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Perchiazzi G, Kawati R, Pellegrini M, Liangpansakul J, Colella R, Bollella P, Rangaiah P, Cannone A, Venkataramana DH, Perez M, Stramaglia S, Torsi L, Bellotti R, Augustine R. Imitating the respiratory activity of the brain stem by using artificial neural networks: exploratory study on an animal model of lactic acidosis and proof of concept. J Clin Monit Comput 2024:10.1007/s10877-024-01208-4. [PMID: 39162839 DOI: 10.1007/s10877-024-01208-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 08/07/2024] [Indexed: 08/21/2024]
Abstract
Artificial neural networks (ANNs) are versatile tools capable of learning without prior knowledge. This study aims to evaluate whether ANN can calculate minute volume during spontaneous breathing after being trained using data from an animal model of metabolic acidosis. Data was collected from ten anesthetized, spontaneously breathing pigs divided randomly into two groups, one without dead space and the other with dead space at the beginning of the experiment. Each group underwent two equal sequences of pH lowering with pre-defined targets by continuous infusion of lactic acid. The inputs to ANNs were pH, ΔPaCO2 (variation of the arterial partial pressure of CO2), PaO2, and blood temperature which were sampled from the animal model. The output was the delta minute volume (ΔVM), (the change of minute volume as compared to the minute volume the animal had at the beginning of the experiment). The ANN performance was analyzed using mean squared error (MSE), linear regression, and the Bland-Altman (B-A) method. The animal experiment provided the necessary data to train the ANN. The best architecture of ANN had 17 intermediate neurons; the best performance of the finally trained ANN had a linear regression with R2 of 0.99, an MSE of 0.001 [L/min], a B-A analysis with bias ± standard deviation of 0.006 ± 0.039 [L/min]. ANNs can accurately estimate ΔVM using the same information that arrives at the respiratory centers. This performance makes them a promising component for the future development of closed-loop artificial ventilators.
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Affiliation(s)
- Gaetano Perchiazzi
- The Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
- Department of Anesthesia, Operation and Intensive Care, Uppsala University Hospital, Uppsala, Sweden.
- Hedenstierna Laboratoriet, Akademiska sjukhuset ing 40 3 tr, Uppsala, 75185, Sweden.
| | - Rafael Kawati
- Department of Anesthesia, Operation and Intensive Care, Uppsala University Hospital, Uppsala, Sweden
| | - Mariangela Pellegrini
- The Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Department of Anesthesia, Operation and Intensive Care, Uppsala University Hospital, Uppsala, Sweden
| | - Jasmine Liangpansakul
- The Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | | | - Paolo Bollella
- Department of Chemistry, University of Bari Aldo Moro, Bari, Italy
| | - Pramod Rangaiah
- Department of Electrical Engineering, Solid-State Electronics, Uppsala University, Uppsala, Sweden
| | - Annamaria Cannone
- Department of Anaesthesia and Intensive Care, "Madonna delle Grazie" Hospital, Matera, Italy
| | | | - Mauricio Perez
- Department of Electrical Engineering, Solid-State Electronics, Uppsala University, Uppsala, Sweden
| | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari, Rome, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
| | - Luisa Torsi
- Department of Chemistry, University of Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari, Rome, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
| | - Robin Augustine
- Department of Electrical Engineering, Solid-State Electronics, Uppsala University, Uppsala, Sweden
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2
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Chichra A, Tickoo M, Honiden S. Managing the Chronically Ventilated Critically Ill Population. J Intensive Care Med 2024; 39:703-714. [PMID: 37787184 DOI: 10.1177/08850666231203601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Advances in intensive care over the past few decades have significantly improved the chances of survival for patients with acute critical illness. However, this progress has also led to a growing population of patients who are dependent on intensive care therapies, including prolonged mechanical ventilation (PMV), after the initial acute period of critical illness. These patients are referred to as the "chronically critically ill" (CCI). CCI is a syndrome characterized by prolonged mechanical ventilation, myoneuropathies, neuroendocrine disorders, nutritional deficiencies, cognitive and psychiatric issues, and increased susceptibility to infections. It is associated with high morbidity and mortality as well as a significant increase in healthcare costs. In this article, we will review disease burden, outcomes, psychiatric effects, nutritional and ventilator weaning strategies as well as the role of palliative care for CCI with a specific focus on those requiring PMV.
