Subarachnoid Hemorrhage (SAH) is a debilitating type of hemorrhagic stroke that affects ~50,000 annually in
the US. It is caused by a rupture of a cerebral aneurysm and hemorrhage into the subarachnoid space around
the brain. In those who survive the initial bleed, ~30% of the patients develop a serious secondary complication
called delayed cerebral ischemia (DCI). DCI typically occurs at 4-21 days after SAH and is characterized by focal
neurological deficits. Though DCI is a potentially preventable contributor to morbidity after SAH, interventional
studies targeting DCI have failed. A major hindrance in SAH research is our inability to prognosticate which SAH
patient will develop DCI. This leads to all SAH patients being observed for prolonged periods in the intensive
care unit leading to higher rates of in-hospital complications, potential delay in treatment and misappropriated
resources. Furthermore, because of the inability to risk-stratify SAH patients, randomized clinical trials require
large populations, are expensive and have failed to identify effective treatments. Inclusion of subjects who are
less likely to develop DCI, have led to exposing them to unwanted side effects from the treatment with no
potential benefits. Identification of patients who will develop DCI is an unmet clinical need. Currently, there are
no biomarkers with sufficient sensitivity and specificity to serve as a clinically useful screening test. This proposal
therefore addresses a major unmet clinical need: a lack of a biomarker that can prognosticate DCI. Through a
series of previous clinical studies, we have demonstrated that SAH leads to increases in systemic
pathophysiological responses. We showed that an early elevated pathophysiological response, quantified by
measuring plasma protein biomarkers, was associated with the development of DCI. We have expanded this
line of investigation and via targeted proteomic profiling in pilot cohorts. First, we identified 6 plasma proteins
that are prognostic of DCI within 48 hours of SAH. Second, using multivariate statistical and machine learning
approaches, we identified a biomarker signature (a combination of proteins) that improved DCI prognostication.
In this study, we propose to identify and confirm additional candidate biomarkers, develop and internally validate
protein detection technology and verify the proof of concept in a prospective cohort. Performance characteristics
of the candidate markers and the signature will be determined and validated for clinical use. Development of
effective, minimally invasive biomarkers for DCI prognostication will lead to significant improvements in SAH
management.
Public/Health/Relevance: SAH has detrimental effects on individual health and the economy as a whole. The proposed research is relevant to public health as it will improve prognostication of DCI (an avoidable complication after SAH). This would lead to discovery of proteins that prognosticate DCI, improve our understanding of the pathophysiology of DCI and lead to improvements in design of clinical trials targeting DCI.
