The Hemodynamic Intelligence System: A Review of Closed-Loop Integration between Bedside Monitors, Biomarker Analysis, and Smart Infusion Pumps
Abstract
Background: The management of acute hemodynamic instability, particularly in sepsis and shock, remains a high-stakes challenge characterized by time-sensitive interventions and dynamic physiological changes. Recent technological advances in smart infusion pumps, continuous physiological monitoring, and rapid biomarker analysis present an unprecedented opportunity for integration.
Aim: This narrative review aims to synthesize the current evidence and conceptual frameworks for a Hemodynamic Intelligence System (HIS)—a closed-loop integration of bedside monitors, biomarker analysis, and smart infusion pumps—to enable autonomous, physiologically adaptive drug delivery for conditions like sepsis and shock.
Methods: A comprehensive literature search was conducted across PubMed, IEEE Xplore, CINAHL, and Web of Science for English-language articles published between 2010 and 2024.
Results: The convergence of these technologies is technically feasible and shows promise in early-stage clinical studies for improving protocol adherence and reducing time-to-therapeutic goals. Laboratory medicine must evolve to provide analyzers with sufficient rapidity and reliability for real-time feedback. Nursing faces a paradigm shift towards system oversight and alarm management, requiring new competencies in data interpretation and human-machine interaction.
Conclusion: The Hemodynamic Intelligence System represents a transformative vision for critical care. Its successful implementation hinges not on technological capability alone, but on rigorous interdisciplinary collaboration to address challenges in system safety, clinical workflow integration, and the preservation of the nurse's indispensable role as clinical contextualizer. Future research must prioritize robust clinical outcome trials and the development of shared governance models for autonomous systems.
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References
Alhazzani, W., Evans, L., Alshamsi, F., Møller, M. H., Ostermann, M., Prescott, H. C., ... & Rhodes, A. (2021). Surviving sepsis campaign guidelines on the management of adults with coronavirus disease 2019 (COVID-19) in the ICU: first update. Critical care medicine, 49(3), e219-e234. DOI: 10.1097/CCM.0000000000004899
Bhangu, A., Notario, L., Pinto, R. L., Pannell, D., Thomas-Boaz, W., Freedman, C., ... & da Luz, L. (2022). Closed loop communication in the trauma bay: identifying opportunities for team performance improvement through a video review analysis. Canadian Journal of Emergency Medicine, 24(4), 419-425. https://doi.org/10.1007/s43678-022-00295-z
Boulain, T., Garot, D., Vignon, P., Lascarrou, J. B., Benzekri-Lefevre, D., & Dequin, P. F. (2016). Predicting arterial blood gas and lactate from central venous blood analysis in critically ill patients: a multicentre, prospective, diagnostic accuracy study. BJA: British Journal of Anaesthesia, 117(3), 341-349. https://doi.org/10.1093/bja/aew261
Chromik, J., Klopfenstein, S. A. I., Pfitzner, B., Sinno, Z. C., Arnrich, B., Balzer, F., & Poncette, A. S. (2022). Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review. Frontiers in digital health, 4, 843747. https://doi.org/10.3389/fdgth.2022.843747
Desebbe, O., Rinehart, J., Van der Linden, P., Cannesson, M., Delannoy, B., Vigneron, M., ... & Joosten, A. (2022). Control of postoperative hypotension using a closed-loop system for norepinephrine infusion in patients after cardiac surgery: a randomized trial. Anesthesia & Analgesia, 134(5), 964-973. DOI: 10.1213/ANE.0000000000005888
DeSimone, A. K., Kapoor, N., Lacson, R., Budiawan, E., Hammer, M. M., Desai, S. P., ... & Khorasani, R. (2023). Impact of an automated closed-loop communication and tracking tool on the rate of recommendations for additional imaging in thoracic radiology reports. Journal of the American College of Radiology, 20(8), 781-788. https://doi.org/10.1016/j.jacr.2023.05.004
Dubin, A., Loudet, C. I., Hurtado, F. J., Pozo, M. O., Comande, D., Gibbons, L., ... & Bardach, A. (2022). Comparison of central venous minus arterial carbon dioxide pressure to arterial minus central venous oxygen content ratio and lactate levels as predictors of mortality in critically ill patients: a systematic review and meta-analysis. Revista Brasileira de Terapia Intensiva, 34, 279-286. https://doi.org/10.5935/0103-507X.20220026-en
