[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118732-en":3,"doc-seo-118732-105":30,"detail-sidebar-cat-0-en-105":92},{"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},118732,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Prediction Models for Chronic Postsurgical Pain in Patients With Breast Cancer Based on Machine Learning Approaches","This study develops prediction models for chronic postsurgical pain (CPSP) after breast cancer surgery using machine learning and assesses their performance. Secondary analysis is conducted on a high-quality dataset from a randomized controlled trial, focusing on patients with primary breast cancer undergoing mastectomy. CPSP at 12 months serves as the primary outcome and is defined by a modified Brief Pain Inventory score greater than zero. Among 1152 patients, CPSP occurred in 22.1%. Machine learning models outperform multivariable logistic regression in specificity and positive likelihood ratio and positive predictive value, enabling more accurate high-risk identification and earlier clinical intervention.","TYPE Original Research PUBLISHED 27 February 2023 DOI 10.3389/fonc.2023.1096468  \nOPEN ACCESS  \nEDITED BY Daqing Ma,  \nImperial College London, United Kingdom  \nREVIEWED BY Hon-Yi Shi,  \nKaohsiung Medical University, Taiwan Heba Taher,  \nCairo University, Egypt Yiting Lei,  \nOrthopedic Laboratory of Chongqing Medical University, China  \n*CORRESPONDENCE Lijian Pei  \n [hazelbeijing@vip.163.com](hazelbeijing@vip.163.com)  \n†These authors have contributed equally to this work  \nSPECIALTY SECTION  \nThis article was submitted to Breast Cancer,  \na section of the journal Frontiers in Oncology  \nRECEIVED 12 November 2022  \nACCEPTED 06 February 2023  \nPUBLISHED 27 February 2023  \nCITATION  \nSun C, Li M, Lan L, Pei L, Zhang Y, Tan G, Zhang Z and Huang Y (2023) Prediction models for chronic postsurgical pain inpatients with breast cancer based on machine learning approaches.  \nFront. Oncol. 13:1096468 .  \ndoi: 10.3389/fonc.2023.1096468  \nCOPYRIGHT  \n© 2023 Sun, Li, Lan, Pei, Zhang, Tan, Zhang and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPrediction models for chronic postsurgical pain in patients with breast cancer based on machine learning approaches  \nChen Sun 1†, Mohan Li 1†, Ling Lan 1†, Lijian Pei 1,2*, Yuelun Zhang 3, Gang Tan 1, Zhiyong Zhang 1 and Yuguang Huang 1  \n1 Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, 2Outcomes Research Consortium, Cleveland, OH, United States, 3 Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China  \nPurpose: This study aimed to develop prediction models for chronic postsurgical pain (CPSP) after breast cancer surgery using machine learning approaches and evaluate their performance.  \nMethods: The study was a secondary analysis based on a high-quality dataset from a randomized controlled trial (NCT00418457), including patients with primary breast cancer undergoing mastectomy. The primary outcome was CPSP at 12 months after surgery, defined as modified Brief Pain Inventory > 0. The dataset was randomly split into a training dataset (90%) and a testing dataset (10%) . Variables were selected using recursive feature elimination combined with clinical experience, and potential predictors were then incorporated into three machine learning models, including random forest, gradient boosting decision tree and extreme gradient boosting models for outcome prediction, as well as logistic regression. The performances of these four models were tested and compared.  \nResults: 1152 patients were finally included, of which 22 . 1% developed CPSP at 12 months after breast cancer surgery. The 6 leading predictors were higher numerical rating scale within 2 days after surgery, post-menopausal status, urban medical insurance, history of at least one operation, under fentanyl with sevoflurane general anesthesia, and received axillary lymph node dissection. Compared with the multivariable logistic regression model, machine learning models showed better specificity, positive likelihood ratio and positive predictive value, helping to identify high-risk patients more accurately and create opportunities for early clinical intervention.  \nConclusions: Our study developed prediction models for CPSP after breast cancer surgery based on machine learning approaches, which may help to identify highrisk patients and improve patients’ management after breast cancer.  \nKEYWORDS  \nchronic postsurgical pain (CPSP), breast cancer, predict","cbCait3JxCKJUCMm","https://ap.wps.com/l/cbCait3JxCKJUCMm","pdf",1147253,1,10,"English","en",105,"# Introduction\n## Breast cancer and CPSP burden\n## Need for early risk prediction and clinical utility\n## Limitations of existing models\n## Role of machine learning in CPSP prediction\n# Methods\n## Data source and study design\n## Outcome definition and dataset split\n## Feature selection and model development\n# Results\n## Patient cohort and CPSP incidence\n## Top predictors\n## Model performance comparison\n# Conclusions","[{\"question\":\"What is the primary goal of the study?\",\"answer\":\"To develop and evaluate prediction models for chronic postsurgical pain (CPSP) after breast cancer surgery using machine learning approaches.\"},{\"question\":\"How is CPSP defined in the analysis?\",\"answer\":\"CPSP at 12 months after surgery is defined as a modified Brief Pain Inventory score greater than 0.\"},{\"question\":\"Which model class performed better than multivariable logistic regression?\",\"answer\":\"Machine learning models (random forest, gradient boosting decision tree, and extreme gradient boosting) showed better specificity, positive likelihood ratio, and positive predictive value than the multivariable logistic regression model.\"}]","Prediction Models for Chronic Postsurgical Pain in Patients With Breast Cancer Based on Machine Learning Approaches | 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is the primary goal of the study?","Question",{"text":76,"@type":77},"To develop and evaluate prediction models for chronic postsurgical pain (CPSP) after breast cancer surgery using machine learning approaches.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is CPSP defined in the analysis?",{"text":81,"@type":77},"CPSP at 12 months after surgery is defined as a modified Brief Pain Inventory score greater than 0.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model class performed better than multivariable logistic regression?",{"text":85,"@type":77},"Machine learning models (random forest, gradient boosting decision tree, and extreme gradient boosting) showed better specificity, positive likelihood ratio, and positive predictive value than the multivariable logistic regression 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