Objective Network analysis is an emerging tool for the study of complex systems. In the current report, the cascade of physiological and neurological changes following aneurysmal subarachnoid haemorrhage (SAH) was modelled as a complex system of interacting parameters. Graph theoretical analysis was then applied to identify parameters at critical topological junctions of the network, which may represent the most effective therapeutic targets.
Methods Correlation matrices were calculated using a combination of Pearson, polyserial and polychoric regressions among 50 variables collected from 120 participants (38 male; mean age 51 years) included in the CONSCIOUS-1 trial. Graph theoretical analysis was performed to identify important topological features within the network formed by the interactions among these variables. Non-parametric resampling was applied to determine thresholds for significance.
Results Several critical network hubs were identified, including the incidence of delayed ischaemic neurological deficit (DIND), anaemia and hypoalbuminaemia/hypoproteinaemia. While not significant hubs, World Federation of Neurosurgical Societies (WFNS) score and use of rescue therapy had widespread connections within the network. Patient sex and history of hypertension also strongly clustered with other variables. A subnetwork (module) was also identified, which was related to neurological outcomes including WFNS score, angiographic vasospasm, DIND, use of rescue therapy and hydrocephalus.
Interpretation Using graph theoretical analysis, we identify critical network topologies following SAH, which may serve as useful therapeutic targets. Importantly, we demonstrate that network analysis is a robust method to model complex interactions following SAH.
- SUBARACHNOID HAEMORRHAGE
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