急性创伤性颈髓损伤患者气管切开预测模型

Predictive model of tracheostomy in acute traumatic cervical spinal cord injury

中文关键词:  颈脊髓损伤  气管切开  预测模型

英文关键词:Cervical spinal cord injury  Tracheostomy  Prediction model

中文摘要:

  【摘要】 目的：通过易获得的急性创伤性颈髓损伤患者床旁资料建立气管切开预测模型，探讨用其预测颈髓损伤患者气管切开的可行性。方法：回顾性分析我院收治的345例急性创伤性颈脊髓损伤患者临床数据。采集其中219例患者人口学资料，是否行气管切开，既往系统性疾病史（除肺病外）、既往肺病史、吸烟史、治疗过程中是否出现呼吸系统并发症，入院时ASIA运动评分、神经损伤节段、ASIA分级、合并伤情况，是否存在颈椎骨折脱位，术前颈椎MRI显示的颈髓信号改变长度及最高病变节段、椎管最大侵占率、脊髓最大受压率、是否出现髓内出血。分别应用多元逻辑回归分析和分类回归树分析建立气管切开的逻辑回归模型和决策树模型。利用交叉验证方法应用另外126例患者资料对两模型进行外部验证，应用敏感性、特异性、预测准确率及ROC曲线下面积评估两模型预测能力。结果：345例患者中，58例行气管切开。决策树模型显示：入院时ASIA运动评分≤1分的患者气管切开率为66.7%；ASIA运动评分≤22分且出现呼吸系统并发症患者气管切开率为69.0%；入院时ASIA运动评分≥23分、不完全颈髓损伤、术前MRI显示髓內信号改变最高节段位于C3或以下的患者气管切开率为0.8%。逻辑回归模型显示的独立预测因素包括ASIA运动评分≤22分，ASIA A级或B级损伤及治疗过程中出现呼吸系统并发症。决策树模型和逻辑回归模型在敏感性、特异性、预测准确率、ROC曲线下面积的比较分别为73.7% vs 81.8%、89.7% vs 86.4%、87.3% vs 85.7%及0.909 vs 0.889。结论：决策树模型可用于进行气管切开的预测，入院时ASIA运动评分≤22分、ASIA A级颈髓损伤、治疗过程中出现呼吸系统并发症及术前颈椎MRI显示髓内信号改变的最高节段位于C2或以上为患者气管切开的独立预测因素。

英文摘要:

  【Abstract】 Objectives: To develop a risk prediction model for tracheostomy in acute traumatic cervical spinal cord injury (SCI) patients by using accessible data obtained from the bedside. Methods: Clinical and radiological data of 345 patients with acute traumatic cervical spinal cord injury were retrospectively reviewed. The information about patients′ demographics, trachestomy placement, pre-existing medical comorbidities (except for lung diseases), pre-existing pulmonary diseases, smoking history, presence of respiratory complications during treatment, admission ASIA motor score(AAMS), neurological level of impairment(NLI), ASIA grade, associated injuries, presence of cervical fracture and/or dislocations was gathered. The preoperative magnetic resonance imaging(MRI) was checked to determine the highest signal change(HSC) level in the spinal cord, lesion length, maximunm spinal cord compression(MSCC), maximum canal compromise(MCC) and the presence of intramedullary haemorrhage. The multiple logistic regression(MLR) analysis and classification and regression tree(CART) analysis were used to develop the prediction models. Cross-validation was used to conduct an external validation in order to assess the prediction performance of both models, using parameters of sensitivity, specificity, overall correction rate and area under receiver operating characteristic curve. Results: Among 345 patients, 58 patients underwent tracheostomy. The CART model suggested that the incidences of tracheostomy for patients with AAMS ≤1 and patients with AAMS ≤22 who suffered from respiratory complications during hospitalization and patients with AAMS ≥23, suffering from incomplete SCI, whose HSC was at C3 or lower was 66.7%, 69.0% and 0.8% respectively. The MLR model suggested that the independent risk factors included AAMS ≤22, ASIA grade A or B and respiratory complications during treatment. The comparison on sensitivity, specificity, overall correction rate and area under receiver operating characteristic curve between the CART model and the MLR model was 73.7% vs 81.8%, 89.7% vs 86.4%, 87.3% vs 85.7% and 0.909 vs 0.889 respectively. Conclusions: The CART model is preferred in prediction. AAMS ≤22, ASIA grade A, respiratory complications during hospitalization and HSC at C2 or higher are considered independent risk factors of tracheostomy for patients with traumatic cervical SCI patients