Carelog's non-invasive ECG algorithm, Diastolytix, designed for the detection of Grade 3 diastolic dysfunction, has received Breakthrough Device designation from the U.S. Food and Drug Administration (FDA). This AI-driven innovation aims to improve early diagnosis of diastolic dysfunction, a key predictor of heart failure with preserved ejection fraction (HFpEF).
The Breakthrough Device designation is intended to accelerate the development and assessment of medical devices that address life-threatening or irreversibly debilitating conditions, where preliminary clinical evidence suggests a potential for significant improvement over existing technologies. Diastolytix utilizes machine learning to analyze ECG data, identifying Grade 3 diastolic dysfunction, often an underdiagnosed precursor to HFpEF.
Significance of Early Detection
According to Carelog, early detection of diastolic dysfunction is crucial because HFpEF patients face significant challenges, including limited treatment options and a poor prognosis. The 5-year survival rate for these patients ranges from 40% to 60%. Current treatments are primarily pharmaceutical, highlighting the need for innovative diagnostic tools that can improve patient outcomes.
Expert Commentary
"I’m thrilled to announce that Carelog has received FDA Breakthrough Device Designation for our innovative ECG algorithm that detects diastolic dysfunction — an often underdiagnosed precursor to HFpEF," said Aman Alok, founder and CEO of Carelog. This designation underscores the potential of Diastolytix to address a critical unmet need in cardiology.
About Diastolic Dysfunction and HFpEF
Diastolic dysfunction is characterized by the impaired ability of the left ventricle to relax and fill with blood during diastole. This condition is an early indicator of HFpEF, a complex syndrome affecting millions worldwide. The prevalence of HFpEF is increasing, and it is associated with significant morbidity and mortality. Accurate and timely diagnosis is essential for effective management and improved patient outcomes.