Liu Guozhen, Liu Lei, Zhang Ze, Tan Rui, Wang Yuntao
Department of Spinal Surgery, General Hospital of Ningxia Medical University, Yinchuan, China; Southeast University, Nanjing, Jiang Su Province, China.
Southeast University, Nanjing, Jiang Su Province, China; Department of Spine Surgery, the Affiliated ZhongDa Hospital of Southeast University, Nanjing, Jiang Su Province, China.
Arch Phys Med Rehabil. 2024 Oct 9. doi: 10.1016/j.apmr.2024.09.016.
To investigate the risk factors relating to the need for mechanical ventilation (MV) in isolated patients with cervical spinal cord injury (cSCI) and to construct a nomogram prediction model.
Retrospective analysis study.
National Spinal Cord Injury Model System Database (NSCID) observation data were initially collected during rehabilitation hospitalization.
A total of 5784 patients (N=5784) who had a cSCI were admitted to the NSCID between 2006 and 2021.
Not applicable.
MAIN OUTCOME MEASURE(S): A univariate and multivariate logistic regression analysis was used to identify the independent factors affecting the use of MV in patients with cSCI, and these independent influencing factors were used to develop a nomogram prediction model. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the efficiency and the clinical application value of the model, respectively.
In a series of 5784 included patients, 926 cases (16.0%) were admitted to spinal cord model system inpatient rehabilitation with the need for MV. Logistic regression analysis demonstrated that associated injury, American Spinal Cord Injury Association Impairment Scale (AIS), the sum of unilateral optimal motor scores for each muscle segment of upper extremities (sUEM), and neurologic level of injury (NLI) were independent predictors for the use of MV (P<.05). The prediction nomogram of MV usage in patients with cSCI was established based on the above independent predictors. The AUROC of the training set, internal verification set, and external verification set were 0.871 (0.857-0.886), 0.867 (0.843-0.891), and 0.850 (0.824-0.875), respectively. The calibration curve and DCA results showed that the model had good calibration and clinical practicability.
The nomograph prediction model based on sUEM, NLI, associated injury, and AIS can accurately and effectively predict the risk of MV in patients with cSCI, to help clinicians screen high-risk patients and formulate targeted intervention measures.
探讨单纯颈脊髓损伤（cSCI）患者机械通气（MV）需求的相关危险因素，并构建列线图预测模型。
回顾性分析研究。
国家脊髓损伤模型系统数据库（NSCID）的观察数据最初在康复住院期间收集。
2006年至2021年期间，共有5784例cSCI患者被纳入NSCID。
不适用。
采用单因素和多因素逻辑回归分析确定影响cSCI患者使用MV的独立因素，并将这些独立影响因素用于构建列线图预测模型。分别采用受试者工作特征曲线下面积（AUROC）、校准曲线和决策曲线分析（DCA）评估模型的效能和临床应用价值。
在纳入的5784例患者中，926例（16.0%）因需要MV而入住脊髓模型系统进行住院康复。逻辑回归分析表明，合并损伤、美国脊髓损伤协会损伤分级（AIS）、上肢各肌肉节段单侧最佳运动评分总和（sUEM）以及损伤神经平面（NLI）是使用MV的独立预测因素（P<0.05）。基于上述独立预测因素建立了cSCI患者MV使用的预测列线图。训练集、内部验证集和外部验证集的AUROC分别为0.871（0.857 - 0.886）、0.867（0.843 - 0.891）和0.850（0.824 - 0.875）。校准曲线和DCA结果显示该模型具有良好的校准度和临床实用性。
基于sUEM、NLI、合并损伤和AIS的列线图预测模型能够准确、有效地预测cSCI患者MV的风险，有助于临床医生筛选高危患者并制定针对性的干预措施。
