[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125221-en":3,"doc-seo-125221-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125221,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning - Original Research","Study develops and validates machine learning models to predict superaverage length of stay in COPD patients with hypercapnic respiratory failure, and compares model performance to select an optimal individualized risk assessment tool. The study analyzes 568 patients, with 10 algorithms trained and validated through an external dataset. Feature selection via Boruta yields 9 candidate variables, and Catboost identifies independent risk factors, enabling a Shiny-based interactive web calculator for clinical risk evaluation and monitoring.","Journal of Inflammation Research downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nJournal of Inflammation Research  \nOpen Access Full Text Article ORIGINAL RESEARCH  \nPredicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning  \nBingqing Zuo 1 , *, Lin Jin2 , *, Zhixiao Sun 1 , Hang Hu 1 , Yuan Yin 3 , Shuanying Yang4 , Zhongxiang Liu 1 ,4  \n1Department of Pulmonary and Critical Care Medicine, The Yancheng Clinical College of Xuzhou Medical University, The First People’s Hospital of Yancheng, Yancheng, Jiangsu, 224006, People’s Republic of China; 2Third Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Kunming Medical University, Kuming, Yunnan, 650000, People’s Republic of China; 3Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, People’s Republic of China; 4Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shanxi, 710004, People’s Republic of China  \n*These authors contributed equally to this work  \nCorrespondence: Shuanying Yang; Zhongxiang Liu, [Email yangshuanying112@163.com](Email yangshuanying112@163.com); [liuzhongxiang711@163.com](liuzhongxiang711@163.com)  \n\n| Objective: The purpose of this study was to develop and validate machine learning models that can predict superaverage length of stay in hypercapnic-type respiratory failure and to compare the performance of each model. Furthermore, screen and select the optimal individualized risk assessment model. This model is capable of predicting in advance whether an inpatient’s length of stay will exceed the average duration, thereby enhancing its clinical application and utility.\u003Cbr>Methods: The study included 568 COPD patients with hypercapnic respiratory failure, 426 inpatients from the Department of Respiratory and Critical Care Medicine of Yancheng First People’s Hospital in the modeling group and 142 inpatients from the Department of Respiratory and Critical Care Medicine of Jiangsu Provincial People’s Hospital in the external validation group. Ten machine learning algorithms were used to develop and validate a model for predicting superaverage length of stay, and the best model was evaluated and selected.\u003Cbr>Results: We screened 83 candidate variables using the Boruta algorithm and identified 9 potentially important variables, including: cerebrovascular disease, white blood cell count, hematocrit, D-dimer, activated partial thromboplastin time, fibrin degradation products, partial pressure of carbon dioxide, reduced hemoglobin, and oxyhemoglobin. Cerebrovascular disease, hematocrit, activated partial thromboplastin time, partial pressure of carbon dioxide, reduced hemoglobin and oxyhemoglobin were independent risk factors for superaverage length of stay in COPD patients with hypercapnic respiratory failure. The Catboost model is the optimal model on both the modeling dataset and the external validation set. The interactive web calculator was developed using the Shiny framework, leveraging a predictive model based on Catboost.\u003Cbr>Conclusion: The Catboost model has the most advantages and can be used for clinical evaluation and patient monitoring. Keywords: chronic obstructive pulmonary disease, COPD, hypercapnic respiratory failure, HRF, superaverage length of stay, machine learning, Catboost model |\n| --- |\n| Introduction\u003Cbr>Hypercapnic respiratory failure (HRF), usually defined as arterial partial pressure of carbon dioxide (PaCO2) ≥45 mmHg and often accompanied by a decrease in arterial partial pressure of oxygen (PaO2) can occur in a variety of etiologies, primarily in chronic respiratory diseases such as exacerbations of chronic obstructive pulmonary disease (COPD), cystic fibrosis, thoracic deformities, and other conditions such as neuromuscular disease.1,2 The ","cbCaiaEJ0Jl78k20","https://ap.wps.com/l/cbCaiaEJ0Jl78k20","pdf",8472085,1,16,"English","en",105,"# Objective\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What is the goal of the study for COPD with hypercapnic respiratory failure?\",\"answer\":\"To develop and validate machine learning models that predict whether an inpatient’s length of stay will exceed the average duration, and to identify the best individualized risk assessment model.\"},{\"question\":\"How many COPD patients were included, and how were they divided?\",\"answer\":\"568 patients were included: 426 in the modeling group and 142 in the external validation group, both drawn from respiratory and critical care medicine departments.\"},{\"question\":\"Which model performed best, and how is it delivered for clinical use?\",\"answer\":\"The Catboost model was optimal on both the modeling dataset and the external validation set, and an interactive web calculator was built using the Shiny framework.\"}]","Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning - Original Research | PDF",1785897575,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-superaverage-length-of-stay-in-copd-patients-with-hypercapnic-respiratory-failure-using-machine-learning-original-research","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-superaverage-length-of-stay-in-copd-patients-with-hypercapnic-respiratory-failure-using-machine-learning-original-research/125221/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of the study for COPD with hypercapnic respiratory failure?","Question",{"text":75,"@type":76},"To develop and validate machine learning models that predict whether an inpatient’s length of stay will exceed the average duration, and to identify the best individualized risk assessment model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How many COPD patients were included, and how were they divided?",{"text":80,"@type":76},"568 patients were included: 426 in the modeling group and 142 in the external validation group, both drawn from respiratory and critical care medicine departments.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best, and how is it delivered for clinical use?",{"text":84,"@type":76},"The Catboost model was optimal on both the modeling dataset and the external validation set, and an interactive web calculator was built using the Shiny framework.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]