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全髋关节置换术后假体周围发生骨折的列线图预测模型构建和评估
Hits: 466   Download times: 173   Received:July 20, 2022    
作者Author单位UnitE-Mail
李欣 LI Xin 达州市中西医结合医院, 四川 达州 635000 Dazhou Hospital of Integrated Traditional Chinese and Western Medicine, Dazhou 635000, Sichuan, China mqko44@163.com 
雷孝勇 LEI Xiao-yong 达州市中西医结合医院, 四川 达州 635000 Dazhou Hospital of Integrated Traditional Chinese and Western Medicine, Dazhou 635000, Sichuan, China  
康大为 KANG Da-wei 达州市中西医结合医院, 四川 达州 635000 Dazhou Hospital of Integrated Traditional Chinese and Western Medicine, Dazhou 635000, Sichuan, China  
期刊信息:《中国骨伤》2023年36卷,第11期,第1036-1040页
DOI:10.12200/j.issn.1003-0034.2023.11.006


目的:构建评估行全髋关节置换术(total hip arthroplasty,THA)患者术后假体周围发生骨折的列线图预测模型。

方法:选取2013年4月至2019年2月行THA患者538例为研究对象,男318例,女220例,年龄40~60(50.79±6.37)岁。根据对所有行THA患者跟踪随访3年的结果,将其分为无骨折组506例和骨折组32例。单因素和多因素Logistic回归分析行THA患者术后假体周围发生骨折的影响因素;构建行THA患者术后假体周围发生骨折的列线图预测模型,评估该预测模型的有效性、区分度。

结果:骨折组行THA患者中存在骨质疏松状态、有外伤史、有髋关节翻修占比高于无骨折组(P<0.05),骨水泥型假体占比低于无骨折组(P<0.05)。骨质疏松状态[OR=4.177,95%CI(1.815,9.617),P<0.05],外伤史[OR=7.481,95%CI(3.104,18.031),P<0.05],髋关节翻修[OR=11.371,95%CI(3.220,40.153),P<0.05]是影响行THA患者术后假体周围发生骨折的独立危险因素,骨水泥型假体[OR=0.067,95%CI(0.019,0.236),P<0.05]是影响行THA患者术后假体周围发生骨折的独立保护因素。Hosmer-Lemeshow拟合优度检验显示, χ2=7.864,P=0.325。受试者工作特征(receiver operating characteristic curve,ROC)曲线评估行THA患者术后假体周围发生骨折的曲线下面积(area under curve,AUC)为0.892,敏感度为87.5%,特异性为77.7%。

结论:本研究构建的行THA患者术后假体周围发生骨折的列线图预测模型区分度较好,有益于临床预测行THA患者假体周围是否发生骨折,为进行个体化预防骨折提供便利。
[关键词]:假体周围骨折  预测模型  全髋关节置换术
 
Construction and evaluation of a nomogram prediction model for periprosthetic fractures after total hip arthroplasty
Abstract:

Objective To construct and evaluate nomogram prediction model for periprosthetic fractures in patients undergoing total hip arthroplasty (THA).

Methods A total of 538 patients who underwent THA from April 2013 to February 2019 were selected as the research subjects,including 318 males and 220 females,aged 40 to 60 years old with an average age of (50.79±6.37) years old. All patients with THA were divided into non-fracture group (506 patients) and fracture group (32 pathents) according to the 3-year follow-up results. Univariate and multivariate Logistic regression analyses were performed to analyze the influencing factors of postoperative periprosthetic fractures in patients with THA. A nomogram prediction model for periprosthetic fractures in patients undergoing THA was constructed,and the validity and discrimination of the prediction model were evaluated.

Results The proportion of patients with osteoporosis,trauma history,and hip revision in the fracture group were higher than those in the non-fracture group(P<0.05),and the proportion of bone cement prosthesis was lower than that in the non-fracture group(P<0.05). The osteoporosis status[OR=4.177,95%CI(1.815,9.617),P<0.05],trauma history[OR=7.481,95%CI(3.104,18.031),P<0.05],and hip revision[OR=11.371,95%CI(3.220,40.153,P<0.05] were independent risk factors for postoperative periprosthetic fractures in patients undergoing THA,cemented prosthesis[OR=0.067,95%CI(0.019,0.236),P<0.05] was an independent protective factor for postoperative periprosthetic fractures in patients undergoing THA(P<0.05). Hosmer-Lemeshow goodness of fit test showed that χ2=7.864,P=0.325;the area under the curve (AUC) for periprosthetic fractures in patients undergoing THA was 0.892 with a sensitivity of 87.5% and a specificity of 77.7% by receiver operating characteristic(ROC) curve.

Conclusion The nomogram prediction model for periprosthetic fractures after THA constructed in this study has good discrimination,which is beneficial to clinical prediction of periprosthetic fractures in patients undergoing THA,and facilitates individualized fracture prevention.
KEYWORDS:Periprosthetic fracture  Prediction model  Total hip arthroplasty(THA)
 
引用本文,请按以下格式著录参考文献:
中文格式:李欣,雷孝勇,康大为.全髋关节置换术后假体周围发生骨折的列线图预测模型构建和评估[J].中国骨伤,2023,36(11):1036~1040
英文格式:LI Xin,LEI Xiao-yong,KANG Da-wei.Construction and evaluation of a nomogram prediction model for periprosthetic fractures after total hip arthroplasty[J].zhongguo gu shang / China J Orthop Trauma ,2023,36(11):1036~1040
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