Heart Failure
Longitudinal, multi-source, curated registries, offering rapid access to deep clinical data, insights and outcomes.
Research-Grade RWD in Heart Failure
Unmet Need
Heart failure costs $11 billion annually and affects 6.2 million Americans and is a growing healthcare epidemic and is associated with significant mortality, morbidity, and resource utilization. Despite recent progresses in new treatments including new drug classes, there is still a large unmet need in the treatment of HF with preserved and mid-range ejection fraction as well those with co-morbidities.
243,000+*
HF patients with deep clinical data – prospective, longitudinal, deep clinical & linked claims data:
- Left Ventricular Ejection Fraction (LVEF) measures
- NYHA functional status
- Lab results & medication data
*Counts complete through Q2 2024
6.6 million+*
HF patients in the OM1 Real-World Data Cloud that can be used for modeling, analysis, and other research purposes
Specialized Data
Longitudinal Changes in LVEF in HF Patients with Mid-Range Ejection Fraction (40% – 50%)
Webinar: Analysis of Real-Word Data in Cardiovascular Disease: Missing Data and Predictive Modeling
HF Insights Ebook
In this eBook, we present heart failure research, models, and insights
Posters & Publications
- Poster (1st place winner): Alves P, Gerber J, Spencer A, Bandaria J, Leavy M, Weiss S, Curhan G, Marci C, Paulus J, Boussios C. Lessons Learned from the Development of Machine Learning Models to Estimate Validated Measures of Disease Activity and Symptom Severity Using Real-World Data for Four Chronic Conditions. ICPE, August 23-27, 2023.
- Poster: Probst J, Jung Y, Su Z, Curhan G, Paulus J. The Association Between Social Determinants of Health and 1-Year Survival Among Patients with Heart Failure in a Real-World Cohort. ICPE, August 23-37, 2023.
- Poster (ICPE 2021) Characteristics of Asymptomatic Heart Failure Patients with Reduced Ejection Fraction in a Large US-Based Real-World Cohort
- Abstract (American College of Cardiology Annual Scientific Sessions. 2018): A Highly Predictive Machine Learning Model to Identify Hospitalized Patients at Risk for 30-day Readmission or Mortality
- Poster (AHA Scientific Sessions 2017): Machine Learning Generated Risk Model to Predict Unplanned Hospital Admission in Heart Failure
- Poster (Journal of the American College of Cardiology 2020): A Simple Predictive Score for Pre-Admission Identification of Risk of 30-Day Hospital Readmission or Death in Heart Failure
- Poster (AHA 2017): Machine Learning Enhanced Predictions of Hospital Readmission or Death in Heart Failure
Award-Winning Research Team
Our team of award-winning researchers have authored hundreds of publications, including scientific posters, articles in peer-reviewed journals, white papers, and books.