老年股骨转子间骨折患者PFNA术后髋关节功能不良风险等级预测模型的构建和评价OA
Construction and evaluation of risk stratification prediction model of hip joint dysfunction after PFNA in elderly patients with intertrochanteric fracture
目的 构建老年股骨转子间骨折患者股骨近端防旋髓内钉(PFNA)术后髋关节功能不良的风险等级预测模型,并评价其效能.方法 选取2021年1月至2023年6月收治的208例老年股骨转子间骨折患者为研究对象,通过一般资料调查表、医院电子病历系统、Harris髋关节功能评分系统、恐动症Tampa量表进行问卷调查.采用单因素、多因素Logistic回归分析老年股骨转子间骨折患者PFNA术后髋关节功能不良的影响因素,将Logistic回归分析中有意义变量纳入列线图模型,采用受试者工作特征(ROC)曲线验证其效能,根据个体携带的危险因素,运用列线图模型计算患者PFNA术后髋关节功能不良的风险总得分及其风险概率,并根据风险总得分进行危险等级划分.结果 最终完成206例老年股骨转子间骨折患者的随访及资料收集,Harris髋关节功能评分(86.12±7.32)分,髋关节功能预后良好率为75.24%(155/206),预后不良率为24.76%(51/206).单因素、多因素Logistic回归分析显示,超重/肥胖、不稳定性骨折、手术时机≥7 d、Clavien-Dindo分级、恐动症、营养不良、骨质疏松症、血清I型胶原氨基端前肽、血清骨织素均为老年股骨转子间骨折患者PFNA术后髋关节功能不良的独立危险因素,而功能锻炼依从性、血清25羟维生素D3是其保护因素(P<0.01).基于上述影响因素构建列线图模型预测PFNA术后髋关节功能不良的曲线下面积为0.861(95%CI:0.793,0.928).依据列线图模型计算髋关节功能不良风险总得分、预测风险概率,依据总得分分为低风险(≤160分)、中风险(>160~200分)、高风险(>200分)3个等级,对应概率分别为≤10%、>10%~50%、>50%.结论 超重/肥胖、营养不良、骨质疏松症等是老年股骨转子间骨折患者PFNA术后髋关节功能不良的影响因素,以此为基础构建的列线图模型具有良好临床预测效用,可指导临床建立风险预警模式.
Objective To construct a risk stratification prediction model for hip joint dysfunction following proximal femoral nail antirotation(PFNA)in elderly patients with intertrochanteric fractures,and to evaluate its performance.Methods A total of 208 elderly patients with intertrochanteric fractures admitted from January 2021 to June 2023 were selected as the research subjects.A questionnaire survey was conducted through the general data questionnaire,hospital electronic medical record system,Harris hip function scoring system,and Tampa Scale for Kinesiophobia.Univariate and multivariate logistic regression analyses were used to analyze the influencing factors of hip joint dysfunction after PFNA in elderly patients with intertrochanteric fractures.Significant variables from logistic regression analysis were included in the nomogram model,and the receiver operating characteristic(ROC)curve was used to verify its efficacy.According to the risk factors carried by individuals,the total risk score and risk probability of hip joint dysfunction after PFNA in elderly patients were calculated using a nomogram model,and the risk stratification was performed based on the total risk scores.Results The follow-up and data collection of 206 elderly patients with intertrochanteric fractures were finally completed.The Harris hip function score was(86.12±7.32)scores,the good prognosis rate of hip function was 75.24%(155/206),and the poor prognosis rate was 24.76%(51/206).Univariate and multivariate logistic regression analyses showed that overweight/obesity,unstable fracture,time to operation≥7 d,Clavien-Dindo classification,kinesiophobia,malnutrition,osteoporosis,serum N-terminal propeptide of type I collagen,and serum osteocalcin were all independent risk factors for hip dysfunction after PFNA in elderly patients with intertrochanteric fracture,while functional exercise compliance and serum 25-hydroxyvitamin D3 were protective factors(P<0.01).Based on the above influencing factors,the area under the ROC curve of the nomogram model for predicting hip joint dysfunction after PFNA was 0.861(95%CI:0.793,0.928).According to the nomogram model,the total risk score and predicted risk probability of hip joint dysfunction were calculated.According to the total score,patients were stratified into three risk levels:low risk(≤ 160 scores),medium risk(>160-200 scores)and high risk(>200 scores),with the corresponding probabilities of ≤ 10%,>10%-50%and>50%,respectively.Conclusion Overweight/obesity,malnutrition and osteoporosis are the influencing factors of hip joint dysfunction after PFNA in elderly patients with intertrochanteric fracture.The nomogram model constructed on this basis has good clinical predictive utility and can guide the clinical establishment of risk warning model.
武志刚;张海东;刘海洋
北京市普仁医院骨二科,北京 100062北京市普仁医院骨二科,北京 100062北京天坛医院麻醉科,北京 100071
股骨转子间骨折老年股骨近端防旋髓内钉髋关节功能危险因素预测模型列线图
intertrochanteric fracture of femurelderlyproximal femoral nail antirotationhip joint functionrisk factorsprediction modelnomogram
《临床误诊误治》 2026 (1)
44-52,9
青海省科技计划项目(2023-ZJ-753)北京市普仁医院科研专项项目(普仁科研[2021]-8)
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