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Clin. Med.","JCM"],"abstract":"<jats:p>Objective: Reducing the length of hospital stay (LOS) is a core objective in the management of acute exacerbations of chronic obstructive pulmonary disease (AECOPD). This study aimed to develop an interpretable and clinically applicable machine learning tool for predicting prolonged LOS in this population. Methods: This retrospective three-center study enrolled 1342 patients, who were randomly allocated to a training set (70%) and a test set (30%). Candidate predictors were screened using least absolute shrinkage and selection operator (LASSO) regression, and six machine learning models were developed and compared: logistic regression, random forest, gradient boosting machine, CatBoost, support vector machine, and neural network. Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis. Model interpretability was achieved through SHapley Additive exPlanations (SHAP) and subgroup analyses. Results: Prolonged LOS, defined as &gt;10 days (the median LOS in the training set), occurred in 42.0% of patients. Nine predictors were identified, including daily inhaled medication use, sputum microbiological examination, antibiotic administration, corticosteroid therapy, diuretic use, ICU admission, oxygenation index, platelet count, and neutrophil count. The random forest model demonstrated superior and consistent discriminative performance, achieving an AUC of 0.778 (95% CI: 0.748\u20130.807) in the training set and 0.721 (95% CI: 0.672\u20130.771) in the test set. SHAP analysis ranked diuretic use, daily inhaled medication use, corticosteroid therapy, sputum microbiological examination, and antibiotic administration as the five most influential features, revealing treatment-related variables associated with prolonged LOS. Subgroup analyses indicated better predictive performance in lower-risk patients (age \u2264 65 years with eGFR 1\u20132 stage). Conclusions: Random Forest may enable to promptly identify prolonged LOS high-risk patients, enhancing clinical vigilance and optimizing healthcare resource allocation in AECOPD patients.<\/jats:p>","DOI":"10.3390\/jcm15156127","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T14:45:47Z","timestamp":1786027547000},"page":"6127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Prolonged Length of Hospital Stay in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease: An Interpretable Machine Learning Tool"],"prefix":"10.3390","volume":"15","author":[{"given":"Jingjing","family":"Xiong","sequence":"first","affiliation":[{"name":"Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, China"},{"name":"State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu 610041, China"},{"name":"Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, West China Hospital, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lian","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, China"},{"name":"State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu 610041, China"},{"name":"Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, West China Hospital, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenni","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Medical Administration, West China Hospital, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8553-0102","authenticated-orcid":false,"given":"Weiling","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, Luojiang District People\u2019s Hospital, Deyang 618000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjun","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Intensive Care Unit, People\u2019s Hospital of Leshan, Leshan 614000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8564-313X","authenticated-orcid":false,"given":"Suli","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, China"},{"name":"State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan 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610041, China"},{"name":"Division of Pulmonary Diseases, State Key Laboratory of Biotherapy of China, West China Hospital, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"ref_1","unstructured":"Global Initiative for Chronic Obstructive Lung Disease (2025). GOLD 2026 Report [Internet], Global Initiative for Chronic Obstructive Lung Disease. Available online: https:\/\/goldcopd.org\/2026-gold-report\/."},{"key":"ref_2","unstructured":"GBD Chronic Respiratory Disease Collaborators (2020). Prevalence and attributable health burden of chronic respiratory diseases, 1990\u20132017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet Resp. Med. Engl. Ed., 8, 585\u2013596."},{"key":"ref_3","first-page":"65","article-title":"Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2019 Report: Future Challenges","volume":"56","author":"Miravitlles","year":"2019","journal-title":"Arch. Bronconeumol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/S0140-6736(07)61382-8","article-title":"COPD exacerbations: Defining their cause and prevention","volume":"370","author":"Wedzicha","year":"2007","journal-title":"Lancet"},{"key":"ref_5","unstructured":"Expert Group on Management of Acute Exacerbation of Chronic Obstructive Pulmonary Disease (2023). Expert consensus on the acute exacerbation of chronic obstructive pulmonary disease in China (revision in 2023). Int. J. Respir., 43, 132\u2013149."