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

Award Number: R01HL144692
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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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 incision) allowing ML approaches to build large common relational database registries, and 4) Defined surgical procedure and stressors (anesthesia induction, intra-abdominal air insufflation, and other surgery-specific interventions), which will alter the impact of CRI on measured variables.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2023 ( Subtotal = $720,500 )
2023	2023	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Cardiovascular Diseases Research	001	5	3/4/2023	NON-COMPETING CONTINUATION	$72,050
2023	2023	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Cardiovascular Diseases Research	000	5	1/13/2023	NON-COMPETING CONTINUATION	$648,450
														Subtotal = $720,500

Issue Date FY: 2022 ( Subtotal = $746,565 )
2022	2022	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Cardiovascular Diseases Research	001	4	6/8/2022	NON-COMPETING CONTINUATION	$74,657
2022	2022	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Cardiovascular Diseases Research	000	4	3/10/2022	NON-COMPETING CONTINUATION	$671,908
														Subtotal = $746,565

Issue Date FY: 2021 ( Subtotal = $734,363 )
2021	2021	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90095	LOS ANGELES	USA	Cardiovascular Diseases Research	000	3	1/18/2021	NON-COMPETING CONTINUATION	$660,927
2021	2021	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90095	LOS ANGELES	USA	Cardiovascular Diseases Research	001	3	5/24/2021	NON-COMPETING CONTINUATION	$73,436
														Subtotal = $734,363

Issue Date FY: 2020 ( Subtotal = $648,089 )
2020	2020	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90095	LOS ANGELES	USA	Cardiovascular Diseases Research	001	2	3/9/2020	NON-COMPETING CONTINUATION	$32,405
2020	2020	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90095	LOS ANGELES	USA	Cardiovascular Diseases Research	000	2	1/21/2020	NON-COMPETING CONTINUATION	$615,684
														Subtotal = $648,089

Issue Date FY: 2019 ( Subtotal = $694,170 )
2019	2019	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90095	LOS ANGELES	USA	Cardiovascular Diseases Research	000	1	1/6/2019	NEW	$694,170
														Subtotal = $694,170

Grand Total All Awards = $3,543,687

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

Award Number: R01HL144692

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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