The Acumen Hypotension Prediction Index (HPI) software is a first-of-its-kind technology that provides you with information regarding the likelihood of a patient trending toward a hypotensive event.*
Multiple studies have shown that Acumen HPI software:
- Achieves statistically significant reduction of hypotension when combined with a treatment protocol in noncardiac surgery vs. standard of care1,2
- Demonstrates superior predictive abilities for hypotension than common hemodynamic parameters such as cardiac output (CO), stroke volume (SV), and changes in mean arterial pressure (MAP)3
- Has proven and reliable accuracy4
Edwards Lifesciences has a heritage of partnering with clinicians to advance patient care. That heritage is at the core of the Acumen Hypotension Prediction Index software. Developed in partnership with clinicians across the world and the first in a new category of products, Acumen Hypotension Prediction Index software offers the only predictive monitoring parameter for hypotension that is commercially available in the United States.
This first-of-its-kind predictive decision support software detects the likelihood of a patient trending towards a hypotensive event before the event occurs, and provides you with insights to understand the root cause and inform a potential course of action for your patient.
Part of the Acumen intelligent decision support suite, the Acumen Hypotension Prediction Index software is unlocked with the Acumen IQ sensor.
Three key elements of the Acumen Hypotension Prediction Index software
The HPI parameter displays as a value ranging from 0 to 100, with higher values indicating higher likelihood of a hypotensive event.
The proprietary algorithm - developed with machine learning, and using data from almost 59,000 hypotensive events and over 144,000 non-hypotensive events - detects potential of hypotensive trending of a patient's mean arterial pressure (MAP). The HPI parameter value is updated every 20 seconds, providing continuous predictive insights into developing hypotensive events.
The higher the value of the HPI parameter, the greater the likelihood a hypotensive event will occur.
The diagnostic performance of the HPI parameter was assessed through clinical validation studies:
The full table of Results of Clinical Validation studies may be found in the Operator's Manual.
- Specificity: ratio of true negatives to total number of non-events (negatives) with a negative defined as a data point that is at least 20 minutes away from any hypotensive event
- Sensitivity: ratio of true positives to total number of events (positives) with positive defined as data point that is at most 5 minutes prior to a hypotensive event.
HPI high alert popup
The HPI high alert popup alerts you when your patient is trending toward or experiencing a hypotensive event.
If the HPI parameter value exceeds 85 for two consecutive 20-second updates or reaches 100 at any time, the HPI high alert popup window will appear, prompting you to review the patient hemodynamics using the HPI secondary screen.
HPI secondary screen
If your patient is trending toward a hypotensive event, or is experiencing a hypotensive event, you can investigate the root cause and proactively inform a potential course of action. The advanced hemodynamic pressure and flow parameters provided on the HPI secondary screen allow you an opportunity to investigate and identify the root cause of potentially developing hypotensive events.
The HPI secondary screen is accessed through the HPI high alert popup, by touching the HPI Information Bar (when enabled); by pressing the button on the HPI Key Parameter, or at any time through the Clinical Actions menu on the monitor. Acumen Hypotension Prediction Index software secondary screen displays consolidated values for patient parameters of MAP, CO, SVR, PR, SV, and SVV/PVV as well as two additional indicators of contractility and afterload to provide a complete hemodynamic profile of the patient. The advanced hemodynamic parameters on the secondary screen are arranged visually by preload, contractility, and afterload.
Using these advanced hemodynamic parameters can provide you potential insights into the cause of a hypotensive event.
Stroke volume variation (SVV) or Pulse pressure variation (PPV)
The percent difference between minimum and maximum stroke volume (SV) or pulse pressure (PP) during a respiratory cycle.
Systolic slope (dP/dt)
A sensitive measure of changes in the contractility of the left ventricle defines as maximal upslope of the arterial pressure waveform from a peripheral artery.
Dynamic arterial elastance (Eadyn)
The ratio of pulse pressure variation to stroke volume variation (PPV/SVV).
