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Computer-Assisted Fluid Therapy

  • Computer-Assisted Anesthesia Management (A Joosten, Section Editor)
  • Published:
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Abstract

Purpose of Review

This review provides an overview of the rapidly evolving field of computer-assisted fluid management systems, aimed at familiarizing clinicians with its key concepts and advancements.

Recent Findings

Over the past two decades, several attempts have been made to develop computerized systems to support clinicians with the complicated task of patient fluid management. These systems vary in their purpose, logic, evaluation methods, and more, but they share the principle of utilizing closed-loop control mechanisms.

Summary

Computer-assisted fluid management systems (CAFMs) provide automated tools to support the task of fluid management, promoting precise fluid therapy that is continuously adjusted to meet the set goal. As advanced physiological sensors and algorithms continue to evolve and mature, the implementation of CAFMs within the realm of anesthesia and critical care will continue to grow.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Thiele RH, Raghunathan K, Brudney CS, Lobo DN, Martin D, Senagore A, et al. American Society for Enhanced Recovery (ASER) and Perioperative Quality Initiative (POQI) joint consensus statement on perioperative fluid management within an enhanced recovery pathway for colorectal surgery. Perioper Med. 2016;5:1–15. https://doi.org/10.1186/S13741-016-0049-9/FIGURES/3.

    Article  Google Scholar 

  2. Bighamian R, Kim C-S, Reisner AT, Hahn J-O. Closed-loop fluid resuscitation control via blood volume estimation. J Dyn Syst Meas Control. 2016. https://doi.org/10.1115/1.4033833.

    Article  Google Scholar 

  3. •• U.S. Food and Drug Administration - Center for Devices and Radiological. Technical considerations for medical devices with physiologic closed-loop control technology - Draft Guidance for Industry and Food and Drug Administration Staff. 2021. FDA draft guidance for PCLC technology.

  4. Banh E, Der Wu W, Rinehart J. Principles of pharmacologic hemodynamic management and closed-loop systems. Best Pract Res Clin Anaesthesiol. 2014;28:453–62. https://doi.org/10.1016/J.BPA.2014.08.004.

    Article  PubMed  Google Scholar 

  5. Cosentino C, Bates D. Feedback control in systems biology. 2011. https://doi.org/10.1201/b11153

  6. Gill F, Corkish V, Robertson J, Samson J, Simmons B, Stewart D. An exploration of pediatric nurses’ compliance with a medication checking and administration protocol. J Spec Pediatr Nurs. 2012;17:136–46. https://doi.org/10.1111/J.1744-6155.2012.00331.X.

    Article  PubMed  Google Scholar 

  7. Mackworth NH. The breakdown of vigilance during prolonged visual search. 2008;1:6–21. https://doi.org/10.1080/17470214808416738

  8. •• Avital G, Snider EJ, Berard D, Vega SJ, Hernandez Torres SI, Convertino VA et al. Closed-Loop controlled fluid administration systems: a comprehensive scoping review. J Pers Med. 2022. https://doi.org/10.3390/jpm12071168. Comprehensive scoping review of CAFM systems the literature.

  9. •• Joosten A, Rinehart J, Bardaji A, Van Der Linden P, Jame V, Van Obbergh L et al. Anesthetic management using multiple closed-loop systems and delayed neurocognitive recovery - a randomized controlled trial. Anesthesiology. 2020;132:253–66. https://doi.org/10.1097/ALN.0000000000003014. RCT demonstrating authomated anesthetic control outperforming manual control.

  10. •• IEC 60601–1–10:2007 - Medical electrical equipment — part 1–10: general requirements for basic safety and essential performance — collateral standard: requirements for the development of physiologic closed-loop controllers. (https://www.iso.org/standard/44104.html). Accessed 17 December 2022. IEC standards for PCLCs.

  11. Marques NR, Ford BJ, Khan MN, Kinsky M, Deyo DJ, Mileski WJ, et al. Automated closed-loop resuscitation of multiple hemorrhages: a comparison between fuzzy logic and decision table controllers in a sheep model. Disaster Mil Med. 2017. https://doi.org/10.1186/S40696-016-0029-0.

