Development of a Dynamic Nomogram for Predicting the Probability of Satisfactory Recovery after 6 Months for Cervical Traumatic Spinal Cord Injury

被引:5
|
作者
Yan, Xin [1 ]
He, Yaozhi [1 ]
Jia, Mengxian [1 ]
Yang, Jiali [2 ]
Huang, Kelun [1 ]
Zhang, Peng [1 ]
Lai, Jiaxin [1 ]
Chen, Minghang [1 ]
Fan, Shikang [1 ]
Li, Sheng [1 ]
Fan, Ziwei [1 ]
Teng, Honglin [1 ,3 ]
机构
[1] First Affiliated Hosp Wenzhou Med Univ, Dept Spine Surg, Wenzhou, Peoples R China
[2] Yuying Childrens Hosp Wenzhou Med Univ, Affiliated Hosp 2, Dept Pediat Allergy & Immunol, Wenzhou, Peoples R China
[3] First Affiliated Hosp Wenzhou Med Univ, Dept Spine Surg, Wenzhou 325000, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Cervical traumatic spinal cord injury; Nomogram; Prognosis; Risk factors; COMPRESSION; MODEL;
D O I
10.1111/os.13679
中图分类号
R826.8 [整形外科学]; R782.2 [口腔颌面部整形外科学]; R726.2 [小儿整形外科学]; R62 [整形外科学(修复外科学)];
学科分类号
摘要
ObjectiveCervical traumatic spinal cord injury (CTSCI) is a seriously disabling disease that severely affects the physical and mental health of patients and imposes a huge economic burden on patients and their families. Accurate identification of the prognosis of CTSCI patients helps clinicians to design individualized treatment plans for patients. For this purpose, a dynamic nomogram was developed to predict the recovery of CTSCI patients after 6 months. MethodsWe retrospectively included 475 patients with CTSCI in our institution between March 2013 and January 2022. The outcome variable of the current study was a satisfactory recovery of patients with CTSCI at 6 months. Univariate analyses and univariate logistic regression analyses were used to assess the factors affecting the prognosis of patients with CTSCI. Subsequently, variables (P < 0.05) were included in the multivariate logistic regression analysis to evaluate these factors further. Eventually, a nomogram model was constructed according to these independent risk factors. The concordance index (C-index) and the calibration curve were utilized to assess the model's predictive ability. The discriminating capacity of the prediction model was measured by the receiver operating characteristic (ROC) area under the curve (AUC). One hundred nine patients were randomly selected from 475 patients to serve as the center's internal validation test cohort. ResultsThe multivariate logistic regression model further screened out six independent factors that impact the recovery of patients with CTSCI. Including admission to the American Spinal Injury Association Impairment Scale (AIS) grade, the length of high signal in the spinal cord, maximum spinal cord compression (MSCC), spinal segment fractured, admission time, and hormonal therapy within 8 h after injury. A nomogram prediction model was developed based on the six independent factors above. In the training cohort, the AUC of the nomogram that included these predictors was 0.879, while in the test cohort, it was 0.824. The nomogram C-index incorporating these predictors was 0.872 in the training cohort and 0.813 in the test cohort, while the calibration curves for both cohorts also indicated good consistency. Furthermore, this nomogram was converted into a Web-based calculator, which provided individual probabilities of recovery to be generated for individuals with CTSCI after 6 months and displayed in a graphical format. ConclusionThe nomogram, including ASIA grade, the length of high signal in the spinal cord, MSCC, spinal segment fractured, admission time, and hormonal therapy within 8 h after injury, is a promising model to predict the probability of content recovery in patients with CTSCI. This nomogram assists clinicians in stratifying patients with CTSCI, enhancing evidence-based decision-making, and individualizing the most appropriate treatment.
引用
收藏
页码:1008 / 1020
页数:13
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