Objectives

Although laboratory parameters have long been recognized as indicators of outcome of traumatic brain injury (TBI), it remains a challenge to predict the recovery of disorder of consciousness (DOC) in severe brain injury including TBI. Recent advances have shown an association between alterations in brain connectivity and recovery from DOC. In the present study, we developed a prognostic model of DOC recovery via a combination of laboratory parameters and resting-state functional magnetic resonance imaging (fMRI).

Methods

Fifty-one patients with DOC (age = 52.3 ± 15.2 y, male/female = 31/20) were recruited from Hangzhou Hospital of Zhejiang CAPR and were sub-grouped into conscious (n = 34) and unconscious (n = 17) groups based upon their Glasgow Outcome Scale-Extended (GOS-E) scores at 12-month follow-ups after injury. Resting-state functional connectivity, network nodal measures (centrality), and laboratory parameters were obtained from each patient and served as features for support vector machine (SVM) classifications.

Results

We found that functional connectivity was the most accurate single-domain model (ACC: 70.1% ± 4.5%, P = 0.038, 1000 permutations), followed by degree centrality, betweenness centrality, and laboratory parameters. The stacked multi-domain prognostic model (ACC: 73.4% ± 3.1%, P = 0.005, 1000 permutations) combining all single-domain models yielded a significantly higher accuracy compared to that of the best-performing single-domain model (P = 0.002).

Conclusion

Our results suggest that laboratory parameters only contribute to the outcome prediction of DOC patients, whereas combining information from neuroimaging and clinical parameters may represent a strategy to achieve a more accurate prognostic model, which may further provide better guidance for clinical management of DOC patients.

中文翻译：

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使用静息态 fMRI 和实验室参数的意识障碍多领域预后模型

目标

尽管实验室参数长期以来被认为是创伤性脑损伤 (TBI) 结果的指标，但预测包括 TBI 在内的严重脑损伤的意识障碍 (DOC) 的恢复仍然是一个挑战。最近的进展表明大脑连接的改变与 DOC 的恢复之间存在关联。在本研究中，我们通过实验室参数和静息状态功能磁共振成像 (fMRI) 的组合开发了 DOC 恢复的预后模型。

方法

51 名 DOC 患者（年龄 = 52.3 ± 15.2 岁，男/女 = 31/20）从浙江 CAPR 杭州医院招募，并被分为清醒（n  = 34）和无意识（n  = 17）组根据他们在受伤后 12 个月的随访中的格拉斯哥结局量表扩展 (GOS-E) 评分。从每位患者获得静息状态功能连接、网络节点测量（中心性）和实验室参数，并作为支持向量机 (SVM) 分类的特征。

结果

我们发现功能连接是最准确的单域模型（ACC：70.1% ± 4.5%，P  = 0.038，1000 个排列），其次是度中心性、中介中心性和实验室参数。与性能最佳的单域模型相比，组合所有单域模型的堆叠多域预后模型（ACC：73.4% ± 3.1%，P = 0.005，1000 个排列）产生了显着更高的准确度（P  = 0.002 ）。

结论

我们的研究结果表明，实验室参数仅有助于 DOC 患者的预后预测，而结合来自神经影像学和临床参数的信息可能代表实现更准确预后模型的策略，这可能进一步为 DOC 患者的临床管理提供更好的指导。