Department of Neurology and Brain Medical Centre, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China.
Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China.
Brain Imaging Behav. 2021 Aug;15(4):1966-1976. doi: 10.1007/s11682-020-00390-8. Epub 2020 Oct 11.
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).
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.
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).
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.
尽管实验室参数长期以来一直被认为是创伤性脑损伤（TBI）结局的指标，但预测包括 TBI 在内的严重脑损伤患者意识障碍（DOC）恢复仍然是一个挑战。最近的进展表明，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态功能磁共振成像（fMRI）相结合，开发了一种用于预测 DOC 恢复的预后模型。
从浙江脑损伤诊治技术研究重点实验室附属杭州医院招募了 51 名患有 DOC 的患者（年龄=52.3±15.2 岁，男/女=31/20），并根据损伤后 12 个月的格拉斯哥预后评分扩展量表（GOS-E）将他们分为有意识（n=34）和无意识（n=17）两组。从每位患者中获取静息态功能连接、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。
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 次置换），其次是度中心性、介数中心性和实验室参数。结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 次置换）与表现最佳的单域模型相比，准确性显著提高（P=0.002）。
我们的结果表明，实验室参数仅有助于预测 DOC 患者的结局，而结合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。
