Wang Ming-Dong, Fu Qian-Hui, Song Ming-Jing, Ma Wen-Bin, Zhang John-H, Wang Zhan-Xiang
Department of Neurosurgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
School of Pharmacy, Minzu University of China, Beijing, China.
Front Aging Neurosci. 2021 Jan 11;12:573454. doi: 10.3389/fnagi.2020.573454. eCollection 2020.
Subarachnoid hemorrhage (SAH) has long been classified into two main forms, aneurysmal SAH (aSAH) and non-aneurysmal SAH (naSAH), but the related risk factors for aSAH and naSAH are heterogeneous. Our objective was to determine the risk factors for SAH of known or unknown origin with respect to diagnostic evaluation in a large patient cohort. We sought to determine whether our classification system can further predict middle long-term stroke and death. We performed a systematic review and meta-analysis to identify risk factors for each SAH subtype. The discovery phase analyzed 11 risk factors from case studies in the literature. Kruskal-Wallis, Cox regression, logistic regression, and Kaplan-Meier analyses were used to compare the two groups. A total of 14,904 (34.53%) male and 22,801 (52.84%) female patients were eligible for this study. At a median follow-up of 45.6 months, the 5-years overall survival was 97.768% (95% CI: 0.259-0.292) for aSAH patients and 87.904% (95% CI: 1.459-1.643) for naSAH patients. The 10-years survival rate was 93.870% (95% CI: 2.075-3.086) and 78.115% (95% CI: 2.810-3.156), respectively. Multi-risk factor subgroups showed significant intergroup differences. We identified eight risk factors (drugs, trauma, neoplastic, vessels lesion, inflammatory lesion, blood disease, aneurysm, peri-mesencephalic hemorrhage) using logistic regression, which were optimally differentiated among the aSAH [aSAH-S (AUC: 1), a-d-SAH (AUC: 0.9998), aSAH-T (AUC: 0.9199), aSAH-N (AUC: 0.9433), aSAH-V (AUC: 1), aSAH-I (AUC: 0.9954), a-bd-SAH (AUC: 0.9955)] and naSAH [na-pmSAH (AUC: 0.9979), na-ni-ivl-SAH (AUC: 1), na-t-SAH (AUC: 0.9997), na-ne-SAH (AUC: 0.9475), na-d-SAH (AUC: 0.7676)] subgroups. These models were applied in a parallel cohort, showing eight risk factors plus survival rates to predict the prognosis of SAH. The classification of risk factors related to aSAH and naSAH is helpful in the diagnosis and prediction of the prognosis of aSAH and naSAH patients. Further validation is needed in future clinical applications.
蛛网膜下腔出血(SAH)长期以来被分为两种主要形式,即动脉瘤性SAH(aSAH)和非动脉瘤性SAH(naSAH),但aSAH和naSAH的相关危险因素是异质性的。我们的目的是在一个大型患者队列中确定已知或未知病因的SAH在诊断评估方面的危险因素。我们试图确定我们的分类系统是否能进一步预测中长期中风和死亡情况。我们进行了一项系统评价和荟萃分析,以确定每种SAH亚型的危险因素。发现阶段分析了文献中病例研究的11个危险因素。使用Kruskal-Wallis检验、Cox回归、逻辑回归和Kaplan-Meier分析来比较两组。共有14904名(34.53%)男性和22801名(52.84%)女性患者符合本研究条件。在中位随访45.6个月时,aSAH患者的5年总生存率为97.768%(95%CI:0.259 - 0.292),naSAH患者为87.904%(95%CI:1.459 - 1.643)。10年生存率分别为93.870%(95%CI:2.075 - 3.086)和78.115%(95%CI:2.810 - 3.156)。多危险因素亚组显示出显著的组间差异。我们使用逻辑回归确定了八个危险因素(药物、创伤、肿瘤、血管病变、炎症性病变、血液疾病、动脉瘤、中脑周围出血),这些因素在aSAH [aSAH-S(AUC:1),a-d-SAH(AUC:0.9998), aSAH-T(AUC:0.9199), aSAH-N(AUC:0.9433), aSAH-V(AUC:1), aSAH-I(AUC:0.9954), a-bd-SAH(AUC:0.9955)]和naSAH [na-pmSAH(AUC:0.9979), na-ni-ivl-SAH(AUC:1), na-t-SAH(AUC:0.9997), na-ne-SAH(AUC:0.9475), na-d-SAH(AUC:0.7676)]亚组之间得到了最佳区分。这些模型应用于一个平行队列,显示八个危险因素加上生存率可预测SAH的预后。与aSAH和naSAH相关的危险因素分类有助于aSAH和naSAH患者的诊断和预后预测。未来临床应用中还需要进一步验证。