# NIH grant: HOPE: Advancing HFpEF Management Through Evidence-based Treatment Optimization and Outcome Prediction (1R01HL185686-01) — retained record

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NIH grant 1R01HL185686-01: HOPE: Advancing HFpEF Management Through Evidence-based Treatment Optimization and Outcome Prediction
Institute: {'code': 'HL', 'abbreviation': 'NHLBI', 'name': 'National Heart Lung and Blood Institute', 'admin_org_id': '12500068', 'admin_funding_url': ''}. PI: NANSU  ZONG. Award: $801170.
Start: 2026-08-01T00:00:00.
PROJECT SUMMARY
Heart failure with preserved ejection fraction (HFpEF), a common subtype of heart failure (HF), presents a unique
clinical challenge. Although HFpEF is characterized by a normal ejection fraction (EF), patients still experience
HF symptoms. This paradox results from normal pumping efficiency being offset by impaired filling capacity,
which complicates treatment in clinical settings. Limited strong clinical evidence further constrains treatment
options, with most therapies rated only as Class IIa or IIb recommendations by AHA/ACC/HFSA guidelines.
Adding to this challenge is HFpEF’s complex and often elusive etiology, involving interrelated comorbidities such
as Hypertension and Diabetes. Managing these comorbidities is particularly difficult due to limited guidelines that
do not fully address the complexity of patients.
To tackle these issues, we propose HOPE, a comprehensive approach to advance HFpEF management through
two innovative strategies that evaluate the benefit of the existing guideline-directed medical therapy (GDMT)
drugs and non-GDMT drugs. First, we aim to discover therapeutic benefits from existing GDMT and non-GDMT
combinations for HFpEF patients. This will be achieved through a drug screening model that assesses changes
in endpoint (e.g., mortality and morbidity) by combining the results of drug-target predictions with EHR-based
emulated trials. Second, we will develop biomarkers response prediction models that use temporal coherence
and intra-visit relationships within EHR data. We plan to learn to learn stratified pheno-groups and develop a
personalized biomarkers response prediction model for each group. To validate our models and their potential
benefits, we plan to collaborate with Florida State University (FSU) by using the One-Florida+ EHR network via
PCORnet, comparing outputs derived from Mayo Clinic EHR. HOPE applies standardized methodologies within
the PCORnet common data model, ensuring consistent data extraction and processing to support widespread
application and validation.
The scope of HOPE is substantial, encompassing analysis of over 390,000 HFpEF patients. With successful
implementation, HOPE is poised to significantly enhance patient care, offering tailored treatment plans that meet
individual patient needs and setting a new standard in HFpEF treatment.
```



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