# Examining Racial Disparities in Predictive Modeling Among Survivors of Critical Illness

> **NIH NIH F32** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $90,932

## Abstract

PROJECT SUMMARY/ABSTRACT:
Survivors of critical illness often face significant challenges, such as neurocognitive disorders, physical
disabilities, and respiratory limitations. Healthcare disparities by race heighten such challenges. Risk
prediction tools designed to identify high-risk patients for interventions such as post-acute care clinics may
exacerbate existing bias among racial and ethnic groups. Intending to address bias in prediction tools for
survivors of critical illness, our team brings expertise in health services research focusing on healthcare
disparities, statistical modeling, and causal inference. In previous work, we have highlighted racial differences
in patients with cardiovascular disease and COVID-19 across different healthcare systems, including the
Veterans Health Administration (VHA). Furthermore, we have begun exploring outcomes among survivors of
critical illness including mortality and re-admission rates and developed an innovative post-ICU care model
showing early indications of reducing hospital readmissions, increasing hospital-free days, and reducing
mortality across diverse patient populations. As an F32 grant recipient, I will integrate and build on the
expertise of my mentorship to identify and characterize racial disparities within three datasets of critical care
illness survivors as defined by mortality, 90-day re-admissions, and hospital-free days (HFDs) at 90 days. In
parallel, I will compare bias within two statistical models used to stratify patients by one-year mortality: (1) the
Care Assessment Needs (CAN) score, a mortality risk model widely used to guide interventions among
Veterans, and (2) the PREDICT score, a simplified one-year mortality risk model used at first patient contact to
guide interventions such as palliative care consultation. Statistical fairness is an emerging concept geared
toward reducing bias within statistical models and algorithms. To address any identified bias within our models,
our team will employ novel approaches to achieve statistical fairness, including double prioritization. In addition
to identifying healthcare disparities within a population of increasing care complexity, my proposal investigates
statistical models as underlying contributors to healthcare disparities, aiming to rectify such inequalities through
refined and equitable modeling approaches. In doing so, we propose establishing a fair care delivery
framework for critical illness survivors. With close mentorship from an experienced team in healthcare
disparities research, healthcare delivery, and innovative research methodologies at the University of
Pittsburgh, this training plan forms a foundation for a Career Development Award and a future career as a
physician-scientist.

## Key facts

- **NIH application ID:** 10901179
- **Project number:** 1F32MD019534-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Hiam Naiditch
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $90,932
- **Award type:** 1
- **Project period:** 2024-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10901179

## Citation

> US National Institutes of Health, RePORTER application 10901179, Examining Racial Disparities in Predictive Modeling Among Survivors of Critical Illness (1F32MD019534-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10901179. Licensed CC0.

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