Gu Yang, Wu Qisen, Luo Shiping, Lin Taotao, Zhou Linquan, Zheng Shengxiong, Lin Bin, Lin Qin, Wu Zhengru, Liu Wenge, Wang Zhenyu
Department of Orthopedics, Affiliated Union Hospital of Fujian Medical University, Fuzhou, Fujian Province, China.
Department of Breast Surgery, Affiliated Union Hospital of Fujian Medical University, Fuzhou, Fujian Province, China.
World Neurosurg. 2020 Jul;139:e245-e254. doi: 10.1016/j.wneu.2020.03.195. Epub 2020 Apr 11.
In this retrospective study, our objective was to establish a nomogram to predict the effectiveness of cervical traction in young and middle-aged chronic nonspecific neck pain (CNNP) patients with unsatisfactory nonsteroidal anti-inflammatory drug (NSAID) control. For CNNP patients with unsatisfactory NSAID control, the effectiveness of cervical traction varies. Neck muscle fat infiltration and clinical features may associate with the effectiveness.
A total of 186 suitable patients were classified into a training data set (from August 2015 to July 2018, n = 118) and a validation data set (from August 2018 to June 2019, n = 68) with time sequence. All patients were included to receive magnetic resonance imaging scan to calculate posterior cervical fat and muscle features, then undergoing unified cervical traction in an outpatient clinic. The least absolute shrinkage and selection operator (LASSO) regression model was used to select potentially relevant features to predict effectiveness possibility of cervical traction. Multivariable logistic regression analysis was used to develop the predicting model, presenting with a nomogram. The performance of the nomogram was assessed based on its calibration, discrimination, and clinical utility.
Through the LASSO regression model, we identified 4 predictors including sex, good exercise compliance, the ratio of the cross-sectional area (CSA) between fat and muscle on C5 level (C5 fat CSA/muscle CSA), the ratio of CSA between fat and centrum on C5 level (C5 fat CSA/centrum muscle CSA). The nomogram provided good calibration and discrimination in the training cohort, showing an area under the curve (AUC) of 0.704 (95% CI, 0.608-0.799) and good concordance between the predicted and actual probabilities with Spiegelhalter's Z-test (P = 0.835). Discrimination of the model in the validation data set was acceptable, with AUC of 0.691 (95% CI, 0.564-0.817). Decision curve analysis revealed the nomogram to be clinically useful.
Male sex, good exercise compliance, lower C5 fat CSA/centrum CSA, and and lower C5 fat CSA/muscle CSA could be favorable features to predict the effectiveness of cervical traction in CNNP patients with unsatisfactory NSAID control.
在这项回顾性研究中,我们的目的是建立一个列线图,以预测在非甾体抗炎药(NSAID)控制效果不佳的中青年慢性非特异性颈部疼痛(CNNP)患者中颈椎牵引的有效性。对于NSAID控制效果不佳的CNNP患者,颈椎牵引的有效性各不相同。颈部肌肉脂肪浸润和临床特征可能与有效性相关。
总共186例合适的患者按时间顺序分为训练数据集(2015年8月至2018年7月,n = 118)和验证数据集(2018年8月至2019年6月,n = 68)。所有患者均接受磁共振成像扫描以计算颈后部脂肪和肌肉特征,然后在门诊接受统一的颈椎牵引。采用最小绝对收缩和选择算子(LASSO)回归模型选择潜在相关特征,以预测颈椎牵引有效性的可能性。多变量逻辑回归分析用于建立预测模型,并呈现列线图。基于其校准、辨别力和临床实用性对列线图的性能进行评估。
通过LASSO回归模型,我们确定了4个预测因子,包括性别、良好的运动依从性、C5水平脂肪与肌肉的横截面积(CSA)之比(C5脂肪CSA/肌肉CSA)、C5水平脂肪与椎体的CSA之比(C5脂肪CSA/椎体肌肉CSA)。列线图在训练队列中提供了良好的校准和辨别力,曲线下面积(AUC)为0.704(95%CI,0.608 - 0.799),并且通过Spiegelhalter's Z检验,预测概率与实际概率之间具有良好的一致性(P = 0.835)。该模型在验证数据集中的辨别力可接受,AUC为0.691(95%CI,0.564 - 0.817)。决策曲线分析表明列线图具有临床实用性。
男性、良好的运动依从性、较低的C5脂肪CSA/椎体CSA以及较低的C5脂肪CSA/肌肉CSA可能是预测NSAID控制效果不佳的CNNP患者颈椎牵引有效性的有利特征。