[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121844-en":3,"doc-seo-121844-105":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},121844,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty - a comparative study","Machine-learning models are evaluated for improving prediction of length of stay (LOS) and postoperative medical morbidity in fast-track total hip and knee arthroplasty, where prior research is limited and often relies on administrative coding with restricted perioperative detail. A prospective cohort study records preoperative comorbidity and prescribed medication, then links outcomes to national registry data. Boosted decision trees with 33 variables are compared against logistic regression using identical inputs and additional parsimonious variants. Model performance is assessed via precision, AUROC/AUPRC, and MCC, with variable importance explained using Shapley values.","Aalborg Universitet  \nMachine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study  \nMichelsen, Christian; Jørgensen, Christoffer C. ; Heltberg, Mathias; Jensen, Mogens H. ; Lucchetti, Alessandra; Petersen, Pelle B. ; Petersen, Troels; Kehlet, Henrik; Center for Fast  \ntrack Hip Knee Replacement Collaborative group; Jakobsen, Thomas Published in:  \nBMC anesthesiology  \nDOI (link to publication from Publisher):  \n10.1186/s12871-023-02354-z  \nCreative Commons License  \nCC BY 4.0  \nPublication date: 2023  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nMichelsen, C. , Jørgensen, C. C. , Heltberg, M. , Jensen, M. H. , Lucchetti, A. , Petersen, P. B. , Petersen, T. , Kehlet, H. , Center for Fast-track Hip Knee Replacement Collaborative group, & Jakobsen, T. (2023) . Machinelearning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study. BMC anesthesiology, 23(1), Article 391. [https://doi.org/10.1186/s12871-023-](https://doi.org/10.1186/s12871-023-)[ ](https://doi.org/10.1186/s12871-023-)[02354-z](02354-z)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n-Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n-You may not further distribute the material or use it for any profit-making activity or commercial gain  \n-You may freely distribute the URL identifying the publication in the public portal-  \nMichelsen et al. BMC Anesthesiology (2023) 23:391  \n[https://doi.org/10.1186/s12871-023-02354-z](https://doi.org/10.1186/s12871-023-02354-z)  \nBMC Anesthesiology  \n RESEARCH Open Access  \nMachine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee  \narthroplasty—a comparative study  \nChristian Michelsen1, Christoffer C. Jørgensen2,3*, Mathias Heltberg1, Mogens H. Jensen 1, Alessandra Lucchetti 1, Pelle B. Petersen2,3, Troels Petersen1, Henrik Kehlet3,4, The Center for Fast-track Hip Knee Replacement Collaborative group, Frank Madsen, Torben B. Hansen, Kirill Gromov, Thomas Jakobsen, Claus Varnum,  \nSoren Overgaard, Mikkel Rathsach and Lars Hansen  \nAbstract  \nBackground Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA) .  \nMethods Cohort study in consecutive unselected primary THA/TKA between 2014–2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting “medical” morbidity leading to LOS >4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014–2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performanc","cbCaifk2iJodVv3w","https://ap.wps.com/l/cbCaifk2iJodVv3w","pdf",1535535,1,13,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions (implied)","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"The study addresses whether machine-learning models can better predict postoperative medical morbidity and length of stay after fast-track hip and knee arthroplasty.\"},{\"question\":\"How were the prediction models built and compared?\",\"answer\":\"Boosted decision trees using 33 preoperative variables were compared with logistic regression based on the same variables, and two parsimonious models using the 10 most important variables were also evaluated.\"},{\"question\":\"How was model performance assessed?\",\"answer\":\"Performance was analyzed using precision, AUROC, precision-recall curves (AUPRC), and the Matthews Correlation Coefficient, with variable importance interpreted using Shapley Additive Explanations values.\"}]","Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty - a comparative study | PDF",1785807185,33,{"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},"machine-learning-vs-logistic-regression-for-preoperative-prediction-of-medical-morbidity-after-fast-track-hip-and-knee-arthroplasty-a-comparative-study","",{"@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/machine-learning-vs-logistic-regression-for-preoperative-prediction-of-medical-morbidity-after-fast-track-hip-and-knee-arthroplasty-a-comparative-study/121844/",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","2026-08-04",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 clinical problem does the study address?","Question",{"text":75,"@type":76},"The study addresses whether machine-learning models can better predict postoperative medical morbidity and length of stay after fast-track hip and knee arthroplasty.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the prediction models built and compared?",{"text":80,"@type":76},"Boosted decision trees using 33 preoperative variables were compared with logistic regression based on the same variables, and two parsimonious models using the 10 most important variables were also evaluated.",{"name":82,"@type":73,"acceptedAnswer":83},"How was model performance assessed?",{"text":84,"@type":76},"Performance was analyzed using precision, AUROC, precision-recall curves (AUPRC), and the Matthews Correlation Coefficient, with variable importance interpreted using Shapley Additive Explanations values.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]