Chen Jingdi, Wu Wei, Xian Chunxing, Wang Taoran, Hao Xiaotian, Chai Na, Liu Tao, Shang Lei, Wang Bo, Gao Jiakai, Bi Long
Department of Orthopedics, Xijing Hospital, Air Force Medical University, Xi'an, 710032, China.
Department of Orthopedics, 95829 Military Hospital in PLA, Wuhan, 430000, China.
Heliyon. 2024 Mar 26;10(7):e28502. doi: 10.1016/j.heliyon.2024.e28502. eCollection 2024 Apr 15.
To explore risk factors for defective non-union of bone and develop a nomogram-based prediction model for such an outcome.
This retrospective study analysed the case data of patients with defective bony non-unions who were treated at the authors' hospital between January 2010 and December 2020. Patients were divided into the union and non-union groups according to their Radiographic Union Score for Tibia scores 1 year after surgery. Univariate analysis was performed to assess factors related to demographic characteristics, laboratory investigations, surgery, and trauma in both groups. Subsequently, statistically significant factors were included in the multivariate logistic regression analysis to identify independent risk factors. A nomogram-based prediction model was established using statistically significant variables in the multivariate analysis. The accuracy and stability of the model were evaluated using receiver operating characteristic (ROC) and calibration curves. The clinical applicability of the nomogram model was evaluated using decision curve analysis.
In total, 204 patients (171 male, 33 female; mean [±SD] age, 39.75 ± 13.00 years) were included. The mean body mass index was 22.95 ± 3.64 kg/m. Among the included patients, 29 were smokers, 18 were alcohol drinkers, and 21 had a previous comorbid systemic disease (PCSD). Univariate analysis revealed that age, occupation, PCSD, smoking, drinking, interleukin-6, C-reactive protein (CRP), procalcitonin, alkaline phosphatase, glucose, and uric acid levels; blood calcium ion concentration; and bone defect size (BDS) were correlated with defective bone union (all P < 0.05). Multivariate logistic regression analysis revealed that PCSD, smoking, interleukin-6, CRP, and glucose levels; and BDS were associated with defective bone union (all P < 0.05), and the variables in the multivariate analysis were included in the nomogram-based prediction model. The value of the area under the ROC curve for the predictive model for bone defects was 0.95.
PCSD, smoking, interleukin-6, CRP, and glucose levels; and BDS were independent risk factors for defective bony non-union, and the incidence of such non-union was predicted using the nomogram. These findings are important for clinical interventions and decision-making.
探讨骨不连的危险因素，并建立基于列线图的骨不连结局预测模型。
本回顾性研究分析了2010年1月至2020年12月在作者所在医院接受治疗的骨不连患者的病例数据。根据术后1年的胫骨影像学愈合评分将患者分为愈合组和未愈合组。对两组患者的人口统计学特征、实验室检查、手术和创伤相关因素进行单因素分析。随后，将具有统计学意义的因素纳入多因素逻辑回归分析，以确定独立危险因素。使用多因素分析中具有统计学意义的变量建立基于列线图的预测模型。采用受试者工作特征（ROC）曲线和校准曲线评估模型的准确性和稳定性。使用决策曲线分析评估列线图模型的临床适用性。
共纳入204例患者（男性171例，女性33例；平均[±标准差]年龄为39.75±13.00岁）。平均体重指数为22.95±3.64kg/m²。纳入患者中，吸烟者29例，饮酒者18例，既往有合并全身性疾病（PCSD）者21例。单因素分析显示，年龄、职业、PCSD、吸烟、饮酒、白细胞介素-6、C反应蛋白（CRP）、降钙素原、碱性磷酸酶、血糖和尿酸水平；血钙离子浓度；以及骨缺损大小（BDS）与骨不连相关（均P<0.05）。多因素逻辑回归分析显示，PCSD、吸烟、白细胞介素-6、CRP和血糖水平；以及BDS与骨不连相关（均P<0.05），多因素分析中的变量被纳入基于列线图的预测模型。骨缺损预测模型的ROC曲线下面积值为0.95。
PCSD、吸烟、白细胞介素-6、CRP和血糖水平；以及BDS是骨不连的独立危险因素，并使用列线图预测了此类不连的发生率。这些发现对临床干预和决策具有重要意义。