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"00034-2015","DOI":"10.1183\/23120541.00034-2015","article-title":"Understanding variation in length of hospital stay for COPD exacerbation: European COPD audit","volume":"2","author":"Ruparel","year":"2016","journal-title":"ERJ Open Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1186\/s12931-018-0951-4","article-title":"Clinical variables predicting the risk of a hospital stay for longer than 7 days in patients with severe acute exacerbations of chronic obstructive pulmonary disease: A prospective study","volume":"19","author":"Crisafulli","year":"2018","journal-title":"Respir. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104246","DOI":"10.1016\/j.amsu.2022.104246","article-title":"Factors affecting length of hospital stay in chronic obstructive pulmonary disease patients in a tertiary hospital of Nepal: A retrospective cross-sectional study","volume":"80","author":"Pokharel","year":"2022","journal-title":"Ann. Med. Surg."},{"key":"ref_9","first-page":"e934392","article-title":"A Risk Prediction Model for Prolonged Length of Stay in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Retrospective Study of 225 Patients in a Single Center in Kunming, China","volume":"28","author":"Yang","year":"2022","journal-title":"Med. Sci. Monit."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s41746-020-00376-2","article-title":"Deep learning-enabled medical computer vision","volume":"4","author":"Esteva","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big Data and Machine Learning in Health Care","volume":"319","author":"Beam","year":"2018","journal-title":"JAMA-J. Am. Med. Assoc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/s41586-023-06221-2","article-title":"Scientific discovery in the age of artificial intelligence","volume":"620","author":"Wang","year":"2023","journal-title":"Nature"},{"key":"ref_15","unstructured":"Lundberg, S.M., and Lee, S.I. (2017). A unified approach to interpreting model predictions. arXiv."},{"key":"ref_16","unstructured":"World Health Organization (2015). International Statistical Classification of Diseases and Related Health Problems, World Health Organization. Available online: https:\/\/icd.who.int\/browse10\/2016\/en."},{"key":"ref_17","unstructured":"Global Initiative for Chronic Obstructive Lung Disease (2021). GOLD 2022 Report [Internet], Global Initiative for Chronic Obstructive Lung Disease. Available online: https:\/\/goldcopd.org\/2022-gold-report\/."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1056\/NEJMoa2102953","article-title":"New Creatinine- and Cystatin C-Based Equations to Estimate GFR without Race","volume":"385","author":"Inker","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1016\/j.kint.2023.10.016","article-title":"Executive summary of the KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease: Known knowns and known unknowns","volume":"105","author":"Levin","year":"2024","journal-title":"Kidney Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1053\/j.ajkd.2014.01.416","article-title":"KDOQI US commentary on the 2012 KDIGO clinical practice guideline for the evaluation and management of CKD","volume":"63","author":"Inker","year":"2014","journal-title":"Am. J. Kidney Dis."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Esenda\u011fl\u0131, D., K\u00f6kt\u00fcrk, N., Baha, A., Yapar, D., \u00d6zkan, S., \u015een, E., \u00c7ift\u00e7i, F., \u00d6zt\u00fcrk, B., Cengiz, S.K., and Ulubay, G. (2024). Risk factors for prolonged hospitalization as a marker for difficult-to-manage exacerbations of chronic obstructive lung disease (COPD): The DiMECO Study. BMC Pulm. Med., 24.","DOI":"10.1186\/s12890-024-03399-7"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1001\/jama.1991.03470010084035","article-title":"The Necessary Length of Hospital Stay for Chronic Pulmonary Disease","volume":"266","author":"Mushlin","year":"1991","journal-title":"JAMA-J. Am. Med. Assoc."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Amann, J., Blasimme, A., Vayena, E., Frey, D., and Madai, V.I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01332-6"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1186\/cc8204","article-title":"Clinical review: Scoring systems in the critically ill","volume":"14","author":"Vincent","year":"2010","journal-title":"Crit. Care"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e65585","DOI":"10.2196\/65585","article-title":"Explainable Machine Learning Framework for Dynamic Monitoring of Disease Prognostic Risk: Retrospective Cohort Study","volume":"9","author":"Ishikawa","year":"2025","journal-title":"JMIR Form. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"132084","DOI":"10.1016\/j.eswa.2026.132084","article-title":"Optimizing real-time phenotyping in critical care using machine learning on electronic health records","volume":"320","author":"Picheta","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"ref_28","unstructured":"Zheng, Z., Nie, X., Wang, X., Zhao, Y., Zhang, H., and Tang, Y. (2026). MATA-Former&SIICU: Semantic Aware Temporal Alignment for High-Fidelity ICU Risk Prediction. