Two distinct chronic obstructive pulmonary disease (COPD) phenotypes are associated with high risk of mortality

Clustering analysis of lipoprotein profiles to identify subtypes of hypertriglyceridemia in Miniature Schnauzers

BACKGROUND

Hypertriglyceridemia (HTG) is prevalent in Miniature Schnauzers, predisposing them to life-threatening diseases. Varied responses to management strategies suggest the possibility of multiple subtypes.

HYPOTHESIS/OBJECTIVE

To identify and characterize HTG subtypes in Miniature Schnauzers through cluster analysis of lipoprotein profiles. We hypothesize that multiple phenotypes of primary HTG exist in this breed.

ANIMALS

Twenty Miniature Schnauzers with normal serum triglyceride concentration (NTG), 25 with primary HTG, and 5 with secondary HTG.

METHODS

Cross-sectional study using archived samples. Lipoprotein profiles, generated using continuous lipoprotein density profiling, were clustered with hierarchical cluster analysis. Clinical data (age, sex, body condition score, and dietary fat content) was compared between clusters.

RESULTS

Six clusters were identified. Dogs with primary HTG were dispersed among 4 clusters. One cluster showed the highest intensities for triglyceride-rich lipoprotein (TRL) and low-density lipoprotein (LDL) fractions and also included 4 dogs with secondary HTG. Two clusters had moderately high TRL fraction intensities and low-to-intermediate LDL intensities. The fourth cluster had high LDL but variable TRL fraction intensities with equal numbers of NTG and mild HTG dogs. The final 2 clusters comprised only NTG dogs with low TRL intensities and low-to-intermediate LDL intensities. The clusters did not appear to be driven by differences in the clinical data.

CONCLUSIONS AND CLINICAL IMPORTANCE

The results of this study support a spectrum of lipoprotein phenotypes within Miniature Schnauzers that cannot be predicted by triglyceride concentration alone. Lipoprotein profiling might be useful to determine if subtypes have different origins, clinical consequences, and response to treatment.

Mortality in individuals with COPD on long-term home non-invasive ventilation

BACKGROUND

Real-world evidence regarding survival of patients with chronic obstructive pulmonary disease (COPD) using chronic non-invasive ventilation (NIV) is scarce.

RESEARCH QUESTION

How do obesity and other factors relate to mortality in patients with COPD on chronic NIV?

STUDY DESIGN

and Methods: We retrospectively analyzed data from COPD patients enrolled in a home ventilation program between 2014 and 2018. Survival was compared between obese and non-obese groups using the Kaplan-Meier method. Factors associated with mortality were identified using multivariable Cox proportional regression analyses with Least Absolute Selection and Shrinkage Operator (LASSO) regularization. Univariable analyses were also done stratified by obesity.

RESULTS

Median survival was 80.0 (95% CI: 71.0-NA) months among obese (n = 205) and 30.0 (95%CI: 19.0-42.0) months in non-obese (n = 61) patients. NIV adherence was high in both groups. Mortality was associated with male gender [HR 1.44], chronic opioids or benzodiazepines use [HR 1.07], home oxygen use [HR 1.82], fixed pressure mode of ventilation [HR 1.55], NIV inspiratory pressure [HR 1.05], and thoracic cancer [HR 1.27]; obesity [HR: 0.43], age [HR 0.99] and NIV expiratory pressure [HR 0.94] were associated with decreased mortality. In the obese, univariable analyses revealed that chest wall disease, thoracic cancer, home oxygen use, FEV1% predicted, and ventilation parameters were associated with mortality. In the non-obese, male gender and respiratory comorbidities were related to mortality.

INTERPRETATION

Obesity is associated with improved survival in COPD patients highly adherent to NIV. Other factors associated with mortality reflect disease severity and ventilator parameters, with differences between obese and non-obese patients.

Evaluation of data processing pipelines on real-world electronic health records data for the purpose of measuring patient similarity

BACKGROUND

The ever-growing size, breadth, and availability of patient data allows for a wide variety of clinical features to serve as inputs for phenotype discovery using cluster analysis. Data of mixed types in particular are not straightforward to combine into a single feature vector, and techniques used to address this can be biased towards certain data types in ways that are not immediately obvious or intended. In this context, the process of constructing clinically meaningful patient representations from complex datasets has not been systematically evaluated.

AIMS

Our aim was to a) outline and b) implement an analytical framework to evaluate distinct methods of constructing patient representations from routine electronic health record data for the purpose of measuring patient similarity. We applied the analysis on a patient cohort diagnosed with chronic obstructive pulmonary disease.

METHODS

Using data from the CALIBER data resource, we extracted clinically relevant features for a cohort of patients diagnosed with chronic obstructive pulmonary disease. We used four different data processing pipelines to construct lower dimensional patient representations from which we calculated patient similarity scores. We described the resulting representations, ranked the influence of each individual feature on patient similarity and evaluated the effect of different pipelines on clustering outcomes. Experts evaluated the resulting representations by rating the clinical relevance of similar patient suggestions with regard to a reference patient.

RESULTS

Each of the four pipelines resulted in similarity scores primarily driven by a unique set of features. It was demonstrated that data transformations according to each pipeline prior to clustering can result in a variation of clustering results of over 40%. The most appropriate pipeline was selected on the basis of feature ranking and clinical expertise. There was moderate agreement between clinicians as measured by Cohen's kappa coefficient.

CONCLUSIONS

Data transformation has downstream and unforeseen consequences in cluster analysis. Rather than viewing this process as a black box, we have shown ways to quantitatively and qualitatively evaluate and select the appropriate preprocessing pipeline.

Severity of Lung Function Impairment Drives Transcriptional Phenotypes of COPD and Relates to Immune and Metabolic Processes

Purpose

This study sought to characterize transcriptional phenotypes of COPD through unsupervised clustering of sputum gene expression profiles, and further investigate mechanisms underlying the characteristics of these clusters.

