Cui Peng, Wang Shuaikang, Zhang Haojie, Wang Peng, Chen Xiaolong, Kong Chao, Lu Shibao
Department of Orthopedics & Elderly Spinal Surgery, National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital of Capital Medical University, Beijing, China.
BMC Surg. 2024 Dec 21;24(1):401. doi: 10.1186/s12893-024-02720-9.
Failure to understand long-term quality of life and functional outcomes hinders effective decision making and prognostication. Therefore, the study aims to predict and analyse the unfavorable outcomes (FOs) in elderly patients undergoing lumbar fusion surgery.
Consecutive 382 patients who underwent lumbar fusion surgery for lumbar degenerative disease from March 2019 to July 2022 were enrolled in this study. The risk factors were selected by the least absolute shrinkage and selection operator method (LASSO) regression. Then, a nomogram prediction model was established to predict unfavorable outcomes (UFOs) by using the risk factors selected from LASSO regression. The performance of the model was assessed by the calibration curve and receiver operating characteristic (ROC) curve. The decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical utility of the model.
Finally, 147 of 382 patients showed UFOs. After splitting data in a 70 - 30 fashion, 267 patients were included in the training set. Ten potential risk factors were selected according to the LASSO regression, that identified the predictor to establish nomogram model. The area under the curve (AUC) value was 0.828, and the calibration curve gained from this prediction model suggested good predictive accuracy between the predicted probability and actual probability. In the validation set, the AUC for the model was 0.858. Likewise, the calibration curve gained from this prediction model suggested good predictive accuracy between the predicted probability and actual probability. And the results of DCA and CIC demonstrated that the model showed good clinical practicability in the validation set.
This nomogram model has good predictive performance and clinical practicability, which could provide a certain basis for predicting UFOs in elderly patients undergoing lumbar fusion surgery.
对长期生活质量和功能结局缺乏了解会妨碍有效的决策制定和预后判断。因此，本研究旨在预测和分析接受腰椎融合手术的老年患者的不良结局。
本研究纳入了2019年3月至2022年7月因腰椎退行性疾病接受腰椎融合手术的382例连续患者。通过最小绝对收缩和选择算子法（LASSO）回归选择危险因素。然后，利用从LASSO回归中选择的危险因素建立列线图预测模型，以预测不良结局（UFOs）。通过校准曲线和受试者工作特征（ROC）曲线评估模型的性能。采用决策曲线分析（DCA）和临床影响曲线（CIC）评估模型的临床实用性。
最终，382例患者中有147例出现UFOs。按70 - 30的比例拆分数据后，267例患者被纳入训练集。根据LASSO回归选择了10个潜在危险因素，这些因素确定了用于建立列线图模型的预测指标。曲线下面积（AUC）值为0.828，该预测模型得到的校准曲线表明预测概率与实际概率之间具有良好的预测准确性。在验证集中，模型的AUC为0.858。同样，该预测模型得到的校准曲线表明预测概率与实际概率之间具有良好的预测准确性。DCA和CIC的结果表明，该模型在验证集中具有良好的临床实用性。
该列线图模型具有良好的预测性能和临床实用性，可为预测接受腰椎融合手术的老年患者的UFOs提供一定依据。
