[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-41772-en":3,"doc-seo-41772-105":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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},41772,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Percutaneous Endoscopic Interlaminar Discectomy in Lumbar Disc Herniation: Risk Factors and Thresholds for Adverse Outcomes","Percutaneous endoscopic interlaminar discectomy (PEID) is widely used for lumbar disc herniation, yet predictors of adverse postoperative outcomes remain disputed. A retrospective cohort of 414 single-level PEID patients (2018–2024) was analyzed using six machine learning algorithms for feature selection and model building. Five key indicators were identified (Modic changes, basal width, BMI, disc herniation ratio, and interspinous ligament injury). An XGB model achieved AUC 0.809/0.718, with actionable risk thresholds reported for BWHD, BMI, and RDH.","European Spine Journal  \n[https://doi.org/10.1007/s00586-026-09987-x](https://doi.org/10.1007/s00586-026-09987-x)  \nRESEARCH  \nPercutaneous endoscopic interlaminar discectomy in patients with lumbar disc herniation: risk factors and thresholds for adverse outcomes  \nChengrui Peng1 · Xuan Deng1 · Xiuqian Wang1 · Li He1 · Jun Ao1 · Hu Qian1  \nReceived: 24 January 2026 / Revised: 17 March 2026 / Accepted: 28 April 2026  \n© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026  \nAbstract  \nBackground Percutaneous endoscopic interlaminar discectomy (PEID) is a common surgical technique for lumbar disc herniation (LDH), but the risk factors for adverse outcomes remain controversial. This study aims to develop and validate a predictive model based on machine learning algorithms to identify key clinical indicators influencing adverse outcomes after PEID.  \nMethods This retrospective study included 414 LDH patients who underwent single-level PEID between October 2018 and June 2024. Data were divided into training (n= 290) and validation (n= 124) sets. Six machine learning algorithms were used for feature selection, identifying core indicators. Models were constructed based on these indicators and evaluated for predictive performance.  \nResult Five core indicators were identified: Modic Changes (MC), Basal Width Of The Herniated Disc (BWHD), Body Mass Index (BMI), Ratio Of Disc Herniation (RDH), and Interspinous Ligament Injury (ILI) . The XGB model performed best, with an AUC of 0.809 in the training set and 0.718 in the validation set. Risk thresholds for BWHD, BMI, and RDH were 1.7 mm, 23.3 kg/m², and 37.2%, respectively.  \nConclusion MC, ILI, BWHD, BMI, and RDH are risk factors for postoperative adverse outcomes in PEID patients. The model provides useful clinical guidance, with validated risk thresholds for key indicators.  \nKeywords PEID · Adverse outcomes · Machine learning · Predictive model · Risk threshold  \nChengrui Peng and Xuan Deng contributed equally to this work.  \n􀀍 Li He [drclover@163.com](drclover@163.com)  \n􀀍 Jun Ao [Ao00jun@163.com](Ao00jun@163.com)  \n􀀍 Hu Qian [moneylakecsu@163.com](moneylakecsu@163.com)  \nChengrui Peng  \n[18485741911@163.com](18485741911@163.com)  \nXuan Deng  \n[18290268886@163.com](18290268886@163.com)  \nXiuqian Wang  \n[909570257@qq.com](909570257@qq.com)  \n1 Department of Orthopedic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, China  \nIntroduction  \nLumbar disc herniation (LDH) is a prevalent degenerative spinal disorder and a leading cause of low back pain and sciatica, substantially impairing patients’ quality of life, work capacity, and social functioning [1, 2] . With advancesin minimally invasive spinal surgery, percutaneous endoscopic interlaminar discectomy (PEID) has become an important treatment option for LDH owing to its minimal tissue trauma, rapid recovery, and shortened hospital stay [3] . Nevertheless, despite its overall favorable efficacy, a subset of patients still experience adverse outcomes, including persistent symptoms, recurrence, or reoperation, which continue to pose clinical challenges [4, 5] .  \nRecent studies have explored potential factors associated with adverse outcomes after PEID, focusing on clinical characteristics and imaging parameters such as baseline patient status, intervertebral disc degeneration, spinal stability, and  \nintraoperative factors [6, 7]. However, most existing studies have primarily identified associations rather than defining actionable risk thresholds, limiting their utility in guiding precise perioperative decision-making. Moreover, traditional regression-based approaches are often insufficient for modeling the complex nonlinear relationships inherent in multidimensional clinical data. As a result, robust and clinically applicable predictive tools for adverse outcomes after PEID remain scarce.  \nWith the rapid development of artificial intelligence and medical big data, mach","cbCaikc60hfj7DB7","https://ap.wps.com/l/cbCaikc60hfj7DB7","pdf",3804341,4,1,12,"English","en",105,"# Abstract\n# Introduction\n# Materials and methods\n## Patients population","[{\"question\":\"What clinical indicators were identified as core risk factors for adverse outcomes after PEID?\",\"answer\":\"Five core indicators were identified: Modic Changes (MC), Basal Width Of The Herniated Disc (BWHD), Body Mass Index (BMI), Ratio Of Disc Herniation (RDH), and Interspinous Ligament Injury (ILI).\"},{\"question\":\"Which machine learning model performed best for predicting adverse outcomes, and what was its performance?\",\"answer\":\"The XGB model performed best, with AUC 0.809 in the training set and 0.718 in the validation set.\"},{\"question\":\"What risk thresholds were reported for BWHD, BMI, and RDH after PEID?\",\"answer\":\"Risk thresholds were reported as BWHD 1.7 mm, BMI 23.3 kg/m², and RDH 37.2%.\"}]",1783333066,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"percutaneous-endoscopic-interlaminar-discectomy-in-lumbar-disc-herniation-risk-factors-and-thresholds-for-adverse-outcomes","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"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":20},"https://docshare.wps.com/document/percutaneous-endoscopic-interlaminar-discectomy-in-lumbar-disc-herniation-risk-factors-and-thresholds-for-adverse-outcomes/41772/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-20","2026-07-06",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 clinical indicators were identified as core risk factors for adverse outcomes after PEID?","Question",{"text":75,"@type":76},"Five core indicators were identified: Modic Changes (MC), Basal Width Of The Herniated Disc (BWHD), Body Mass Index (BMI), Ratio Of Disc Herniation (RDH), and Interspinous Ligament Injury (ILI).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning model performed best for predicting adverse outcomes, and what was its performance?",{"text":80,"@type":76},"The XGB model performed best, with AUC 0.809 in the training set and 0.718 in the validation set.",{"name":82,"@type":73,"acceptedAnswer":83},"What risk thresholds were reported for BWHD, BMI, and RDH after PEID?",{"text":84,"@type":76},"Risk thresholds were reported as BWHD 1.7 mm, BMI 23.3 kg/m², and RDH 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