Kudos to Xiao Hu, Ann Henshaw Gardiner Distinguished Professor of Nursing, and his entire team for the submission of their NIH R01 application entitled: "Learning to Predict Delayed Cerebral Ischemia with Novel Continuous Cerebral Arterial State Index." This proposal requests funding for a five-year period with a start date of September 1, 2020.

Delayed cerebral ischemia (DCI) is the most devastating complication after aneurysmal subarachnoid hemorrhage (aSAH) and has an incidence rate of 30 percent. Current practice relies on intermittent assessment of neurological status and daily cerebral blood flow velocity (CBFV) by Transcranial Doppler ultrasound (TCD) to guide medical management to prevent DCI. Only after medical management fails is endovascular treatment (EVT), including intraarterial vasodilator infusion and/or intracranial angioplasty, initiated. This reactive practice does not account for early predictors of DCI and may miss the optimal EVT window at an early stage of DCI development before symptoms or severe deviations from normal hemodynamics.

The goal of this project is to develop algorithms to predict DCI and related targets at an early stage in their development. An accurate prediction of DCI will enable a more proactive strategy to prevent and treat the underlying cause of DCI. The following three aims will be pursued towards the goal of the project: 1) Develop aSAH-specific intracranial pressure (ICP) pulse-based cerebral arterial state index; 2) Develop and validate predictive models of targets related to delayed cerebral ischemia after aSAH; 3) Conduct a prospective institution-specific adaption and validation of the developed models.

Our DCI predictive algorithms only need data available in current clinical practice hence they can be readily adopted. If validated, these algorithms will enable clinicians to monitor risk of DCI continuously and to proactively deliver appropriate treatment. The proposed prospective study of algorithm implementation and adaptation will well prepare future clinical trials to test the efficacy of algorithm-informed interventions.

Thank you to all those who gave their time and expertise in guiding the application process as well as to all those who helped to prepare for this submission.