Wu Linghong, Peng Xiaozhong, Lu Yao, Fu Cuiping, She Liujun, Zhu Guangwei, Zhuo Xianglong, Hu Wei, Xie Xiangtao
Guangxi Key Laboratory of Orthopaedic Biomaterials Development and Clinical Translation, The Fourth Affiliated Hospital of Guangxi Medical University/Liu Zhou Worker's Hospital, Liuzhou, 545005, China.
Medical Records Data Center, The Fourth Affiliated Hospital of Guangxi Medical University/Liu Zhou Worker's Hospital, Liuzhou, 545005, China.
BMC Med Inform Decis Mak. 2024 Dec 5;24(1):373. doi: 10.1186/s12911-024-02787-7.
To develop a nomogram model for the prediction of the risk of prolonged length of hospital stay (LOS) in spinal fusion patients.
A retrospective cohort study was carried out on 6272 patients who had undergone spinal fusion surgery. Least absolute shrinkage and selection operator (LASSO) regression was performed on the training sets to screen variables, and the importance of independent variables was ranked via random forest. In addition, various independent variables were used in the construction of models 1 and 2. A receiver operating characteristic curve was used to evaluate the models' predictive performance. We employed Delong tests to compare the area under the curve (AUC) of the different models. Assessment of the models' capability to improve classification efficiency was achieved using continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The Hosmer-Lemeshow method and calibration curve was utilised to assess the calibration degree, and decision curve to evaluate its clinical practicality. A bootstrap technique that involved 10 cross-validations and was performed 10,000 times was used to conduct internal and external validation. The were outcomes of the model exhibited in a nomogram graphics. The developed nomogram was validated both internally and externally.
Model 1 was identified as the optimal model. The risk factors for prolonged LOS comprised blood transfusion, operation type, use of tranexamic acid (TXA), diabetes, electrolyte disturbance, body mass index (BMI), surgical procedure performed, the number of preoperative diagnoses and operative time. The diagnostic performance of the nomogram model was satisfactory, with AUC values of 0.784 and 0.795 for the internal and external validation sets, respectively. Model discrimination was favourable in both the internal (C-statistic, 0.811) and external (C-statistic, 0.814) validation sets. Calibration curve and Hosmer-Lemeshow test showed acceptable agreement between predicted and actual results. The decision curve shows that the model provides net clinical benefit within a certain decision threshold range.
This study developed and validated a nomogram to identify the risk of prolonged LOS in spinal fusion patients, which may help clinicians to identify high-risk groups at an early stage. Predictors identified included blood transfusion, operation type, use of TXA, diabetes, electrolyte disturbance, BMI, surgical procedure performed, number of preoperative diagnoses and operative time.
建立一种列线图模型，用于预测脊柱融合手术患者住院时间延长（LOS）的风险。
对6272例行脊柱融合手术的患者进行回顾性队列研究。对训练集进行最小绝对收缩和选择算子（LASSO）回归以筛选变量，并通过随机森林对自变量的重要性进行排序。此外，在模型1和模型2的构建中使用了各种自变量。采用受试者工作特征曲线评估模型的预测性能。我们采用德龙检验比较不同模型的曲线下面积（AUC）。使用连续净重新分类改善（NRI）和综合判别改善（IDI）评估模型提高分类效率的能力。采用Hosmer-Lemeshow方法和校准曲线评估校准程度，并使用决策曲线评估其临床实用性。采用涉及10次交叉验证且进行10000次的自助法进行内部和外部验证。模型结果以列线图图形展示。所开发的列线图在内部和外部均得到验证。
模型1被确定为最佳模型。住院时间延长的危险因素包括输血、手术类型、氨甲环酸（TXA）的使用、糖尿病、电解质紊乱、体重指数（BMI）、所施行的外科手术、术前诊断数量和手术时间。列线图模型的诊断性能令人满意，内部验证集和外部验证集的AUC值分别为0.784和0.795。在内部（C统计量，0.811）和外部（C统计量，0.814）验证集中，模型判别效果均良好。校准曲线和Hosmer-Lemeshow检验显示预测结果与实际结果之间具有可接受的一致性。决策曲线表明，该模型在一定的决策阈值范围内提供了净临床效益。
本研究开发并验证了一种列线图，用于识别脊柱融合手术患者住院时间延长的风险，这可能有助于临床医生在早期识别高危人群。确定的预测因素包括输血、手术类型、TXA的使用、糖尿病、电解质紊乱、BMI、所施行的外科手术、术前诊断数量和手术时间。
