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Evaluating and improving the efficacy of Extracorporeal Cardiopulmonary Resuscitation (ECPR) in pediatric patients using interactive Machine Learning

Award Number: R21HL167181
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 01/01/2024
PERIOD OF PERFORMANCE END DATE: 12/31/2026

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Evaluating and improving the efficacy of Extracorporeal Cardiopulmonary Resuscitation (ECPR) in pediatric patients using interactive Machine Learning - Project Summary/Abstract Pediatric cardiac arrest is a serious life-threatening problem affecting more than 15,000 hospitalized children each year in the US alone. Fewer than 50% of these children survive to hospital discharge, and neurological morbidity is common among those who survive. Importantly, pediatric cardiac arrest survival outcomes plateaued more than a decade ago, and there is hence a critical need for evidence-based and innovative therapeutic approaches. In particular, a signiﬁcant number of patients fail to achieve return of spontaneous circulation (ROSC) even af- ter 30 minutes of conventional CPR and may be candidates for what is termed Extracorporeal cardiopulmonary resuscitation (ECPR). ECPR is a treatment that involves the use of veno-arterial extracorporeal membrane oxy- genation (VA-ECMO) and has been used successfully for resuscitation from shock or cardiac arrest in adult and pediatric patients. It is often utilized as an alternative resuscitation intervention for in-hospital Cardiac Arrest (IHCA) patients. Currently it is not clear if and which subpopulation of cardiac arrest victims may beneﬁt from this intervention. Hence this proposal aims to develop advanced machine learning and signal processing algo- rithms using a sizeable, high-quality dataset which will identify speciﬁc underlying characteristics of the patient who would beneﬁt from ECPR. In particular, in Aim 1, we will develop a model using pre-arrest demographic, physiologic, and biochemical data to predict failure to achieve ROSC within 30 minutes of CPR. We will also de- velop a model using pre-arrest and intra-arrest physiologic data, including continuous invasive and non-invasive waveform data over the ﬁrst <5, <10, <15, <20, <30 minutes of CPR to predict failure to achieve ROSC. In Aim 2, we will identify pre- and intra-arrest characteristics from discontinuous data and continuous invasive and non-invasive waveform data of ECPR and develop a model to predict survivability to hospital discharge. Such a model would enable initiation of ECPR in critically ill patients who are unlikely to survive otherwise and hence lead to overall improvement of survival for in-hospital CPR patients.


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: 2026 ( Subtotal = $0 )
2026	2025	VILLANOVA UNIVERSITY IN THE STATE OF PENNSYLVANIA	800 E LANCASTER AVE	VILLANOVA	PA	19085	DELAWARE	USA	Cardiovascular Diseases Research	000	2	10/8/2025	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2025 ( Subtotal = $111,651 )
2025	2025	VILLANOVA UNIVERSITY IN THE STATE OF PENNSYLVANIA	800 E LANCASTER AVE	VILLANOVA	PA	19085	DELAWARE	USA	Cardiovascular Diseases Research	000	2	1/9/2025	NON-COMPETING CONTINUATION	$111,651
														Subtotal = $111,651

Issue Date FY: 2024 ( Subtotal = $110,809 )
2024	2024	VILLANOVA UNIVERSITY IN THE STATE OF PENNSYLVANIA	800 LANCASTER AVE	VILLANOVA	PA	19085	DELAWARE	USA	Cardiovascular Diseases Research	000	1	12/13/2023	NEW	$110,809
														Subtotal = $110,809

Grand Total All Awards = $222,460

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Evaluating and improving the efficacy of Extracorporeal Cardiopulmonary Resuscitation (ECPR) in pediatric patients using interactive Machine Learning

Award Number: R21HL167181

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 01/01/2024

PERIOD OF PERFORMANCE END DATE: 12/31/2026

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