全膝关节置换术后慢性术后疼痛风险的术前预测:来自单中心病例对照研究的见解

IF 1.3 Q3 MEDICINE, GENERAL & INTERNAL
Cureus Pub Date : 2025-10-04 eCollection Date: 2025-10-01 DOI:10.7759/cureus.93815
Bhavesh Santani, Shubhabrata De, Diego Vergara-Jalandoni, Abhina George, Venancio Manipol, Ulfat Sardar, Rakshya Upreti, Ashwin Unnithan
{"title":"全膝关节置换术后慢性术后疼痛风险的术前预测:来自单中心病例对照研究的见解","authors":"Bhavesh Santani, Shubhabrata De, Diego Vergara-Jalandoni, Abhina George, Venancio Manipol, Ulfat Sardar, Rakshya Upreti, Ashwin Unnithan","doi":"10.7759/cureus.93815","DOIUrl":null,"url":null,"abstract":"<p><p>Background Chronic post-surgical pain (CPSP) is a common and distressing complication following total knee arthroplasty (TKA). Despite advancements, CPSP remains a significant challenge, necessitating the development of a predictive model to identify at-risk patients. Materials and methods This level III case-control study analyzed 869 patients who underwent TKA between 2019 and 2021 at a single center. Data on demographics, comorbidities, mental health conditions, pre-existing pain, opioid use, substance abuse, and surgical factors were collected. Univariate and multivariate logistic regression analyses were conducted to identify significant preoperative risk factors. Independent predictors from multivariate analysis were assigned weighted scores proportional to their odds ratios, which were then adjusted according to previous literature and investigator consensus to create a clinically usable tool. Receiver operating characteristic (ROC) curve analysis was performed, and cutoffs were identified to optimize sensitivity and specificity. Results Out of 869 patients who underwent TKA, 15.7% (n = 136) developed CPSP. Out of these 136 patients, 22% (29) were referred to specialist pain management clinics. Univariate analysis identified seven significant predictors: diabetes mellitus, coronary artery disease (CAD), heart failure, mental health conditions, pre-existing pain, and preoperative opioid use. The multivariate analysis excluded heart failure (p = 0.098). The strongest association was found with CAD (OR = 2.98). A scoring system was developed, and an ROC analysis yielded an area under the curve of 0.616 (95% CI: 0.562-0.671, p = 0.001). Conclusions Identifying preoperative risk factors and using this score as a predictive model, pending further prospective validation, can help stratify patients and enable targeted interventions, potentially minimizing the impact and financial burden of CPSP after TKA.</p>","PeriodicalId":93960,"journal":{"name":"Cureus","volume":"17 10","pages":"e93815"},"PeriodicalIF":1.3000,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12496033/pdf/","citationCount":"0","resultStr":"{\"title\":\"Preoperative Prediction of Chronic Post-surgical Pain Risk After Total Knee Arthroplasty: Insights From a Single-Center Case-Control Study.\",\"authors\":\"Bhavesh Santani, Shubhabrata De, Diego Vergara-Jalandoni, Abhina George, Venancio Manipol, Ulfat Sardar, Rakshya Upreti, Ashwin Unnithan\",\"doi\":\"10.7759/cureus.93815\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Background Chronic post-surgical pain (CPSP) is a common and distressing complication following total knee arthroplasty (TKA). Despite advancements, CPSP remains a significant challenge, necessitating the development of a predictive model to identify at-risk patients. Materials and methods This level III case-control study analyzed 869 patients who underwent TKA between 2019 and 2021 at a single center. Data on demographics, comorbidities, mental health conditions, pre-existing pain, opioid use, substance abuse, and surgical factors were collected. Univariate and multivariate logistic regression analyses were conducted to identify significant preoperative risk factors. Independent predictors from multivariate analysis were assigned weighted scores proportional to their odds ratios, which were then adjusted according to previous literature and investigator consensus to create a clinically usable tool. Receiver operating characteristic (ROC) curve analysis was performed, and cutoffs were identified to optimize sensitivity and specificity. Results Out of 869 patients who underwent TKA, 15.7% (n = 136) developed CPSP. Out of these 136 patients, 22% (29) were referred to specialist pain management clinics. Univariate analysis identified seven significant predictors: diabetes mellitus, coronary artery disease (CAD), heart failure, mental health conditions, pre-existing pain, and preoperative opioid use. The multivariate analysis excluded heart failure (p = 0.098). The strongest association was found with CAD (OR = 2.98). A scoring system was developed, and an ROC analysis yielded an area under the curve of 0.616 (95% CI: 0.562-0.671, p = 0.001). Conclusions Identifying preoperative risk factors and using this score as a predictive model, pending further prospective validation, can help stratify patients and enable targeted interventions, potentially minimizing the impact and financial burden of CPSP after TKA.</p>\",\"PeriodicalId\":93960,\"journal\":{\"name\":\"Cureus\",\"volume\":\"17 10\",\"pages\":\"e93815\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2025-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12496033/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cureus\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7759/cureus.93815\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/10/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cureus","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7759/cureus.93815","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/10/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0

