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Karch, A.* ; Vogelmeier, C.* ; Welte, T.* ; Bals, R.* ; Kauczor, H.U.* ; Biederer, J.* ; Heinrich, J. ; Schulz, H. ; Gläser, S.* ; Holle, R. ; Watz, H.* ; Korn, S.* ; Adaskina, N.* ; Biertz, F.* ; Vogel, C.* ; Vestbo, J.* ; Wouters, E.F.M.* ; Rabe, K.F.* ; Söhler, S.* ; Koch, A.* ; Jörres, R.A.*

The German COPD cohort COSYCONET: Aims, methods and descriptive analysis of the study population at baseline.

Respir. Med. 114, 27-37 (2016)

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Background The German COPD cohort study COSYCONET ("COPD and SYstemic consequences-COmorbidities NETwork") investigates the interaction of lung disease, comorbidities and systemic inflammation. Recruitment took place from 2010 to 2013 in 31 study centers. In addition to the baseline visit, follow-up visits are scheduled at 6, 18, 36 and 54 months after baseline. The study also comprises a biobank, image bank, and includes health economic data. Here we describe the study design of COSYCONET and present baseline data of our COPD cohort. Methods Inclusion criteria were broad in order to cover a wide range of patterns of the disease. In each visit, patients undergo a large panel of assessments including e.g. clinical history, spirometry, body plethysmography, diffusing capacity, blood samples, 6-min walk-distance, electrocardiogram and echocardiography. Chest CTs are collected if available and CTs and MRIs are performed in a subcohort. Data are entered into eCRFs and subjected to several stages of quality control. Results Overall, 2741 subjects with a clinical diagnosis of COPD were included (59% male; mean age 65 ± 8.6 years (range 40-90)). Of these, 8/35/32/9% presented with GOLD stages I-IV; 16% were uncategorized, including the former GOLD-0 category. 24% were active smokers, 68% ex-smokers and 8% never-smokers. Data completeness was 96% for the baseline items. Conclusion The German COPD cohort comprises patients with advanced and less advanced COPD. This is particularly useful for studying the time course of COPD in relation to comorbidities. Baseline data indicate that COSYCONET offers the opportunity to investigate our research questions in a large-scale, high-quality dataset.

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