided into two phenotypic cohorts based on the presence (COPD dysfunctional, n=25) or absence (COPD functional, n=12) of muscle dysfunction. These cohorts were compared to 28 healthy, age matched controls. Muscle strength (dynamometry), cognitive performance (Trail Making Test and STROOP Test), body composition (Dual-energy X-Ray Absorptiometry), habitual physical activity, comorbidities and mood status (questionnaires) were measured. Pulse administration of stable amino acid tracers was performed to measure whole body production rates.

RESULTS

Presence of muscle dysfunction in COPD was independent of muscle mass or severity of airflow obstruction but associated with impaired STROOP Test performance (p=0.04), reduced resting O₂ saturation (p=0.003) and physical inactivity (p=0.01), and specific amino acid metabolic disturbances (enhanced leucine (p=0.02) and arginine (p=0.06) production). In contrast, COPD patients with normal muscle function presented with anxiety, increased fat mass, plasma glucose concentration, and metabolic syndrome related comorbidities (hypertension and dyslipidemia).

CONCLUSION

COPD patients with muscle dysfunction show characteristics of a cognitive - metabolic impairment phenotype, influenced by the presence of hypoxia, whereas those with normal muscle function present a phenotype of metabolic syndrome and mood disturbances.

"High-Risk" Clinical and Inflammatory Clusters in COPD of Chinese Descent

BACKGROUND

COPD is a heterogeneous disease demonstrating inter-individual variation. A high COPD prevalence in Chinese populations is described, but little is known about disease clusters and prognostic outcomes in the Chinese population across Southeast Asia. We aim to determine if clusters of Chinese patients with COPD exist and their association with systemic inflammation and clinical outcomes.

RESEARCH QUESTION

We aim to determine if clusters of Chinese patients with COPD exist and their association with clinical outcomes and inflammation.

STUDY DESIGN AND METHODS

Chinese patients with stable COPD were prospectively recruited into two cohorts (derivation and validation) from six hospitals across three Southeast Asian countries (Singapore, Malaysia, and Hong Kong; n = 1,480). Each patient was followed more than 2 years. Clinical data (including co-morbidities) were employed in unsupervised hierarchical clustering (followed by validation) to determine the existence of patient clusters and their prognostic outcome. Accompanying systemic cytokine assessments were performed in a subset (n = 336) of patients with COPD to determine if inflammatory patterns and associated networks characterized the derived clusters.

RESULTS

Five patient clusters were identified including: (1) ex-TB, (2) diabetic, (3) low comorbidity: low-risk, (4) low comorbidity: high-risk, and (5) cardiovascular. The cardiovascular and ex-TB clusters demonstrate highest mortality (independent of Global Initiative for Chronic Obstructive Lung Disease assessment) and illustrate diverse cytokine patterns with complex inflammatory networks.

INTERPRETATION

We describe clusters of Chinese patients with COPD, two of which represent high-risk clusters. The cardiovascular and ex-TB patient clusters exhibit high mortality, significant inflammation, and complex cytokine networks. Clinical and inflammatory risk stratification of Chinese patients with COPD should be considered for targeted intervention to improve disease outcomes.

Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year longitudinal chest imaging, spirometry, and molecular data, is a rich resource for relating COPD phenotypes to underlying genetic and molecular mechanisms. In this article, we place COPDGene clustering studies in context with other highly cited COPD clustering studies, and summarize the main COPD subtype findings from COPDGene. First, most manifestations of COPD occur along a continuum, which explains why continuous aspects of COPD or disease axes may be more accurate and reproducible than subtypes identified through clustering methods. Second, continuous COPD-related measures can be used to create subgroups through the use of predictive models to define cut-points, and we review COPDGene research on blood eosinophil count thresholds as a specific example. Third, COPD phenotypes identified or prioritized through machine learning methods have led to novel biological discoveries, including novel emphysema genetic risk variants and systemic inflammatory subtypes of COPD. Fourth, trajectory-based COPD subtyping captures differences in the longitudinal evolution of COPD, addressing a major limitation of clustering analyses that are confounded by disease severity. Ongoing longitudinal characterization of subjects in COPDGene will provide useful insights about the relationship between lung imaging parameters, molecular markers, and COPD progression that will enable the identification of subtypes based on underlying disease processes and distinct patterns of disease progression, with the potential to improve the clinical relevance and reproducibility of COPD subtypes.

A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses

The study of diagnostic associations entails a large number of methodological problems regarding the application of machine learning algorithms, collinearity and wide variability being some of the most prominent ones. To overcome these, we propose and tested the usage of uniform manifold approximation and projection (UMAP), a very recent, popular dimensionality reduction technique. We showed its effectiveness by using it on a large Spanish clinical database of patients diagnosed with depression, to whom we applied UMAP before grouping them using a hierarchical agglomerative cluster analysis. By extensively studying its behavior and results, validating them with purely unsupervised metrics, we show that they are consistent with well-known relationships, which validates the applicability of UMAP to advance the study of comorbidities.