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Affiliation(s)
- Astha Chichra
- Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Mayanka Tickoo
- Division of Pulmonary, Critical Care and Sleep Medicine, Tufts University School of Medicine, Boston, MA, USA
| | - Shyoko Honiden
- Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
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Chalumuri YR, Arabidarrehdor G, Tivay A, Sampson CM, Khan M, Kinsky M, Kramer GC, Hahn JO, Scully CG, Bighamian R. A Lumped-Parameter Model of the Cardiovascular System Response for Evaluating Automated Fluid Resuscitation Systems. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2024; 12:62511-62525. [PMID: 38872754 PMCID: PMC11170980 DOI: 10.1109/access.2024.3395008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Physiological closed-loop controlled (PCLC) medical devices, such as those designed for blood pressure regulation, can be tested for safety and efficacy in real-world clinical settings. However, relying solely on limited animal and clinical studies may not capture the diverse range of physiological conditions. Credible mathematical models can complement these studies by allowing the testing of the device against simulated patient scenarios. This research involves the development and validation of a low-order lumped-parameter mathematical model of the cardiovascular system's response to fluid perturbation. The model takes rates of hemorrhage and fluid infusion as inputs and provides hematocrit and blood volume, heart rate, stroke volume, cardiac output and mean arterial blood pressure as outputs. The model was calibrated using data from 27 sheep subjects, and its predictive capability was evaluated through a leave-one-out cross-validation procedure, followed by independent validation using 12 swine subjects. Our findings showed small model calibration error against the training dataset, with the normalized root-mean-square error (NRMSE) less than 10% across all variables. The mathematical model and virtual patient cohort generation tool demonstrated a high level of predictive capability and successfully generated a sufficient number of subjects that closely resembled the test dataset. The average NRMSE for the best virtual subject, across two distinct samples of virtual subjects, was below 12.7% and 11.9% for the leave-one-out cross-validation and independent validation dataset. These findings suggest that the model and virtual cohort generator are suitable for simulating patient populations under fluid perturbation, indicating their potential value in PCLC medical device evaluation.
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Affiliation(s)
- Yekanth Ram Chalumuri
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
| | - Ghazal Arabidarrehdor
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
| | - Ali Tivay
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
| | - Catherine M Sampson
- Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Muzna Khan
- Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Michael Kinsky
- Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - George C Kramer
- Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
| | - Christopher G Scully
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Ramin Bighamian
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD 20993, USA
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von Platen P, Pickerodt PA, Russ M, Taher M, Hinken L, Braun W, Köbrich R, Pomprapa A, Francis RCE, Leonhardt S, Walter M. SOLVe: a closed-loop system focused on protective mechanical ventilation. Biomed Eng Online 2023; 22:47. [PMID: 37193969 DOI: 10.1186/s12938-023-01111-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 05/02/2023] [Indexed: 05/18/2023] Open
Abstract
BACKGROUND Mechanical ventilation is an essential component in the treatment of patients with acute respiratory distress syndrome. Prompt adaptation of the settings of a ventilator to the variable needs of patients is essential to ensure personalised and protective ventilation. Still, it is challenging and time-consuming for the therapist at the bedside. In addition, general implementation barriers hinder the timely incorporation of new evidence from clinical studies into routine clinical practice. RESULTS We present a system combing clinical evidence and expert knowledge within a physiological closed-loop control structure for mechanical ventilation. The system includes multiple controllers to support adequate gas exchange while adhering to multiple evidence-based components of lung protective ventilation. We performed a pilot study on three animals with an induced ARDS. The system achieved a time-in-target of over 75 % for all targets and avoided any critical phases of low oxygen saturation, despite provoked disturbances such as disconnections from the ventilator and positional changes of the subject. CONCLUSIONS The presented system can provide personalised and lung-protective ventilation and reduce clinician workload in clinical practice.