摘要

背景:慢性术后疼痛(CPSP)是全膝关节置换术(TKA)后常见且令人痛苦的并发症。尽管取得了进展,但CPSP仍然是一个重大挑战,需要开发一种预测模型来识别高危患者。本III级病例对照研究分析了2019年至2021年在单个中心接受TKA的869例患者。收集了人口统计学、合并症、精神健康状况、既往疼痛、阿片类药物使用、药物滥用和手术因素的数据。进行单因素和多因素logistic回归分析以确定重要的术前危险因素。来自多变量分析的独立预测因子被赋予与其比值比成正比的加权分数,然后根据先前的文献和研究者的共识进行调整,以创建临床可用的工具。进行受试者工作特征(ROC)曲线分析,并确定截断点以优化灵敏度和特异性。结果869例TKA患者中,15.7% (n = 136)发生CPSP。在这136名患者中,22%(29)被转介到专科疼痛管理诊所。单变量分析确定了七个重要的预测因素:糖尿病、冠状动脉疾病(CAD)、心力衰竭、精神健康状况、既往疼痛和术前阿片类药物使用。多变量分析排除心力衰竭(p = 0.098)。与CAD的相关性最强(OR = 2.98)。建立了评分系统,ROC分析得出曲线下面积为0.616 (95% CI: 0.562-0.671, p = 0.001)。结论:确定术前危险因素并使用该评分作为预测模型,有待进一步的前瞻性验证,可以帮助患者分层并实现有针对性的干预,潜在地减少TKA后CPSP的影响和经济负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Preoperative Prediction of Chronic Post-surgical Pain Risk After Total Knee Arthroplasty: Insights From a Single-Center Case-Control Study.

Preoperative Prediction of Chronic Post-surgical Pain Risk After Total Knee Arthroplasty: Insights From a Single-Center Case-Control Study.

Background Chronic post-surgical pain (CPSP) is a common and distressing complication following total knee arthroplasty (TKA). Despite advancements, CPSP remains a significant challenge, necessitating the development of a predictive model to identify at-risk patients. Materials and methods This level III case-control study analyzed 869 patients who underwent TKA between 2019 and 2021 at a single center. Data on demographics, comorbidities, mental health conditions, pre-existing pain, opioid use, substance abuse, and surgical factors were collected. Univariate and multivariate logistic regression analyses were conducted to identify significant preoperative risk factors. Independent predictors from multivariate analysis were assigned weighted scores proportional to their odds ratios, which were then adjusted according to previous literature and investigator consensus to create a clinically usable tool. Receiver operating characteristic (ROC) curve analysis was performed, and cutoffs were identified to optimize sensitivity and specificity. Results Out of 869 patients who underwent TKA, 15.7% (n = 136) developed CPSP. Out of these 136 patients, 22% (29) were referred to specialist pain management clinics. Univariate analysis identified seven significant predictors: diabetes mellitus, coronary artery disease (CAD), heart failure, mental health conditions, pre-existing pain, and preoperative opioid use. The multivariate analysis excluded heart failure (p = 0.098). The strongest association was found with CAD (OR = 2.98). A scoring system was developed, and an ROC analysis yielded an area under the curve of 0.616 (95% CI: 0.562-0.671, p = 0.001). Conclusions Identifying preoperative risk factors and using this score as a predictive model, pending further prospective validation, can help stratify patients and enable targeted interventions, potentially minimizing the impact and financial burden of CPSP after TKA.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信