Data_Sheet_5_Novel Subgroups in Subarachnoid Hemorrhage and Their Association With Outcomes—A Systematic Review and Meta-Regression.DOCX (7.96 MB)
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Data_Sheet_5_Novel Subgroups in Subarachnoid Hemorrhage and Their Association With Outcomes—A Systematic Review and Meta-Regression.DOCX

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posted on 11.01.2021, 04:42 by Ming-Dong Wang, Qian-Hui Fu, Ming-Jing Song, Wen-Bin Ma, John-H. Zhang, Zhan-Xiang Wang

Background and Purpose: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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