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Titel: Prediction of lung emphysema in COPD by spirometry and clinical symptoms : results from COSYCONET
VerfasserIn: Kellerer, Christina
Jörres, Rudolf A.
Schneider, Antonius
Alter, Peter
Kauczor, Hans-Ulrich
Jobst, Bertram
Biederer, Jürgen
Bals, Robert
Watz, Henrik
Behr, Jürgen
Kauffmann-Guerrero, Diego
Lutter, Johanna
Hapfelmeier, Alexander
Magnussen, Helgo
Trudzinski, Franziska C.
Welte, Tobias
Vogelmeier, Claus F.
Kahnert, Kathrin
Sprache: Englisch
Titel: Respiratory Research
Bandnummer: 22
Heft: 1
Verlag/Plattform: BMC
Erscheinungsjahr: 2021
Freie Schlagwörter: Emphysema
CT scan
Decision trees
Random forest
Adaboost
COPD phenotypes
DDC-Sachgruppe: 610 Medizin, Gesundheit
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: Background: Lung emphysema is an important phenotype of chronic obstructive pulmonary disease (COPD), and CT scanning is strongly recommended to establish the diagnosis. This study aimed to identify criteria by which physi‑ cians with limited technical resources can improve the diagnosis of emphysema. Methods: We studied 436 COPD patients with prospective CT scans from the COSYCONET cohort. All items of the COPD Assessment Test (CAT) and the St George’s Respiratory Questionnaire (SGRQ), the modifed Medical Research Council (mMRC) scale, as well as data from spirometry and CO difusing capacity, were used to construct binary decision trees. The importance of parameters was checked by the Random Forest and AdaBoost machine learning algorithms. Results: When relying on questionnaires only, items CAT 1 & 7 and SGRQ 8 & 12 sub-item 3 were most important for the emphysema- versus airway-dominated phenotype, and among the spirometric measures FEV1/FVC. The combina‑ tion of CAT item 1 (≤2) with mMRC (>1) and FEV1/FVC, could raise the odds for emphysema by factor 7.7. About 50% of patients showed combinations of values that did not markedly alter the likelihood for the phenotypes, and these could be easily identifed in the trees. Inclusion of CO difusing capacity revealed the transfer coefcient as dominant measure. The results of machine learning were consistent with those of the single trees. Conclusions: Selected items (cough, sleep, breathlessness, chest condition, slow walking) from comprehensive COPD questionnaires in combination with FEV1/FVC could raise or lower the likelihood for lung emphysema in patients with COPD. The simple, parsimonious approach proposed by us might help if diagnostic resources regarding respiratory diseases are limited.
DOI der Erstveröffentlichung: 10.1186/s12931-021-01837-2
URL der Erstveröffentlichung: https://respiratory-research.biomedcentral.com/articles/10.1186/s12931-021-01837-2
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-367207
hdl:20.500.11880/33362
http://dx.doi.org/10.22028/D291-36720
ISSN: 1465-993X
Datum des Eintrags: 8-Jul-2022
Bezeichnung des in Beziehung stehenden Objekts: Supplementary Information
In Beziehung stehendes Objekt: https://static-content.springer.com/esm/art%3A10.1186%2Fs12931-021-01837-2/MediaObjects/12931_2021_1837_MOESM1_ESM.docx
Fakultät: M - Medizinische Fakultät
Fachrichtung: M - Innere Medizin
Professur: M - Prof. Dr. Robert Bals
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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