Background: It remains unknown whether and to what extent members of online “long COVID” peer support groups remain symptomatic and limited over time. Therefore, we aimed to evaluate symptoms in members of online long COVID peer support groups up to 6 months after the onset of coronavirus disease 2019 (COVID-19)-related symptoms.Methods: Demographics, symptoms, health status, work productivity, functional status and health-related quality of life were assessed about 3 and 6 months after the onset of COVID-19-related symptoms in members of online long COVID peer support groups.Results: Data from 239 patients with a confirmed COVID-19 diagnosis (83% women; median (interquartile range) age 50 (39–56) years) were analysed. During the infection, a median (interquartile range) of 15 (11–18) symptoms was reported, which was significantly lower 3 and 6 months later: 6 (4–9) and 6 (3–8), respectively (p<0.05). From 3 to 6 months follow-up, the proportion of patients without symptoms increased from 1.3% to only 5.4% (p<0.001). Patients also reported a significantly improved work productivity (work absenteeism and presenteeism: 73%versus52% and 66%versus60%, respectively), self-reported good health (9.2%versus16.7%), functional status (mean±sdPost-COVID-19 Functional Status scale: 2.4±0.9versus2.2±1.0) and health-related quality of life (all p<0.05).Conclusion: Although patients with confirmed COVID-19, who were all members of online long COVID peer support groups, reported significant improvements in work productivity, functional status and quality of life between 3 and 6 months follow-up, these data clearly highlight the long-term impact of COVID-19, as approximately 6 months after the onset of COVID-19-related symptoms a large proportion still experienced persistent symptoms, a moderate-to-poor health, moderate-to-severe functional limitations, considerable loss in work productivity, and/or an impaired quality of life. Action is needed to improve the management and healthcare of these patients.

Background: Patients with chronic obstructive pulmonary disease (COPD) often suffer from multiple morbidities, which occur in clusters and are sometimes related to accelerated aging. This study aimed to assess the disease specificity of comorbidity clusters in COPD and their association with a biomarker of accelerated aging as a potential mechanistic factor. Methods: Body composition, metabolic, cardiovascular, musculoskeletal, and psychological morbidities were objectively evaluated in 208 COPD patients (age 62 ± 7 years, 58% males, FEV₁ 50 ± 16% predicted) and 200 non-COPD controls (age 61 ± 7 years, 45% males). Based on their presence and severity, the morbidities were clustered to generate distinct clusters in COPD and controls. Telomere length in circulating leukocytes was compared across the clusters. Results: (co)morbidities were more prevalent in COPD patients compared to controls (3.9 ± 1.7 vs. 2.4 ± 1.5, p < 0.05). A “Psychologic” and “Cachectic” cluster were only present in the COPD population. “Less (co)morbidity”, “Cardiovascular”, and “Metabolic” clusters were also observed in controls, although with less complexity. Telomere length was reduced in COPD patients, but did not differ between the (co)morbidity clusters in both populations. Conclusions: Two COPD-specific comorbidity clusters, a “Cachectic” and “Psychologic” cluster, were identified and warrant further studies regarding their development. Accelerated aging was present across various multimorbidity clusters in COPD.

The degree of lung function is frequently used as referral criterion for pulmonary rehabilitation. The efficacy of pulmonary rehabilitation was assessed in 518 chronic obstructive pulmonary disease (COPD) patients, after clustering based on a comprehensive pre-rehabilitation lung function assessment. Mean improvements in dyspnea, exercise performance, health status, mood status and problematic activities of daily life after pulmonary rehabilitation were mostly comparable between the seven clusters, despite significant differences in the degree of lung function. The current study demonstrates no significant relationship between the seven lung-function-based clusters and response to pulmonary rehabilitation. Therefore, baseline lung function cannot be used to identify those who will respond well to pulmonary rehabilitation, and moreover, cannot be used as a criterion for referral to pulmonary rehabilitation in patients with COPD.

Rationale: COPD has been associated repeatedly with single biomarkers of systemic inflammation, ignoring the complexity of inflammatory pathways. This study aimed to cluster patients with COPD based on systemic markers of inflammatory processes and to evaluate differences in their clinical characterisation and examine how these differences may relate to altered biological pathways.Methods: 213 patients with moderate-to-severe COPD in a clinically stable state were recruited and clinically characterised, which included a venous blood sample for analysis of serum biomarkers. Patients were clustered based on the overall similarity in systemic levels of 57 different biomarkers. To determine interactions among the regulated biomarkers, protein networks and biological pathways were examined for each patient cluster.Results: Four clusters were identified: two clusters with lower biomarker levels (I and II) and two clusters with higher biomarker levels (III and IV), with only a small number of biomarkers with similar trends in expression. Pathway analysis indicated that three of the four clusters were enriched in Rage (receptor for advanced glycation end-products) and Oncostatin M pathway components. Although the degree of airflow limitation was similar, the clinical characterisation of clusters ranged from 1) better functional capacity and health status and fewer comorbidities; 2) more underweight, osteoporosis and static hyperinflation; 3) more metabolically deranged; and 4) older subjects with worse functional capacity and higher comorbidity load.Conclusions: These new insights may help to understand the functionally relevant inflammatory interactions in the pathophysiology of COPD as a heterogeneous disease.