    Article  PubMed  PubMed Central  Google Scholar 

  12. • Salinas J, Drew G, Gallagher J, Cancio LC, Wolf SE, Wade CE et al. Closed-loop and decision-assist resuscitation of burn patients. J. Trauma. 2008. https://doi.org/10.1097/TA.0B013E31816BF4F7. “Burn navigator” system.

  13. • Snider EJ, Berard D, Vega SJ, Avital G, Boice EN. Evaluation of a proportional integral derivative controller for hemorrhage resuscitation using a hardware-in-loop test platform. J Pers Med. 2022. https://doi.org/10.3390/jpm12060979. Hardware-in-loop test platform for a PID controlled system.

  14. • Rinehart J, Alexander B, Manach YL, Hofer C, Tavernier B, Kain ZN et al. Evaluation of a novel closed-loop fluid-administration system based on dynamic predictors of fluid responsiveness: an in silico simulation study. Crit Care. 2011;15:1–12. https://doi.org/10.1186/CC10562/TABLES/6. “Learning Intravenous Resuscitator” (LIR) system.

  15. Salinas J, Chung KK, Mann EA, Cancio LC, Kramer GC, Serio-Melvin ML, et al. Computerized decision support system improves fluid resuscitation following severe burns: an original study. Crit Care Med. 2011;39:2031–8. https://doi.org/10.1097/CCM.0B013E31821CB790.

    Article  PubMed  Google Scholar 

  16. Jin X, Bighamian R, Hahn JO. Development and in silico evaluation of a model-based closed-loop fluid resuscitation control algorithm. IEEE Trans Biomed Eng. 2019;66:1905–14. https://doi.org/10.1109/TBME.2018.2880927.

    Article  Google Scholar 

  17. Schöning J, Riechmann A, Pfisterer H-J. AI for closed-loop control systems - new opportunities for modeling, designing, and tuning control systems. ACM Int Conf Proceeding Ser. 2022:318–23. https://doi.org/10.1145/3529836.3529952.

  18. Padmanabhan R, Meskin N, Haddad WM. Closed-loop control of anesthesia and mean arterial pressure using reinforcement learning. Biomed Signal Process Control. 2015;22:54–64. https://doi.org/10.1016/J.BSPC.2015.05.013.

    Article  Google Scholar 

  19. Nian R, Liu J, Huang B. A review on reinforcement learning: introduction and applications in industrial process control. Comput Chem Eng. 2020;139:106886. https://doi.org/10.1016/J.COMPCHEMENG.2020.106886.

    Article  CAS  Google Scholar 

  20. Bogatu L, Turco S, Mischi M, Schmitt L, Woerlee P, Bezemer R, et al. New hemodynamic parameters in peri-operative and critical care—challenges in translation. Sensors. 2023;23:2226. https://doi.org/10.3390/S23042226.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Argueta E, Berdine G, Pena C, Nugent KM. FloTrac® monitoring system: what are its uses in critically ill medical patients? Am J Med Sci. 2015;349:352–6. https://doi.org/10.1097/MAJ.0000000000000393.

    Article  PubMed  Google Scholar 

  22. Taniguchi Y, Emoto N, Miyagawa K, Nakayama K, Kinutani H, Tanaka H, et al. Noninvasive and simple assessment of cardiac output and pulmonary vascular resistance with whole-body impedance cardiography is useful for monitoring patients with pulmonary hypertension. Circ J. 2013;77:2383–9. https://doi.org/10.1253/CIRCJ.CJ-13-0172.

    Article  PubMed  Google Scholar 

  23. Spöhr F, Hettrich P, Bauer H, Haas U, Martin E, Böttiger BW. Comparison of two methods for enhanced continuous circulatory monitoring in patients with septic shock. Intensive Care Med. 2007;33:1805–10. https://doi.org/10.1007/S00134-007-0703-2.

    Article  PubMed  Google Scholar 

  24. Kendrick J, Kaye A, Tong Y, Belani K, Urman R, Hoffman C, et al. Goal-directed fluid therapy in the perioperative setting. J Anaesthesiol Clin Pharmacol. 2019;35:29–34. https://doi.org/10.4103/JOACP.JOACP_26_18.

    Article  Google Scholar 

  25. Ljungqvist O, Scott M, Fearon KC. Enhanced recovery after surgery: a review. JAMA Surg. 2017;152:292–8. https://doi.org/10.1001/JAMASURG.2016.4952.