Patients and methods

Induced sputum samples were collected from patients with stable COPD (n = 72) and healthy controls (n = 15). Induced sputum was collected for inflammatory cell counts, and RNA extracted. Transcriptional profiles were generated (Illumina Humanref-8 V2) and analyzed by GeneSpring GX14.9.1. Unsupervised hierarchical clustering and differential gene expression analysis were performed, and gene alterations validated in the ECLIPSE dataset (GSE22148).

Results

We identified 2 main clusters (Cluster 1 [n = 35] and Cluster 2 [n = 37]), which further divided into 4 sub-clusters (Sub-clusters 1.1 [n = 14], 1.2 [n = 21], 2.1 [n = 20] and 2.2 [n = 17]). Compared with Cluster 1, Cluster 2 was associated with significantly lower lung function (p = 0.014), more severe disease (p = 0.009) and breathlessness (p = 0.035), and increased sputum neutrophils (p = 0.031). Sub-cluster 1.1 had significantly higher proportion of people with comorbid cardiovascular disease compared to the other 3 sub-clusters (92.5% vs 57.1%, 50% and 52.9%, p < 0.013). Through supervised analysis we determined that degree of airflow limitation (GOLD stage) was the predominant factor driving gene expression differences in our transcriptional clusters. There were 452 genes (adjusted p < 0.05 and ≥2 fold) altered in GOLD stage 3 and 4 versus 1 and 2, of which 281 (62%) were also found to be significantly expressed between these GOLD stages in the ECLIPSE data set (GSE22148). Differentially expressed genes were largely downregulated in GOLD stages 3 and 4 and connected in 5 networks relating to lipoprotein and cholesterol metabolism; metabolic processes in oxidation/reduction and mitochondrial function; antigen processing and presentation; regulation of complement activation and innate immune responses; and immune and metabolic processes.

Conclusion

Severity of lung function drives 2 distinct transcriptional phenotypes of COPD and relates to immune and metabolic processes.

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.

Evaluation of the multimorbidity network and its relationship with clinical phenotypes in chronic obstructive pulmonary disease: The GALAXIA study

Background

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous condition, in which taking into consideration clinical phenotypes and multimorbidity is relevant to disease management. Network analysis, a procedure designed to study complex systems, allows to represent connections between the distinct features found in COPD.

Methods

Network analysis was applied to a cohort of patients with COPD in order to explore the degree of connectivity between different diseases, taking into account the presence of two phenotypic traits commonly used to categorize patients in clinical practice: chronic bronchitis (CB⁺/CB⁻) and the history of previous severe exacerbations (Ex⁺/Ex⁻). The strength of association between diseases was quantified using the correlation coefficient Phi (ɸ).

Results

A total of 1726 patients were included, and 91 possible links between 14 diseases were established. Although the four phenotypically defined groups presented a similar underlying comorbidity pattern, with special relevance for cardiovascular diseases and/or risk factors, classifying patients according to the presence or absence of CB implied differences between groups in network density (mean ɸ: 0.098 in the CB⁻ group and 0.050 in the CB⁺ group). In contrast, between‐group differences in network density were small and of questionable significance when classifying patients according to prior exacerbation history (mean ɸ: 0.082 among Ex⁻ subjects and 0.072 in the Ex⁺ group). The degree of connectivity of any given disease with the rest of the network also varied depending on the selected phenotypic trait. The classification of patients according to the CB⁻/CB⁺ groups revealed significant differences between groups in the degree of conectivity between comorbidities. On the other side, grouping the patients according to the Ex⁻/Ex⁺ trait did not disclose differences in connectivity between network nodes (diseases).

Conclusions

The multimorbidity network of a patient with COPD differs according to the underlying clinical characteristics, suggesting that the connections linking comorbidities between them vary for different phenotypes and that the clinical heterogeneity of COPD could influence the expression of latent multimorbidity. Network analysis has the potential to delve into the interactions between COPD clinical traits and comorbidities and is a promising tool to investigate possible specific biological pathways that modulate multimorbidity patterns.

COPD and diabetes mellitus: down the rabbit hole

Abstract:

: One of the important comorbidities that has a longstanding research history in COPD is diabetes. Although there are multiple studies on COPD and diabetes the exact links between these two conditions is still controversial. The exact prevalence of diabetes in COPD varies between 2 and 37 %. The true nature of this relationship is complex and may be partially related to the traditional risk factors for diabetes such as smoking, cardiovascular disease and use of steroids. However, COPD is a disease that has multiple phenotypes and is no longer regarded as a homogeneous condition. It seems that some COPD patients who have overlap with asthma or the obese phenotype at a particular risk for T2DM. The aim of this review is to analyze the prevalence, risk factors and possible interactions between COPD and diabetes mellitus.

Clinical and biological clusters of sepsis patients using hierarchical clustering

Background

Heterogeneity in sepsis expression is multidimensional, including highly disparate data such as the underlying disorders, infection source, causative micro-organismsand organ failures. The aim of the study is to identify clusters of patients based on clinical and biological characteristic available at patients’ admission.

Methods

All patients included in a national prospective multicenter ICU cohort OUTCOMEREA and admitted for sepsis or septic shock (Sepsis 3.0 definition) were retrospectively analyzed. A hierarchical clustering was performed in a training set of patients to build clusters based on a comprehensive set of clinical and biological characteristics available at ICU admission. Clusters were described, and the 28-day, 90-day, and one-year mortality were compared with log-rank rates. Risks of mortality were also compared after adjustment on SOFA score and year of ICU admission.

Results

Of the 6,046 patients with sepsis in the cohort, 4,050 (67%) were randomly allocated to the training set. Six distinct clusters were identified: young patients without any comorbidities, admitted in ICU for community-acquired pneumonia (n = 1,603 (40%)); young patients without any comorbidities, admitted in ICU for meningitis or encephalitis (n = 149 (4%)); elderly patients with COPD, admitted in ICU for bronchial infection with few organ failures (n = 243 (6%)); elderly patients, with several comorbidities and organ failures (n = 1,094 (27%)); patients admitted after surgery, with a nosocomial infection (n = 623 (15%)); young patients with immunosuppressive conditions (e.g., AIDS, chronic steroid therapy or hematological malignancy) (n = 338 (8%)). Clusters differed significantly in early or late mortality (p < .001), even after adjustment on severity of organ dysfunctions (SOFA) and year of ICU admission.

