Anumana and Pfizer Partner to Enable Early Detection of Underdiagnosed Cardiovascular Disease

Cardiac amyloidosis is a serious, progressive, and underdiagnosed rare disease that leads to heart failure.¹ In patients with cardiac amyloidosis, the heart walls become stiff, making it difficult for the left ventricle to properly relax and fill with blood, while at the same time hampering its ability to squeeze and pump blood out of the heart. Symptoms and comorbidities associated with cardiac amyloidosis, such as shortness of breath, knee pain, bilateral carpal tunnel syndrome, kidney disease, and gastrointestinal issues, can vary and seem unrelated, making diagnosis difficult.^2,3

“The challenge in diagnosing cardiac amyloidosis can prevent patients from getting treatment while the disease continues to progress,” said David McMullin, chief business officer of Anumana. “We believe this collaboration will demonstrate the power of Anumana’s AI-ECG algorithms to help clinicians intervene earlier, giving them greater ability to improve patient outcomes and prolong lives.”

The research agreement with Pfizer will help deepen Anumana’s efforts to implement AI-enabled early detection software that can reveal signals from ECGs that humans cannot interpret. The ubiquitous nature of the painless, non-invasive 100-year-old ECG test gives AI-ECG algorithms the potential to reach a larger number of patients earlier.

“AI-ECG solutions alert clinicians to humanly imperceptible patterns in ECG signals, providing an early warning for serious occult or impending disease,” said Paul Friedman, M.D., Chair of the Department of Cardiovascular Medicine at Mayo Clinic and Chair of Anumana’s Mayo Clinic Board of Advisors. “This stands to improve the lives of people with cardiac amyloidosis by improving the speed of triage and care of this group.”

Anumana was founded in 2021 in collaboration with the Mayo Clinic Platform. Anumana has licensed Mayo Clinic AI-ECG algorithms for low ejection fraction, pulmonary hypertension, and hyperkalemia, all of which have received Breakthrough Device Designation from the U.S. FDA. These algorithms have been validated by over 75 peer-reviewed publications, including a first of its kind prospective clinical impact study on low ventricular ejection fraction that was published in Nature Medicine in 2021, and another first of its kind batch enrolled prospective clinical impact study that demonstrated the effectiveness of AI-ECG to screen for undetected atrial fibrillation in normal sinus rhythm that was published in The Lancet in 2022.

About Anumana

Anumana, an AI-driven health technology company and portfolio company of nference, is harnessing industry-leading AI and translational science to unlock the electrical language of the heart as never before. Anumana algorithms have been developed, trained, and validated using clinical data from Mayo Clinic and other leading institutions to detect hidden diseases using AI-ECG algorithms and to enhance and improve interventional procedures through real-time AI techniques. Multiple Anumana algorithms have received FDA Breakthrough Device Designation and are currently undergoing rigorous clinical trial validation. Anumana software devices are designed for real-world physician use, integrating into existing workflows at the point of care to reduce costs and enhance quality of life. Anumana algorithms are investigational medical devices and have not yet received regulatory approval or clearance. For more information, visit anumana.ai and follow Anumana on LinkedIn and Twitter.

References

1. Rapezzi C, Lorenzini M, Longhi S et al. Cardiac amyloidosis: the great pretender. Heart Fail Rev. 2015;20(2):117-124.
2. Maurer MS, Elliott P, Comenzo R, Semigran M, Rapezzi C. Addressing Common Questions Encountered in the Diagnosis and Management of Cardiac Amyloidosis. Circulation. 2017;135(14):1357-1377.
3. Ash S, Shorer E, Ramgobin D, Vo M, Gibbons J, Golamari R, Jain R, Jain R. Cardiac amyloidosis-A review of current literature for the practicing physician. Clin Cardiol. 2021 Mar;44(3):322-331.