[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120313-en":3,"doc-seo-120313-105":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":20,"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},120313,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Impact of Inflammation After Cardiac Surgery on 30-Day Mortality and Machine Learning Risk Prediction - read online","Systemic inflammatory response syndrome (SIRS) after cardiac surgery is assessed for its relationship with 30-day mortality and its predictability using machine learning. A retrospective cohort study includes 1,908 elective or urgent patients undergoing cardiopulmonary bypass from 2016–2020 at a single tertiary hospital. SIRS is evaluated on the first postoperative day and is associated with significantly higher 30-day mortality. Machine learning models show improved discrimination for predicting SIRS using baseline and procedure-adjusted risk features.","ARTICLE IN PRESS  \nJournal of Cardiothoracic and Vascular Anesthesia 000 (2024) 1􀀂9  \nContents lists available at ScienceDirect  \nJournal of Cardiothoracic and Vascular Anesthesia  \njournal [homepage: www.jcvaonline.com](homepage: www.jcvaonline.com)  \nOriginal Article  \nImpact of Inflammation After Cardiac Surgery on 30-Day Mortality and Machine Learning Risk Prediction  \nEnrico Squiccimarro*, y, Roberto Lorussoy, z, Antonio Consiglio *, Cataldo Labriolax, Renard G. Haumann||, zz, Felice Piancone *, Giuseppe Speziale{, Richard P. Whitlock\\#, **,  \nDomenico Paparella*, yy, 1  \n*Division of Cardiac Surgery, Department of Medical and Surgical Sciences, University of Foggia, Foggia,  \nItaly  \nyCardio-Thoracic Surgery Department, Heart & Vascular Centre, Maastricht University Medical Centre,  \nMaastricht, The Netherlands  \nzCardiovascular Research Institute Maastricht, Maastricht, The Netherlands  \nxDivision of Cardiac Anesthesia and Intensive Care, Montevergine Hospital, GVM Care & Research, Merco-|| gliano, Italy  \nDepartment of Cardio-Thoracic Surgery, Thoraxcentrum Twente, Medisch Spectrum Twente, Enschede, The  \nNetherlands  \n{Division of Cardiac Surgery, Anthea Hospital, GVM Care & Research, Bari, Italy  \n\\#Division of Cardiac Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada  \n**Population Health Research Institute, Hamilton, ON, Canada  \nyyDivision of Cardiac Surgery, Santa Maria Hospital, GVM Care & Research, Bari, Italy zzDepartment of Biomechanical Engineering, TechMed Centre, University of Twente, Enschede, The  \nNetherlands  \nObjectives: To investigate the impact of systemic inflammatory response syndrome (SIRS) on 30-day mortality following cardiac surgery and develop a machine learning model to predict SIRS.  \nDesign: Retrospective cohort study.  \nSetting: Single tertiary care hospital.  \nParticipants: Patients who underwent elective or urgent cardiac surgery with cardiopulmonary bypass (CPB) from 2016 to 2020 (N = 1,908) .  \nInterventions: Mixed cardiac surgery operations were performed on CPB. Data analysis was made of preoperative, intraoperative, and postoperative variables without direct interventions.  \nMeasurements and Main Results: SIRS, defined using American College of Chest Physicians/Society of Critical Care Medicine parameters, was assessed on the first postoperative day. The primary outcome was 30-day mortality. SIRS incidence was 28.7%, with SIRS-positive patients showing higher 30-day mortality (12.2% v 1.5%, p \u003C 0.001) . A multivariate logistic model identified predictors of SIRS. Propensity score matching balanced 483 patient pairs. SIRS was associated with increased mortality (OR 2.77; 95% CI 1.40-5.47, p = 0.003) . Machine learning models to predict SIRS were developed. The baseline risk model achieved an area under the curve of 0.77 § 0.04 in cross-validation and 0.73 (95% CI 0.70-0.85) on the test set, while the procedure-adjusted risk model showed improved performance with an area under the curve of 0.81 § 0.02 in cross-validation and 0.82 (95% CI 0.76-0.85) on the test set.  \n1Address correspondence to Domenico Paparella, MD, Division of Cardiac Surgery, Department of Medical and Surgical Sciences, University of Foggia, Viale Luigi Pinto 1, 71122 Foggia, Italy.  \n[E-mail address:](E-mail address: domenico.paparella@unifg.it)[ domenico.paparella@unifg.it](E-mail address: domenico.paparella@unifg.it) (D. Paparella).  \n[https://doi.org/10.1053/j.jvca.2024.12.013](https://doi.org/10.1053/j.jvca.2024.12.013)  \n1053-0770/􀀁 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/))  \nARTICLE IN PRESS  \n2 E. Squiccimarro et al. / Journal of Cardiothoracic and Vascular Anesthesia 00 (2024) 1􀀂9  \nConclusions: SIRS is significantly associated with increased 30-day mortality following cardiac surgery. Machine learning models effectively predict SIRS, pa","cbCailrQ7unqgxzo","https://ap.wps.com/l/cbCailrQ7unqgxzo","pdf",1250106,1,9,"English","en",105,"# Introduction\n# Methods\n## Ethical statement\n## Study design and data collection\n# Results and main findings\n# Conclusions","[{\"question\":\"What was the study’s primary objective?\",\"answer\":\"To investigate how postoperative systemic inflammatory response syndrome (SIRS) impacts 30-day mortality after cardiac surgery and to develop a machine learning model to predict SIRS.\"},{\"question\":\"How were patients and outcomes defined in the study?\",\"answer\":\"The retrospective cohort included 1,908 elective or urgent cardiac surgery patients on cardiopulmonary bypass from 2016 to 2020. The primary outcome was mortality within 30 days, and SIRS was assessed on the first postoperative day using specified clinical parameters.\"},{\"question\":\"What did the machine learning models achieve for SIRS prediction?\",\"answer\":\"Baseline and procedure-adjusted risk machine learning models were evaluated with cross-validation and test-set performance measured by area under the curve (AUC). The procedure-adjusted model showed improved discrimination compared with the baseline model.\"}]","Impact of Inflammation After Cardiac Surgery on 30-Day Mortality and Machine Learning Risk Prediction - read online | PDF",1785729399,23,{"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},"impact-of-inflammation-after-cardiac-surgery-on-30-day-mortality-and-machine-learning-risk-prediction-read-online","",{"@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/impact-of-inflammation-after-cardiac-surgery-on-30-day-mortality-and-machine-learning-risk-prediction-read-online/120313/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the study’s primary objective?","Question",{"text":75,"@type":76},"To investigate how postoperative systemic inflammatory response syndrome (SIRS) impacts 30-day mortality after cardiac surgery and to develop a machine learning model to predict SIRS.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were patients and outcomes defined in the study?",{"text":80,"@type":76},"The retrospective cohort included 1,908 elective or urgent cardiac surgery patients on cardiopulmonary bypass from 2016 to 2020. The primary outcome was mortality within 30 days, and SIRS was assessed on the first postoperative day using specified clinical parameters.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the machine learning models achieve for SIRS prediction?",{"text":84,"@type":76},"Baseline and procedure-adjusted risk machine learning models were evaluated with cross-validation and test-set performance measured by area under the curve (AUC). The procedure-adjusted model showed improved discrimination compared with the baseline model.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]