Conclusions

Clinical and biological features commonly available at ICU admission of patients with sepsis or septic shock enabled to set up six clusters of patients, with very distinct outcomes. Considering these clusters may improve the care management and the homogeneity of patients in future studies.

The cardiovascular phenotype of Chronic Obstructive Pulmonary Disease (COPD): Applying machine learning to the prediction of cardiovascular comorbidities

BACKGROUND

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous group of lung conditions that are challenging to diagnose and treat. As the presence of comorbidities often exacerbates this scenario, the characterization of patients with COPD and cardiovascular comorbidities may allow early intervention and improve disease management and care.

METHODS

We analysed a 4-year observational cohort of 6883 UK patients who were ultimately diagnosed with COPD and at least one cardiovascular comorbidity. The cohort was extracted from the UK Royal College of General Practitioners and Surveillance Centre database. The COPD phenotypes were identified prior to diagnosis and their reproducibility was assessed following COPD diagnosis. We then developed four classifiers for predicting cardiovascular comorbidities.

RESULTS

Three subtypes of the COPD cardiovascular phenotype were identified prior to diagnosis. Phenotype A was characterised by a higher prevalence of severe COPD, emphysema, hypertension. Phenotype B was characterised by a larger male majority, a lower prevalence of hypertension, the highest prevalence of the other cardiovascular comorbidities, and diabetes. Finally, phenotype C was characterised by universal hypertension, a higher prevalence of mild COPD and the low prevalence of COPD exacerbations. These phenotypes were reproduced after diagnosis with 92% accuracy. The random forest model was highly accurate for predicting hypertension while ruling out less prevalent comorbidities.

CONCLUSIONS

This study identified three subtypes of the COPD cardiovascular phenotype that may generalize to other populations. Among the four models tested, the random forest classifier was the most accurate at predicting cardiovascular comorbidities in COPD patients with the cardiovascular phenotype.

One-Year Evolution of Symptoms and Health Status of the COPD Multi-Dimensional Phenotypes: Results from the Follow-Up of the STORICO Observational Study

Aim

Describing the 1-year evolution of symptoms and health status in COPD patients enrolled in the STORICO study (observational study on characterization of 24-h symptoms in patients with COPD) classified in multidimensional phenotypes (m-phenotypes).

Methods

In our previous study, we performed an exploratory factor analysis to identify clinical and pathophysiological variables having the greatest classificatory properties, followed by a cluster analysis to group patients into m-phenotypes (mild COPD (MC), mild emphysematous (ME), severe bronchitic (SB), severe emphysematous (SE), and severe mixed COPD (SMC)). COPD symptoms were recorded at baseline, 6-, and 12-month follow-up and their evolution was described as frequency of patients with always present, always absent, arising’, ‘no more present symptoms. QoL and quality of sleep were evaluated using the SGRQ and CASIS questionnaires, respectively.

Results

We analyzed 379 subjects (144 MC, 71 ME, 96 SB, 14 SE, 54 SMC). M-phenotypes were stable over time in terms of presence of symptoms and health status with selected differences in evolution of symptoms in mild vs severe m-phenotypes. Indeed, 28.1% SB, 50.0% SE and 24.1% SMC vs 0.7% MC and 5.6% ME with night-time symptoms at baseline had no more symptoms at 6-month (p-value night-time symptom evolution MC vs SB, SE, SMC and ME vs SB, SE, SMC <0.0001). All m-phenotypes improved in quality of sleep, more markedly the severe than the mild ones (p-values CASIS score change between baseline and 6- or 12-month in MC, ME vs SB, SE, SMC <0.0001). QoL did not change during observation, irrespectively of m-phenotype.

Conclusion

Over 1 year, severe m-phenotypes showed an improvement in night-time symptoms and quality of sleep, but not QoL. Being stable over time, m-phenotypes seem worthy of testing for classificatory and prognostic purposes.

Diffusing capacity in chronic obstructive pulmonary disease assessment: A meta-analysis

To achieve a multidimensional evaluation of chronic obstructive pulmonary disease (COPD) patients, the spirometry measures are supplemented by assessment of symptoms, risk of exacerbations, and CT imaging. However, the measurement of diffusing capacity of the lung for carbon monoxide (DLCO) is not included in most common used models of COPD assessment. Here, we conducted a meta-analysis to evaluate the role of DLCO in COPD assessment.The studies were identified by searching the terms "diffusing capacity" OR "diffusing capacity for carbon monoxide" or "DLCO" AND "COPD" AND "assessment" in Pubmed, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, Scopus, and Web of Science databases. The mean difference of DLCO % predict was assessed in COPD patient with different severity (according to GOLD stage and GOLD group), between COPD patients with or without with frequent exacerbation, between survivors and non-survivors, between emphysema dominant and non-emphysema dominant COPD patients, and between COPD patients with or without pulmonary hypertension.43 studies were included in the meta-analysis. DLCO % predicted was significantly lower in COPD patients with more severe airflow limitation (stage II/IV), more symptoms (group B/D), and high exacerbation risk (group C/D). Lower DLCO % predicted was also found in exacerbation patients and non-survivors. Low DLCO % predicted was related to emphysema dominant phenotype, and COPD patients with PH.The current meta-analysis suggested that DLCO % predicted might be an important measurement for COPD patients in terms of severity, exacerbation risk, mortality, emphysema domination, and presence of pulmonary hypertension. As diffusion capacity reflects pulmonary ventilation and perfusion at the same time, the predictive value of DLCO or DLCO combined with other criteria worth further exploration.

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.

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.