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Affiliation(s)
- Philip von Platen
- Chair for Medical Information Technology, RWTH Aachen University, Aachen, Germany.
| | - Philipp A Pickerodt
- Department of Anesthesiology and Operative Intensive Care Medicine CCM CVK, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Martin Russ
- Department of Anesthesiology and Operative Intensive Care Medicine CCM CVK, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Mahdi Taher
- Department of Anesthesiology and Operative Intensive Care Medicine CCM CVK, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | | | | | | | - Anake Pomprapa
- Chair for Medical Information Technology, RWTH Aachen University, Aachen, Germany
| | - Roland C E Francis
- Department of Anesthesiology and Operative Intensive Care Medicine CCM CVK, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Department of Anesthesiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Uniklinikum Erlangen, Erlangen, Germany
| | - Steffen Leonhardt
- Chair for Medical Information Technology, RWTH Aachen University, Aachen, Germany
| | - Marian Walter
- Chair for Medical Information Technology, RWTH Aachen University, Aachen, Germany
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Ryumin VE, Kinzhalova SV, Chistyakova GN, Remizova II, Kadochnikova PA. Protective technologies of modern methods of respiratory support in neonatal practice. MESSENGER OF ANESTHESIOLOGY AND RESUSCITATION 2023. [DOI: 10.24884/2078-5658-2023-20-1-69-80] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
Abstract
The article presents an analysis of literature data on modern protective regimens for invasive respiratory support in premature newborns with respiratory distress syndrome. We have considered positive and negative aspects of the used methods of invasive ventilation of the lungs, which are currently widely used as a method of respiratory therapy in obstetric hospitals at any level, even in the category of children with extremely and very low birth weight. Modern protective mechanical ventilation provides for 2 main directions for reducing ventilator-induced lung damage: a decrease in tidal volume (Vt) and the principle of tolerable (permissive) hypercapnia. The use of the technique of permissive hypercapnia and regimens with a target volume can reduce the likelihood of ventilator-induced lung injury in newborns. Despite the limited indications for mechanical ventilation in modern neonatology and the widespread use of non-invasive ventilation, for patients who really need mechanical ventilation, the use of volume-targeted regimens offers the best chance of reducing ventilation complications.
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Affiliation(s)
- V. E. Ryumin
- Ural Scientific Research Institute of Maternity and Infancy Protection
| | - S. V. Kinzhalova
- Ural Scientific Research Institute of Maternity and Infancy Protection
| | - G. N. Chistyakova
- Ural Scientific Research Institute of Maternity and Infancy Protection
| | - I. I. Remizova
- Ural Scientific Research Institute of Maternity and Infancy Protection
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6
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Abstract
PURPOSE OF REVIEW The last 25 years have seen considerable development in modes of closed-loop ventilation and there are now several of them commercially available. They not only offer potential benefits for the individual patient, but may also improve the organization within the intensive care unit (ICU). Clinicians are showing both greater interest and willingness to address the issues of a caregiver shortage and overload of bedside work in the ICU. This article reviews the clinical benefits of using closed-loop ventilation modes, with a focus on control of oxygenation, lung protection, and weaning. RECENT FINDINGS Closed-loop ventilation modes are able to maintain important physiological variables, such as oxygen saturation measured by pulse oximetry, tidal volume (VT), driving pressure (ΔP), and mechanical power (MP), within target ranges aimed at ensuring continuous lung protection. In addition, these modes adapt the ventilator support to the patient's needs, promoting diaphragm activity and preventing over-assistance. Some studies have shown the potential of these modes to reduce the duration of both weaning and mechanical ventilation. SUMMARY Recent studies have primarily demonstrated the safety, efficacy, and feasibility of using closed-loop ventilation modes in the ICU and postsurgery patients. Large, multicenter randomized controlled trials are needed to assess their impact on important short- and long-term clinical outcomes, the organization of the ICU, and cost-effectiveness.
