# Harnessing the power of CTSA-CDRN data networks: Using social determinants of health, frailty and functional status to identify at-risk patients and improve risk adjustment

> **NIH NIH U01** · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · 2020 · $754,897

## Abstract

Postoperative complications and readmissions rates are higher in minority and low socioeconomic status (SES)
patients. Low SES is associated with frailty, one of the best predictors of 30-day postoperative complications
and early hospital readmission. Despite their influence on health outcomes, frailty and social risk factors are not
considered in risk adjustment for reimbursement and quality measures. CMS developed financial incentive-
based programs to improve quality of care. Yet this strategy disproportionately penalizes minority-serving, major
teaching and safety net hospitals (SNH), further constraining resources for the care of vulnerable populations.
Our long-term goal is to use frailty and social risk factors to identify at-risk patients to design more
effective clinical care pathways. Frailty can be derived retrospectively using the American College of Surgeons
National Surgical Quality Improvement Program (ACS NSQIP) dataset.
Data networks are powerful research tools that can be used to answer important questions. However, extracting
data from EHR is challenging. The Patient-Centered Outcomes Research Institute (PCORI) developed 13
Clinical Data Research Networks (CDRN) that have considerable overlapping membership with Clinical
Translational Science Award (CTSA) institutions. While steady progress has been made, multiple barriers exist
to efficiently access and use data. We will engage 3 CTSA hubs, each members of a different CDRN, to locally
merge identified datasets developing data accessing and linking strategies at diverse institutions for
dissemination across sites within CDRNs and to ultimately perform similar studies across CDRNs. We will use
the SMART IRB reliance platform to harmonize the regulatory approval process as much as possible for each
step of this project to identify barriers to use in data networks. We propose the following Aims:
1) Determine the predictive power of ethnicity, race, SES, and frailty for postoperative complications, mortality
 and readmissions to improve risk adjustment at 3 CTSA/CDRNs
2) Estimate postoperative functional status using natural language processing (NLP) and machine learning
 algorithms on inpatient physical therapy (PT), occupational therapy (OT) and nursing notes for ACS NSQIP
 patients to predict long-term functional status
3) Develop methods to predict long-term loss of independence after major surgery
4) Determine hospital resource utilization stratified by SES, frailty and minority status
The significance of our study is the incorporation of social risk factors, frailty and functional status in risk
adjustment forming the basis for future interventions by targeting patients at the highest risk for postoperative
complications and reducing health care disparities. Our innovative approach harnesses data sources at diverse
institutions with the goal of disseminating these methods across 3 CDRNs and the CTSA network.

## Key facts

- **NIH application ID:** 9981049
- **Project number:** 5U01TR002393-03
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
- **Principal Investigator:** Paula K Shireman
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $754,897
- **Award type:** 5
- **Project period:** 2018-07-25 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9981049

## Citation

> US National Institutes of Health, RePORTER application 9981049, Harnessing the power of CTSA-CDRN data networks: Using social determinants of health, frailty and functional status to identify at-risk patients and improve risk adjustment (5U01TR002393-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9981049. Licensed CC0.

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