Aspects extrapulmonaires des exacerbations de bronchopneumopathie chronique obstructive

La bronchopneumopathie chronique obstructive (BPCO) est une maladie systémique aux multiples atteintes, émaillée d’exacerbations. L’inflammation, l’hypoxémie, les troubles de la coagulation et les perturbations des interactions coeur–poumon expliquent en partie les atteintes non pulmonaires observées notamment lors d’exacerbations aiguës. Les événements cardiovasculaires sont la première cause de mortalité des patients BPCO, et leur recrudescence est observée pendant plusieurs semaines après une exacerbation. Aux urgences, la prise en charge des patients en exacerbation de BPCO repose donc en plus du support ventilatoire sur une bonne évaluation des potentielles pathologies associées telles que l’insuffisance cardiaque, l’ischémie myocardique, l’insuffisance rénale ou encore la maladie thromboembolique. Cette évaluation globale permet d’adapter les thérapeutiques parfois délétères sur la fonction cardiaque ou l’équilibre acide−base et de prendre en compte le risque extrapulmonaire dans le choix du parcours de soins du patient. Dans cette mise au point, nous abordons quelques explications physiologiques des multiples perturbations observées au cours de l’exacerbation de BPCO, et proposons une vision globale de l’évaluation de ces patients admis pour une dyspnée ou un autre motif pouvant être en rapport avec la BPCO.

Metabolic profiles among COPD and controls in the CanCOLD population-based cohort

A high prevalence of intermediate cardiometabolic risk factors and obesity in chronic obstructive pulmonary disease (COPD) has suggested the existence of pathophysiological links between hypertriglyceridemia, insulin resistance, visceral adiposity, and hypoxia or impaired pulmonary function. However, whether COPD contributes independently to the development of these cardiometabolic risk factors remains unclear. Our objective was to compare ectopic fat and metabolic profiles among representative individuals with COPD and control subjects and to evaluate whether the presence of COPD alters the metabolic risk profile. Study participants were randomly selected from the general population and prospectively classified as non-COPD controls and COPD, according to the Global Initiative for Chronic Obstructive Lung Disease classification. The metabolic phenotype, which consisted of visceral adipose tissue area, metabolic markers including homeostasis model assessment of insulin resistance (HOMA-IR), and blood lipid profile, was obtained in 144 subjects with COPD and 119 non-COPD controls. The metabolic phenotype was similar in COPD and controls. The odds ratios for having pathologic values for HOMA-IR, lipids and visceral adipose tissue area were similar in individuals with COPD and control subjects in multivariate analyses that took into account age, sex, body mass index, tobacco status and current medications. In a population-based cohort, no difference was found in the metabolic phenotype, including visceral adipose tissue accumulation, between COPD and controls. Discrepancies between the present and previous studies as to whether or not COPD is a risk factor for metabolic abnormalities could be related to differences in COPD phenotype or disease severity of the study populations.

Sex Hormone Phenotypes in Young Girls and the Age at Pubertal Milestones

CONTEXT

The age of pubertal onset is influenced by many variables in young girls. Previous studies have not examined sex hormones longitudinally around the time of breast development and their relationship to pubertal onset.

OBJECTIVE

We sought to use an unbiased statistical approach to identify phenotypes of sex hormones in young girls and examine their relationship with pubertal milestones.

DESIGN AND SETTING

Longitudinal observational study.

PARTICIPANTS AND MAIN OUTCOME MEASURES

In 269 girls, serum concentrations of steroid sex hormones [estradiol (E2), estrone, testosterone, and dehydroepiandrosterone sulfate] were measured by HPLC-mass spectrometry at time points before, at, and after thelarche. Girls were classified into four hormone phenotypes using objective principal components and cluster analyses of longitudinal hormone data. The association between the identified phenotypes and age of pubertal milestones was estimated using Cox proportional hazards modeling.

RESULTS

Mean ages at thelarche, pubarche, and menarche were 9.02, 9.85, and 12.30 years, respectively. Girls with low levels of all four hormones, phenotype 3b, were youngest at thelarche (8.67 years); those in phenotype 2, with the highest E2 levels and E2 surge 6 months after thelarche, were youngest at menarche (11.87 years) with shortest pubertal tempo. When controlling for race, maternal age of menarche, caregiver education, and body mass, different phenotypes were associated with the age of pubertal events.

CONCLUSIONS

Hormone phenotypic clustering can identify clinically relevant subgroups with differing ages of thelarche, pubarche, and menarche. These findings may enhance the understanding of timing of pubertal milestones and risk of adult disease.

The Impact of Interprofessional Monitoring and Education on the Usage of Systemic Glucocorticoids in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Retrospective, Medication Use Review

Background

Systemic corticosteroid therapy for chronic obstructive pulmonary disease (COPD) exacerbations is routine in clinical practice, however, dosing is variable. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) panel recommends a short course of systemic corticosteroids for acute COPD exacerbation treatment. Despite these recommendations, institutions continue to use higher doses and longer durations of systemic corticosteroid therapies.

Methods

This single-center, retrospective, cohort study evaluated systemic corticosteroid use in inpatient treatment of COPD exacerbations. Data were collected on patients with a diagnosis of COPD exacerbation from October 2017 to February 2018 in both the control and education groups. An interprofessional, learner-centric, quality improvement, educational seminar was performed. Providers were given accompanying pocket reference material for improved adherence to GOLD guidelines for the management of acute COPD exacerbations.

Results

Of the 137 charts reviewed in the control group, 130 of 137 patients (94.9%) received systemic corticosteroid doses exceeding GOLD guideline recommendations. These patients received an average daily dose of 147.5 mg of prednisone equivalents. These patients also experienced more adverse drug reactions as compared to their post-intervention counterparts. The 105 charts examined post-educational intervention revealed 47 of 105 patients (44.8%) received GOLD guideline-directed doses of systemic corticosteroids. This was an improvement from 2.9% (4 of 137) in the control group (p-value < 0.001). The average daily dose decreased to 58 mg daily (p-value < 0.001), and the number of doses over the recommended 40 mg of prednisone equivalents (54 of 105) was a 43.5% reduction (p-value < 0.001). Length of stay also decreased in the education group from 6.1 +/- 4.1 to 4.7 +/- 2.8 days (p-value 0.009). The 30-day readmission rate, however, was not statistically different between the two groups, 31.4% pre- and 21.0% post-educational intervention (p-value 0.098).

Conclusions

The interprofessional education seminar and pocket reference sheet realigned clinical practice with guideline-based therapy in this tertiary care, community hospital. These data validate that learner-centric innovation will benefit patient outcomes and improve the educational potential of the interdisciplinary rounding team.