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Affiliation(s)
- Jean-Michel Arnal
- Service de réanimation polyvalente, Hôpital Sainte Musse, Toulon, France
- Department of Research and New Technologies, Hamilton Medical, Bonaduz, Switzerland
| | - Shinshu Katayama
- Division of Intensive Care, Department of Anesthesiology and Intensive Care Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Christopher Howard
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, Texas, USA
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7
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The Local and Systemic Exposure to Oxygen in Children With Severe Bronchiolitis on Invasive Mechanical Ventilation: A Retrospective Cohort Study. Pediatr Crit Care Med 2023; 24:e115-e120. [PMID: 36661429 PMCID: PMC9848215 DOI: 10.1097/pcc.0000000000003130] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
OBJECTIVES Oxygen supplementation is a cornerstone treatment in critically ill children with bronchiolitis in the PICU. However, potential deleterious effects of high-dose oxygen are well-known. In this study, we aim to describe the pulmonary (local) and arterial (systemic) oxygen exposure over the duration of invasive mechanical ventilation (IMV) in children with severe bronchiolitis. Our secondary aim was to estimate potentially avoidable exposure to high-dose oxygen in these patients. DESIGN Retrospective cohort study. SETTING Single-center, tertiary-care PICU. PATIENTS Children younger than 2 years old admitted to the PICU for severe bronchiolitis receiving IMV. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Hourly measurements of Fio2 and peripheral oxygen saturation (Spo2), and arterial blood gas data were collected up to day 10 of IMV. A total of 24,451 hours of IMV were observed in 176 patients (median age of 1.0 mo [interquartile range (IQR), 1.0-2.3 mo]). The pulmonary exposure to oxygen was highest during the first day of IMV (median time-weighted average [TWA]-Fio2 0.46 [IQR, 0.39-0.53]), which significantly decreased over subsequent days. The systemic exposure to oxygen was relatively low, as severe hyperoxemia (TWA-Pao2 > 248 Torr [> 33 kPa]) was not observed. However, overuse of oxygen was common with 52.3% of patients (n = 92) having at least 1 day of possible excessive oxygen exposure and 14.8% (n = 26) with severe exposure. Furthermore, higher oxygen dosages correlated with increasing overuse of oxygen (rrepeated measures, 0.59; 95% CI, 0.54-0.63). Additionally, caregivers were likely to keep Fio2 greater than or equal to 0.50 when Spo2 greater than or equal to 97%. CONCLUSIONS Moderate to high-dose pulmonary oxygen exposure and potential overuse of oxygen were common in this cohort of severe bronchiolitis patients requiring IMV; however, this was not accompanied by a high systemic oxygen burden. Further studies are needed to determine optimal oxygenation targets to prevent overzealous use of oxygen in this vulnerable population.
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8
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Morris AH, Horvat C, Stagg B, Grainger DW, Lanspa M, Orme J, Clemmer TP, Weaver LK, Thomas FO, Grissom CK, Hirshberg E, East TD, Wallace CJ, Young MP, Sittig DF, Suchyta M, Pearl JE, Pesenti A, Bombino M, Beck E, Sward KA, Weir C, Phansalkar S, Bernard GR, Thompson BT, Brower R, Truwit J, Steingrub J, Hiten RD, Willson DF, Zimmerman JJ, Nadkarni V, Randolph AG, Curley MAQ, Newth CJL, Lacroix J, Agus MSD, Lee KH, deBoisblanc BP, Moore FA, Evans RS, Sorenson DK, Wong A, Boland MV, Dere WH, Crandall A, Facelli J, Huff SM, Haug PJ, Pielmeier U, Rees SE, Karbing DS, Andreassen S, Fan E, Goldring RM, Berger KI, Oppenheimer BW, Ely EW, Pickering BW, Schoenfeld DA, Tocino I, Gonnering RS, Pronovost PJ, Savitz LA, Dreyfuss D, Slutsky AS, Crapo JD, Pinsky MR, James B, Berwick DM. Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy. J Am Med Inform Assoc 2022; 30:178-194. [PMID: 36125018 PMCID: PMC9748596 DOI: 10.1093/jamia/ocac143] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 07/27/2022] [Accepted: 08/22/2022] [Indexed: 12/15/2022] Open
Abstract
How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.