Evans, L., Rhodes, A., Alhazzani, W., Antonelli, M., Coopersmith, C. M., French, C., ... & Levy, M. (2021). Executive summary: surviving sepsis campaign: international guidelines for the management of sepsis and septic shock 2021. Critical care medicine, 49(11), 1974-1982. DOI: 10.1097/CCM.0000000000005357
Fang, D. Z., Patil, T., Belitskaya-Levy, I., Yeung, M., Posley, K., & Allaudeen, N. (2018). Use of a hands free, instantaneous, closed-loop communication device improves perception of communication and workflow integration in an academic teaching hospital: a pilot study. Journal of Medical Systems, 42(1), 4. https://doi.org/10.1007/s10916-017-0864-7
Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The lancet digital health, 3(11), e745-e750. https://doi.org/10.1016/S2589-7500(21)00208-9
Heilmann, E., Gregoriano, C., Wirz, Y., Luyt, C. E., Wolff, M., Chastre, J., ... & Schuetz, P. (2021). Association of kidney function with effectiveness of procalcitonin-guided antibiotic treatment: a patient-level meta-analysis from randomized controlled trials. Clinical Chemistry and Laboratory Medicine (CCLM), 59(2), 441-453. https://doi.org/10.1515/cclm-2020-0931
Intelligence, A., & Learning, M. (2021). Based software as a medical device (samd) action plan. Food and Drug Administration, 2021-06.
Jansen, T. C., van Bommel, J., Schoonderbeek, F. J., Sleeswijk Visser, S. J., van der Klooster, J. M., Lima, A. P., ... & Bakker, J. (2010). Early lactate-guided therapy in intensive care unit patients: a multicenter, open-label, randomized controlled trial. American journal of respiratory and critical care medicine, 182(6), 752-761. https://doi.org/10.1164/rccm.200912-1918OC
Khatab, Z., & Yousef, G. M. (2021). Disruptive innovations in the clinical laboratory: catching the wave of precision diagnostics. Critical reviews in clinical laboratory sciences, 58(8), 546-562. https://doi.org/10.1080/10408363.2021.1943302
Kokol, P., Vošner, H. B., & Završnik, J. (2022). Knowledge Development in Artificial Intelligence Use in Paediatrics. Knowledge, 2(2), 185-190. https://doi.org/10.3390/knowledge2020011
Kost, G. J., Zadran, A., Zadran, L., & Ventura, I. (2019). Point-of-care testing curriculum and accreditation for public health—Enabling preparedness, response, and higher standards of care at points of need. Frontiers in public health, 6, 385. https://doi.org/10.3389/fpubh.2018.00385
Lieneck, C., McLauchlan, M., & Phillips, S. (2023, November). Healthcare cybersecurity ethical concerns during the COVID-19 global pandemic: a rapid review. In Healthcare (Vol. 11, No. 22, p. 2983). MDPI. https://doi.org/10.3390/healthcare11222983
Mallat, J., Baghdadi, F. A., Mohammad, U., Lemyze, M., Temime, J., Tronchon, L., ... & Fischer, M. O. (2020). Central venous-to-arterial PCO2 difference and central venous oxygen saturation in the detection of extubation failure in critically ill patients. Critical care medicine, 48(10), 1454-1461. DOI: 10.1097/CCM.0000000000004446
Martinez-Millana, A., & Martinez-Piqueras, M. (2019). Closed-Loop Ergonomics in the Factory of the Future: A Practical Approach from FASyS Project. In Transforming Ergonomics with Personalized Health and Intelligent Workplaces (pp. 65-84). IOS Press. Doi: 10.3233/978-1-61499-973-7-65
Marwitz, K. K., Fritschle, A. C., Trivedi, V., Covert, M. L., Walroth, T. A., DeLaurentis, P., ... & Degnan, D. (2020). Investigating multiple sources of data for smart infusion pump and electronic health record interoperability. American Journal of Health-System Pharmacy, 77(17), 1417-1423. https://doi.org/10.1093/ajhp/zxaa115
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Patel, N. T., Goenaga-Diaz, E. J., Lane, M. R., Austin Johnson, M., Neff, L. P., & Williams, T. K. (2022). Closed-loop automated critical care as proof-of-concept study for resuscitation in a swine model of ischemia–reperfusion injury. Intensive Care Medicine Experimental, 10(1), 30. https://doi.org/10.1186/s40635-022-00459-2
Pinsky, M. R., Cecconi, M., Chew, M. S., De Backer, D., Douglas, I., Edwards, M., ... & Vincent, J. L. (2022). Effective hemodynamic monitoring. Critical Care, 26(1), 294. https://doi.org/10.1186/s13054-022-04173-z
Pinsky, M. R., & Payen, D. (2005). Functional hemodynamic monitoring: foundations and future. In Functional Hemodynamic Monitoring (pp. 3-6). Berlin, Heidelberg: Springer Berlin Heidelberg.