Assessing pulmonary hypertension in COPD. Is there a role for computed tomography?

Pulmonary hypertension (PH) is a common complication of chronic obstructive pulmonary disease (COPD) and is associated with increased morbidity and mortality. Reference standard method to diagnose PH is right heart catheterization. Several non-invasive imaging techniques have been employed in the detection of PH. Among them, computed tomography (CT) is the most commonly used for phenotyping and detecting complications of COPD. Several CT findings have also been described in patients with severe PH. Nevertheless, CT analysis is currently based on visual findings which can lead to reproducibility failure. Therefore, there is a need for quantification in order to assess objective criteria. In this review, progresses in automated analyses of CT parameters and their values in predicting PH and COPD outcomes are presented.

Chronic obstructive pulmonary disease classification, phenotypes and risk assessment

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease. Various classification systems and phenotypes have been proposed. This review highlights the current classifications of COPD, describes the major phenotypes and provides a blue print for risk assessment of COPD. It is likely that more phenotypes and endotypes of COPD will be described paving the way to personalized medicine for patients with COPD.

Low Vitamin K Status Is Associated with Increased Elastin Degradation in Chronic Obstructive Pulmonary Disease

Elastin degradation is accelerated in chronic obstructive pulmonary disease (COPD) and is partially regulated by Matrix Gla Protein (MGP), via a vitamin K-dependent pathway. The aim was to assess vitamin K status in COPD as well as associations between vitamin K status, elastin degradation, lung function parameters and mortality. A total of 192 COPD patients and 186 age-matched controls were included. In addition to this, 290 COPD patients from a second independent longitudinal cohort were also included. Vitamin K status was assessed by measuring plasma inactive MGP levels and rates of elastin degradation by measuring plasma desmosine levels. Reduced vitamin K status was found in COPD patients compared to smoking controls (p < 0.0005) and controls who had never smoked (p = 0.001). Vitamin K status was inversely associated with desmosine (cohort 1: p = 0.001; cohort 2: p = 0.004). Only few significant associations between vitamin K status and lung function parameters were found. Mortality was higher in COPD patients within the quartile with the lowest vitamin K status compared to those within the other quartiles (hazard ratio 1.85, 95% confidence interval (CI), 1.21-2.83, p = 0.005). In conclusion, we demonstrated reduced vitamin K status in COPD and an inverse association between vitamin K status and elastin degradation rate. Our results therefore suggest a potential role of vitamin K in COPD pathogenesis.

Role of Diet in Chronic Obstructive Pulmonary Disease Prevention and Treatment

Chronic obstructive pulmonary disease is one of the leading causes of morbidity and mortality worldwide and a growing healthcare problem. Identification of modifiable risk factors for prevention and treatment of COPD is urgent, and the scientific community has begun to pay close attention to diet as an integral part of COPD management, from prevention to treatment. This review summarizes the evidence from observational and clinical studies regarding the impact of nutrients and dietary patterns on lung function and COPD development, progression, and outcomes, with highlights on potential mechanisms of action. Several dietary options can be considered in terms of COPD prevention and/or progression. Although definitive data are lacking, the available scientific evidence indicates that some foods and nutrients, especially those nutraceuticals endowed with antioxidant and anti-inflammatory properties and when consumed in combinations in the form of balanced dietary patterns, are associated with better pulmonary function, less lung function decline, and reduced risk of COPD. Knowledge of dietary influences on COPD may provide health professionals with an evidence-based lifestyle approach to better counsel patients toward improved pulmonary health.

Quantitative Imaging Markers of Lung Function in a Smoking Population Distinguish COPD Subgroups with Differential Lung Cancer Risk

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition with respect to onset, progression, and response to therapy. Incorporating clinical- and imaging-based features to refine COPD phenotypes provides valuable information beyond that obtained from traditional clinical evaluations. We characterized the spectrum of COPD-related phenotypes in a sample of former and current smokers and evaluated how these subgroups differ with respect to sociodemographic characteristics, COPD-related comorbidities, and subsequent risk of lung cancer.

METHODS

White (N = 659) and African American (N = 520) male and female participants without lung cancer (controls) in the INHALE study who completed a chest CT scan, interview, and spirometry test were used to define distinct COPD-related subgroups based on hierarchical clustering. Seven variables were used to define clusters: pack years, quit years, FEV1/FVC, % predicted FEV1, and from quantitative CT (qCT) imaging, % emphysema, % air trapping, and mean lung density ratio. Cluster definitions were then applied to INHALE lung cancer cases (N = 576) to evaluate lung cancer risk.

RESULTS

Five clusters were identified that differed significantly with respect to sociodemographic (e.g., race, age) and clinical (e.g., BMI, limitations due to breathing difficulties) characteristics. Increased risk of lung cancer was associated with increasingly detrimental lung function clusters (when ordered from most detrimental to least detrimental).

CONCLUSIONS

Measures of lung function vary considerably among smokers and are not fully explained by smoking intensity.

IMPACT

Combining clinical (spirometry) and radiologic (qCT) measures of COPD defines a spectrum of lung disease that predicts lung cancer risk differentially among patient clusters.

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.

[The impact of acute exacerbations of COPD on mortality]

INTRODUCTION

The purpose of the study was to evaluate the impact of acute exacerbations of chronic obstructive pulmonary disease (COPD) on mortality and to identify the predictive factors in the Algerian population where COPD occurs as frequently as in the rest of the world.

METHODS

An observational study of a cohort of 400 patients with an established diagnosis of COPD was performed in the pulmonology department of the University of Algiers. The patients were divided into two groups according to the number of exacerbations they experienced and were followed prospectively for 3 years. Mortality was analyzed comparatively in both groups.