The COPD multi-dimensional phenotype: A new classification from the STORICO Italian observational study

Background

This paper is aimed to (i) develop an innovative classification of COPD, multi-dimensional phenotype, based on a multidimensional assessment; (ii) describe the identified multi-dimensional phenotypes.

Methods

An exploratory factor analysis to identify the main classificatory variables and, then, a cluster analysis based on these variables were run to classify the COPD-diagnosed 514 patients enrolled in the STORICO (trial registration number: NCT03105999) study into multi-dimensional phenotypes.

Results

The circadian rhythm of symptoms and health-related quality of life, but neither comorbidity nor respiratory function, qualified as primary classificatory variables. Five multidimensional phenotypes were identified: the MILD COPD characterized by no night-time symptoms and the best health status in terms of quality of life, quality of sleep, level of depression and anxiety, the MILD EMPHYSEMATOUS with prevalent dyspnea in the early-morning and day-time, the SEVERE BRONCHITIC with nocturnal and diurnal cough and phlegm, the SEVERE EMPHYSEMATOUS with nocturnal and diurnal dyspnea and the SEVERE MIXED COPD distinguished by higher frequency of symptoms during 24h and worst quality of life, of sleep and highest levels of depression and anxiety.

Conclusions

Our results showed that properly collected respiratory symptoms play a primary classificatory role of COPD patients. The longitudinal observation will disclose the discriminative and prognostic potential of the proposed multidimensional phenotype.

Trial registration

Trial registration number: NCT03105999, date of registration: 10th April 2017.

Symptom Subtypes of Obstructive Sleep Apnea Predict Incidence of Cardiovascular Outcomes

Rationale: Symptom subtypes have been described in clinical and population samples of patients with obstructive sleep apnea (OSA). It is unclear whether these subtypes have different cardiovascular consequences.Objectives: To characterize OSA symptom subtypes and assess their association with prevalent and incident cardiovascular disease in the Sleep Heart Health Study.Methods: Data from 1,207 patients with OSA (apnea-hypopnea index ≥ 15 events/h) were used to evaluate the existence of symptom subtypes using latent class analysis. Associations between subtypes and prevalence of overall cardiovascular disease and its components (coronary heart disease, heart failure, and stroke) were assessed using logistic regression. Kaplan-Meier survival analysis and Cox proportional hazards models were used to evaluate whether subtypes were associated with incident events, including cardiovascular mortality.Measurements and Main Results: Four symptom subtypes were identified (disturbed sleep [12.2%], minimally symptomatic [32.6%], excessively sleepy [16.7%], and moderately sleepy [38.5%]), similar to prior studies. In adjusted models, although no significant associations with prevalent cardiovascular disease were found, the excessively sleepy subtype was associated with more than threefold increased risk of prevalent heart failure compared with each of the other subtypes. Symptom subtype was also associated with incident cardiovascular disease (P < 0.001), coronary heart disease (P = 0.015), and heart failure (P = 0.018), with the excessively sleepy again demonstrating increased risk (hazard ratios, 1.7-2.4) compared with other subtypes. When compared with individuals without OSA (apnea-hypopnea index < 5), significantly increased risk for prevalent and incident cardiovascular events was observed mostly for patients in the excessively sleepy subtype.Conclusions: OSA symptom subtypes are reproducible and associated with cardiovascular risk, providing important evidence of their clinical relevance.

[The GLORI-COPD score: detection of COPD patients at risk of complications]

DEVELOPMENT OF THE GLORI-COPD SCORE

GLObal RIsk of severe outcomes in COPD patients.

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) is a commonly under-diagnosed disease. This study aimed to develop a screening tool for COPD patients with a high risk of complications, taking into account COPD severity and associated co-morbidity.

METHODS

Two Delphi rounds were conducted to select the items for a preliminary score. Subsequently, this score was submitted to patients with a possible diagnosis of COPD attending for pulmonary function tests in hospital and primary care. Items associated with a diagnosis of COPD and its severity were examined with multivariate logistic regressions. Associated items in our analyses and in the literature were integrated into the score. The score was developed with a factorial analysis and optimized according to ROC curves.

RESULTS

Fifteen items were selected with the Delphi method, of which six were retained after logistic regression. They were submitted to 64 patients (mean age: 59+/-13.6 years). Factors associated with COPD were smoking ≥10 pack-years and a history of acute exacerbations. Low levels of activity and coughing up sputum were associated with COPD severity. Age ≥40 years and co-morbidities were added to the score. According to the factorial analysis, a two-stage score was developed assessing first the diagnosis of COPD and then the risk of severe outcomes. It showed a sensitivity of 71 %, a specificity of 77 %. The positive and negative predictive value were respectively 28 % and 96 %.