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Affiliation(s)
- Alan H Morris
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Christopher Horvat
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Brian Stagg
- Department of Ophthalmology and Visual Sciences, Moran Eye Center, University of Utah, Salt Lake City, Utah, USA
| | - David W Grainger
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - Michael Lanspa
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - James Orme
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Terry P Clemmer
- Department of Internal Medicine (Critical Care), Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Lindell K Weaver
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Frank O Thomas
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Colin K Grissom
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Ellie Hirshberg
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Thomas D East
- SYNCRONYS - Chief Executive Officer, Albuquerque, New Mexico, USA
| | - Carrie Jane Wallace
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Michael P Young
- Department of Critical Care, Renown Regional Medical Center, Reno, Nevada, USA
| | - Dean F Sittig
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas, USA
| | - Mary Suchyta
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - James E Pearl
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
- Department of Internal Medicine, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Antinio Pesenti
- Faculty of Medicine and Surgery—Anesthesiology, University of Milan, Milano, Lombardia, Italy
| | - Michela Bombino
- Department of Emergency and Intensive Care, San Gerardo Hospital, Monza (MB), Italy
| | - Eduardo Beck
- Faculty of Medicine and Surgery - Anesthesiology, University of Milan, Ospedale di Desio, Desio, Lombardia, Italy
| | - Katherine A Sward
- Department of Biomedical Informatics, College of Nursing, University of Utah, Salt Lake City, Utah, USA
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Shobha Phansalkar
- Wolters Kluwer Health—Clinical Solutions—Medical Informatics, Wolters Kluwer Health, Newton, Massachusetts, USA
| | - Gordon R Bernard
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - B Taylor Thompson
- Pulmonary and Critical Care Division, Department of Internal Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Roy Brower
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Jonathon Truwit
- Department of Internal Medicine, Pulmonary and Critical Care, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Jay Steingrub
- Department of Internal Medicine, Pulmonary and Critical Care, University of Massachusetts Medical School, Baystate Campus, Springfield, Massachusetts, USA
| | - R Duncan Hiten
- Department of Internal Medicine, Pulmonary and Critical Care, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Douglas F Willson
- Pediatric Critical Care, Department of Pediatrics, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Jerry J Zimmerman
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington School of Medicine, Seattle, Washington, USA
| | - Vinay Nadkarni
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Adrienne G Randolph
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Martha A Q Curley
- University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, USA
| | - Christopher J L Newth
- Childrens Hospital Los Angeles, Department of Anesthesiology and Critical Care, University of Southern California Keck School of Medicine, Los Angeles, California, USA
| | - Jacques Lacroix
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Université de Montréal Faculté de Médecine, Montreal, Quebec, Canada
| | - Michael S D Agus
- Division of Medical Pediatric Critical Care, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kang Hoe Lee
- Department of Intensive Care Medicine, Ng Teng Fong Hospital and National University Centre of Transplantation, National University Singapore Yong Loo Lin School of Medicine, Singapore
| | - Bennett P deBoisblanc
- Department of Internal Medicine, Pulmonary and Critical Care, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA
| | - Frederick Alan Moore
- Department of Surgery, University of Florida College of Medicine, Gainesville, Florida, USA
| | - R Scott Evans
- Department of Medical Informatics, Intermountain Healthcare, and Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Dean K Sorenson
- Department of Medical Informatics, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Anthony Wong
- Department of Data Science Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, Illinois, USA
| | - Michael V Boland
- Department of Ophthalmology, Massachusetts Ear and Eye Infirmary, Harvard Medical School, Boston, Massachusetts, USA
| | - Willard H Dere
- Endocrinology and Metabolism Division, Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Alan Crandall
- Department of Ophthalmology and Visual Sciences, Moran Eye Center, University of Utah, Salt Lake City, Utah, USA
- Posthumous
| | - Julio Facelli
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Stanley M Huff
- Department of Medical Informatics, Intermountain Healthcare, Department of Biomedical Informatics, University of Utah, and Graphite Health, Salt Lake City, Utah, USA
| | - Peter J Haug
- Department of Medical Informatics, Intermountain Healthcare, and Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Ulrike Pielmeier
- Aalborg University Faculty of Engineering and Science - Department of Health Science and Technology, Respiratory and Critical Care Group, Aalborg, Nordjylland, Denmark
| | - Stephen E Rees
