[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-456488-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-456488-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","preoperative-serum-raman-spectroscopy-and-machine-learning-for-predicting-postoperative-acute-kidney-injury-after-acute-type-a-aortic-dissection","Preoperative serum Raman spectroscopy and machine learning for predicting postoperative acute kidney injury after acute type A aortic dissection","","Acute type A aortic dissection represents an aggressive cardiovascular condition with substantial morbidity and mortality. Postoperative acute kidney injury is a major complication that worsens prognosis after total aortic arch replacement. Existing approaches lack effective predictors, and evidence on using preoperative serum Raman spectroscopy for early identification is limited. This study analyzes Raman spectra from preoperative serum samples and preoperative biochemical indicators, integrates machine learning classifiers with feature selection, and applies SHAP for interpretable personalized risk assessment.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/preoperative-serum-raman-spectroscopy-and-machine-learning-for-predicting-postoperative-acute-kidney-injury-after-acute-type-a-aortic-dissection/456488/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/preoperative-serum-raman-spectroscopy-and-machine-learning-for-predicting-postoperative-acute-kidney-injury-after-acute-type-a-aortic-dissection/456488.png","ImageObject",300,407,{"name":42,"@type":43},"awa","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-09-30",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the study’s primary objective?","Question",{"text":63,"@type":64},"To develop and evaluate a predictive model for postoperative acute kidney injury after total aortic arch replacement in patients with acute type A aortic dissection, using preoperative serum Raman spectroscopy and machine learning.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How are Raman spectroscopy data and biochemical indicators used?",{"text":68,"@type":64},"Preoperative blood biochemical indicators are examined, and Raman spectra from preoperative serum samples are analyzed by quantifying specific peak intensities. Feature selection is then applied to build classification models.",{"name":70,"@type":61,"acceptedAnswer":71},"What model performed best and how is it evaluated?",{"text":72,"@type":64},"The logistic regression model emerged as the optimal model, achieving a test set area under the curve (AUC) of 0.848 with a 95% confidence interval of 0.777–0.918. SHAP interpretation is used to support personalized risk assessment.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},456488,1790792311,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,120,123,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":55,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145,"preview_page_count":4},3985747858093,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Renal FailuRe  \n2025, VOl. 47, nO. 1, 2603854  \n[https://doi.org/10.1080/0886022X.2025.2603854](https://doi.org/10.1080/0886022X.2025.2603854)  \nCliniCal Study    \nPreoperative serum Raman spectroscopy and machine learning for predicting postoperative acute kidney injury after acute type A aortic dissection  \nXiaopeng Meia*, Shuman Jia*, yongpo Jiangb*, Runlu Caic, Qiang Chend, He Zhange, tao Shid, yang yand and Zhanqin Zhangd,f  \naDepartment of Pain Management, The First affiliated Hospital of Xi’an Jiaotong university, Xi’an, China; bDepartment of Critical Care Medicine, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical university, Taizhou, China; cDepartment of anesthesiology, The First affiliated Hospital of Xi’an Jiaotong university, Xi’an, China; dDepartment of Cardiovascular Surgery, The First affiliated Hospital of Xi’an Jiaotong university, Xi’an, China; eSchool of nursing and Rehabilitation, Xi’an FanYi university, Xi’an, China; fShaanxi Provincial Center for Regenerative Medicine and Surgical engineering, First affiliated Hospital of Xi’an Jiaotong university, Xi’an, China  \nABSTRACT  \nacute type a aortic dissection (ataad) represents an aggressive cardiovascular condition with substantial morbidity and mortality rates. Postoperative acute kidney injury (aKi) is a complication that contributes significantly to worse prognosis following total aortic arch replacement (taaR) surgery. Currently, there are neither effective predictive methods nor data regarding the application of preoperative serum Raman spectroscopy for early identification of aKi after taaR surgery. Our study aimed to develop a predictive model and assess its effectiveness in prediction of aKi after taaR surgery. Preoperative blood biochemical indicators were investigated while Raman spectra from preoperative serum samples were analyzed by quantifying the peak intensities. Following appropriate feature selection procedures, multiple machine learning classification models were integrated to identify the optimal model for predicting postoperative aKi. to facilitate personalized risk assessment, Shapley additive exPlanations (SHaP) interpretation was developed. in summary, 396 patients were enrolled in this study and incidence of postoperative aKi was 56.1%(222/396). Out of the 15 biochemical parameters and 5 intensities of specific Raman peaks possessing statistical differences, 6 indictors (creatinine, cystatin C, neutrophil, platelet, glucose, carbon dioxide combining power [CO2cp]) and 3 Raman peak intensities (505, 947 and 1208 cm−1) were identified as predictors of postoperative aKi. the logistic regression model emerged as the optimal model, with a test set area under the curve (auC) of 0.848 (95% confidence interval [Ci]: 0.777–0.918) to our knowledge, this is the first study to integrate preoperative serum Raman spectroscopy with machine learning for predicting aKi risk in patients undergoing ataad surgery.  \n1. Introduction  \nARTICLE HISTORY  \nReceived 26 august 2025 Revised 5 november 2025  \naccepted 28 november 2025  \nKEYWORDS  \nRaman spectroscopy; machine learning; acute kidney injury; aortic dissection; predictive model; SHaP  \nacute type a aortic dissection (ataad) is a highly perilous condition, with an annual incidence rate of 4–6 cases per 100,000 emergency department patients [1] . Emergency cardiac surgery remains the primary therapeutic option for managing ataad, effectively enhancing systemic circulatory function. However, despite standard surgical intervention, some ataad patients faced reduced life expectancy due to various postoperative complications, notably acute kidney injury (aKi), which occurs in 26–72% of patients [2] . aKi symptoms can range from mild renal dysfunction to severe kidney failure requiring renal  \nCONTACT Zhanqin Zhang  [zhangzhanqin12@sina.com](zhangzhanqin12@sina.com)  Department of anesthesiology, The First affiliated Hospital of Xi’an Jiaotong university, Xi’an 710061, China; Yang Yan  [y","cbCaitL4h2Zf6nYw","https://ap.wps.com/l/cbCaitL4h2Zf6nYw","pdf",2502645,18,"English","# ABSTRACT\n# 1. Introduction\n# KEYWORDS","[{\"question\":\"What is the study’s primary objective?\",\"answer\":\"To develop and evaluate a predictive model for postoperative acute kidney injury after total aortic arch replacement in patients with acute type A aortic dissection, using preoperative serum Raman spectroscopy and machine learning.\"},{\"question\":\"How are Raman spectroscopy data and biochemical indicators used?\",\"answer\":\"Preoperative blood biochemical indicators are examined, and Raman spectra from preoperative serum samples are analyzed by quantifying specific peak intensities. Feature selection is then applied to build classification models.\"},{\"question\":\"What model performed best and how is it evaluated?\",\"answer\":\"The logistic regression model emerged as the optimal model, achieving a test set area under the curve (AUC) of 0.848 with a 95% confidence interval of 0.777–0.918. SHAP interpretation is used to support personalized risk assessment.\"}]","Preoperative serum Raman spectroscopy and machine learning for predicting postoperative acute kidney injury after acute type A aortic dissection | PDF",1790746218,45]