Predictive decision support. Individualised patient care solutions for smart recovery1,2
HemoSphere advanced monitoring platform provides a comprehensive view of haemodynamics and tissue oximetry, giving you data to inform your clinical decisions. From one monitor, you can continuously monitor your patient using both oxygen saturation and haemodynamic parameters to ensure they remain adequately perfused.
The only platform to offer full-range cuff, sensor and catheter compatibility along with first-of-its-kind hypotension predictive decision support software*, HemoSphere advanced monitor enables proactive, individualised patient management.
References
- McGee, W. A Simple Physiologic Algorithm for Managing Hemodynamics Using Stroke Volume and Stroke Volume Variation: Physiologic Optimization Program. J Intensive Care Med. 2009.
- Benes J, Giglio M, Brienza N, Michard F. The effects of goal-directed fluid therapy based on dynamic parameters on post-surgical outcome: a meta-analysis of randomized controlled trials. Crit Care. 2014 Oct 28;18(5):584.
- Pugsley and Lerner. “Cardiac Output Monitoring: Is There a Gold Standard and How Do the Newer Technologies Compare?” Seminars in Cardiothoracic and Vascular Anesthesia, vol. 14, no. 4, 2010, p. 274–282. DOI:10.1177/1089253210386386. http://scv.sagepub.com.
- Lee, Matthew, et al. The SwanGanz Catheter Remains a Critically Important Component of Monitoring in Cardiovascular Critical Care. Canadian Journal of Cardiology 33 (2017), p.142-147.
- Ranucci, M., et al. Continuous monitoring of Central venous oxygen saturation (PediaSat) in pediatric patients undergoing cardiac surgery: a validation study of a new technology. Journal of cardiothoracic and vascular anesthesia, Vol. 22, No. 6, December 2008, p. 847-852.
- Mohseni-Bod, eta al. Evaluation of a new pediatric continuous oximetry catheter. Pediatric Crit Care Med 2011;12:4. 437-441.
- 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 after noncardiac surgery: A retrospective cohort analysis. Anesthesiology, 126(1), 47-65.
- Sun LY, Wijeysundera DN, Tait GA, Beattie WS. Association of intraoperative hypotension with acute kidney injury after elective noncardiac surgery. Anesthesiology. 2015 Sep;123(3):515- 23.
- Maheshwari K, Khanna S, Bajracharya GR, Makarova N, Riter Q, Raza S, Cywinski JB, Argalious M, Kurz A, Sessler DI. A Randomized Trial of Continuous Noninvasive Blood Pressure Monitoring During Noncardiac Surgery. Anesth Analg. 2018 Aug;127(2):424-431.
- Walsh M, Devereaux PJ, Garg AX, Kurz A, Turan A, Rodseth RN, Cywinski J, Thabane L, Sessler DI. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension. Anesthesiology. 2013 Sep;119(3):507-15.
- Berkenstadt. Et al. Stroke Volume Variation as a Predictor of Fluid Responsiveness in Patients Undergoing Brain Surgery. Neurosurgical Anesthesia. 2001; 92: 984-9.
- Peng, K et al. Goal-Directed Fluid Therapy Based on Stroke Volume Variations Improves Fluid Management and Gastrointestinal Perfusion in Patients Undergoing Major Orthopedic Surgery. Med Principle and Practice. 2014; 23:413- 420.
- Monnet, X. Passive let raising: five rules, not a drop in fluid! Critical Care 19:18 2015. 1-3.
- Pratt, B. et al. Calculating Arterial PressureBased Cardiac Output Using a Novel Measurement and Analysis Method. Biomedical Instrumentation & Technology. 403-411.
- Ward H. van der Ven, Denise P. Veelo, Marije Wijnberge, Björn J.P. van der Ster, Alexander P.J. Vlaar, Bart F. Geerts, One of the first validations of an artificial intelligence algorithm for clinical use: The impact on intraoperative hypotension prediction and clinical decision-making, Surgery, Volume 169, Issue 6, 2021, Pages 1300-1303, ISSN 0039-6060, https://doi.org/10.1016/j.surg.2020.09.041.
- Feras Hatib, Zhongping Jian, Sai Buddi, Christine Lee, Jos Settels, Karen Sibert, Joseph Rinehart, Maxime Cannesson; Machine-learning Algorithm to Predict Hypotension Based on Highfidelity Arterial Pressure Waveform Analysis. Anesthesiology 2018; 129:663–674 doi: https://doi.org/10.1097/ALN.0000000000002300
- Cannesson M, Pearse R. Perioperative Hemodynamic Monitoring and Goal Directed Therapy: From theory to practice. Cambridge University Press. 2014.
For a listing of indications, contraindications, precautions, warnings, and potential adverse events, please refer to the Instructions for Use (consult eifu.edwards.com where applicable).
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