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Background: The objective of this study was to compare fatigue levels between subjects with and without COPD, and to investigate the relationship between fatigue, demographics, clinical features and disease severity. Methods: A total of 1290 patients with COPD [age 65 ± 9 years, 61% male, forced expiratory volume in 1 s (FEV1) 56 ± 19% predicted] and 199 subjects without COPD (age 63 ± 9 years, 51% male, FEV1 112 ± 21% predicted) were assessed for fatigue (Checklist Individual Strength-Fatigue), demographics, clinical features and disease severity. Results: Patients with COPD had a higher mean fatigue score, and a higher proportion of severe fatigue (CIS-Fatigue score 35 ± 12 versus 21 ± 11 points, p < 0.001; 49 versus 10%, p < 0.001). Fatigue was significantly, but poorly, associated with the degree of airflow limitation [FEV1 (% predicted) Spearman correlation coefficient = −0.08, p = 0.006]. Multiple regression indicated that 30% of the variance in fatigue was explained by the predictor variables. Conclusions: Severe fatigue is prevalent in half of the patients with COPD, and correlates poorly with the degree of airflow limitation. Future studies are needed to better understand the physical, psychological, behavioural, and systemic factors that precipitate or perpetuate fatigue in COPD.

Background: Physical capacity (PC) and physical activity (PA) represent associated but separate domains of physical function. It remains unknown whether this framework may support a better understanding of the impaired physical function in patients with chronic obstructive pulmonary disease (COPD). The current study had two aims: (1) to determine the distribution of patients with COPD over the PC-PA quadrants, and (2) to explore whether differences exist in clinical characteristics between these quadrants. Methods: In this retrospective study, PC was measured using the six-minute walk distance (6MWD), and PA was assessed with an accelerometer. Moreover, patients’ clinical characteristics were obtained. Patients were divided into the following quadrants: (I) low PC (6MWD <70% predicted), low PA, using a step-defined inactivity index (<5000 steps/day, ”can’t do, don’t do” quadrant); (II) preserved PC, low PA (“can do, don’t do” quadrant); (III) low PC, preserved PA (“can’t do, do do” quadrant); and (IV) preserved PC, preserved PA (“can do, do do” quadrant). Results: The distribution of the 662 COPD patients over the quadrants was as follows: “can’t do, don’t do”: 34%; “can do, don’t do”: 14%; “can’t do, do do”: 21%; and “can do, do do”: 31%. Statistically significant differences between quadrants were found for all clinical characteristics, except for educational levels. Conclusions: This study proves the applicability of the PC-PA quadrant concept in COPD. This concept serves as a pragmatic clinical tool, that may be useful in the understanding of the impaired physical functioning in COPD patients and therefore, may improve the selection of appropriate interventions to improve physical function.

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BackgroundOur objective was to develop a tool to identify patients with COPD for proactive palliative care. Since palliative care needs increase during the disease course of COPD, the prediction of mortality within 1 year, measured during hospitalizations for acute exacerbation COPD (AECOPD), was used as a proxy for the need of proactive palliative care.Patients and methodsPatients were recruited from three general hospitals in the Netherlands in 2014. Data of 11 potential predictors, a priori selected based on literature, were collected during hospitalization for AECOPD. After 1 year, the medical files were explored for the date of death. An optimal prediction model was assessed by Lasso logistic regression, with 20-fold cross-validation for optimal shrinkage. Missing data were handled using complete case analysis.ResultsOf 174 patients, 155 patients were included; of those 30 (19.4%) died within 1 year. The optimal prediction model was internally validated and had good discriminating power (AUC =0.82, 95% CI 0.81–0.82). This model relied on the following seven predictors: the surprise question, Medical Research Council dyspnea questionnaire (MRC dyspnea), Clinical COPD Questionnaire (CCQ), FEV1% of predicted value, body mass index, previous hospitalizations for AECOPD and specific comorbidities. To ensure minimal miss out of patients in need of proactive palliative care, we proposed a cutoff in the model that prioritized sensitivity over specificity (0.90 over 0.73, respectively). Our model (ProPal-COPD tool) was a stronger predictor of mortality within 1 year than the CODEX (comorbidity, age, obstruction, dyspnea, and previous severe exacerbations) index.ConclusionThe ProPal-COPD tool is a promising multivariable prediction tool to identify patients with COPD for proactive palliative care.