- Aalborg University Faculty of Engineering and Science - Department of Health Science and Technology, Respiratory and Critical Care Group, Aalborg, Nordjylland, Denmark
| | - Dan S Karbing
- Aalborg University Faculty of Engineering and Science - Department of Health Science and Technology, Respiratory and Critical Care Group, Aalborg, Nordjylland, Denmark
| | - Steen Andreassen
- Aalborg University Faculty of Engineering and Science - Department of Health Science and Technology, Respiratory and Critical Care Group, Aalborg, Nordjylland, Denmark
| | - Eddy Fan
- Internal Medicine, Pulmonary and Critical Care Division, Institute of Health Policy, Management and Evaluation, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
| | - Roberta M Goldring
- Department of Internal Medicine, Pulmonary and Critical Care, New York University School of Medicine, New York, New York, USA
| | - Kenneth I Berger
- Department of Internal Medicine, Pulmonary and Critical Care, New York University School of Medicine, New York, New York, USA
| | - Beno W Oppenheimer
- Department of Internal Medicine, Pulmonary and Critical Care, New York University School of Medicine, New York, New York, USA
| | - E Wesley Ely
- Internal Medicine, Pulmonary and Critical Care, Critical Illness, Brain Dysfunction, and Survivorship (CIBS) Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Tennessee Valley Veteran’s Affairs Geriatric Research Education Clinical Center (GRECC), Nashville, Tennessee, USA
| | - Brian W Pickering
- Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota, USA
| | - David A Schoenfeld
- Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Irena Tocino
- Department of Radiology, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Russell S Gonnering
- Department of Ophthalmology and Visual Sciences, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Peter J Pronovost
- Department of Anesthesiology and Critical Care Medicine, University Hospitals, Highland Hills, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Lucy A Savitz
- Northwest Center for Health Research, Kaiser Permanente, Oakland, California, USA
| | - Didier Dreyfuss
- Assistance Publique—Hôpitaux de Paris, Université de Paris, Sorbonne Université - INSERM unit UMR S_1155 (Common and Rare Kidney Diseases), Paris, France
| | - Arthur S Slutsky
- Interdepartmental Division of Critical Care Medicine, Keenan Research Center, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, University of Toronto, Toronto, Ontario, Canada
| | - James D Crapo
- Department of Internal Medicine, National Jewish Health, Denver, Colorado, USA
| | - Michael R Pinsky
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Brent James
- Department of Internal Medicine, Clinical Excellence Research Center (CERC), Stanford University School of Medicine, Stanford, California, USA
| | - Donald M Berwick
- Institute for Healthcare Improvement, Cambridge, Massachusetts, USA
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9
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Kanal V, Pathmanathan P, Hahn JO, Kramer G, Scully C, Bighamian R. Development and validation of a mathematical model of heart rate response to fluid perturbation. Sci Rep 2022; 12:21463. [PMID: 36509856 PMCID: PMC9744837 DOI: 10.1038/s41598-022-25891-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/06/2022] [Indexed: 12/15/2022] Open
Abstract
Physiological closed-loop controlled (PCLC) medical devices monitor and automatically adjust the patient's condition by using physiological variables as feedback, ideally with minimal human intervention to achieve the target levels set by a clinician. PCLC devices present a challenge when it comes to evaluating their performance, where conducting large clinical trials can be expensive. Virtual physiological patients simulated by validated mathematical models can be utilized to obtain pre-clinical evidence of safety and assess the performance of the PCLC medical device during normal and worst-case conditions that are unlikely to happen in a limited clinical trial. A physiological variable that plays a major role during fluid resuscitation is heart rate (HR). For in silico assessment of PCLC medical devices regarding fluid perturbation, there is currently no mathematical model of HR validated in terms of its predictive capability performance. This paper develops and validates a mathematical model of HR response using data collected from sheep subjects undergoing hemorrhage and fluid infusion. The model proved to be accurate in estimating the HR response to fluid perturbation, where averaged between 21 calibration datasets, the fitting performance showed a normalized root mean square error (NRMSE) of [Formula: see text]. The model was also evaluated in terms of model predictive capability performance via a leave-one-out procedure (21 subjects) and an independent validation dataset (6 subjects). Two different virtual cohort generation tools were used in each validation analysis. The generated envelope of virtual subjects robustly met the defined acceptance criteria, in which [Formula: see text] of the testing datasets presented simulated HR patterns that were within a deviation of 50% from the observed data. In addition, out of 16000 and 18522 simulated subjects for the leave-one-out and independent datasets, the model was able to generate at least one virtual subject that was close to the real subject within an error margin of [Formula: see text] and [Formula: see text] NRMSE, respectively. In conclusion, the model can generate valid virtual HR physiological responses to fluid perturbation and be incorporated into future non-clinical simulated testing setups for assessing PCLC devices intended for fluid resuscitation.
