Address the potential root cause of hemodynamic instability sooner with AI-guided predictive decision support
You have the power to prevent hypotension.
Acumen Hypotension Prediction Index™ (HPI™) Software provides earlier warnings, deeper insights and more time to act. With advanced predictive intelligence, it’s a smarter way to monitor and personalize care for your patient’s needs.
Predictive Hemodynamic Monitoring
Predicts hypotension up to 15 minutes in advance, enabling you to proactively treat hemodynamic instability.
Personalized insights for your patient
Provides AI-guided predictive decision support to help clinicians respond faster with targeted interventions.
The right access for the right patient
Tailor monitoring to each patient’s level of acuity with minimally-invasive and noninvasive solutions.
Clinically Proven to reduce the burden of intraoperative hypotension
Backed by extensive clinical evidence, Acumen HPI™ Software has been rigorously studied for accuracy and efficacy compared to the standard of care.1 The EU HYPROTECT study demonstrated that patients monitored with Acumen HPI™ Software experienced a low incidence, duration and severity of hypotension.*2
Low incidence of hypotension
41% of patients avoided hypotension when monitored with Acumen HPI™ Software, compared to 12% of patients in another large multicenter study where Acumen HPI™ Software was not used.*2,3
Brief duration of hypotension
Patients in this study experienced a median of 2 minutes of hypotension, or 1% of surgery time, when Acumen HPI™ Software was used compared with 28 minutes in another large multicenter study where Acumen HPI™ Software was not used.*2,3
Low severity of hypotension
Patients monitored with Acumen HPI™ Software experienced a very low time-weighted average (TWA) MAP <65 mmHg at 0.03. The TWA MAP <60 mmHg was 0.*2
Intraoperative hypotension (IOH) is common. The consequences may be severe. Even low-risk surgical patients may face serious consequences.
Solares G, Garcia D. The value of the Hypotension Prediction Index as a tool for treatment of haemodynamic instability: a clinical review. Anesth Clin Res. 2024;15(6):1–8.
Kouz K, Garcia MIM, Cerutti E, et al. Intraoperative hypotension when using hypotension prediction index software during major noncardiac surgery: a European multicentre prospective observational registry (EU HYPROTECT). BJA Open. 2023;6:100140.
Shah N, Mentz G, Kheterpal S, et al. The incidence of intraoperative hypotension in moderate to high risk patients undergoing non-cardiac surgery: a retrospective multicenter observational analysis. J Clin Anesth. 2020;66:1-12.
Treat the root cause of hemodynamic instability
Acumen HPI™ Software doesn’t just sound an alarm — it provides AI-guided predictive decision support to help you drill down to the potential root cause of hemodynamic instability and intervene sooner.
Smart Alerts and Trends
When hemodynamic instability is detected, HPI™ Smart Alerts and Trends provide you with a focused view of the potential targets for intervention, such as preload, afterload and contractility.
Relationship View
The Relationship View screen provides a simplified, focused view of a patient's hemodynamic status, with red and yellow indicators to guide you towards the potential root cause of hemodynamic instability.
Beyond blood pressure
Blood pressure alone does not reflect perfusion. Acumen HPI™ Software provides continuous insights into hemodynamic trends that can help clinicians detect instability and identify the potential root cause. Additional advanced parameters, dP/dt and Eadyn, provide deeper insights into the underlying cause of hemodynamic instability, supporting targeted interventions.
Detect hemodynamic instability and unlock comprehensive insights into preload, contractility and afterload:
The EU HYPROTECT study demonstrated that patients monitored with Acumen HPI™ Software experienced a low incidence, duration and severity of hypotension.*1
The multicenter, prospective, observational study of over 700 noncardiac surgery patients demonstrated:
*Multicenter, prospective, observational study of patients across 5 EU countries monitored with Acumen IQ™ Arterial Line Sensor where hypotension was defined as >1 min below MAP 65 mmHg
Highlighted clinical evidence for Acumen HPI™ Software
Wijnberge M, et al. J Amer Med Assoc. 2020
Effect of a Machine Learning–Derived Early Warning System for Intraoperative Hypotension Versus Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial*
This study demonstrated that Acumen HPI™ Software combined with a treatment protocol achieved a statistically significant reduction in hypotension versus standard of care. Elective, noncardiac surgery patients monitored with Acumen HPI™ Software had a median time of hypotension per patient of 8 minutes compared with 32.7 minutes in the control group.3
Schneck et al. J Clin Monit Comput. 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 Randomised Blinded Prospective Interventional Trial*
In this single center randomized blinded prospective feasibility study, Acumen HPI™ Software combined with protocolized treatment was shown to reduce the incidence and duration—both absolute and relative—of hypotensive events in patients undergoing primary total hip arthroplasty, when compared with historical and prospective control groups receiving routine care.7
Highlighted validation studies for Acumen HPI™ Software
Davies et al. Anesth Analg. 2019
Ability of an Arterial Waveform Analysis–Derived Hypotension Prediction Index to Predict Future Hypotensive Events in Surgical Patients*
In this 255 patient, 2-center retrospective analysis study, Acumen HPI™ Software showed a high predictive performance at 5 and 10 minutes before hypotension.8
Hatib et al. Anesthesiology. 2019
Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis**
Acumen HPI™ Software demonstrated high accuracy in predicting hypotensive events (MAP< 65 mmHg for at least 1 min).
An internal validation of 350 surgical patient demonstrated that Acumen HPI™ Software predicted hypotensive events 15 minutes before hey occurred with a sensitivity and specificity o 88% and 87%, respectively, and an AUC of 0.95 (CI 95%).9
*Studies conducted with arterial line monitoring.
Wijnberge M, Geerts BF, Hol L, et al. 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;323(11):1052-1060.
Maheshwari K, Shimada T, Yang D, et al. Hypotension Prediction Index for prevention of hypotension during moderate- to high-risk noncardiac surgery. Anesthesiology. 2020;133(6):1214-1222.
Tsoumpa M, Kyttari A, Matiatou S, et al. The use of the Hypotension Prediction Index integrated in an algorithm of goal-directed hemodynamic treatment during moderate- and high-risk surgery. J Clin Med. 2021;10(24):5884.
Grundmann CD, Wischermann JM, Fassbender P, et al. Hemodynamic monitoring with Hypotension Prediction Index versus arterial waveform analysis alone and incidence of perioperative hypotension. Acta Anaesthesiol Scand. 2021;65(10):1404-1412.
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. 2020;130(2):352-359.
Hatib F, Jian Z, Buddi S, et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology. 2018;129(4):663-674.
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