He Ming, Zhu Qingsheng, Yin Dayu, Duan Yonghong, Sun Pengxiao, Fang Qing
Department of Arthrology I, Xi'an International Medical Center Hospital No. 777 Xitai Road, Chang'an District, Xi'an 710100, Shaanxi, China.
Am J Transl Res. 2024 Feb 15;16(2):557-566. doi: 10.62347/NXYD3115. eCollection 2024.
To explore the relationship of serum levels of IL-1β, IL-6, and TNF-α with prosthesis loosening after hip arthroplasty, and to establish a predictive model for prosthesis loosening.
We retrospectively analyzed the data of 501 patients who underwent hip arthroplasty in Xi'an International Medical Center Hospital from January 2020 to August 2022. Based on radiological diagnosis, the patients were divided into a prosthesis loosening group and a non-loosening group. Clinical data including postoperative serum levels of inflammatory cytokines were collected. Univariant analysis, Lasso regression, decision tree, and random forest models were used to screen feature variables. Based on the screening results, a nomogram model for predicting the risk of prosthesis loosening was established and then validated using ROC curve, and calibration curve, and other methods.
There were 50 cases in the loosening group and 451 cases in the non-loosening group. Postoperative levels of IL-1β, IL-6, and TNF-α were found to be significantly higher in the loosening group (P<0.0001). Univariant analysis showed that osteoporosis and postoperative infection were risk factors for prosthesis loosening (P<0.001). The machine learning algorithm identified osteoporosis, postoperative infection, IL-1β, IL-6, and TNF-α as 5 relevant variables. The predictive model based on these 5 variables exhibited an area under the ROC curve of 0.763. The calibration curve and DCA curve verified the accuracy and practicality of the model.
Serum levels of IL-1β, IL-6, and TNF-α were significantly elevated in patients with postoperative prosthesis loosening. Osteoporosis, postoperative infection, and inflammatory cytokines are independent risk factors for prosthesis loosening. The predictive model we established through machine learning can effectively determine the risk of prosthesis loosening. Monitoring inflammatory cytokines and postoperative infections, combined with prevention of osteoporosis, can help reduce the risk of prosthesis loosening.
探讨血清白细胞介素-1β(IL-1β)、白细胞介素-6(IL-6)和肿瘤坏死因子-α(TNF-α)水平与髋关节置换术后假体松动的关系,并建立假体松动的预测模型。
回顾性分析2020年1月至2022年8月在西安国际医学中心医院行髋关节置换术的501例患者的数据。根据影像学诊断,将患者分为假体松动组和未松动组。收集包括术后血清炎症细胞因子水平在内的临床资料。采用单因素分析、套索回归、决策树和随机森林模型筛选特征变量。根据筛选结果,建立预测假体松动风险的列线图模型,然后采用ROC曲线、校准曲线等方法进行验证。
松动组50例,未松动组451例。发现松动组术后IL-1β、IL-6和TNF-α水平显著更高(P<0.0001)。单因素分析显示骨质疏松和术后感染是假体松动的危险因素(P<0.001)。机器学习算法确定骨质疏松、术后感染、IL-1β、IL-6和TNF-α为5个相关变量。基于这5个变量的预测模型在ROC曲线下面积为0.763。校准曲线和DCA曲线验证了模型的准确性和实用性。
术后假体松动患者血清IL-1β、IL-6和TNF-α水平显著升高。骨质疏松、术后感染和炎症细胞因子是假体松动的独立危险因素。我们通过机器学习建立的预测模型可以有效确定假体松动的风险。监测炎症细胞因子和术后感染,结合预防骨质疏松,有助于降低假体松动的风险。