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1097\/01.EEM.0000000000000092","article-title":"How Predictive AI Models Will Reshape Emergency Medicine\u2019s Magic","volume":"47","author":"Walker","year":"2025","journal-title":"Emerg. Med. News"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.pccm.2026.05.001","article-title":"Structural and cellular mechanisms of mucus plugging in the larger airways","volume":"4","author":"Hu","year":"2026","journal-title":"Chin. Med. J. Pulm. Crit."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Patil, N., Ambj\u00f6rnsson, J., Kallin, S., Malla, D., Sayedali, H., \u00c5kesson, E., R\u00e5dinger, M., Hansson, G.C., and Johansson, K. (2026). Dysregulated MUC5B and MUC5AC impair epithelial barrier function and alter granulocyte frequency and activation in the lung and distal sites. Mucosal Immunol., 100385.","DOI":"10.1016\/j.mucimm.2026.100385"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s41030-026-00354-1","article-title":"Oscillating Positive Expiratory Pressure (OPEP) Therapy in Bronchiectasis and Chronic Obstructive Pulmonary Disease (COPD): A Narrative Review of Clinical Studies","volume":"12","author":"Schloss","year":"2026","journal-title":"Pulm. Ther."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"193","DOI":"10.5152\/TurkThoracJ.2018.18004","article-title":"A Revised Treatment Approach for Hospitalized Patients with Eosinophilic and Neutrophilic Exacerbations of Chronic Obstructive Pulmonary Disease","volume":"19","author":"Aksoy","year":"2018","journal-title":"Turk. Thorac. J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"47","DOI":"10.4103\/0970-2113.197116","article-title":"Mechanisms of hypoxemia","volume":"34","author":"Sarkar","year":"2017","journal-title":"Lung India"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"108879","DOI":"10.1016\/j.rmed.2026.108879","article-title":"Development and validation of a nomogram prediction model for prolonged length of hospital stay in patients with AECOPD","volume":"258","author":"Tang","year":"2026","journal-title":"Resp. Med."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, Z., Yang, S., Jiang, H., and Xie, C. (2025). Development of an interpretable machine learning model for predicting prolonged hospital stay in patients with acute exacerbation of chronic obstructive pulmonary disease: A retrospective cohort study. BMC Med. Inform. Decis. Mak., 25.","DOI":"10.1186\/s12911-025-03220-3"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.2147\/PPA.S410725","article-title":"Current Status and Influencing Factors of Readiness for Discharge of Elderly Patients with Chronic Obstructive Pulmonary Disease","volume":"17","author":"Wu","year":"2023","journal-title":"Patient Prefer. Adherence"},{"key":"ref_38","first-page":"79","article-title":"Social and clinical predictors associated with prolonged hospital stays for patients with severe exacerbation of chronic obstructive pulmonary disease","volume":"220","author":"Villar","year":"2020","journal-title":"Rev. Clin. Esp."},{"key":"ref_39","first-page":"A170","article-title":"P126 Frailty in hospitalised COPD patients","volume":"73","author":"Fei","year":"2018","journal-title":"Thorax"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"35044","DOI":"10.1038\/s41598-025-21370-2","article-title":"Indicators of different outcomes after prolonged weaning","volume":"15","author":"Neetz","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3895","DOI":"10.2147\/COPD.S185001","article-title":"Nasal high-flow in acute hypercapnic exacerbation of COPD","volume":"13","author":"Wirtz","year":"2018","journal-title":"Int. J. Chronic Obstr. Pulm. Dis."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1186\/s13063-021-05692-w","article-title":"Extracorporeal carbon dioxide removal for treatment of exacerbated chronic obstructive pulmonary disease (ORION): Study protocol for a randomised controlled trial","volume":"22","author":"Tonetti","year":"2021","journal-title":"Trials"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1602426","DOI":"10.1183\/13993003.02426-2016","article-title":"Official ERS\/ATS clinical practice guidelines: Noninvasive ventilation for acute respiratory failure","volume":"50","author":"Rochwerg","year":"2017","journal-title":"Eur. Respir. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"105970","DOI":"10.1016\/j.ijmedinf.2025.105970","article-title":"Show and tell: A critical review on robustness and uncertainty for a more responsible medical AI","volume":"202","author":"Marconi","year":"2025","journal-title":"Int. J. Med. Inform."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pennestr\u00ec, F., Cabitza, F., Picerno, N., and Banfi, G. (2025). Sharing reliable information worldwide: Healthcare strategies based on artificial intelligence need external validation. Position paper. BMC Med. Inform. Decis. Mak., 25.","DOI":"10.1186\/s12911-025-02883-2"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/15\/6127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T09:29:12Z","timestamp":1786094952000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/15\/6127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,6]]},"references-count":46,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["jcm15156127"],"URL":"https:\/\/doi.org\/10.3390\/jcm15156127","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,6]]}}}