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Development and Validation of a Prediction Model for Longitudinal Fall Risk in Older Adults

Award Number: K25AG068253
ORGANIZATION: NATIONAL INSTITUTE ON AGING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 06/15/2021
PERIOD OF PERFORMANCE END DATE: 03/31/2027

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Development and Validation of a Prediction Model for Longitudinal Fall Risk in Older Adults - ABSTRACT My goal is to establish myself as an independent biostatistical researcher who develops and validates prediction models that are critical to identifying older adults at high risk of adverse health outcomes using novel machine learning methodology. My passion for developing prediction models for health outcomes in older adults stems from my desire to help people, especially older adults who are vulnerable. I developed a machine learning method called Binary Mixed Model (BiMM) forest, which is particularly suited for developing prediction models for repeated measures of outcomes in older adults because it accommodates dynamic fluctuations over time which are common in aging (e.g., functional status). Environment: Wake Forest School of Medicine is a nationally-recognized leader in geriatric research and provides an outstanding environment for accomplishing my goals. Mentors: I have an excellent interdisciplinary mentoring team consisting of experts in aging, biostatistics and informatics who are dedicated to supporting me in completing the research and training aims proposed in this K25 grant. Training: I will complete activities aimed to address gaps in my training with the guidance of my mentoring team. My Training Objectives are to 1) acquire knowledge about gerontology and geriatrics, 2) deepen my understanding of missing data mechanisms and techniques, 3) learn about the appropriate use of electronic health record (EHR) data for research purposes, and 4) develop leadership, networking, communication and grant writing skills. To accomplish these objectives, I will attend conferences, seminars, journal club, case conferences and writing workshops, observe a geriatric clinician, and complete coursework. The K25 grant will allow me to extend previous training, which will provide me superior knowledge, experience and skills for my future as a biostatistical researcher in aging. Research: Early identification of older adults at-risk of falling is critical so that preventative care plans may be implemented to improve patient outcomes and reduce burden on the health care system. Most current prediction models cannot handle repeated measurements over time and have not been used with EHR data. I propose to use my BiMM forest method to develop a prediction model for fall risk over time. My Research Specific Aims are to 1) impute (fill in) missing predictor data in Health, Aging, and Body Composition (Health ABC) Study with a novel machine learning method (BiMM forest); 2) develop a prediction model for identifying older adults at-risk for falls; and 3) assess the feasibility of using EHR data to externally validate the prediction model. The prediction model developed in this proposal will aid in identification of at-risk individuals for falls, allowing providers to intervene early to reduce the burden of falls for patients, caregivers and healthcare systems. Results from this grant will provide a basis for future R01 submissions to further validate the prediction model using EHR data from multiple health care centers and evaluate the clinical utility of the model in practice.


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	WAKE FOREST UNIVERSITY HEALTH SCIENCES	MEDICAL CENTER BLVD	WINSTON SALEM	NC	27157	FORSYTH	USA	Aging Research	000	5	2/11/2026	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2025 ( Subtotal = $111,087 )
2025	2025	WAKE FOREST UNIVERSITY HEALTH SCIENCES	MEDICAL CENTER BLVD	WINSTON SALEM	NC	27157	FORSYTH	USA	Aging Research	000	5	7/24/2025	NON-COMPETING CONTINUATION	$111,087
														Subtotal = $111,087

Issue Date FY: 2024 ( Subtotal = $111,087 )
2024	2024	WAKE FOREST UNIVERSITY HEALTH SCIENCES	MEDICAL CENTER BLVD	WINSTON SALEM	NC	27157	FORSYTH	USA	Aging Research	000	4	4/12/2024	NON-COMPETING CONTINUATION	$111,087
														Subtotal = $111,087

Issue Date FY: 2023 ( Subtotal = $115,407 )
2023	2023	WAKE FOREST UNIVERSITY HEALTH SCIENCES	MEDICAL CENTER BLVD	WINSTON SALEM	NC	27157	FORSYTH	USA	Aging Research	000	3	3/10/2023	NON-COMPETING CONTINUATION	$115,407
														Subtotal = $115,407

Issue Date FY: 2022 ( Subtotal = $118,247 )
2022	2022	WAKE FOREST UNIVERSITY HEALTH SCIENCES	MEDICAL CENTER BLVD	WINSTON SALEM	NC	27157	FORSYTH	USA	Aging Research	000	2	3/15/2022	NON-COMPETING CONTINUATION	$118,247
														Subtotal = $118,247

Issue Date FY: 2021 ( Subtotal = $122,627 )
2021	2021	WAKE FOREST UNIVERSITY HEALTH SCIENCES	MEDICAL CENTER BLVD	WINSTON SALEM	NC	27157	FORSYTH	USA	Aging Research	000	1	6/15/2021	NEW	$122,627
														Subtotal = $122,627

Grand Total All Awards = $578,455

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Development and Validation of a Prediction Model for Longitudinal Fall Risk in Older Adults

Award Number: K25AG068253

ORGANIZATION: NATIONAL INSTITUTE ON AGING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 06/15/2021

PERIOD OF PERFORMANCE END DATE: 03/31/2027

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