Data from Anumana’s New Study Show AI Algorithm Has the Potential to Detect Pulmonary Hypertension Early Using ECG Data
June 27 2024 - 12:26PM
Business Wire
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
- McLaughlin VV et al. .J Am Coll Cardiol 2018;
71(7):7522–763.
- Gall H et al. J Heart Lung Transplant 2017; 36(9):9572–967.
- Frost AE et al. Chest 2013; 144(5):1521–1529.
- Nickel H et al. Eur Respir J 2012; 39(3):589–596.
- Vachiéry JL et al. Eur Respir Rev 2012; 21(123):40–47.
- 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.
- 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.
- 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.
- Maron BA, Humbert M. Finding pulmonary arterial
hypertension-switching to offense to mitigate disease burden. JAMA
cardiology 2022: 7(4): 369-370.
- Hajian-Tilaki, K. Caspian J Intern Med. 2013 Spring; 4(2):
627-635.
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Anumana Media Contact: Sam Choinski Pazanga Health
Communications schoinski@pazangahealth.com (860) 301-5058