CONCLUSION

The score was an acceptable screening tool to identify COPD patients with high risk of complications. Nevertheless, validation needs be performed in a larger population to allow its use in primary care.

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.

Early Endotyping: A Chance for Intervention in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a syndrome that comprises several lung pathologies, but subphenotyping the various disease subtypes has been difficult. One reason may be that current efforts focused on studying COPD once it has occurred do not allow tracing back to the different origins of disease. This perspective proposes that emphysema originates when susceptible airway, endothelial, and/or hematopoietic cells are exposed to environmental toxins such as cigarette smoke, biomass fuel, or traffic emissions. These susceptible cell types may initiate distinct pathobiological mechanisms ("COPD endotypes") that ultimately manifest the emphysematous destruction of the lung. On the basis of evidence from the "airway" endotype, we suggest that grading these endotypes by severity may allow better diagnosis of disease at early stages when intervention can be designed on the basis of the mechanisms involved. Therefore, genomic, proteomic, and metabolomic studies on at-risk patients will be important in the identification of biomarkers that help designate each endotype. Together with understanding of the involved molecular pathways that lead to disease manifestation, these efforts may lead to development of intervention strategies.

Cluster and CART analyses identify large subgroups of adults with cystic fibrosis at low risk of 10-year death

Our goal was to identify subgroups of adults with cystic fibrosis (CF) at low risk of death within 10 years.Factor analysis for mixed data followed by Ward's cluster analysis was conducted using 25 variables from 1572 French CF adults in 2005. Rates of death by subgroups were analysed over 10 years. An algorithm was developed using CART (classification and regression tree) analysis to provide rules for the identification of subgroups of CF adults with low rates of death within 10 years. This algorithm was validated in 1376 Canadian CF adults.Seven subgroups were identified by cluster analysis in French CF adults, including two subgroups with low (∼5%) rates of death at 10 years: one subgroup (22% of patients) was composed of patients with nonclassic CF, the other subgroup (17% of patients) was composed of patients with classic CF but low rates of Pseudomonas aeruginosa infection and diabetes. An algorithm based on CART analysis of data in 2005 allowed us to identify most French adults with low rates of death. When tested using data from Canadian CF adults in 2005, the algorithm identified 287 out of 1376 (21%) patients at low risk (10-year death: 7.7%).Large subgroups of CF adults share low risk of 10-year mortality.

An evaluation of roflumilast and PDE4 inhibitors with a focus on the treatment of asthma

INTRODUCTION

Asthma is a common chronic airway inflammatory disease characterized by diverse inflammatory events leading to airway hyperresponsiveness and reversible airflow obstruction. Corticosteroids have been the mainstay for asthma treatment due to their broad anti-inflammatory actions; however, other medications such as phosphodiesterase 4 inhibitors also demonstrate anti-inflammatory activity in the airways.

AREAS COVERED

This review describes tissue expression of phosphodiesterase 4 in the airways, the different phosphodiesterase 4 isoenzymes identified, and the anti-inflammatory activities of phosphodiesterase 4 inhibition in asthma and related findings in chronic obstructive pulmonary disease (COPD). The authors further review clinical trials demonstrating that drugs such as roflumilast have an excellent safety profile and efficacy in patients with asthma and COPD.

EXPERT OPINION

Phosphodiesterase 4 inhibitors suppress the activity of immune cells, an effect similar to corticosteroids although by acting through different anti-inflammatory pathways and uniquely blocking neutrophilic inflammation. Roflumilast and other phosphodiesterase 4 inhibitors have been shown to provide additive protection in asthma when added to corticosteroid and anti-leukotriene treatment. Developmental drugs with dual phosphodiesterase 3 and 4 inhibition are thought to be able to provide bronchodilation and anti-inflammatory activities and will consequently be pushed forward in their clinical development for the treatment of asthma and COPD.

[Personalised COPD care: Where are we going?]

The concept of personalised medicine is recent but the underlying notions are not new: knowing how to adapt care to patients' characteristics is one of the components of the "art of medicine". The advances of science allow to refine considerably the applications of the concept in many fields of medicine including COPD: research has identified phenotypes, endotypes and treatable traits. Personalisation can be applied to all components of care. For instance, the decision to perform screening spirometry relies not only on risk factors (age, smoking, other exposures) but also on symptoms. Assessment of comorbidities often associated with COPD is based on risk factors and their combinations, variable between individuals. Rehabilitation and its components are in essence highly individualised, which a major condition for their success. Last but not least, personalisation of pharmacological therapy, which has long been rather poor, could not benefit from biomarkers of interest (predictive of response), such as blood eosinophil count. Practical strategies using these still need to be established, and new biomarkers may usefully enrich the collection!