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Multi-institutional validation of a multi-modal machine learning algorithm to predict and reduce acute care during cancer therapy

Award Number: R01CA277782
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 03/01/2023
PERIOD OF PERFORMANCE END DATE: 02/29/2028

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Multi-institutional validation of a multi-modal machine learning algorithm to predict and reduce acute care during cancer therapy - PROJECT ABSTRACT An estimated 650,000 patients with cancer receive systemic therapy or radiation therapy (RT) annually in the United States. Many of these patients undergoing outpatient cancer therapy will require acute care with an emergency department visit or hospital admission due to symptoms from treatment, disease, or comorbidities. This can impact cancer outcomes, patient treatment decisions, and costs to patients and the healthcare system. While there has been much enthusiasm for artificial intelligence and machine learning (ML) to improve healthcare delivery, high quality prospective data are lacking, especially across diverse clinical practice settings. We previously completed one of the first randomized controlled studies in healthcare ML, demonstrating that ML based on EHR data can accurately generate personalized predictions and guide supportive interventions to decrease acute care requirements and costs in patients undergoing RT and chemoradiotherapy (CRT) (NCT04277650). We have also developed a ML model for predicting hospitalizations based on prospective clinical trials of daily step counts collected in patients undergoing CRT. The research objective of this application is to leverage a geographically, racially, socioeconomically, and technically diverse network of healthcare settings and patients to assess and maximize how accurately and equitably these approaches generalize. Our team includes the University of California, San Francisco (UCSF), Duke University, Beth Israel Deaconess Medical Center, Essentia Health in Duluth, MN and Ashland, WI, Washington Hospital in Fremont, CA, Duke Regional Hospital in Durham, NC, and Duke Raleigh Hospital in Raleigh, NC. Specifically, we seek to: (1) prospectively evaluate the validity of an EHR-based acute care prediction ML algorithm across our network and establish a framework for equity, generalizability, and portability and (2) validate our existing patient-generated health data (PGHD; step count) models that predict hospitalization during CRT at a second institution and integrate with our EHR-based ML algorithm to enhance prediction of acute care needs. We hypothesize that our approaches will be accurate across institutions though require adjustments for both generalizability and fairness, and that EHR- and PGHD-based approaches will offer complementary predictive performance. The long-term goal is to develop informatics-based tools that can be broadly and equitably deployed to improve the delivery of cancer care and subsequent treatment outcomes. This research will generate data regarding the generalizability and fairness of EHR- and PGHD-based approaches and a platform for a future multi-institutional randomized controlled trial.


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 = $374,948 )
2026	2026	REGENTS OF THE UNIVERSITY OF CALIFORNIA, SAN FRANCISCO, THE	1855 FOLSOM ST STE 425	SAN FRANCISCO	CA	94103	SAN FRANCISCO	USA	Cancer Cause and Prevention Research	000	4	1/26/2026	NON-COMPETING CONTINUATION	$337,453
2026	2026	REGENTS OF THE UNIVERSITY OF CALIFORNIA, SAN FRANCISCO, THE	1855 FOLSOM ST STE 425	SAN FRANCISCO	CA	94103	SAN FRANCISCO	USA	Cancer Cause and Prevention Research	001	4	4/20/2026	NON-COMPETING CONTINUATION	$37,495
														Subtotal = $374,948

Issue Date FY: 2025 ( Subtotal = $475,152 )
2025	2025	REGENTS OF THE UNIVERSITY OF CALIFORNIA, SAN FRANCISCO, THE	1855 FOLSOM ST STE 425	SAN FRANCISCO	CA	94103	SAN FRANCISCO	USA	Cancer Cause and Prevention Research	002	3	8/4/2025	NON-COMPETING CONTINUATION	$99,999
2025	2025	REGENTS OF THE UNIVERSITY OF CALIFORNIA, SAN FRANCISCO, THE	1855 FOLSOM ST STE 425	SAN FRANCISCO	CA	94103	SAN FRANCISCO	USA	Cancer Cause and Prevention Research	000	3	2/14/2025	NON-COMPETING CONTINUATION	$337,638
2025	2025	REGENTS OF THE UNIVERSITY OF CALIFORNIA, SAN FRANCISCO, THE	1855 FOLSOM ST STE 425	SAN FRANCISCO	CA	94103	SAN FRANCISCO	USA	Cancer Cause and Prevention Research	001	3	7/25/2025	NON-COMPETING CONTINUATION	$37,515
														Subtotal = $475,152

Issue Date FY: 2024 ( Subtotal = $356,096 )
2024	2024	REGENTS OF THE UNIVERSITY OF CALIFORNIA, SAN FRANCISCO, THE	1855 FOLSOM ST STE 425	SAN FRANCISCO	CA	94103	SAN FRANCISCO	USA	Cancer Cause and Prevention Research	001	2	4/17/2024	NON-COMPETING CONTINUATION	$18,742
2024	2024	REGENTS OF THE UNIVERSITY OF CALIFORNIA, SAN FRANCISCO, THE	1855 FOLSOM ST STE 425	SAN FRANCISCO	CA	94143	SAN FRANCISCO	USA	Cancer Cause and Prevention Research	000	2	2/5/2024	NON-COMPETING CONTINUATION	$337,354
														Subtotal = $356,096

Issue Date FY: 2023 ( Subtotal = $389,126 )
2023	2023	REGENTS OF THE UNIVERSITY OF CALIFORNIA, SAN FRANCISCO, THE	1855 FOLSOM ST STE 425	SAN FRANCISCO	CA	94143	SAN FRANCISCO	USA	Cancer Cause and Prevention Research	000	1	2/27/2023	NEW	$389,126
														Subtotal = $389,126

Grand Total All Awards = $1,595,322

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Multi-institutional validation of a multi-modal machine learning algorithm to predict and reduce acute care during cancer therapy

Award Number: R01CA277782

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 03/01/2023

PERIOD OF PERFORMANCE END DATE: 02/29/2028

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