# Machine Learning of Physiological Waveforms and Electronic Health Record Data to Predict, Diagnose, and Treat Hemodynamic Instability in Surgical Patients

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $648,089

## Abstract

Project Summary / Abstract
If one could accurately predict who, when and why patients develop cardiorespiratory instability (CRI) during
surgery, then effective preemptive treatments could be given to improve postoperative outcome and more
effectively use healthcare resources. But signs of shock often occur late once organ injury is already present.
The goal of this proposal is to develop, validate, and test real-time intraoperative risk prediction tools based on
electronic health record (EHR) data and high-fidelity physiological waveforms to predict CRI and make the
databases of intraoperative data and waveforms used for these developments freely accessible. This is
extremely relevant because although 5.7 million Americans are admitted to an Intensive Care Units (ICU) in one
year, more than 42 millions undergo surgery annually. Previous and ongoing studies conducted in the ICU and
in the step down unit have built the architecture to collect real-time high-fidelity physiological waveform data
streams and integrate them with patient demographics from the EHR to build large data sets, and derive
actionable fused parameters based on machine learning (ML) analytics as well as display information in real
time at the bedside to drive clinical decision support (CDS) in the critical care setting. The goal of this proposal
is to apply these ML approaches to the complex and time compressed environment of high-risk surgery where
greater patient and disease variability exist and shorter period of time is available to deliver truly personalized
medicine approaches. The work will be initiated using an already existing annotated intraoperative database
from the University of California Irvine including EHR and high-fidelity waveform data. This operating room
database already exists and needs only to be extracted. This data will be used for the initial training and
development of the ML model that will then be tested on prospectively collected University of California Los
Angeles and University of Pittsburgh Medical Center databases. Simultaneously, this approach will use existing
knowledge of CRI patterns derived from previous step down unit / intensive care unit cohorts, MIMIC II data,
University of California Irvine data, and animal studies to create smart alarms and graphic user interface for
clinical decision support based on functional hemodynamic monitoring principles. The next step will then
leverage the focus on the issues and strengths of the intraoperative environment, some of which can be listed
as: 1) Known patients characteristics before surgery to define pre-stress baseline, allowing functional
hemodynamic monitoring stress evaluations, preconditioning, and other preoperative calibrations, 2) High
degree of direct observation and data density during most phases of surgery allowing close semi-autonomous
monitoring and titration of novel treatment algorithms early, 3) Defined stages in the initial part of surgery
(induction, intubation, skin incisio...

## Key facts

- **NIH application ID:** 9846016
- **Project number:** 5R01HL144692-02
- **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:** $648,089
- **Award type:** 5
- **Project period:** 2019-01-07 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9846016

## Citation

> US National Institutes of Health, RePORTER application 9846016, Machine Learning of Physiological Waveforms and Electronic Health Record Data to Predict, Diagnose, and Treat Hemodynamic Instability in Surgical Patients (5R01HL144692-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9846016. Licensed CC0.

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