# Improving Outcomes in Veterans with Heart Failure and Chronic Kidney Disease

> **NIH VA I01** · U.S. DEPT/VETS AFFAIRS MEDICAL CENTER · 2020 · —

## Abstract

Project Summary
Heart failure (HF) is a major public health problem with high mortality (~50% at 5 years) and hospital
readmission (~25% at 30 days). Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor
blockers (ARBs) improve both outcomes in patients with HF with reduced ejection fraction (HFrEF). However,
these drugs also adversely affect kidney function, and may increase the risk of acute kidney injury (AKI),
chronic kidney disease (CKD) progression, and incident kidney failure, leading to end-stage renal disease
(ESRD) requiring renal replacement therapy. All these risks are higher in HFrEF patients with CKD and those
receiving these drugs in high doses. We have demonstrated that ACEIs or ARBs may reduce mortality in
HFrEF with CKD (PMC3324926). Findings from our work also suggest that clinical benefits of ACEIs or ARBs
might be similar at both low and high doses. The objectives of the proposed study are to test the hypotheses
that low-dose ACEIs and ARBs are safe and beneficial in patients HFrEF with CKD. We will then develop a
machine-learning algorithm to identify individual HF patients who might benefit from these drugs given their
unique ejection fraction, kidney function, and other baseline characteristics. These aims will be achieved by
using VA's national data (over 1 million HF patients) and the American Heart Association's Get With The
Guideline (GWTG) HF data (over 1.5 million HF patients) linked to the United States Renal Data System
(USRDS) data. HF will be adjudicated using an automated machine-learning algorithm. An active-comparator
new-user design with propensity score matching and sensitivity analysis will be used to compare clinical and
renal outcomes in patients receiving low-dose vs. high-dose ACEIs or ARBs. Machine learning will be used to
develop a risk prediction model to maximize clinical benefit and minimize renal harm for individual patients.
The investigative team consists of national experts in key content areas and has the collective experience and
expertise to complete the project in a timely manner. Nearly half of the Class-I recommendations (benefit
greater than risk) in national HF guideline are based on Level-C evidence (mostly expert opinion) and there is
a need to expand the evidence base from which clinical practice guidelines are derived. Findings from the
proposed project will provide evidence that will help clinicians use a personalized approach in the use of ACEIs
and ARBs in patients with HFrEF so that potential risks and benefits are optimized.

## Key facts

- **NIH application ID:** 9932798
- **Project number:** 5I01HX002422-02
- **Recipient organization:** U.S. DEPT/VETS AFFAIRS MEDICAL CENTER
- **Principal Investigator:** ALI AHMED
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9932798

## Citation

> US National Institutes of Health, RePORTER application 9932798, Improving Outcomes in Veterans with Heart Failure and Chronic Kidney Disease (5I01HX002422-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9932798. Licensed CC0.

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