Novel Subgroups in Subarachnoid Hemorrhage and Their Association with Outcomes – A Systematic Review and Meta-Regression Analysis

Autor: Zhan-Xiang Wang, John-H. Zhang, Qian-Hui Fu, Ming-Dong Wang, Wenbin Ma, Ming-Jing Song
Rok vydání: 2019
Předmět:
Zdroj: SSRN Electronic Journal.
ISSN: 1556-5068
Popis: Background: For a long time, the international classification of subarachnoid hemorrhage classified into two main forms, type aneurysmal SAH(a-SAH) and type non-aneurysmal SAH (na-SAH), but related risk factors for a-SAH and na-SAH is heterogeneous. An optimizing classification system could provide a powerful tool to predict outcome of SAH, individualise treatment and identify individuals with risk factors associated with the merger at diagnosis. Methods: From clinical practice-led multiple risk factors search was conducted in the context of multiple database studies in patients with diagnosed SAH. we performed a systematic review and using meta-analysis to estimate the subtyping. The discovery phase measured 11 risk factors in aSAH and naSAH group and used the regression model to Calculate, evaluated interaction between study variable, with all analyses adjusted for selected SAH risk factors. Findings: Multi-risk factor subgroups showed significant intergroup differences. Logistic regression identified 9 risk factors, optimally differentiated between aSAH subgroup [aSAH-S(AUC:1),a-d-SAH(AUC:0.9998),aSAH-T(AUC:0.9199),a-SAH-N(AUC:0.9433), aSAH-V (AUC:1), aSAH-I (AUC:0.9954), a-bd-SAH (AUC:0.9955)], and five [(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) subgroup]. aSAH-S subgroup (Major independent risk factors)had significantly higher risk than other subgroup in aSAH. na-ni-ivl-SAH had the higher risk of mortality. These models applied in parallel cohort, nine risk factors plus survival ratio predicted SAH progression to outcome. Interpretation: We stratified these risk factors in 12 subgroups with differing associated with disease. This novel substratification might prediction of stroke course, and provides information about underlying disease mechanisms, thereby guiding choice of therapy. Funding Statement: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Interests: The authors declare no competing interests. Ethical Approval Statement: Not required.
Databáze: OpenAIRE