Charles Sternberg, Associate Editor10.02.23
Anumana Inc., an AI-driven health technology and nference portfolio company working in collaboration with Mayo Clinic, has received U.S. Food and Drug Administration (FDA) 510(k) clearance for ECG-AI LEF, a breakthrough artificial intelligence (AI)-powered medical device to detect low ejection fraction in patients at risk of heart failure.
“Anumana’s ECG-AI LEF fills an important unmet need – the lack of an easily accessible point-of-care, noninvasive, and inexpensive tool to screen for a weak heart pump,” said Paul Friedman, M.D., chair of the Department of Cardiovascular Medicine at Mayo Clinic in Rochester, Minn. and chair of Anumana’s Board of Advisors. “It allows identification of otherwise hidden disease, for which many effective, lifesaving treatments are available – once the presence of the disease is known.”
Developed in partnership with Mayo Clinic, Anumana’s ECG-AI LEF is a software-as-a-medical device (SaMD) designed to screen for LEF in adults at risk for heart failure using data from a routine 12-lead electrocardiogram (ECG), a rapid and common test used in both primary and specialty care. Based on research from Mayo Clinic,4 the algorithm has been developed utilizing over 100,000 ECG and echocardiogram data pairs from unique patients and has been clinically tested in more than 25 studies involving more than 40,000 patients in the U.S. and internationally.5
Anumana’s ECG-AI LEF was clinically validated in a multi-site, retrospective clinical study of 16,000 racially diverse patients, achieving an 83.6 percent specificity and 84.5 percent sensitivity (n=13,960).6 ECG-AI LEF correctly identified LEF from a routine ECG with an AUROC of 0.932, a high performance level that compares favorably to tests currently used in heart failure standard of care.7
Additionally, the EAGLE study, a groundbreaking prospective, randomized, controlled clinical trial by Mayo Clinic evaluated the use of an investigational version of the algorithm in routine clinical care of 22,641 adults by 120 primary care teams from 45 clinics or hospitals, demonstrated that ECG-AI LEF implementation improved clinician’s ability to diagnosis of LEF by 31 percent versus standard of care without increasing the overall rate of echocardiogram usage.8
“Anumana was founded by nference in partnership with Mayo Clinic to unlock the electrical language of the heart through deep learning and improve disease diagnosis and patient care,” said Murali Aravamudan, co-founder and CEO of Anumana and nference. “Achieving FDA clearance of our breakthrough ECG-AI LEF device is a significant milestone and the first of more than a dozen innovative algorithms currently in development. We are excited about the next phase of the journey, deploying our technology in the U.S. and globally to empower clinicians and enhance real-world clinical care.”
Anumana is focused on driving fast-paced adoption of the new ECG-AI category, clinically developing and commercializing its novel technologies in healthcare. The newly FDA-cleared ECG-AI LEF can be easily integrated with various ECG information management systems or directly with a patient’s electronic health record via Anumana’s web-based ECG Viewer to support clinical decision-making.
Anumana spearheaded the effort to bring reimbursement to ECG-AI, receiving approval for two Category III CPT codes from the American Medical Association in 2022. These codes are now available and designed to facilitate the use, adoption, and potential reimbursement of emerging technologies in clinical workflows.
References:
1. Jaskanwal D Sara, Takumi Toya, Riad Taher, Amir Lerman, Bernard J Gersh, Nandan S Anavekar. Asymptomatic Left Ventricle Systolic Dysfunction. European Cardiology Review 2020;15:e13. (https://doi.org/10.15420/ecr.2019.14).
2. Tsao, C.W., Aday, A.W., Almarzooq Z.I., et al. Heart Disease and Stroke Statistics—2023 Update: A Report From the American Heart Association. Circulation Vol. 147, No. 8;https://www.ahajournals.org/doi/10.1161/CIR.0000000000001123#d330256e1.
3. Khazanie P, Allen LA. Systematizing Heart Failure Population Health. Heart Fail Clin. 2020 Oct;16(4):457-466. Doi: 10.1016/j.hfc.2020.06.006. Epub 2020 Jul 21. PMID: 32888640; PMCID: PMC7737815.
4. Attia, Z.I., Kapa, S., Lopez-Jimenez, F. et al. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat Med 25, 70–74 (2019).https://doi.org/10.1038/s41591-018-0240-2.
5. Anumana data on file and published studies.
6. Anumana data on file (NCT04963218).
7. Such as NT-proBNP, a common blood test used to help identify LEF today; Koschack, J., Scherer, M., Lüers, C. et al. Natriuretic peptide vs. clinical information for diagnosis of left ventricular systolic dysfunction in primary care. BMC Fam Pract 9, 14 (2008). https://doi.org/10.1186/1471-2296-9-14h
8. Yao, X., Rushlow, D.R., Inselman, J.W. et al. Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med 27, 815–819 (2021); https://doi.org/10.1038/s41591-021-01335-4.
