Developed with machine learning1 and powered by a first-of-its-kind algorithm,1,2 Acumen HPI software accurately predicts hypotension 10 minutes before it occurs.1
Multiple studies have shown:
Incidence, duration, and severity of IOH* during noncardiac surgery were significantly reduced vs standard of care.3,4
Acumen HPI software was highly accurate in predicting hypotension*.1,5
Acumen HPI software was highly accurate in identifying hemodynamic instabilities.6
Key elements of the Acumen HPI software
Clinical evidence
Reductions in incidence, duration, and severity of IOH3,4
From the HYPE trial, elective noncardiac surgery patients monitored with Acumen HPI software had a median duration of IOH per patient of 8 minutes compared to 32.7 minutes in a control group; p<0.001.3
Intervention (n=31), control (n=29)
Incidence of IOH (events per hour) in total hip arthroplasty patients monitored with Acumen HPI software was 0 events compared to 5 events in a prospective control and 2 events in a historical control; p<0.001.4
Intervention (n=25), prospective control (n=24), historical control (n=50)
Accurate prediction of IOH1,6
Acumen HPI software had superior ability to predict IOH 5 and 10 minutes before an event vs. all other measured hemodynamic parameters.6
At 10 minutes before an event, Acumen HPI software predicted IOH with a specificity of 89%, a sensitivity of 90% and an AUC of 0.95.1
Acumen intelligent decision support suite
The Acumen intelligent decision support suite offers both predictive and retrospective decision support for the management of hypotension.
Acumen IQ cuff
(noninvasive)
Acumen IQ sensor
(minimally-invasive)
HemoSphere advanced monitoring platform
Acumen Analytics software
Edwards clinical education
Hemodynamic education empowering clinical advancement
With a long-term commitment to improving the quality of care for surgical and critical care patients through education, Edwards clinical education meets you no matter where you are in the learning process — with a continuum of resources and tools that continuously support you as you solve the clinical challenges facing you today, and in the future.
TopMedTalk podcasts on Intraoperative Hypotension (IOH)
The TopMedTalk podcast is a leading source of high value medical discussion and education in Anaesthesia, Perioperative Care and Enhanced Recovery. We've featured interviews with key opinion leaders, academics, medical practitioners and policy makers.
Listen below to podcasts on hypotension and Acumen Hypotension Prediction Index (HPI) software. You can also find these episodes of TopMedTalk through wherever you get your podcasts.
Avoiding the hypotension hazard
The Acumen Hypotension Prediction Index (HPI) software takes 2.6 million features from a single waveform which it then applies to 133 million different wave forms looking for factors which can predict hypotension. It's the first fully approved foray into predictive analytics for the world of anaesthesia.
This conversation looks at how it works and how it is being adopted in detail.
Presented by Desiree Chappell with Monty Mythen and Feras Hatib PhD, Director, Research and Development, Algorithms and Signal Processing at Edwards Lifesciences and Dr Simon Davies, Consultant Anaesthetist at York Teaching Hospital NHS Foundation Trust.
TopMedTalk with Tim Miller
The Perioperative Quality Initiative (POQI) have featured frequently on TopMedTalk, here we discuss their focus on hypotension and blood pressure.
Presented by Monty Mythen and Desiree Chappell with Joff Lacey, registrar in anaesthesia at St George's Hospital in London, currently undertaking a fellowship in Perioperative Medicine at UCLH as well as being a regular presenter on TopMedTalk and Tim Miller, Associate Professor of Anaesthesiology from Duke University Medical Centre.
TopMedTalk with Feras Hatib
Streamed live from the 2019 Prehabilitation World Congress and Annual London Peri-Operative Medicine Congress. This is a break out session held at 'Montague on The Gardens', focusing on artificial intelligence and was sponsored by Edwards Lifesciences.
Presented by Desiree Chappell and Monty Mythen with their guest, Feras Hatib, PhD, Director, Research and Development, Algorithms and Signal Processing at Edwards Lifesciences.
The AI Doctor will see you now...
Streamed live from the 2019 Prehabilitation World Congress and Annual London Peri-Operative Medicine Congress. This is a break out session held at 'Montague on The Gardens', focusing on artificial intelligence and was sponsored by Edwards Lifesciences.
Presented by Monty Mythen and Desiree Chappell, this is a short explanation of the technology in practice.
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References
- Hatib, F., Zhongping, J., Buddi, S., Lee, C., Settels, J., Sibert, K., Rinehart, J., Cannesson, M. (2018). Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis. Anesthesiology 129, 663-674.
- Edwards Lifesciences data on file [US Food and Drug Administration. Acumen HPI Software De Novo clearance].
- Wijnberge, M., Geerts, B., Hol, L., Lemmers, N., Mulder, M., Berge, P., Schenk, J., Terwindt, L., Hollman, M., Vlaar, A., Veelo, D. (2020) Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA Online, February 17, 2020.
- Schneck E, Schulte D, Habig L, et al. Hypotension prediction index based protocolized haemodynamic management reduces the incidence and duration of intraoperative hypotension in primary total hip arthroplasty: a single centre feasibility randomised blinded prospective interventional trial. J Clin Monit Comput. 2020;34(6):1149-1158.
- Davies SJ, Vistisen ST, Jian Z, et al. Ability of an arterial waveform analysis-derived hypotension prediction index to predict future hypotensive events in surgical patients. Anesth Analg 2019.
- Davies SJ, Mythen M. Hemodynamic and intestinal microcirculatory changes in a phenylephrine corrected porcine model of hemorrhage. Anesth Analg. 2021 March 23. doi: 10.1213/ANE.0000000000005388. Online ahead of print.
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