Acumen HPI software combined with a treatment protocol achieved statistically significant reduction in hypotension vs. standard of care1,2
Two randomized controlled trials have shown that using Acumen HPI software in combination with a hemodynamic treatment protocol significantly reduced the incidence and duration of hypotensive events in patients undergoing noncardiac surgery.1,2
HYPE trial results featured in JAMA1
Highlights from 2020 Wijnberge, et al.1
Publication in JAMA: “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”
- Elective, noncardiac surgery patients monitored with Acumen HPI software had a median time of hypotension per patient of 8 minutes compared to 32.7 minutes in the control group
- Time-weighted average of hypotension combines the duration and the severity of hypotension corrected for the total duration of the procedure. With Acumen HPI software, the study showed a median .38 mmHg difference between the interventional and control group.
- The Acumen HPI software secondary screen provided insight into the potential root cause of that hypotension, enabling clinicians to identify the appropriate treatment course
HYPE trial: primary and secondary endpoints1
HYPE trial: hemodynamic diagnostic guidance and treatment protocol12
The trial protocol is included below for reference purposes only as it relates to this particular study.
In the HYPE trial, there was no significant difference in the cumulative dose of vasopressors or fluids, and hypotension was prevented without increasing the number of hypertensive events.1
Results from Acumen HPI implementation in total hip arthroplasty prospective trial2
Highlights from 2019 Schneck, et al.2
Publication in the Journal of Clinical Monitoring and Computing: "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"
Acumen Hypotension Prediction Index software combined with protocolized treatment was shown to reduce the relative and absolute duration of hypotensive events in total hip arthroplasty patients, in comparison to a historical and prospective control group.
Trial: primary endpoints2
|Number of hypotensive events per hour (n/hr)||0||5||2|
|Absolute IOH time (sec)||0||640||660|
|Relative IOH time (IOH time as % of total anesthesia time)||0||6||7|
Total hip arthroplasty prospective trial: protocol2
The trial protocol is included below for reference purposes only as it relates to this particular study.
Acumen HPI software had superior ability to predict hypotensive events than common hemodynamic parameters3
Highlights from 2019 Davies, et al.3
Publication in Anesthesia and Analgesia: "Ability of an Arterial Waveform Analysis–Derived Hypotension Prediction Index to Predict Future Hypotensive Events in Surgical Patients"
When compared with hemodynamic parameters such as SV, CO, SVV, and MAP, Acumen HPI software showed a higher predictive performance at 5 and 10 minutes before hypotension in this study3.
Prediction of hypotension at 5 minutes before an event3
Prediction of hypotension at 10 minutes before an event3
5 and 10 min before the event.
Acumen HPI software demonstrated high accuracy in predicting hypotension4
Highlights from Hatib, et al4
Publication in Anesthesiology: "Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis"
- Predictive algorithms are usually assessed via a ROC curve, with the AUC showing the predictive power of the algorithm4,14
- At 10 minutes before an event, Acumen HPI software predicted hypotension with a specificity and sensitivity of 89% and 90% respectively, and with an AUC of 0.95 in this study.4
ROC closest to the y-axis approaches a perfect model, with fewer false-positive and false-negative values13
ROC at 10 minutes before a hypotensive event4
In noncardiac surgery patients, research findings have revealed strong associations between intraoperative hypotension and elevated risk of both acute kidney injury (AKI) and myocardial injury after noncardiac surgery (MINS).5-7
MINS — the most common cardiovascular complication that occurs after noncardiac surgery — is the leading cause of mortality within one month following surgery.5,8 It is a substantial public health issue.8
More than 200 million adults across the world will undergo noncardiac surgery annually, and this number continues to increase each year.9,10 Globally, an estimated 8 million patients over 45 years old — more than 1 in 12 patients — suffer myocardial injury after noncardiac surgery per year.8,10,11
Once a patient’s mean arterial pressure (MAP) reaches 65 mmHg, it only takes 10 minutes of exposure to see higher associations between intraoperative hypotension and MINS.5
Additionally, if a patient's MAP reaches 50 mmHg, it only takes one minute of exposure to see a significant escalation in risk of MINS making early identification of a hypotensive event critical.5
Acumen Hypotension Prediction Index software is the first predictive technology that provides you with information regarding the likelihood of a patient trending toward a hypotensive event* and assists you in understanding the root cause of deteriorating cardiovascular stability.
