Health Technology/clinical-trials

ASTEREO Trial: AI-Powered Sound Analysis for Early Heart Failure Decompensation Detection — Innovative Remote Monitoring Approach

247 Health News Editorial

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18 May 2026

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3 min read

·clinical-trials

The ASTEREO trial is investigating whether AI analysis of voice, breathing, and cough sounds can predict heart failure decompensation before symptoms appear. This innovative smartphone-based approach could revolutionise remote monitoring for heart failure patients.

Originally reported by ClinicalTrials.gov

References

  1. 1.Abraham, W. T., Adamson, P. B., Bourge, R. C., Aaron, M. F., Costanzo, M. R., Stevenson, L. W., Strickland, W., Neelagaru, S., Raval, N., Krueger, S., Weiner, S., Shavelle, D., Jeffries, B., & Yadav, J. S. (2011). Wireless pulmonary artery haemodynamic monitoring in chronic heart failure: a randomised controlled trial. The Lancet, 377(9766), 658-666. https://doi.org/10.1016/S0140-6736(11)60101-3
  2. 2.Cambridge University Hospitals NHS Foundation Trust. (2024). Utilising AI Analysis of Sounds To prEdict heaRt failurE decOmpensation. ClinicalTrials.gov. NCT06555757. https://clinicaltrials.gov/study/NCT06555757
  3. 3.Conrad, N., Judge, A., Tran, J., Mohseni, H., Hedgecott, D., Crespillo, A. P., Allison, M., Hemingway, H., Cleland, J. G., McMurray, J. J. V., & Rahimi, K. (2018). Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. The Lancet, 391(10120), 572-580. https://doi.org/10.1016/S0140-6736(17)32520-5
  4. 4.Koehler, F., Winkler, S., Schieber, M., Sechtem, U., Stangl, K., Böhm, M., Boll, H., Baumann, G., Honold, M., Koehler, K., Gelbrich, G., Kirwan, B. A., & Anker, S. D. (2011). Impact of remote telemedical management on mortality and hospitalizations in ambulatory patients with chronic heart failure. Circulation, 123(17), 1873-1880. https://doi.org/10.1161/CIRCULATIONAHA.111.018473
  5. 5.Segar, M. W., Patel, K. V., Ayers, C., Basit, M., Tang, W. H. W., Willett, D., Berry, J., Grodin, J. L., & Pandey, A. (2019). Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis. European Heart Journal, 40(47), 3843-3852. https://doi.org/10.1093/eurheartj/ehz621
  6. 6.Yancy, C. W., Jessup, M., Bozkurt, B., Butler, J., Casey, D. E., Colvin, M. M., Drazner, M. H., Filippatos, G. S., Fonarow, G. C., Givertz, M. M., Hollenberg, S. M., Lindenfeld, J., Masoudi, F. A., McBride, P. E., Peterson, P. N., Stevenson, L. W., & Westlake, C. (2017). 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure. Circulation, 136(6), e137-e161. https://doi.org/10.1161/CIR.0000000000000509
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