    Article  PubMed  Google Scholar 

  26. Chong MA, Wang Y, Berbenetz NM, McConachie I. Does goal-directed haemodynamic and fluid therapy improve peri-operative outcomes? Eur J Anaesthesiol. 2018;35:469–83. https://doi.org/10.1097/EJA.0000000000000778.

    Article  PubMed  Google Scholar 

  27. Jessen MK, Vallentin MF, Holmberg MJ, Bolther M, Hansen FB, Holst JM, et al. Goal-directed haemodynamic therapy during general anaesthesia for noncardiac surgery: a systematic review and meta-analysis. Br J Anaesth. 2022;128:416–33. https://doi.org/10.1016/J.BJA.2021.10.046.

    Article  CAS  PubMed  Google Scholar 

  28. Rinehart J, Chung E, Canales C, Cannesson M. Intraoperative stroke volume optimization using stroke volume, arterial pressure, and heart rate: closed-loop (learning intravenous resuscitator) versus anesthesiologists. J Cardiothorac Vasc Anesth. 2012;26:933–9. https://doi.org/10.1053/j.jvca.2012.05.015.

    Article  PubMed  Google Scholar 

  29. • Maheshwari K, Malhotra G, Bao X, Lahsaei P, Hand WR, Fleming NW et al. Assisted fluid management software guidance for intraoperative fluid administration. Anesthesiology. 2021;135:273–83. https://doi.org/10.1097/ALN.0000000000003790. Clinical trial of the “Acumen Assisted Fluid Management” system.

  30. Gricourt Y, PrinDerre C, Demattei C, Bertran S, Louart B, Muller L, et al. A pilot study assessing a closed-loop system for goal-directed fluid therapy in abdominal surgery patients. J Pers Med. 2022;12:1409. https://doi.org/10.3390/JPM12091409.

    Article  PubMed  PubMed Central  Google Scholar 

  31. •• Joosten A, Coeckelenbergh S, Delaporte A, Ickx B, Closset J, Roumeguere T et al. Implementation of closed-loop-assisted intra-operative goal-directed fluid therapy during major abdominal surgery: a case-control study with propensity matching. Eur J Anaesthesiol. 2018;35:650–8. https://doi.org/10.1097/EJA.0000000000000827. computer-assisted GDFT demonstrated improved postoperative outcomes.

  32. Klingert W, Peter J, Thiel C, Thiel K, Rosenstiel W, Klingert K, et al. Fully automated life support: an implementation and feasibility pilot study in healthy pigs. Intensive Care Med Exp. 2018;6:1–12. https://doi.org/10.1186/S40635-018-0168-3/FIGURES/4.

    Article  Google Scholar 

  33. Gholami B, Haddad WM, Bailey JM, Geist B, Ueyama Y, Muir WW. A pilot study evaluating adaptive closed-loop fluid resuscitation during states of absolute and relative hypovolemia in dogs. J Vet Emerg Crit Care. 2018;28:436–46. https://doi.org/10.1111/VEC.12753.

    Article  Google Scholar 

  34. Gholami B, Haddad WM, Bailey JM, Muir WW. Closed-loop control for fluid resuscitation: recent advances and future challenges. Front Vet Sci. 2021;8:145. https://doi.org/10.3389/FVETS.2021.642440/BIBTEX.

    Article  Google Scholar 

  35. Cannon JW. Hemorrhagic shock. 2018;378:370–9. https://doi.org/10.1056/NEJMRA1705649.

  36. Jenkins DH, Rappold JF, Badloe JF, Berséus O, Blackbourne CL, Brohi KH, et al. Trauma hemostasis and oxygenation research position paper on remote damage control resuscitation: definitions, current practice, and knowledge gaps. Shock. 2014;41:3–12. https://doi.org/10.1097/SHK.0000000000000140.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Vaid SU, Shah A, Michell MW, Rafie AD, Deyo DJ, Prough DS, et al. Normotensive and hypotensive closed-loop resuscitation using 30% NaCl to treat multiple hemorrhages in sheep. Crit Care Med. 2006;34:1185–92. https://doi.org/10.1097/01.CCM.0000207341.78696.3A.

