The importance of symptoms in the longitudinal variability of clusters in COPD patients: A validation study

Health-Related Quality of Life and Daily Physical Activity Level in Patients with COPD- a Cluster Analysis

Patients with chronic obstructive pulmonary disease (COPD) may have a limited level of physical activity in daily life (PADL) and health-related quality of life (HRQOL). The interrelationships of these variables should be measure by cluster analysis to characterize this population and enable rehabilitation programs to target each patient profile identified. This study investigates different phenotypes in COPD according to PADL and HRQOL. A cross-sectional study with cluster analysis was done, in which 76 people with COPD were submitted to measurements to characterize the sample on first day, followed by used of physical activity monitor, which was worn for 7 days. After 7 days, the six-minute walk test (6MWT) and HRQOL questionnaires were applied (St. George's Respiratory Questionnaire). The main results: three phenotypes were identified (A, B and C), with phenotype A who exhibited an inactive physical activity level and HRQOL scores above the value deemed satisfactory, phenotype B those with active physical activity level and poor HRQOL scores, and phenotype C subjects with inactive physical activity level and HRQOL scores but the value is close to cutoff point. To conclude, three phenotypes were found, with one indicating disproportionality between PADL and HRQOL.

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.

The HUNT study: Association of comorbidity clusters with long-term survival and incidence of exacerbation in a population-based Norwegian COPD cohort

BACKGROUND AND OBJECTIVE

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease often viewed as part of a multimorbidity complex. There is a need for better phenotyping of the disease, characterization of its interplay with other comorbidities and its association with long-term outcomes. This study aims to examine how clusters of comorbidities are associated with severe exacerbations and mortality in COPD.

METHODS

Participants with potential COPD were recruited from the second (1995-1997) and third (2006-2008) survey of the HUNT Study and followed up until April 2020. Ten objectively identified comorbidities were clustered using self-organizing maps. Severe COPD exacerbations requiring hospitalization were assessed using hospital data. All-cause mortality was collected from national registries. Multivariable Cox regression was used to calculate hazard ratios (HRs) with 95% CIs for the association between comorbidity clusters and all-cause mortality. Poisson regression was used to calculate incidence rate ratios (IRRs) with 95% CI for the cumulative number of severe exacerbations for each cluster.

RESULTS

Five distinct clusters were identified, including 'less comorbidity', 'psychological', 'cardiovascular', 'metabolic' and 'cachectic' clusters. Using the less comorbidity cluster as reference, the psychological and cachectic clusters were associated with all-cause mortality (HR 1.23 [1.04-1.45] and HR 1.83 [1.52-2.20], adjusted for age and sex). The same clusters also had increased risk of exacerbations (unadjusted IRR of 1.24 [95% CI 1.04-1.48] and 1.50 [95% CI 1.23-1.83], respectively).

CONCLUSION

During 25 years of follow-up, individuals in the psychological and cachectic clusters had increased mortality. Furthermore, these clusters were associated with increased risk of severe COPD exacerbations.

Utilizing an Artificial Neural Network to Predict Self-Management in Patients With Chronic Obstructive Pulmonary Disease: An Exploratory Analysis

PURPOSE

The main objective of this study was to utilize an artificial neural network in an exploratory fashion to predict self-management behaviors based on reported symptoms in a sample of stable patients with chronic obstructive pulmonary disease (COPD).

DESIGN AND METHODS

Patient symptom data were collected over 21 consecutive days. Symptoms included distress due to cough, chest tightness, distress due to mucus, dyspnea with activity, dyspnea at rest, and fatigue. Self-management abilities were measured and recorded periodically throughout the study period and were the dependent variable for these analyses. Self-management ability scores were broken into three equal tertiles to signify low, medium, and high self-management abilities. Data were entered into a simple artificial neural network using a three-layer model. Accuracy of the neural network model was calculated in a series of three models that respectively used 7, 14, and 21 days of symptom data as input (independent variables). Symptom data were used to determine if the model could accurately classify participants into their respective self-management ability tertiles (low, medium, or high scores). Through analysis of synaptic weights, or the strength or amplitude of a connection between variables and parts of the neural network, the most important variables in classifying self-management abilities could be illuminated and served as another outcome in this study.

FINDINGS

The artificial neural network was able to predict self-management ability with 93.8% accuracy if 21 days of symptom data were included. The neural network performed best when predicting the low and high self-management abilities but struggled in predicting those with medium scores. By analyzing the synaptic weights, the most important variables determining self-management abilities were gender, followed by chest tightness, age, cough, breathlessness during activity, fatigue, breathlessness at rest, and phlegm.

