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Predicting Pediatric Postoperative Complications using Waveform and Electronic Health Record Data

Award Number: K08EB038393
ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 03/10/2026
PERIOD OF PERFORMANCE END DATE: 02/28/2030

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Predicting Pediatric Postoperative Complications using Waveform and Electronic Health Record Data - PROJECT SUMMARY/ABSTRACT Nearly 5% of children undergo surgery annually in the US, with postoperative complications after pediatric surgery occurring in 9-14% of cases and 30-day mortality in 0.3-3.6% cases. Existing predictive models of pediatric postoperative complications rely on databases with limited features and have limited predictive ability beyond mortality. Additionally, whereas adult prediction models incorporate hemodynamic data and waveform data from pulse oximetry plethysmography, arterial pressure waveforms, and electrocardiograms, these data elements have not been utilized in the context of pediatric perioperative outcomes. Our team will apply artificial intelligence and machine learning approaches, using structured electronic health record and raw physiological waveform data, to develop and externally validate algorithms that predict pediatric postoperative complications. The primary objective of this award is to support Dr. Wingert with the necessary training to become a leader in perioperative pediatrics research. The career development and mentorship plans were developed to fill gaps in current knowledge and experience. With the support of the mentors, Dr. Wingert’s career development goals are to develop proficiency and independence in applying artificial intelligence and machine learning statistical methodologies, expertise in waveform analysis, and practical and theoretical expertise in Clinical Decision Support in Artificial Intelligence Implementation. The primary goal of this study is to develop and externally validate high-performing, implementable models that predict pediatric perioperative complications using physiologic waveform and electronic health record data. The aims of the proposal are as follows: Aim 1 is to develop and validate a model to predict pediatric postoperative complications based upon high-fidelity electronic health record data. In Aim 2, we will characterize baseline waveform features for children of different age cohorts and add the resultant waveform features to the prediction model to create a combined electronic health record-waveform model. Finally, in Aim 3, we will perform external validation of the combined electronic health record-waveform model.


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 = $174,861 )
2026	2026	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	1	3/10/2026	NEW	$174,861
														Subtotal = $174,861

Grand Total All Awards = $174,861

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Predicting Pediatric Postoperative Complications using Waveform and Electronic Health Record Data

Award Number: K08EB038393

ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 03/10/2026

PERIOD OF PERFORMANCE END DATE: 02/28/2030

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