    Article  PubMed  Google Scholar 

  38. Hundeshagen G, Kramer GC, Ribeiro Marques N, Salter MG, Koutrouvelis AK, Li H, et al. Closed-loop and decision-assist guided fluid therapy of human hemorrhage. Crit Care Med. 2017;45:e1068. https://doi.org/10.1097/CCM.0000000000002593.

    Article  PubMed  PubMed Central  Google Scholar 

  39. • Alsalti M, Tivay A, Jin X, Kramer GC, Hahn JO. Design and in silico evaluation of a closed-loop hemorrhage resuscitation algorithm with blood pressure as controlled variable.J Dyn Syst Meas Control. Trans. ASME. 2022. https://doi.org/10.1115/1.4052312/1119319. “Composite adaptive control” (CAC) system.

  40. Libert N, Chenegros G, Harrois A, Baudry N, Cordurie G, Benosman R, et al. Performance of closed-loop resuscitation of haemorrhagic shock with fluid alone or in combination with norepinephrine: an experimental study. Ann Intensive Care. 2018;8:1–10. https://doi.org/10.1186/S13613-018-0436-0/TABLES/2.

    Article  CAS  Google Scholar 

  41. • Libert N, Chenegros G, Harrois A, Baudry N, Decante B, Cordurie G et al. Performance of closed-loop resuscitation in a pig model of haemorrhagic shock with fluid alone or in combination with norepinephrine, a pilot study. J ClinMonit Comput. 2021;35:835–47. https://doi.org/10.1007/S10877-020-00542-7/TABLES/3. “TraumaTab” system.

  42. Richards JE, Harris T, Dünser MW, Bouzat P, Gauss T. Vasopressors in trauma: a never event? Anesth Analg. 2021;133:68–79. https://doi.org/10.1213/ANE.0000000000005552.

    Article  CAS  PubMed  Google Scholar 

  43. Snider EJ, Vega SJ, Ross E, Berard D, Hernandez-Torres SI, Salinas J, et al. Supervisory algorithm for autonomous hemodynamic management systems. Sensors. 2022. https://doi.org/10.3390/s22020529.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Snider EJ, Berard D, Vega SJ, Ross E, Knowlton ZJ, Avital G, et al. Hardware-in-loop comparison of physiological closed-loop controllers for the autonomous management of hypotension. Bioengineering. 2022. https://doi.org/10.3390/bioengineering9090420.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Vega S, Berard D, Avital G, Ross ES E. Adaptive closed-loop resuscitation controllers for hemorrhagic shock resuscitation. Transfusion (accepted for publication).

  46. Snider EJ, Berard D, Vega SJ, Ross E, Knowlton ZJ, Avital G, et al. Hardware-in-loop comparison of physiological closed-loop controllers for the autonomous management of hypotension. Bioengineering. 2022;9:420. https://doi.org/10.3390/BIOENGINEERING9090420/S1.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Snider EJ, Berard D, Vega SJ, Hernandez Torres SI, Avital G, Boice EN. An automated hardware-in-loop testbed for evaluating hemorrhagic shock resuscitation controllers. Bioengineering. 2022;9:373. https://doi.org/10.3390/BIOENGINEERING9080373/S1.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Ahuja RB, Puri V, Gibran N, Greenhalgh D, Jeng J, Mackie D, et al. ISBI practice guidelines for burn care. Burns. 2016;42:953–1021. https://doi.org/10.1016/J.BURNS.2016.05.013.

    Article  Google Scholar 

  49. Pham TN, Cancio LC, Gibran NS. American Burn Association practice guidelines burn shock resuscitation. J Burn Care Res. 2008;29:257–66. https://doi.org/10.1097/BCR.0B013E31815F3876.

    Article  PubMed  Google Scholar 

  50. Salinas J, Chung KK, Mann EA, Cancio LC, Kramer GC, Serio-Melvin ML, et al. Computerized decision support system improves fluid resuscitation following severe burns: an original study. Crit Care Med. 2011;39:2031–8. https://doi.org/10.1097/CCM.0B013E31821CB790.

    Article  PubMed  Google Scholar 

  51. Cancio LC, Salinas J, Kramer GC. Protocolized resuscitation of burn patients. Crit Care Clin. 2016;32:599–610. https://doi.org/10.1016/j.ccc.2016.06.008.

