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一种用于预测颈椎脊髓损伤后不可逆性神经功能障碍概率的动态列线图:基于临床特征和 MRI 数据的研究。

A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data.

机构信息

Department of Orthopaedics, People's Hospital of Chongqing Banan District, Chongqing, China.

Department of Orthopaedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Street, Jiangyang District, Sichuan, 646000, China.

出版信息

BMC Musculoskelet Disord. 2023 Jun 5;24(1):459. doi: 10.1186/s12891-023-06570-z.

Abstract

BACKGROUND

Irreversible neurological dysfunction (IND) is an adverse event after cervical spinal cord injury (CSCI). However, there is still a shortage of objective criteria for the early prediction of neurological function. We aimed to screen independent predictors of IND and use these findings to construct a nomogram that could predict the development of neurological function in CSCI patients.

METHODS

Patients with CSCI attending the Affiliated Hospital of Southwest Medical University between January 2014 and March 2021 were included in this study. We divided the patients into two groups: reversible neurological dysfunction (RND) and IND. The independent predictors of IND in CSCI patients were screened using the regularization technique to construct a nomogram, which was finally converted into an online calculator. Concordance index (C-index), calibration curves analysis and decision curve analysis (DCA) evaluated the model's discrimination, calibration, and clinical applicability. We tested the nomogram in an external validation cohort and performed internal validation using the bootstrap method.

RESULTS

We enrolled 193 individuals with CSCI in this study, including IND (n = 75) and RND (n = 118). Six features, including age, American spinal injury association Impairment Scale (AIS) grade, signal of spinal cord (SC), maximum canal compromise (MCC), intramedullary lesion length (IMLL), and specialized institution-based rehabilitation (SIBR), were included in the model. The C-index of 0.882 from the training set and its externally validated value of 0.827 demonstrated the model's prediction accuracy. Meanwhile, the model has satisfactory actual consistency and clinical applicability, verified in the calibration curve and DCA.

CONCLUSION

We constructed a prediction model based on six clinical and MRI features that can be used to assess the probability of developing IND in patients with CSCI.

摘要

背景

不可逆性神经功能障碍(IND)是颈椎脊髓损伤(CSCI)后的一种不良事件。然而,对于神经功能的早期预测仍然缺乏客观标准。我们旨在筛选 IND 的独立预测因子,并利用这些发现构建一个列线图,以预测 CSCI 患者神经功能的发展。

方法

本研究纳入了 2014 年 1 月至 2021 年 3 月期间在西南医科大学附属医院就诊的 CSCI 患者。我们将患者分为两组:可逆性神经功能障碍(RND)和 IND。使用正则化技术筛选 CSCI 患者 IND 的独立预测因子,构建列线图,最终转换为在线计算器。一致性指数(C 指数)、校准曲线分析和决策曲线分析(DCA)评估模型的判别、校准和临床适用性。我们在外部验证队列中测试了该列线图,并使用自举法进行内部验证。

结果

我们共纳入了 193 例 CSCI 患者,其中包括 IND(n=75)和 RND(n=118)。模型中包括 6 个特征,包括年龄、美国脊髓损伤协会损伤分级(AIS)、脊髓信号、最大椎管狭窄程度(MCC)、脊髓内病变长度(IMLL)和专科机构康复(SIBR)。来自训练集的 0.882 的 C 指数及其外部验证值 0.827 表明了模型的预测准确性。同时,该模型在校准曲线和 DCA 中验证了具有良好的实际一致性和临床适用性。

结论

我们构建了一个基于 6 个临床和 MRI 特征的预测模型,可用于评估 CSCI 患者发生 IND 的概率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e60/10240743/4aff5a348eb9/12891_2023_6570_Fig1_HTML.jpg

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