# Biomedical Informatics Tools for Applied Perioperative Physiology

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $672,624

## Abstract

Project Summary / Abstract
Even though US hospitals have widely adopted electronic health record (EHR) documentation of patient care,
interoperability of these systems remains an issue, leading to challenges in data integration. In the operating
room (OR) setting, during surgery, physiological waveforms (arterial pressure, EKG, SpO2, central venous
pressure, etc.) represent a large source of information used by clinical monitors to extract and display information
in order for healthcare providers to make clinical decisions. Integration and synchronization of high-quality EHR
and physiological waveform data in large datasets of surgical patients would allow machine learning and deep
learning approaches to plumb these datasets for clinically relevant signatures that would promote advanced OR
patient monitoring systems to define present state, predict state trajectory, suggest effective counter measures
to minimize patients decompensated states, and define the usefulness and efficacy of new monitoring devices.
The objective of this proposal is to focus the resources of an interdisciplinary team from academia (University of
California Los Angeles (UCLA), University of California Irvine (UCI), and Carnegie Mellon University Computer
Sciences), industry (Edwards Lifesciences Critical Care), and clinical medicine (anesthesiology, surgery, and
critical care at UCLA, UCI, Beth Israel, and University of Pittsburgh Medical Center) to create, develop, and
organize large surgical datasets combining EHR and high fidelity physiological waveform data, to make these
datasets freely accessible, and to develop new predictive/forecasting monitoring systems for the surgical
patients. The study will begin with the development of a machine learning algorithm to predict cardiovascular
collapse during surgery. This algorithm development will be based on physiological signatures predictive of
cardiovascular collapse identified in the animal models of shock. The study hypothesis is that the combination
of two separate OR databases containing EHR and physiological waveforms will allow for training and
development of monitoring solutions, predictive and/or prescriptive analytics tools, clinical decision support, and
validate them on an independent, external validation database. The surgical setting is relevant because although
5.7 million Americans are admitted annually to an Intensive Care Unit, more than 50 million undergo surgery.
OR databases are unique in medicine because: 1) Changes occur quickly and the lead-time before an event is
compressed; 2) Knowledge of baseline/pre-stress status of surgical patients allows normalization, calibration,
and markedly enhances prediction; 3) Continuous and immediate presence of dense skilled acute care
practitioners allows faster implementation of complex treatment algorithms in the OR; and 4) Defined stages,
procedures, and stressors allow building large common relational database registries. By helping to focus the
provider's atte...

## Key facts

- **NIH application ID:** 9971901
- **Project number:** 1R01EB029751-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Maxime Cannesson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $672,624
- **Award type:** 1
- **Project period:** 2020-05-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9971901

## Citation

> US National Institutes of Health, RePORTER application 9971901, Biomedical Informatics Tools for Applied Perioperative Physiology (1R01EB029751-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9971901. Licensed CC0.

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