Xu Zhe, Zhang Ruguo, Chen Qiuhan, Peng Guoxuan, Luo Shanpeng, Liu Chen, Zeng Ling, Deng Jin
Department of Emergency, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, China.
Department of Orthopedics, Guihang Guiyang Hospital, Guiyang, 550025, China.
BMC Geriatr. 2025 Aug 4;25(1):591. doi: 10.1186/s12877-025-06213-z.
Sepsis after hip fracture in elderly people is a risk factor for mortality. The purpose of this study was to screen for risk factors for 60-day sepsis morbidity after hip fracture and to establish a predictive model using various machine learning algorithms.
A total of 697 patients who were older than 65 years of age were selected; 590 patients were divided into training and test sets (at a ratio of 7:3), and 107 individuals were used for external validation. Least absolute shrinkage and selection operator (LASSO), the Boruta algorithm, and random survival forest (RSF) were used to screen for risk factors for sepsis incidence. Correlation coefficients and variance inflation factors (VIFs) were calculated to assess multicollinearity. Cox proportional hazards model analysis was used to establish a model. A restricted cubic spline (RCS) was used. The discrimination, calibration, and decision curve analysis (DCA) results were evaluated. The prediction model that was constructed was compared with those of the qSOFA.
There was no multicollinearity between variables (r < 0.6, variance inflation factor (VIF) < 5). Pneumonia, nutritional status, age, diabetes status, hemoglobin level, and BMI were the final variables. The C-indices of the training and test sets and the external validation set were 0.83 (95% CI 0.78 ~ 0.88), 0.78 (95% CI 0.70 ~ 0.85), and 0.76 (95% CI 0.70 ~ 0.83), respectively. The calibration and DCA results indicated that the model had good predictive value. The RCS revealed a linear relationship between age, BMI, hemoglobin and sepsis incidence (p > 0.05). The discrimination ability and clinical applicability of the model were superior to those of the qSOFA.
This study, which uses various feature selection methods, can effectively construct a 60-day sepsis morbidity prediction model after hip fracture in elderly people and could be superior to the qSOFA.
Date of registry: 02/08/2023; Trial number: ChiCTR2300074246.
老年人髋部骨折后的脓毒症是死亡的危险因素。本研究的目的是筛查髋部骨折后60天脓毒症发病的危险因素，并使用各种机器学习算法建立预测模型。
共选取697例年龄大于65岁的患者；590例患者被分为训练集和测试集（比例为7:3），107例个体用于外部验证。使用最小绝对收缩和选择算子（LASSO）、Boruta算法和随机生存森林（RSF）来筛查脓毒症发病的危险因素。计算相关系数和方差膨胀因子（VIF）以评估多重共线性。采用Cox比例风险模型分析建立模型。使用受限立方样条（RCS）。评估判别、校准和决策曲线分析（DCA）结果。将构建的预测模型与qSOFA的模型进行比较。
变量之间不存在多重共线性（r < 0.6，方差膨胀因子（VIF）< 5）。肺炎、营养状况、年龄、糖尿病状况、血红蛋白水平和BMI是最终变量。训练集、测试集和外部验证集的C指数分别为0.83（95%CI 0.78 ~ 0.88）、0.78（95%CI 0.70 ~ 0.85）和0.76（95%CI 0.70 ~ 0.83）。校准和DCA结果表明该模型具有良好的预测价值。RCS显示年龄、BMI、血红蛋白与脓毒症发病率之间存在线性关系（p > 0.05）。该模型的判别能力和临床适用性优于qSOFA。
本研究使用各种特征选择方法，能够有效构建老年人髋部骨折后60天脓毒症发病预测模型，且可能优于qSOFA。
注册日期：2023年8月2日；试验编号：ChiCTR2300074246。