    Article  PubMed  Google Scholar 

  52. • Rizzo JA, Liu NT, Coates EC, Serio-Melvin ML, Foster KN, Shabbir M et al. Initial results of the american burn association observational multicenter evaluation on the effectiveness of the burn navigator. J Burn Care Res. 2022;43:728–34. https://doi.org/10.1093/JBCR/IRAB182. Multicenter observational study on the Burn Navigator.

  53. Parvinian B, Scully C, Wiyor H, Kumar A, Weininger S. Regulatory considerations for physiological closed-loop controlled medical devices used for automated critical care: food and drug administration workshop discussion topics hhs public access. Anesth Analg. 2018;126:1916–25. https://doi.org/10.1213/ANE.0000000000002329.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Varvel JR, Donoho DL, Shafer SL. Measuring the predictive performance of computer-controlled infusion pumps. J Pharmacokinet Biopharm. 1992;20:63–94. https://doi.org/10.1007/BF01143186.

    Article  CAS  PubMed  Google Scholar 

  55. Beckers R, Kwade Z, Zanca F. The EU medical device regulation: implications for artificial intelligence-based medical device software in medical physics. Phys Medica. 2021;83:1–8. https://doi.org/10.1016/J.EJMP.2021.02.011.

    Article  CAS  Google Scholar 

  56. Convertino VA, Schauer SG, Weitzel EK, Cardin S, Stackle ME, Talley MJ, et al. Wearable sensors incorporating compensatory reserve measurement for advancing physiological monitoring in critically injured trauma patients. Sensors. 2020;20:6413. https://doi.org/10.3390/S20226413.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Convertino VA, Koons NJ, Suresh MR. Physiology of human hemorrhage and compensation. Compr Physiol. 2021;11:1531–74. https://doi.org/10.1002/CPHY.C200016.

    Article  PubMed  Google Scholar 

  58. Joosten A, Huynh T, Suehiro K, Canales C, Cannesson M, Rinehart J. Goal-directed fluid therapy with closed-loop assistance during moderate risk surgery using noninvasive cardiac output monitoring: a pilot study. Br J Anaesth. 2015;114:886–92. https://doi.org/10.1093/bja/aev002.

    Article  CAS  PubMed  Google Scholar 

  59. 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;18:1–11. https://doi.org/10.1186/S13054-014-0584-Z/FIGURES/6.

    Article  Google Scholar 

  60. Dave C, Shen J, Chaudhuri D, Herritt B, Fernando SM, Reardon PM, et al. Dynamic assessment of fluid responsiveness in surgical ICU patients through stroke volume variation is associated with decreased length of stay and costs: a systematic review and meta-analysis. J Intensive Care Med. 2020;35:14–23. https://doi.org/10.1177/0885066618805410/ASSET/IMAGES/LARGE/10.1177_0885066618805410-FIG2.JPEG.

    Article  PubMed  Google Scholar 

  61. Marik PE, Cavallazzi R, Vasu T, Hirani A. Dynamic changes in arterial waveform derived variables and fluid responsiveness in mechanically ventilated patients: a systematic review of the literature. Crit Care Med. 2009;37:2642–7. https://doi.org/10.1097/CCM.0B013E3181A590DA.

    Article  PubMed  Google Scholar 

  62. Warltier DC, Dé F, Michard R. Changes in arterial pressure during mechanical ventilation. Anesthesiology. 2005;103:419–28. https://doi.org/10.1097/00000542-200508000-00026.

    Article  Google Scholar 

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Funding

The author EJS is the recipient of a US Department of Defense Research Award, with the payment from this award made directly to the US Army Institute of Surgical Research. The rest of the authors have no relevant financial relationships.

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Authors and Affiliations

  1. Division of Anesthesia, Sourasky Medical Center, Sackler Faculty of Medicine, Intensive Care & Pain Management, Tel-Aviv, Tel-Aviv University, Tel-Aviv, Israel

    Ron Eshel & Guy Avital

  2. U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX, 78234, USA

    Eric J. Snider

  3. Israel Defense Forces Medical Corps, Ramat Gan, Israel

    Guy Avital

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  1. Ron Eshel
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Correspondence to Ron Eshel.

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Eshel, R., Snider, E.J. & Avital, G. Computer-Assisted Fluid Therapy. Curr Anesthesiol Rep 13, 41–48 (2023). https://doi.org/10.1007/s40140-023-00559-z

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