{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T02:10:22Z","timestamp":1792030222405,"version":"4.4.0"},"reference-count":39,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":21,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Clinical &amp; Translational All"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Small airway dysfunction (SAD) is critical in chronic obstructive pulmonary disease (COPD) and asthma\u2010COPD overlap (ACO), impacting disease severity, acute exacerbation (AE) risk, and prognosis. Traditional spirometry may miss SAD due to its reliance on forced vital capacity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>This study investigates the role of impulse oscillometry system (IOS) for early detection, disease monitoring, and AE prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Pathological specimens from 64 patients with normal lung function were divided into small airway pathological abnormalities (PAs, <jats:italic>n<\/jats:italic>\u00a0=\u00a038) and normal pathology (PN, <jats:italic>n<\/jats:italic>\u00a0=\u00a026). Logistic regression and receiver operating characteristic (ROC) curve analysis evaluated IOS's predictive value for SAD. Additionally, 37 healthy volunteers, 125 COPD patients, and 128 ACO patients underwent spirometry, IOS, FeNO, CT scans, and blood tests. Correlations between IOS and spirometry indices were evaluated. One\u2010year follow\u2010up of 140 patients assessed IOS's predictive capability for AE.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>ROC analysis indicated that R5\u00a0\u2212\u00a0R20 combined with FEF<jats:sub>75<\/jats:sub>%pred best predicted PAs (areas under the ROC curves [AUC]\u00a0=\u00a00.80). R5\u00a0\u2212\u00a0R20, with a cut\u2010off of 0.09\u00a0kPa\/[L\/s], demonstrated 85.6% sensitivity and 72.9% specificity in distinguishing COPD from healthy individuals, and 89.1% sensitivity with 72.9% specificity for ACO. In COPD, R5\u00a0\u2212\u00a0R20 correlated strongly with spirometry indices (<jats:italic>r<\/jats:italic>\u00a0=\u00a00.60), while Fres correlated well in ACO (<jats:italic>r<\/jats:italic>\u00a0=\u00a00.48) for FEV<jats:sub>1<\/jats:sub>%pred\u00a0\u2265\u00a050%, with slightly weaker correlations for FEV<jats:sub>1<\/jats:sub>%pred\u00a0&lt;\u00a050%. For predicting AE, a model combining R5\u00a0\u2212\u00a0R20, FEV<jats:sub>1<\/jats:sub>%Pred and body mass index had an AUC of 0.860 in COPD, while a model with Fres, FEV<jats:sub>1<\/jats:sub>%pred and fraction of exhaled nitric oxide achieved an AUC of 0.874 in ACO.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>IOS is valuable for early detection, monitoring, and AE prediction in COPD and ACO, enhancing diagnostic precision.<\/jats:p><\/jats:sec><jats:sec><jats:title>Clinical Trial Registration<\/jats:title><jats:p>No. ChiCTR2400089625, <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.chictr.org.cn\">www.chictr.org.cn<\/jats:ext-link>.<\/jats:p><\/jats:sec>","DOI":"10.1002\/clt2.70057","type":"journal-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T13:35:32Z","timestamp":1745328932000},"source":"Crossref","is-referenced-by-count":8,"title":["Role of impulse oscillometry in chronic 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General Hospital Shanghai Jiao Tong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0145-5819","authenticated-orcid":false,"given":"Wuping","family":"Bao","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine Shanghai General Hospital Shanghai Jiao Tong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengjian","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine Shanghai General Hospital Shanghai Jiao Tong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine Shanghai General Hospital Shanghai Jiao Tong University School of Medicine  Shanghai 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