Data from Anumana’s New Study Show AI Algorithm Has the Potential to Detect Pulmonary Hypertension Early Using ECG Data

Anumana, a leading AI-driven health technology company and portfolio company of nference, announced a new study that revealed promising results in support of further development of its investigational pulmonary hypertension (PH) algorithm. The study, “An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension,” which was published in the European Respiratory Journal, determined that the algorithm used in the study can detect PH using routine 12-lead electrocardiogram (ECG) data.

PH is a severe, progressive disease where delayed diagnosis is associated with a higher risk of morbidity and mortality.^1-5 Despite an increasing number of available treatments for the disease, diagnostic delays – often of more than two years from symptom onset – are common, due to the non-specific symptoms at presentation.^6-9 To address this challenge, Anumana, in collaboration with Mayo Clinic, Vanderbilt University Medical Center (VUMC), and Janssen Research & Development, LLC, a Johnson & Johnson company, has developed an Al algorithm designed to detect PH using routinely collected 12-lead ECG data.

In the newly published study, a convolutional neural network developed for detecting PH was trained and validated using retrospective ECG and either right heart catheterization or echocardiogram data from 39,823 PH-likely patients and 219,404 control patients from Mayo Clinic. The algorithm used in the study was further validated on an additional 6,045 PH-likely patients and 24,256 control patients from VUMC. The algorithm demonstrated promising performance in identifying PH, achieving an area under the receiver operating characteristic curve (AUC) of 0.92 in the diagnostic test set at Mayo Clinic and 0.88 at VUMC, where AUC values range from 0 to 1.¹⁰

The PH algorithm received Breakthrough Device designation from the FDA in 2022, and Anumana is continuing to develop the PH algorithm in pursuit of FDA clearance and CE marking. Anumana has previously received FDA 510(k) clearance for its ECG-AI™ LEF algorithm, which helps clinicians detect low ejection fraction in patients at risk of heart failure.

“The promising data from our study suggest that an AI algorithm has the potential to non-invasively detect PH at an early stage using standard ECGs. This finding marks a significant step forward in the care and management of PH patients who often have a long diagnostic journey,” said Dr. Hilary DuBrock, a Mayo Clinic pulmonologist and lead author of the study.

“These new data underscore the potential of AI algorithms to empower clinicians to uncover diseases earlier, improve patient outcomes and bring us one step closer to our mission to transform cardiac care,” said Maulik Nanavaty, CEO of Anumana. “We’re continuing to work closely with our partners to further clinically validate this much-needed algorithm, which can help clinicians worldwide get PH patients into treatment sooner to address symptoms and prolong life.”

Mayo Clinic has a financial interest in the technology referenced in this press release. Mayo Clinic will use any revenue it receives to support its not-for-profit mission in patient care, education and research.

About Anumana

Anumana is a leading AI-driven health technology company leveraging cutting-edge AI and industry-leading translational science to unlock the electrical language of the heart as never before. The company was founded by nference in collaboration with Mayo Clinic to leverage the clinical and technical expertise of both organizations to develop innovative ECG-AI technology into a clinically meaningful, medical-grade, and easy to use tool for clinicians to advance patient care. Anumana’s software-as-a-medical device (SaMD) ECG-AI™ solutions aim to detect diseases earlier using standard-of-care ECG readings, enabling clinicians to enhance and improve care with real-time AI insights.

Anumana’s lead algorithm, ECG-AI™ LEF is now available in the U.S. To learn more about how the algorithm can help clinicians identify low ejection fraction earlier and schedule a demo, visit us at ECG-AI LEF.

References

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5. Vachiéry JL et al. Eur Respir Rev 2012; 21(123):40–47.
6. Brown LM, Chen H, Halpern S, et al. Delay in recognition of pulmonary arterial hypertension: factors identified from the REVEAL Registry. Chest 2011: 140(1): 19-26.
7. Didden EM, Lee E, Wyckmans J, et al. Time to diagnosis of pulmonary hypertension and diagnostic burden: A retrospective analysis of nationwide US healthcare data. Pulm Circ 2023: 13(1): e12188.
8. Armstrong I, Billings C, Kiely DG, et al. The patient experience of pulmonary hypertension: a large cross-sectional study of UK patients. BMC Pulm Med 2019: 19(1): 67.
9. Maron BA, Humbert M. Finding pulmonary arterial hypertension-switching to offense to mitigate disease burden. JAMA cardiology 2022: 7(4): 369-370.
10. Hajian-Tilaki, K. Caspian J Intern Med. 2013 Spring; 4(2): 627-635.

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