Background: Clusters of COPD patients have been reported in order to individualize the treatment program. Neither co-morbidity clusters, nor integrated respiratory physiomics clusters contributed to a better prediction of outcomes. Based on a thoroughly assessed set of pulmonary and extra-pulmonary traits at the start of a pulmonary rehabilitation (PR) program, we recently described seven clusters of COPD patients. The aims of this study are to confirm multidimensional differential response and to assess the potential of pulmonary and extra-pulmonary traits-based clusters to predict this multidimensional response to PR pulmonary in COPD patients.Methods: Outcomes of a 40-session PR program for COPD patients, referred by a chest physician, were evaluated based on the minimal clinically important difference (MCID) for 6-minute walk distance (6MWD), cycle endurance time, Canadian Occupational Performance Measure performance and satisfaction scores, Hospital Anxiety and Depression Scale anxiety and depression scores, MRC dyspnea grade and St George’s Respiratory Questionnaire. The aforementioned response indicators were used to calculate the overall multidimensional response and patients were grouped in very good, good, moderate and poor responders. In the same way, responses to pulmonary rehabilitation were compared based on seven previously identified pulmonary and extra-pulmonary traits-based clusters.Results: Of the whole sample, drop out was 19% and 419 patients (55.4% males, age: 64.3 ± 8.8, FEV₁% of predicted: 48.9 ± 20) completed the pulmonary rehabilitation program. Very good responders had significantly worse baseline characteristics with a higher burden of disease, a higher proportion of rollator-users, higher body mass index (BMI), more limitations of activities in daily life, emotional dysfunction, higher symptoms of dyspnea and worse quality of life. Of the seven pre-identified clusters, ‘the overall best functioning cluster’ and ‘the low disease burden cluster’ both including the best 6MWD, the lowest dyspnea score and the overall best health status, demonstrated attenuated outcomes, while in ‘the cluster of disabled patients’, 76% of the patients improved health status with at least 2 times MCID. This ‘cluster of disabled patients’ as well as ‘the multimorbid cluster’, ‘the emotionally dysfunctioning cluster’, ‘the overall worst-functioning cluster’ and ‘the physically dysfunctioning cluster’ all demonstrated improvements in performance and satisfaction for occupational activities (more than 65% of patients improved with > 1MCID), emotional functioning (more than 50% of patients improved with > 1 MCID) and overall health status (more than 58%).Conclusion: The current study confirms the differential response to pulmonary rehabilitation based on multidimensional response profiling. Cluster analysis of baseline traits illustrates that non-linear, clinically important differences can be achieved in the most functionally and emotionally impaired clusters and that ‘the overall best functional cluster’ as well as ‘the low disease burden cluster’ had an attenuated outcome.

Background: Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality. While tools predicting short-term prognosis exist, there is urgent need for the early identification of patients requiring close follow-up monitoring for post-acute mortality. We therefore conducted cluster analysis of baseline clinical data to investigate predictors of post-acute mortality in CAP. Methods: We analysed 7840 participants from the German CAPNETZ cohort, using self-organising map (SOM)-clustering and survival analyses. Random survival forest (RSF) models were used to identify key predictors of mortality, which were then analysed using time-dependent area under the curve and Cox proportional hazard regression models. Results: SOM-clustering based on 10 predictors identified 879 (12%, in four clusters) patients with high risk for post-acute (30–180 days) mortality. Across the cohort, age and urea were the most important predictors of post-acute mortality, while in the high-risk cohort, body mass index emerged as the strongest predictor, as identified by RSF modelling. In one high-risk cluster, there was an association with elevated platelet counts (HR: 1.13, 95% CI 1.03–1.21, p=0.01; increments of 40 platelets·nL ⁻¹ , c14, 35% of high-risk patients), in another (c15, 50% of high-risk patients) with elevated urea (HR: 1.06, 95% CI 1.01–1.11, p=0.02) and C-reactive protein (CRP) (HR: 1.27, 95% CI 1.01–1.58, p=0.04). Conclusion: Using 10 clinical predictors for post-acute mortality in CAP, predictive SOM-clustering revealed several high-risk subgroups, with heterogeneous biomarkers, suggestive of differences in the underlying pathophysiology (thrombocytes, urea, CRP). Adapting medical therapy to these high-risk subgroups may reduce post-acute mortality following CAP.