CONCLUSIONS

The results of this study suggest that self-management abilities could potentially be predicted through understanding and reporting of patient's symptoms and use of an artificial neural network. Future research is clearly needed to expand on these findings.

CLINICAL RELEVANCE

Symptom presentation in chronically ill patients directly impacts self-management behaviors. Patients with COPD experience a number of symptoms that have the potential to impact their ability to manage their chronic disease, and artificial neural networks may help clinicians identify patients at risk for poor self-management abilities.

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.

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.

Predictors of Respiratory Decline Following Stereotactic Ablative Radiotherapy to Multiple Lung Tumors

INTRODUCTION

Stereotactic ablative radiotherapy (SABR) is highly effective at controlling early stage primary lung cancer and lung metastases. Although previous studies have suggested that treating multiple lung tumors with SABR is safe, post-treatment changes in respiratory function have not been analyzed in detail.

PATIENTS AND METHODS

We retrospectively identified patients with 2 or more primary lung cancers or lung metastases treated with SABR and analyzed clinical outcomes and predictors of toxicity. We defined a composite respiratory decline endpoint to include increased oxygen requirement, increased dyspnea scale, or death from respiratory failure not owing to disease progression.

RESULTS

A total of 86 patients treated with SABR to 203 lung tumors were analyzed. A total of 21.8% and 41.8% of patients developed composite respiratory decline at 2 and 4 years, respectively. When accounting for intrathoracic disease progression, 12.7% of patients developed composite respiratory decline at 2 years. Of the patients, 7.9% experienced grade 2 or greater radiation pneumonitis. No patient- or treatment-related factor predicted development of respiratory decline. The median overall survival was 46.9 months, and the median progression-free survival was 14.8 months. The cumulative incidence of local failure was 9.7% at 2 years.

CONCLUSION

Although our results confirm that SABR is an effective treatment modality for patients with multiple lung tumors, we observed a high rate of respiratory decline after treatment, which may be owing to a combination of treatment and disease effects. Future studies may help to determine ways to avoid pulmonary toxicity from SABR.

The physical, mental, and social impact of COPD in a population-based sample: results from the Longitudinal Aging Study Amsterdam

Chronic obstructive pulmonary disease (COPD) is associated with substantial health impact that may already become apparent in early disease. This study aims to examine the features of subjects with COPD in a Dutch population-based sample and compare their physical status, mental status, and social status to non-COPD subjects. This study made use of Longitudinal Aging Study Amsterdam (LASA) data. Demographics, clinical characteristics, self-reported diseases, post-bronchodilator spirometry, physical, mental, and social status were assessed. A number of 810 subjects (50.5% male, mean age 60.5 ± 2.9 years) were included. Subjects with COPD (n = 68, mean FEV₁ 67.6 [IQR 60.4–80.4] %.) had a slower walking speed than non-COPD subjects, p = 0.033. When compared to non-COPD subjects, COPD subjects gave a lower rating on their health (physical subscale of SF-12: 15 [IQR 16.0–19.0] vs. 18 [IQR 11.0–17.0] points) and life (EQ5D VAS: 75 [IQR 70.0–90.0] vs. 80 points [IQR 65.0–85.5]) surveys. COPD subjects also had a more impaired disease-specific health status (CAT: 9.5 ± 5.9 vs. 6.7 ± 5.2, respectively), were less likely to have a partner (69% vs. 84%, respectively) and received emotional support less often (24% vs. 36%, respectively) compared to non-COPD subjects (All comparisons p < 0.001). In a population-based sample, subjects with COPD had a reduced physical performance, a more impaired disease-specific health status and were more socially deprived compared to non-COPD subjects. These impairments need to be taken into consideration when setting up a management program for patients with mild COPD.

Patients with early-stage chronic lung disease need holistic support to limit the physical, mental and social impacts of the condition. There is more to chronic obstructive pulmonary disease (COPD) than persistent airflow limitation; systemic effects, including loss of muscle strength and higher risk of heart conditions, mental health and social problems can manifest from the early stages. Frits Franssen at CIRO, the Netherlands, and co-workers interviewed 810 participants aged 55–65 from the Longitudinal Aging Study Amsterdam to investigate the physical, mental and social status of COPD sufferers and compare them with healthy controls. Those with COPD were more likely to walk slower, tire easily and perceive themselves as having poor overall health. Socially, COPD patients were less likely to have long-term partners and felt the need for more emotional support than their healthy peers.