Rajsic, S., Breitkopf, R., Bachler, M., & Treml, B. (2021). Diagnostic modalities in critical care: point-of-care approach. Diagnostics, 11(12), 2202. https://doi.org/10.3390/diagnostics11122202
Ray, S., Sundaram, V., Dutta, S., & Kumar, P. (2021). Ensuring administration of first dose of antibiotics within the golden hour of management in neonates with sepsis. BMJ Open Quality, 10(Suppl 1). https://doi.org/10.1136/bmjoq-2021-001365
Rinehart, J., Ma, M., Calderon, M. D., Bardaji, A., Hafiane, R., Van der Linden, P., & Joosten, A. (2019). Blood pressure variability in surgical and intensive care patients: Is there a potential for closed-loop vasopressor administration?. Anaesthesia Critical Care & Pain Medicine, 38(1), 69-71. https://doi.org/10.1016/j.accpm.2018.11.009
Rinehart, J., Lee, S., Saugel, B., & Joosten, A. (2021, February). Automated blood pressure control. In Seminars in respiratory and critical care medicine (Vol. 42, No. 01, pp. 047-058). Thieme Medical Publishers, Inc.. DOI: 10.1055/s-0040-1713083
Saraswat, D., Bhattacharya, P., Verma, A., Prasad, V. K., Tanwar, S., Sharma, G., ... & Sharma, R. (2022). Explainable AI for healthcare 5.0: opportunities and challenges. IEEe Access, 10, 84486-84517. https://doi.org/10.1109/ACCESS.2022.3197671
Storm, J., & Chen, H. C. (2021). The relationships among alarm fatigue, compassion fatigue, burnout and compassion satisfaction in critical care and step‐down nurses. Journal of clinical nursing, 30(3-4), 443-453. https://doi.org/10.1111/jocn.15555
Subramanian, S., Pamplin, J. C., Hravnak, M., Hielsberg, C., Riker, R., Rincon, F., ... & Herasevich, V. (2020). Tele-critical care: an update from the society of critical care medicine tele-ICU committee. Critical care medicine, 48(4), 553-561. DOI: 10.1097/CCM.0000000000004190
Wingert, T., Lee, C., & Cannesson, M. (2021). Machine learning, deep learning, and closed loop devices—anesthesia delivery. Anesthesiology clinics, 39(3), 565-581. https://doi.org/10.1016/j.anclin.2021.03.012
Yilmaz, O., Radermacher, K., Beger, F., Roth, J., & Janß, A. (2023). Usability evaluation of a process optimized integrated workstation based on the IEEE 11073 SDC standard. Healthcare and Medical Devices, 133. https://doi.org/10.54941/ahfe1003481
Zhou, A., Johnson, B. C., & Muller, R. (2018). Toward true closed-loop neuromodulation: artifact-free recording during stimulation. Current opinion in neurobiology, 50, 119-127. https://doi.org/10.1016/j.conb.2018.01.012
Authors
Copyright (c) 2024 Fahad Khalid Alotaibi, Mohammed ALeqidi Alruwili, Abdullah Maashi Harran Alruwili, Menwer Ata Maashi Alruwili, Zamil Ashwi Muhayris Alruwaili, Nasser Mohammed Albaqami, Rakan Ashwi Alruwaili, Hussein Nasser Mansour Aldawsari, Obaid Saad Aldwassary, Mohammed Mubarak Al-Shuraidah, Nader Awad R. Alotaibi, Mesfer Zaid Hathal Alkhamis, Eman Ali Mohammed Alrazqi, Zahra Jaber Majrashi

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