# Machine Learning Risk Prediction of Kidney Disease After Extremely Preterm Birth

> **NIH NIH K23** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $200,718

## Abstract

PROJECT SUMMARY
The overall goals of this 3 year mentored patient-oriented research career development award are to develop
tools to improve recognition of kidney disease risks among extremely preterm born children and to support the
development of Dr. Keia Sanderson into an independent investigator. Dr. Sanderson is a motivated clinical
researcher at the University of North Carolina at Chapel Hill (UNC), with a specific interest in early recognition
of kidney disease in children. Over the next 3 years, Dr. Sanderson will work with her mentorship committee
toward her goal of independence building upon her prior 3 years of KL2-supported training. Her mentorship
committee includes experts in long-term neonatal outcomes research (O’Shea, Laughon), neonatal nephrology
(Askenazi), machine learning (Kosorok), qualitative research (Flythe), and clinical decision support science
(Kistler). Each member has an established track record of mentoring junior faculty, consistent peer-reviewed
support, and high research productivity. Dr. Sanderson’s career development objectives are to: 1) gain multi-
center research leadership experience; 2) develop skills in machine learning methods for large data risk
stratification; 3) develop qualitative research skills to support practical application of machine learning models;
4) acquire skills in research mentorship; and 5) improve publication and grant writing in preparation for NIH
R01 applications. The research and training environment at UNC is well established to support these
objectives. To achieve her career development objectives, Dr. Sanderson will participate in structured
coursework, conduct mentored research, and workshops through North Carolina Translational and Clinical
Sciences Institute R-Writing Group. The specific aims of this research project are to 1) utilize machine learning
approaches within large databases of prenatal, neonatal, and early life exposure variables to predict kidney
disease in adolescents born extremely premature; 2) develop and evaluate the usability and acceptability of a
prototype web-based risk stratification tool for pediatric kidney disease after extremely preterm birth using
variables hypothesized to predict kidney disease. Expanding on two existing prospective cohorts (Extremely
Low Gestational Age Newborn-Environmental influences on Child Health Outcomes (ELGAN-ECHO) and the
Preterm Erythropoietin Neuroprotection Trial (PENUT) cohorts), Dr. Sanderson will utilize clinical variables to
identify the combination of “at-highest risk,” and “at-risk” predictors for pediatric chronic kidney disease in
children after extremely preterm birth. This research will be the basis for R-level NIH applications to develop a
finalized web-based risk prediction tool informed by this proposal for public dissemination, to expand the use of
machine learning derived risk stratification tools to other medically complex pediatric populations, and to test
whether use of risk prediction tools increases...

## Key facts

- **NIH application ID:** 10815855
- **Project number:** 5K23DK131289-02
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Keia Sanderson
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $200,718
- **Award type:** 5
- **Project period:** 2023-07-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10815855

## Citation

> US National Institutes of Health, RePORTER application 10815855, Machine Learning Risk Prediction of Kidney Disease After Extremely Preterm Birth (5K23DK131289-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10815855. Licensed CC0.

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