# Building a Platform for Precision Anesthesia for the Geriatric Surgical Patient

> **NIH NIH R21** · STANFORD UNIVERSITY · 2020 · $422,731

## Abstract

PROJECT SUMMARY
Perioperative neurocognitive disorders (PNCD) affect about 25% of patients in the period
following surgery, and can persist for months or years in 10% of geriatric surgical patients. The
presence of acute cognitive disturbances post-surgery increases the risk of patients eventually
developing dementias such as Alzheimer's disease. Poor cognitive outcomes lead to longer
hospital stays, decreased quality of life, and increased morbidity and mortality. Unfortunately,
about half of elderly individuals require a surgical procedure at some time in their remaining
years, and no interventions exist to prevent PNCD because the etiology is unclear. One of the
main challenges in identifying the factors leading to chronic cognitive impairment is the lack of
routine, comprehensive cognitive testing in the surgical care plan. This shortcoming is in part
due to the lack of mobile, easy-to-use cognitive testing platforms. To establish the perioperative
biomarkers contributing to cognitive decline, we will (1) integrate routine, comprehensive
cognitive testing pre- and post-surgery, and (2) build a database and an analysis platform to
mine this multidimensional dataset. Together, this aims to yield accurate models to pre-identify
patients at risk and create targeted therapeutic interventions.
We propose to build the foundational infrastructure for a precision medicine approach in
geriatric surgical patients. In the R21 phase, we will build a novel comprehensive database of
demographic and risk factor questionnaire responses, banked blood specimens, intraoperative
electroencephalography (EEG), and inclusive cognitive testing. These data will be collected
throughout the patient interaction, from the preoperative appointment through a year following
the surgical procedure and available to other research teams. We will incorporate cognitive
testing and collect large-scale data in the geriatric surgical setting, establishing a new precedent
for subsequent multidisciplinary studies. This structure will afford us the opportunity to
accurately track cognitive decline towards chronic conditions, such as dementia.
In the R33 phase, we will develop an analysis platform capable of mining this multidimensional
dataset. This phase will include EEG analysis and deep immune profiling using mass cytometry.
Layered on these analyses we will build innovative machine learning tools to identify features
and interactions contributing to both acute and chronic PNCD pathology. Our novel machine
learning tools use prior knowledge to refine feature selection, thus addressing a common
challenge faced by clinical research studies (having many measurements in a limited patient
population), and will thus be of broad interest to other clinical research projects.

## Key facts

- **NIH application ID:** 10057189
- **Project number:** 1R21AG065744-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** David Raymond Drover
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $422,731
- **Award type:** 1
- **Project period:** 2020-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10057189

## Citation

> US National Institutes of Health, RePORTER application 10057189, Building a Platform for Precision Anesthesia for the Geriatric Surgical Patient (1R21AG065744-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10057189. Licensed CC0.

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