RESULTS

During the follow-up period 39 patients died, mainly frequent exacerbators (84.6%), with a high mortality occurring during and in the months following hospitalisation (69.2%). By multivariate analysis the phenotype "frequent exacerbator" (RR=6.20; 95% CI: 2.6 to 14.8), GOLD category C (RR=7; 95% CI: 1.28 to 14.7), GOLD category D (RR=7.11; 95% CI: 1.38 to 15.6), age≥80 years (RR=2.7, 95% CI: 2.23 to 3.76) and chronic core pulmonale (RR=2.35; 95% CI 1.05 to 5.25) were shown to be independent risk factors of death.

CONCLUSION

The frequency and the severity of exacerbations impact negatively on the survival of patients with 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!

Impact of Comorbidities on the Treatment of Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) frequently exists alongside other chronic diseases. Comorbidities can have a significant influence on the course of COPD, affecting disease-related symptoms and increasing morbidity and mortality in patients. Studies indicate that management of comorbid COPD can be improved by engaging in a multidisciplinary team-based approach. A collaborative effort from different disease specialists and health care professionals, together with disease self-management and management programs, could improve the outcomes of patients with comorbid COPD.

CT-derived muscle remodelling after bronchoscopic lung volume reduction in advanced emphysema

Muscle wasting frequently occurs in severe emphysema. Improving respiratory mechanics by bronchoscopic lung volume reduction using endobronchial valves (EBV) might prevent further loss or even increase in muscle mass. CT-derived skeletal muscle mass gain was observed in 39/49 patients 6 months after EBV. Multiple linear regression showed that gain in muscle (β=2.4; 95% CI 0.2 to 4.6; p=0.036) and intramuscular fat (β=3.1; 95% CI 0.2 to 5.9; p=0.035) is associated with improved 6 min walk distance independent of the change in residual volume. Skeletal muscle remodelling associates with improved exercise capacity after EBV, independent of hyperinflation reduction.

Trial registration number

Clinical trial registered with the Dutch trial register www.trialregister.nl (NTR2876), Results.

Obesity and Metabolic Abnormalities in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) and obesity are major causes of morbidity and mortality worldwide, and a relationship between COPD and obesity is increasingly recognized. The nature and underlying mechanisms of the link between obesity and COPD are still largely unknown. Obesity, and abdominal adiposity in particular, is linked to metabolic syndrome. Hypertension, hyperglycemia, and abdominal adiposity seem to be the most prevalent components of metabolic syndrome in COPD. Adipose tissues function as a source of a variety of signaling molecules in the pathobiology of respiratory diseases. Computed tomography allows measurement of various fat depots and allows better understanding of the impact of abdominal visceral fat in the pathophysiology of COPD. Innovative statistical methodology has identified obesity as a relevant and distinguishing characteristic in patients with COPD. Integrated research combining COPD and its multimorbidity network may unravel underlying endotypes to direct future interventions in this specific COPD cluster.

Personalized medicine and chronic obstructive pulmonary disease

PURPOSE OF REVIEW

The current review summarizes ongoing developments in personalized medicine and precision medicine in chronic obstructive pulmonary disease (COPD). Our current approach is far away of personalized management algorithms as current recommendations for COPD are largely based on a reductionist disease description, operationally defined by results of spirometry.

RECENT FINDINGS

Besides precision medicine developments, a personalized medicine approach in COPD is described based on a holistic approach of the patient and considering illness as the consequence of dynamic interactions within and between multiple interacting and self-adjusting systems. Pulmonary rehabilitation is described as a model of personalized medicine. Largely based on current understanding of inflammatory processes in COPD, targeted interventions in COPD are reviewed. Augmentation therapy for α-1-antitrypsine deficiency is described as model of precision medicine in COPD based in profound understanding of the related genetic endotype.

SUMMARY

Future developments of precision medicine in COPD require identification of relevant endotypes combined with proper identification of phenotypes involved in the complex and heterogeneous manifestations of COPD.

A simple algorithm for the identification of clinical COPD phenotypes

This study aimed to identify simple rules for allocating chronic obstructive pulmonary disease (COPD) patients to clinical phenotypes identified by cluster analyses.Data from 2409 COPD patients of French/Belgian COPD cohorts were analysed using cluster analysis resulting in the identification of subgroups, for which clinical relevance was determined by comparing 3-year all-cause mortality. Classification and regression trees (CARTs) were used to develop an algorithm for allocating patients to these subgroups. This algorithm was tested in 3651 patients from the COPD Cohorts Collaborative International Assessment (3CIA) initiative.Cluster analysis identified five subgroups of COPD patients with different clinical characteristics (especially regarding severity of respiratory disease and the presence of cardiovascular comorbidities and diabetes). The CART-based algorithm indicated that the variables relevant for patient grouping differed markedly between patients with isolated respiratory disease (FEV₁, dyspnoea grade) and those with multi-morbidity (dyspnoea grade, age, FEV₁ and body mass index). Application of this algorithm to the 3CIA cohorts confirmed that it identified subgroups of patients with different clinical characteristics, mortality rates (median, from 4% to 27%) and age at death (median, from 68 to 76 years).A simple algorithm, integrating respiratory characteristics and comorbidities, allowed the identification of clinically relevant COPD phenotypes.

Precision medicine for all? Challenges and opportunities for a precision medicine approach to critical illness

All of medicine aspires to be precise, where a greater understanding of individual data will lead to personalized treatment and improved outcomes. Prompted by specific examples in oncology, the field of critical care may be tempted to envision that complex, acute syndromes could bend to a similar reductionist philosophy-where single mutations could identify and target our critically ill patients for treatment. However, precision medicine faces many challenges in critical care. These include confusion about terminology, uncertainty about how to divide patients into discrete groups, the challenges of multi-morbidity, scale, and the need for timely interventions. This review addresses these challenges and provides a translational roadmap spanning preclinical work to identify putative treatment targets, novel designs for clinical trials, and the integration of the electronic health record to implement precision critical care for all.

Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea

BACKGROUND

Obstructive sleep apnoea (OSA) is a heterogeneous disorder, and improved understanding of physiologic phenotypes and their clinical implications is needed. We aimed to determine whether routine polysomnographic data can be used to identify OSA phenotypes (clusters) and to assess the associations between the phenotypes and cardiovascular outcomes.