The 2018 update of the Global Strategy for Asthma Management and Prevention does not mention fatigue-related symptoms. Nevertheless, patients with asthma frequently report tiredness, lack of energy, and daytime sleepiness. Quantitative research regarding the prevalence of fatigue in asthmatic patients is lacking. This retrospective cross-sectional study of outpatients with asthma upon referral to a chest physician assessed fatigue (Checklist Individual Strength-Fatigue (CIS-Fatigue)), lung function (spirometry), asthma control (Asthma Control Questionnaire (ACQ)), dyspnea (Medical Research Council (MRC) scale), exercise capacity (six-minute walk test (6MWT)), and asthma-related Quality-of-Life (QoL), Asthma Quality of Life Questionnaire (AQLQ) during a comprehensive health-status assessment. In total, 733 asthmatic patients were eligible and analyzed (47.4 ± 16.3 years, 41.1% male). Severe fatigue (CIS-Fatigue ≥ 36 points) was detected in 62.6% of patients. Fatigue was not related to airflow limitation (FEV1, ρ = −0.083); was related moderately to ACQ (ρ = 0.455), AQLQ (ρ = −0.554), and MRC (ρ = 0.435; all p-values < 0.001); and was related weakly to 6MWT (ρ = −0.243, p < 0.001). In stepwise multiple regression analysis, 28.9% of variance in fatigue was explained by ACQ (21.0%), MRC (6.5%), and age (1.4%). As for AQLQ, 42.2% of variance was explained by fatigue (29.8%), MRC (8.6%), exacerbation rate (2.6%), and age (1.2%). Severe fatigue is highly prevalent in asthmatic patients; it is an important determinant of disease-specific QoL and a crucial yet ignored patient-related outcome in patients with asthma.

Several composite markers have been proposed for risk assessment in chronic obstructive pulmonary disease (COPD). However, choice of parameters and score complexity restrict clinical applicability. Our aim was to provide and validate a simplified COPD risk index independent of lung function.The PROMISE study (n=530) was used to develop a novel prognostic index. Index performance was assessed regarding 2-year COPD-related mortality and all-cause mortality. External validity was tested in stable and exacerbated COPD patients in the ProCOLD, COCOMICS and COMIC cohorts (total n=2988).Using a mixed clinical and statistical approach, body mass index (B), severe acute exacerbations of COPD frequency (AE), modified Medical Research Council dyspnoea severity (D) and copeptin (C) were identified as the most suitable simplified marker combination. 0, 1 or 2 points were assigned to each parameter and totalled to B-AE-D or B-AE-D-C. It was observed that B-AE-D and B-AE-D-C were at least as good as BODE (body mass index, airflow obstruction, dyspnoea, exercise capacity), ADO (age, dyspnoea, airflow obstruction) and DOSE (dyspnoea, obstruction, smoking, exacerbation) indices for predicting 2-year all-cause mortality (c-statistic: 0.74, 0.77, 0.69, 0.72 and 0.63, respectively; Hosmer–Lemeshow test all p>0.05). Both indices were COPD specific (c-statistic for predicting COPD-related 2-year mortality: 0.87 and 0.89, respectively). External validation of B-AE-D was performed in COCOMICS and COMIC (c-statistic for 1-year all-cause mortality: 0.68 and 0.74; c-statistic for 2-year all-cause mortality: 0.65 and 0.67; Hosmer–Lemeshow test all p>0.05).The B-AE-D index, plus copeptin if available, allows a simple and accurate assessment of COPD-related risk.

Background and objective Asthma and chronic obstructive pulmonary disease (COPD) are two prevalent and complex diseases that require personalized management. Although a strategy based on treatable traits (TTs) has been proposed, the prevalence and relationship of TTs to the diagnostic label and disease severity established by the attending physician in a real‐world setting are unknown. We assessed how the presence/absence of specific TTs relate to the diagnosis and severity of ‘asthma’, ‘COPD’ or ‘asthma + COPD’. Methods The authors selected 30 frequently occurring TTs from the NOVELTY study cohort (NOVEL observational longiTudinal studY; NCT02760329), a large (n = 11,226), global study that systematically collects data in a real‐world setting, both in primary care clinics and specialized centres, for patients with ‘asthma’ (n = 5932, 52.8%), ‘COPD’ (n = 3898, 34.7%) or both (‘asthma + COPD’; n = 1396, 12.4%). Results The results indicate that (1) the prevalence of the 30 TTs evaluated varied widely, with a mean ± SD of 4.6 ± 2.6, 5.4 ± 2.6 and 6.4 ± 2.8 TTs/patient in those with ‘asthma’, ‘COPD’ and ‘asthma + COPD’, respectively (p < 0.0001); (2) there were no large global geographical variations, but the prevalence of TTs was different in primary versus specialized clinics; (3) several TTs were specific to the diagnosis and severity of disease, but many were not; and (4) both the presence and absence of TTs formed a pattern that is recognized by clinicians to establish a diagnosis and grade its severity. Conclusion These results provide the largest and most granular characterization of TTs in patients with airway diseases in a real‐world setting to date.