Filling an Important Unmet Need
Low ejection fraction (LEF), or a weak heart pump, is a significant, at times asymptomatic, and commonly undiagnosed indicator of heart failure.1 The increasing prevalence of heart failure and its associated morbidity, mortality, rehospitalizations, and societal costs2,3 underscores the need to identify and manage patients with LEF.“Anumana’s ECG-AI LEF fills an important unmet need – the lack of an easily accessible point-of-care, noninvasive, and inexpensive tool to screen for a weak heart pump,” said Paul Friedman, M.D., chair of the Department of Cardiovascular Medicine at Mayo Clinic in Rochester, Minn. and chair of Anumana’s Board of Advisors. “It allows identification of otherwise hidden disease, for which many effective, lifesaving treatments are available – once the presence of the disease is known.”
Developed in partnership with Mayo Clinic, Anumana’s ECG-AI LEF is a software-as-a-medical device (SaMD) designed to screen for LEF in adults at risk for heart failure using data from a routine 12-lead electrocardiogram (ECG), a rapid and common test used in both primary and specialty care. Based on research from Mayo Clinic,4 the algorithm has been developed utilizing over 100,000 ECG and echocardiogram data pairs from unique patients and has been clinically tested in more than 25 studies involving more than 40,000 patients in the U.S. and internationally.5
Anumana’s ECG-AI LEF was clinically validated in a multi-site, retrospective clinical study of 16,000 racially diverse patients, achieving an 83.6 percent specificity and 84.5 percent sensitivity (n=13,960).6 ECG-AI LEF correctly identified LEF from a routine ECG with an AUROC of 0.932, a high performance level that compares favorably to tests currently used in heart failure standard of care.7
Additionally, the EAGLE study, a groundbreaking prospective, randomized, controlled clinical trial by Mayo Clinic evaluated the use of an investigational version of the algorithm in routine clinical care of 22,641 adults by 120 primary care teams from 45 clinics or hospitals, demonstrated that ECG-AI LEF implementation improved clinician’s ability to diagnosis of LEF by 31 percent versus standard of care without increasing the overall rate of echocardiogram usage.8
“Anumana was founded by nference in partnership with Mayo Clinic to unlock the electrical language of the heart through deep learning and improve disease diagnosis and patient care,” said Murali Aravamudan, co-founder and CEO of Anumana and nference. “Achieving FDA clearance of our breakthrough ECG-AI LEF device is a significant milestone and the first of more than a dozen innovative algorithms currently in development. We are excited about the next phase of the journey, deploying our technology in the U.S. and globally to empower clinicians and enhance real-world clinical care.”
Anumana is focused on driving fast-paced adoption of the new ECG-AI category, clinically developing and commercializing its novel technologies in healthcare. The newly FDA-cleared ECG-AI LEF can be easily integrated with various ECG information management systems or directly with a patient’s electronic health record via Anumana’s web-based ECG Viewer to support clinical decision-making.
Anumana spearheaded the effort to bring reimbursement to ECG-AI, receiving approval for two Category III CPT codes from the American Medical Association in 2022. These codes are now available and designed to facilitate the use, adoption, and potential reimbursement of emerging technologies in clinical workflows.
References:
1. Jaskanwal D Sara, Takumi Toya, Riad Taher, Amir Lerman, Bernard J Gersh, Nandan S Anavekar. Asymptomatic Left Ventricle Systolic Dysfunction. European Cardiology Review 2020;15:e13. (https://doi.org/10.15420/ecr.2019.14).
2. Tsao, C.W., Aday, A.W., Almarzooq Z.I., et al. Heart Disease and Stroke Statistics—2023 Update: A Report From the American Heart Association. Circulation Vol. 147, No. 8;https://www.ahajournals.org/doi/10.1161/CIR.0000000000001123#d330256e1.
3. Khazanie P, Allen LA. Systematizing Heart Failure Population Health. Heart Fail Clin. 2020 Oct;16(4):457-466. Doi: 10.1016/j.hfc.2020.06.006. Epub 2020 Jul 21. PMID: 32888640; PMCID: PMC7737815.
4. Attia, Z.I., Kapa, S., Lopez-Jimenez, F. et al. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat Med 25, 70–74 (2019).https://doi.org/10.1038/s41591-018-0240-2.
5. Anumana data on file and published studies.
6. Anumana data on file (NCT04963218).
7. Such as NT-proBNP, a common blood test used to help identify LEF today; Koschack, J., Scherer, M., Lüers, C. et al. Natriuretic peptide vs. clinical information for diagnosis of left ventricular systolic dysfunction in primary care. BMC Fam Pract 9, 14 (2008). https://doi.org/10.1186/1471-2296-9-14h
8. Yao, X., Rushlow, D.R., Inselman, J.W. et al. Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med 27, 815–819 (2021); https://doi.org/10.1038/s41591-021-01335-4.