# Validating the GRACE 2.0 Score Derived from Administrative Data to Define Appropriateness of Invasive Care for the Management of Acute Coronary Syndrome

> **NIH VA I21** · VETERANS HEALTH ADMINISTRATION · 2020 · —

## Abstract

Background: Clinical practice guidelines recommend the use of invasive care to treat acute coronary syndrome
(ACS) in most patients with chronic kidney disease (CKD). Preliminary unadjusted results from our pilot work
indicate that such care is commonly not provided to Veterans with CKD. However, these preliminary findings did
not account for the appropriateness of invasive care. To address this gap, we submitted a VA HSR&D Merit
Review proposal last year to examine whether invasive care was implemented less frequently in Veterans with
CKD accounting for the appropriateness of invasive care. We proposed assessing the appropriateness of
invasive care using Global Registry of Acute Coronary Events (GRACE) scores derived from VA administrative
data and using natural language processing (NLP) to extract component variables contained as free text in the
VA Corporate Data Warehouse (CDW). A key issue raised in the review of our proposal was the need for proof
of concept testing of GRACE scores calculated using this approach. Accordingly, we propose to conduct a pilot
study to develop a method to calculate GRACE scores in this manner and assess their accuracy.
Significance/Impact: By developing a method to calculate GRACE scores using VA administrative data and NLP,
our proposal addresses the VA priorities to advance research methods (i.e., Data/Measurement Science) that
cut across conditions and/or care settings and to transform VA data into a national resource. Furthermore, as
our long-term goal is to ensure the equitable provision of evidence-based care for ACS in Veterans with CKD,
this pilot is the first step in a series of studies that will address multiple VA HSR&D priority areas, including
management of complex chronic diseases, access to care, health equity, and quality and safety of heath care.
Innovation: Our use of NLP to extract free text from VA administrative data and incorporate those data elements
into the calculation of GRACE scores is highly innovative and has broad relevance to health services research.
Specific Aim 1: To develop a precise method for retrospective GRACE score calculation in Veterans with and
without CKD who were hospitalized with ACS using standard administrative data extraction from the VA CDW
combined with rule-based and machine-learning NLP techniques for extraction of free text from the CDW.
Specific Aim 2: To assess the accuracy of GRACE scores calculated using administrative and free text data from
the CDW by comparing to reference standard scores derived from manual chart reviews.
Methodology: We will use a national cohort of Veterans with and without CKD who were hospitalized within VA
with ACS between 1/1/2013 and 12/31/17. We will calculate GRACE scores for each patient’s ACS
hospitalization by extracting 6 of the 8 variables of the score from the CDW and using NLP to extract the
remaining 2 free text variables from text integration utility notes in CDW files. For 300 randomly selected patients,
we will per...

## Key facts

- **NIH application ID:** 9950650
- **Project number:** 1I21HX003111-01
- **Recipient organization:** VETERANS HEALTH ADMINISTRATION
- **Principal Investigator:** Steven Weisbord
- **Activity code:** I21 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2020-02-01 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9950650

## Citation

> US National Institutes of Health, RePORTER application 9950650, Validating the GRACE 2.0 Score Derived from Administrative Data to Define Appropriateness of Invasive Care for the Management of Acute Coronary Syndrome (1I21HX003111-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9950650. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*