Introduction After aneurysmal subarachnoid hemorrhage (SAH), both proximal and distal cerebral vasospasm can contribute to the development of delayed cerebral ischemia. Intra-arterial (IA) vasodilators are a mainstay of treatment for distal arterial vasospasm, but no methods of assessing the efficacy of interventions in real time have been established.
Objective To introduce a new method for continuous intraprocedural assessment of endovascular treatment for cerebral vasospasm.
Methods The premise of our approach was that distal cerebral arterial changes induce a consistent pattern in the morphological changes of intracranial pressure (ICP) pulse. This premise was demonstrated using a published algorithm in previous papers. In this study, we applied the algorithm to calculate the likelihood of cerebral vasodilation (VDI) and cerebral vasoconstriction (VCI) from intraprocedural ICP signals that are synchronized with injection of the IA vasodilator, verapamil. Cerebral blood flow velocities (CBFVs) on bilateral cerebral arteries were studied before and after IA therapy.
Results 192 recordings of patients with SAH were reviewed, and 27 recordings had high-quality ICP waveforms. The VCI was significantly lower after the first verapamil injection (0.47±0.017) than VCI at baseline (0.49±0.020, p<0.001). A larger dose of injected verapamil resulted in a larger and longer VDI increase. CBFV of the middle cerebral artery increases across the days before the injection of verapamil and decreases after IA therapy.
Conclusion This study provides preliminary validation of an algorithm for continuous assessment of distal cerebral arterial changes in response to IA vasodilator infusion in patients with vasospasm and aneurysmal SAH.
- cerebral vasospasm
- endovascular treatment
- intra-arterial treatment
- subarachnoid hemorrhage
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Contributors XH and SWH conceived and designed the study. XL, KG, NH, NK collected data. XL performed data analysis and drafted the manuscript. JRV, XH, SWH, NK, XL interpreted the data. All authors reviewed the manuscript and approved the submission.
Funding This work was supported by National Institutes of Health grant number (R01NS089771A1 and R01EB012031) and Middle Career Scientist Award, UCSF Institute for Computational Health Sciences.
Competing interests None declared.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data are available upon reasonable request. Please feel free to contact email@example.com.