METHODS

Cross-sectional and longitudinal analyses of a multisite, observational US Veteran (n=1247) cohort were performed. Principal components-based clustering was used to identify polysomnographic features in OSA's four pathophysiological domains (sleep architecture disturbance, autonomic dysregulation, breathing disturbance and hypoxia). Using these features, OSA phenotypes were identified by cluster analysis (K-means). Cox survival analysis was used to evaluate longitudinal relationships between clusters and the combined outcome of incident transient ischaemic attack, stroke, acute coronary syndrome or death.

RESULTS

Seven patient clusters were identified based on distinguishing polysomnographic features: 'mild', 'periodic limb movements of sleep (PLMS)', 'NREM and arousal', 'REM and hypoxia', 'hypopnoea and hypoxia', 'arousal and poor sleep' and 'combined severe'. In adjusted analyses, the risk (compared with 'mild') of the combined outcome (HR (95% CI)) was significantly increased for 'PLMS', (2.02 (1.32 to 3.08)), 'hypopnoea and hypoxia' (1.74 (1.02 to 2.99)) and 'combined severe' (1.69 (1.09 to 2.62)). Conventional apnoea-hypopnoea index (AHI) severity categories of moderate (15≤AHI<30) and severe (AHI ≥30), compared with mild/none category (AHI <15), were not associated with increased risk.

CONCLUSIONS

Among patients referred for OSA evaluation, routine polysomnographic data can identify physiological phenotypes that capture risk of adverse cardiovascular outcomes otherwise missed by conventional OSA severity classification.

Noninvasive ventilation with helium-oxygen mixture in hypercapnic COPD exacerbation: aggregate meta-analysis of randomized controlled trials

When used as a driving gas during NIV in hypercapnic COPD exacerbation, a helium–oxygen (He/O₂) mixture reduces the work of breathing and gas trapping. The potential for He/O₂ to reduce the rate of NIV failure leading to intubation and invasive mechanical ventilation has been evaluated in several RCTs. The goal of this meta-analysis is to assess the effect of NIV driven by He/O₂ compared to air/O₂ on patient-centered outcomes in hypercapnic COPD exacerbation. Relevant RCTs were searched using standard procedures. The main endpoint was the rate of NIV failure. The effect size was computed by a fixed-effect model, and estimated as odds ratio (OR) with 95% confidence interval (CI). Additional endpoints were ICU mortality, NIV-related side effects, and the length and costs of ICU stay. Three RCTs fulfilled the selection criteria and enrolled a total of 772 patients (386 patients received He/O₂ and 386 received air/O₂). Pooled analysis showed no difference in the rate of NIV failure when using He/O₂ mixture compared to air/O₂: 17 vs 19.7%, respectively; OR 0.84, 95% CI 0.58–1.22; p = 0.36; I² for heterogeneity = 0%, and no publication bias. ICU mortality was also not different: OR 0.8, 95% CI 0.45–1.4; p = 0.43; I² = 5%. However, He/O₂ was associated with less NIV-related adverse events (OR 0.56, 95% CI 0.4–0.8, p = 0.001), and a shorter length of ICU stay (difference in means = −1.07 day, 95% CI −2.14 to −0.004, p = 0.049). Total hospital costs entailed by hospital stay and NIV gas were not different: difference in means = −279$, 95% CI −2052–1493, p = 0.76. Compared to air/O₂, He/O₂ does not reduce the rate of NIV failure in hypercapnic COPD exacerbation. It is, however, associated with a lower incidence of NIV-related adverse events and a shortening of ICU length of stay with no increase in hospital costs.

Senescence in COPD and Its Comorbidities

Chronic obstructive pulmonary disease (COPD) is regarded as a disease of accelerated lung aging. This affliction shows all of the hallmarks of aging, including telomere shortening, cellular senescence, activation of PI3 kinase-mTOR signaling, impaired autophagy, mitochondrial dysfunction, stem cell exhaustion, epigenetic changes, abnormal microRNA profiles, immunosenescence, and a low-grade chronic inflammation (inflammaging). Many of these pathways are driven by chronic exogenous and endogenous oxidative stress. There is also a reduction in antiaging molecules, such as sirtuins and Klotho, which further accelerate the aging process. COPD is associated with several comorbidities (multimorbidity), such as cardiovascular and metabolic diseases, that share the same pathways of accelerated aging. Understanding these mechanisms has helped identify several novel therapeutic targets, and several drugs and dietary interventions are now in development to treat multimorbidity.

Chronic obstructive pulmonary disease phenotypes using cluster analysis of electronic medical records

Chronic obstructive pulmonary disease is a heterogeneous disease. In this retrospective study, we hypothesize that it is possible to identify clinically relevant phenotypes by applying clustering methods to electronic medical records. We included all the patients >40 years with a diagnosis of chronic obstructive pulmonary disease admitted to the University of New Mexico Hospital between 1 January 2011 and 1 May 2014. We collected admissions, demographics, comorbidities, severity markers and treatments. A total of 3144 patients met the inclusion criteria: 46 percent were >65 years and 52 percent were males. The median Charlson score was 2 (interquartile range: 1-4) and the most frequent comorbidities were depression (36%), congestive heart failure (25%), obesity (19%), cancer (19%) and mild liver disease (18%). Using the sphere exclusion method, nine clusters were obtained: depression-chronic obstructive pulmonary disease, coronary artery disease-chronic obstructive pulmonary disease, cerebrovascular disease-chronic obstructive pulmonary disease, malignancy-chronic obstructive pulmonary disease, advanced malignancy-chronic obstructive pulmonary disease, diabetes mellitus-chronic kidney disease-chronic obstructive pulmonary disease, young age-few comorbidities-high readmission rates-chronic obstructive pulmonary disease, atopy-chronic obstructive pulmonary disease, and advanced disease-chronic obstructive pulmonary disease. These clusters will need to be validated prospectively.

Classification and characterization of class III malocclusion in Chinese individuals

BACKGROUND

Class III malocclusion is a maxillofacial disorder that is characterised by a concave profile and can be attributed to both genetic inheritance and environmental factors. It is a clinical challenge due to our limited understanding of its aetiology. Revealing its prototypical diversity will contribute to our sequential exploration of the underlying aetiological information. The objective of this study was to characterize phenotypic variations of Class III malocclusion via a lateral cephalometric analysis in a community of Chinese individuals.