The Acumen IQ sensor unlocks the Acumen Hypotension Prediction Index feature
The Acumen IQ sensor — part of the minimally invasive family of hemodynamic sensors — unlocks the Acumen Hypotension Prediction Index software. The minimally invasive Acumen IQ sensor connects to any existing radial arterial line. The Acumen IQ system automatically updates advanced parameters every 20 seconds, reflecting rapid physiological changes in moderate-to high-risk surgery. Advanced hemodynamic parameters provided by the Acumen IQ sensor offer you continuous insight into your patient’s hemodynamic status.
|Model||Description||Length||Unit of Measure|
|AIQS8||Acumen IQ sensor||84 in / 213 cm||EA|
|AIQS85||Acumen IQ sensor||84 in / 213 cm||5|
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- 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. doi:10.1001/jama.2020.0592 https://jamanetwork.com/journals/jama/article-abstract/2761469
- Schneck, E., Schulte, D., Habig, L., Ruhrmann, S., Edinger, F., Markmann, M., Habicher, M., Rickert, M., Koch, C., Sander, M. (2019) Hypotension Prediction Index based protocolized haemodynamic management reduces the incidence and duration of intraoperative hypotension in primary total hip arthroplasty: a single centre feasibility randomized blinded prospective interventional trial. Journal of Clinical Monitoring and Computing online, November 29, 2019. https://link.springer.com/article/10.1007/s10877-019-00433-6
- 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;doi: 10.1213/ANE.0000000000004121. https://journals.lww.com/anesthesia-analgesia/Citation/2020/02000/Ability-of-an-Arterial-Waveform-Analysis-Derived.16.aspx
- 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, 663-74. https://anesthesiology.pubs.asahq.org/article.aspx?articleid=2685008
- Salmasi, V., Maheshwari, K., Yang, G., Mascha, E.J., Singh, A., Sessler, D.I., & Kurz, A. (2017). Relationship between intraoperative hypotension, defined by either reduction from baseline or absolute thresholds, and acute kidney injury and myocardial injury. Anesthesiology, 126(1), 47-65.
- Sun, L.Y., Wijeysundera, D.N., Tait, G.A., & Beattie, W.S. (2015). Association of Intraoperative Hypotension with Acute Kidney Injury after Elective non-cardiac Surgery. Anesthesiology, 123(3), 515-523.
- Walsh, M., Devereaux, P.J., Garg, A.X., Kurz, A., Turan, A., Rodseth, R.N., Cywinski, J., Thabane, L., & Sessler, D.I. (2013). Relationship between Intraoperative Mean Arterial Pressure and Clinical Outcomes after non-cardiac Surgery. Anesthesiology, 119(3), 507-515.
- Kahn, Alonso-Coello and Devereaux, Myocardial injury after non-cardiac surgery, Curr Opin Cardiol, 2014, 29:307-311
- Devereaux and Sessler, Cardiac complications in patients undergoing major non-cardiac surgery, N Engl J Med, 2015, 373(23):2258-2269.
- Sellers, D., Srinivas, C., Djaiani, G. (2018). Cardiovascular complications after non-cardiac surgery. Anaesthesia, 73 (Suppl. 1), 34 - 42.
- van Waes, J., Nathoe, H., Graaff, J., Kemperman, H., de Borst, G., Peelen, L., van Klei, W. (2013). Myocardial Injury After Noncardiac Surgery and its Association With Short-Term Mortality. Circulation, 127, 2264 - 2271.
- Wijnberge et al. HYPE Protocol supplement: Intraoperative implementation of the hypotension probability indicator (HPI) algorithm – A pilot randomized controlled clinical trial, Supplemental to 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 2020.16 Appendix I.
- Vining, David & Gladish, Gregory. (1992). Receiver operating characteristic curves: a basic understanding. Radiographics : a review publication of the Radiological Society of North America, Inc. 12. 1147-54. 10.1148/radiographics.12.6.1439017.
- Huang, Jin, and C.X. Ling. (2005, Using AUC and Accuracy in Evaluating Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 3, 2005, pp. 299–310., doi:10.1109/tkde.2005.50.
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