Jian Yunbo, Sun Dawei, Zhang Zhengfeng
Department of Orthopedics, Xinqiao Hospital, Army Medical University, Chongqing, China.
Neurospine. 2022 Dec;19(4):1084-1092. doi: 10.14245/ns.2244596.298. Epub 2022 Dec 31.
To develop a nomogram for the prediction of tracheostomy in patients with traumatic cervical spinal cord injury (TCSCI).
A total of 689 TCSCI patients were included in our study. First, the variable selection was performed using between-group comparisons and LASSO regression analysis. Second, a multivariate logistic regression analysis (MLRA) with a step-by-step method was performed. A nomogram model was developed based on the MLRA. Finally, the model was validated on the training set and validation set.
The nomogram prediction model incorporated 5 predictors, including smoking history, dislocation, thoracic injury, American Spinal Injury Association (ASIA) grade, and neurological level of injury (NLI). The area under curve in the training group and in the validation group were 0.883 and 0.909, respectively. The Hosmer-Lemeshow test result was p = 0.153. From the decision curve analysis curve, the model performed well and was feasible to make beneficial clinical decisions.
The nomogram combining dislocation, thoracic injury, ASIA grade A, NLI, and smoking history was validated as a reliable model for the prediction of tracheostomy.
建立一种预测创伤性颈脊髓损伤（TCSCI）患者气管切开术的列线图。
本研究共纳入689例TCSCI患者。首先，采用组间比较和LASSO回归分析进行变量选择。其次，采用逐步法进行多因素逻辑回归分析（MLRA）。基于MLRA建立列线图模型。最后，在训练集和验证集上对模型进行验证。
列线图预测模型纳入了5个预测因素，包括吸烟史、脱位、胸部损伤、美国脊髓损伤协会（ASIA）分级和神经损伤平面（NLI）。训练组和验证组的曲线下面积分别为0.883和0.909。Hosmer-Lemeshow检验结果为p = 0.153。从决策曲线分析曲线来看，该模型表现良好，在做出有益的临床决策方面是可行的。
结合脱位、胸部损伤、ASIA A级、NLI和吸烟史的列线图被验证为预测气管切开术的可靠模型。