METHOD

One-hundred-and-forty-four individuals (58 males ≥18 and 86 females ≥16) with Class III malocclusion ranging from mild to severe were enrolled in this study. Principal component analysis and cluster analysis were performed using 61 lateral cephalometric measurements.

RESULTS

Six principal components were discovered in the examined population and were responsible for 73.7 % of the variability. Four subtypes were revealed by cluster analysis. Subtype 1 included subjects with mild mandibular prognathism with a steep mandibular plane. Subjects in subtype 2 showed a combination of prognathic mandibular and retrusive maxillary with a flat or normal mandibular plane. Subtype 3 included individuals with purely severe mandibular prognathism and a normal mandibular plane. Individuals in subtype 4 had a mild maxillary deficiency and severe mandibular prognathism with the lowest mandibular plane angle.

CONCLUSION

The six principal components extracted among the 61 variables improve our knowledge of lateral cephalometric analysis for diagnoses. We successfully identified four Class III malocclusion subtypes, indicating that cluster analysis could supplement the classification of Class III malocclusion among a Chinese population and may assist in our on-going genetic study.

Distribution and Outcomes of a Phenotype-Based Approach to Guide COPD Management: Results from the CHAIN Cohort

Rationale

The Spanish guideline for COPD (GesEPOC) recommends COPD treatment according to four clinical phenotypes: non-exacerbator phenotype with either chronic bronchitis or emphysema (NE), asthma-COPD overlap syndrome (ACOS), frequent exacerbator phenotype with emphysema (FEE) or frequent exacerbator phenotype with chronic bronchitis (FECB). However, little is known on the distribution and outcomes of the four suggested phenotypes.

Objective

We aimed to determine the distribution of these COPD phenotypes, and their relation with one-year clinical outcomes.

Methods

We followed a cohort of well-characterized patients with COPD up to one-year. Baseline characteristics, health status (CAT), BODE index, rate of exacerbations and mortality up to one year of follow-up were compared between the four phenotypes.

Results

Overall, 831 stable COPD patients were evaluated. They were distributed as NE, 550 (66.2%); ACOS, 125 (15.0%); FEE, 38 (4.6%); and FECB, 99 (11.9%); additionally 19 (2.3%) COPD patients with frequent exacerbations did not fulfill the criteria for neither FEE nor FECB. At baseline, there were significant differences in symptoms, FEV₁ and BODE index (all p<0.05). The FECB phenotype had the highest CAT score (17.1±8.2, p<0.05 compared to the other phenotypes). Frequent exacerbator groups (FEE and FECB) were receiving more pharmacological treatment at baseline, and also experienced more exacerbations the year after (all p<0.05) with no differences in one-year mortality. Most of NE (93%) and half of exacerbators were stable after one year.

Conclusions

There is an uneven distribution of COPD phenotypes in stable COPD patients, with significant differences in demographics, patient-centered outcomes and health care resources use.

Chronic Obstructive Pulmonary Disease Subtypes. Transitions over Time

BACKGROUND

Although subtypes of chronic obstructive pulmonary disease are recognized, it is unknown what happens to these subtypes over time. Our objectives were to assess the stability of cluster-based subtypes in patients with stable disease and explore changes in clusters over 1 year.

METHODS

Multiple correspondence and cluster analysis were used to evaluate data collected from 543 stable patients included consecutively from 5 respiratory outpatient clinics.

RESULTS

Four subtypes were identified. Three of them, A, B, and C, had marked respiratory profiles with a continuum in severity of several variables, while the fourth, subtype D, had a more systemic profile with intermediate respiratory disease severity. Subtype A was associated with less dyspnea, better health-related quality of life and lower Charlson comorbidity scores, and subtype C with the most severe dyspnea, and poorer pulmonary function and quality of life, while subtype B was between subtypes A and C. Subtype D had higher rates of hospitalization the previous year, and comorbidities. After 1 year, all clusters remained stable. Generally, patients continued in the same subtype but 28% migrated to another cluster. Together with movement across clusters, patients showed changes in certain characteristics (especially exercise capacity, some variables of pulmonary function and physical activity) and changes in outcomes (quality of life, hospitalization and mortality) depending on the new cluster they belonged to.

CONCLUSIONS

Chronic obstructive pulmonary disease clusters remained stable over 1 year. Most patients stayed in their initial subtype cluster, but some moved to another subtype and accordingly had different outcomes.

Distinct symptom experiences in subgroups of patients with COPD

Background

In addition to their respiratory symptoms, patients with COPD experience multiple, co-occurring symptoms.

Objectives

The aims of this study were to identify subgroups of COPD patients based on their distinct experiences with 14 symptoms and to determine how these subgroups differed in demographic and clinical characteristics and disease-specific quality of life.

Patients and methods

Patients with moderate, severe, and very severe COPD (n=267) completed a number of self-report questionnaires. Latent class analysis was used to identify subgroups of patients with distinct symptom experiences based on the occurrence of self-reported symptoms using the Memorial Symptom Assessment Scale.

Results

Based on the probability of occurrence of a number of physical and psychological symptoms, three subgroups of patients (ie, latent classes) were identified and named “high”, “intermediate”, and “low”. Across the three latent classes, the pairwise comparisons for the classification of airflow limitation in COPD were not significantly different, which suggests that measurements of respiratory function are not associated with COPD patients’ symptom burden and their specific needs for symptom management. While patients in both the “high” and “intermediate” classes had high occurrence rates for respiratory symptoms, patients in the “high” class had the highest occurrence rates for psychological symptoms. Compared with the “intermediate” class, patients in the “high” class were younger, more likely to be women, had significantly more acute exacerbations in the past year, and reported significantly worse disease-specific quality of life scores.

Conclusion

These findings suggest that subgroups of COPD patients with distinct symptom experiences can be identified. Patients with a higher symptom burden warrant more detailed assessments and may have therapeutic needs that would not be identified using current classifications based only on respiratory function.