Real-World Evidence
egnite’s database contributes to a body of evidence that helps physicians uncover and close gaps in patient care.
A preview into our work…
Outcomes with Guideline-Directed Medical Therapy and Cardiac Implantable Electronic Device Therapies for Patients with Heart Failure with Reduced Ejection Fraction
Presented at Heart Rhythm 2023, data utilizing egnite’s database underscore the significance of combined GDMT and device therapy, indicating even greater survival rates among critically ill heart failure patients and the need to standardize care to improve the health of our communities.1
1 Mignone JL, et al. Presented at: Heart Rhythm Society 44th Annual meeting (Heart Rhythm 2023); May 19-21, 2023; New Orleans, LA.
Untreated Aortic Stenosis Mortality by Diagnosis Severity: Results from a Large Real-World Database
Published in the Journal of the American College of Cardiology, data utilizing egnite’s database demonstrated that moderate and moderate-to-severe AS have clinically similar mortality to severe AS, but are at best, half as likely to be treated.2
2 Généreux P, et al. J Am Coll Cardiol. 2023;81(8_Suppl):1989.
Prognostic Impact of Cardiac Damage Across the Spectrum of Aortic Stenosis Severity: Results from a Large Real-World Database
Published in the Journal of the American College of Cardiology, data utilizing egnite’s database demonstrated that cardiac damage is very common in all degrees of AS severity, driving poor patient outcomes with increased mortality at 2 years.3
3 Généreux P, et al. J Am Coll Cardiol. 2023;81(8_Suppl):1930.
The Contemporary Prevalence of Valvular Heart Disease
The largest contemporary analysis of VHD in the US was performed on over 929,000 de-identified echocardiograms from egnite’s database.4,5
Utilizing Artificial Intelligence to Predict the Progression from Moderate to Severe Aortic Stenosis
Published in Intelligence-Based Medicine, leveraged over 1 million de-identified echocardiograms from egnite’s database.6,7
6 Moualla SK, et al. J Am Coll Cardiol. 2021;78(19_Suppl_S):B24.
7 Moualla SK, et al. Intelligence-Based Medicine. 2022;6:100062.
Artificial Intelligence to Assist Physicians in Identifying Patients with Severe Aortic Stenosis
Published in Intelligence-Based Medicine, leveraged over 1 million de-identified echocardiograms from egnite’s database.8
8 Thomas JD, et al. Intelligence-Based Medicine. 2022;6:100059.