Developing an Intraoperative EEG-Guided Precision Anesthesia Management System to Prevent Postoperative Delirium in Elderly and Patients at Risk for Alzheimer's & Related Dementias

Pascall Systems, Incorporated, Boston MA

Investigators

Abstract

Each day in the United States, over 100,000 patients receive general anesthesia, with about 40% being aged 60 and above. The current practice of general anesthesia is prone to over-sedating patients, either due to the lack of brain monitoring or using brain monitors with inaccurate indices. Over-sedation (at 28% incidence) contributes to post-operative delirium (POD) in elderly population (≥65yrs). Notably, 11% of these elderly patients suffer from Alzheimer’s Disease and Related Dementias, placing them at an even higher risk of POD. PASCALL was founded to introduce a novel neuroscience-based EEG-guided personalized anesthetic management. In our original grant, AG066325, we achieved a) (AG066325 Aim 1,2) the design, development, and FDA 510(k) pre-market clearance of a wireless anesthetic brain monitor, designated as M0 and b) (AG066325 Aim 3) the development of personalized algorithms to monitor anesthetic brain state in aging, dementia, and Alzheimer’s disease patients. In this grant, we propose concrete steps to commercialize M0 and the personalized algorithms developed in AG066325, collectively designated as PASCALL M1. We realize this innovation through 4 specific aims: Aim 1: Ensure the accuracy of M1 index in aging & ADRD patients by performing verification and validation on proprietary data sets (N=1597 retrospective, N=564 prospective); Aim 2: Implement an integrated IP, clinical and marketing strategy for M1; Aim 3: Accelerate the pathways to regulatory clearance and reimbursement for the M1 by: requesting FDA Breakthrough Device designation, evaluating suitable CMS programs, and existing modifying circumstances codes code; and Aim 4: Optimize the potential for adoption and effective use of the device by: conducting human factor testing with anesthesia caregivers in diverse hospital settings, and incorporate their feedback to refine the hardware and software for an improved user experience; creating an extensive education program that includes essential scientific knowledge, clinical simulations, and interactive peer-to-peer training to increase device accessibility; and optimizing manufacturing processes to an industry-aligned 65 percent device gross margin. If successful, this effort will bring neuroscience-based precision anesthesia care to millions of older patients, saving Medicare ~$44,000 per patient per year and eliminating needless suffering including long-term functional and cognitive disability.