Real-World Evidence

By leveraging the power of big data, egnite improves patient care.

mXcnnpxp_400x400
Real-World Evidence

Outcomes With Guideline-Directed Medical Therapy and Cardiac Implantable Electronic Device Therapies For Patients With Heart Failure With Reduced Ejection Fraction

“Over the last five years, new therapies to treat heart failure emerged with promising improvements in survival benefit. This study represents the first time we’ve seen an assessment of ‘5-class’ guideline-directed therapy — up to 4 foundational medication classes plus ICD/CRT-D therapy — for these patients. The next big challenge to overcome is implementing care […]

Read More
512x512bb
Real-World Evidence

Contemporary Prevalence of Valvular Heart Disease & Diagnostic Variability Across Centers

BACKGROUND Valvular heart disease (VHD) is progressive and deadly, requiring timely diagnosis for optimal outcomes1 Prior landmark analyses of VHD prevalence in the United States (US), including the Framingham Heart Study2 and Nkomo et al.3 , have reported notable prevalence of disease However, these analyses were limited in scope (e.g., reporting only valvular regurgitation or […]

Read More
X26665212
Real-World Evidence

Artificial Intelligence to Assist Physicians in Identifying Patients with Severe Aortic Stenosis

BACKGROUND Severe aortic stenosis (AS) remains a life-threatening form of valvular heart disease. Missed diagnosis of severe AS can lead to a delay in treatment and poor outcomes, but there are limited tools available to help physicians minimize the risk of missed diagnoses. OBJECTIVE Here, a Diagnostic Precision Algorithm was developed from a deidentified dataset […]

Read More
X26665212
Real-World Evidence

Artificial Intelligence-Enabled Predictive Model of Progression From Moderate to Severe Aortic Stenosis

BACKGROUND Progression from moderate to severe aortic stenosis (AS) warrants careful monitoring due to the increased risk of sudden death and heart failure, with disease progression significantly varying among patients and no accurate predictive tools presently available. OBJECTIVE A Disease Progression Algorithm was developed from a deidentified database of 1,163,923 echocardiographic (echo) reports from 35 […]

Read More