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Affiliation(s)
- Varun Kanal
- grid.417587.80000 0001 2243 3366Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD USA
| | - Pras Pathmanathan
- grid.417587.80000 0001 2243 3366Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD USA
| | - Jin-Oh Hahn
- grid.164295.d0000 0001 0941 7177Department of Mechanical Engineering, University of Maryland, College Park, MD USA
| | - George Kramer
- grid.176731.50000 0001 1547 9964Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX USA
| | - Christopher Scully
- grid.417587.80000 0001 2243 3366Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD USA
| | - Ramin Bighamian
- grid.417587.80000 0001 2243 3366Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD USA
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10
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Pelosi P, Blanch L, Jabaudon M, Constantin JM. Automated systems to minimise asynchronies and personalise mechanical ventilation: A light at the end of the tunnel! Anaesth Crit Care Pain Med 2022; 41:101157. [PMID: 36108918 DOI: 10.1016/j.accpm.2022.101157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Paolo Pelosi
- Department of Surgical Sciences and Integrated Diagnostics, University of Genoa, Genoa, Italy; Anaesthesia and Critical Care, San Martino Policlinico Hospital, IRCCS for Oncology and Neurosciences, Genoa, Italy.
| | - Lluis Blanch
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació I Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain; Centro de Investigación Biomédica en Red Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Matthieu Jabaudon
- Department of Perioperative Medicine, CHU Clermont-Ferrand, Clermont-Ferrand, France; iGReD, CNRS, INSERM, Université Clermont Auvergne, Clermont-Ferrand, France
| | - Jean-Michel Constantin
- Sorbonne Université, GRC 29, Assistance Publique-Hôpitaux de Paris (AP-HP), Groupe Hospitalier La Pitié-Salpêtrière, Département d'Anesthésie Réanimation, F-75013 Paris, France
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11
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Boerger E, Russ M, von Platen P, Taher M, Hinken L, Pomprapa A, Koebrich R, Konietschke F, Graw JA, Lachmann B, Braun W, Leonhardt S, Pickerodt PA, Francis RCE. Induction of severe hypoxemia and low lung recruitability for the evaluation of therapeutic ventilation strategies: a translational model of combined surfactant-depletion and ventilator-induced lung injury. Intensive Care Med Exp 2022; 10:32. [PMID: 35902450 PMCID: PMC9334469 DOI: 10.1186/s40635-022-00456-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/09/2022] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Models of hypoxemic lung injury caused by lavage-induced pulmonary surfactant depletion are prone to prompt recovery of blood oxygenation following recruitment maneuvers and have limited translational validity. We hypothesized that addition of injurious ventilation following surfactant-depletion creates a model of the acute respiratory distress syndrome (ARDS) with persistently low recruitability and higher levels of titrated "best" positive end-expiratory pressure (PEEP) during protective ventilation. METHODS Two types of porcine lung injury were induced by lung lavage and 3 h of either protective or injurious ventilation, followed by 3 h of protective ventilation (N = 6 per group). Recruitment maneuvers (RM) and decremental PEEP trials comparing oxygenation versus dynamic compliance were performed after lavage and at 3 h intervals of ventilation. Pulmonary gas exchange function, respiratory mechanics, and ventilator-derived parameters were assessed after each RM to map the course of injury severity and recruitability. RESULTS Lung lavage impaired respiratory system compliance (Crs) and produced arterial oxygen tensions (PaO2) of 84±13 and 80±15 (FIO2 = 1.0) with prompt increase after RM to 270-395 mmHg in both groups. After subsequent 3 h of either protective or injurious ventilation, PaO2/FIO2 was 104±26 vs. 154±123 and increased to 369±132 vs. 167±87 mmHg in response to RM, respectively. After additional 3 h of protective ventilation, PaO2/FIO2 was 120±15 vs. 128±37 and increased to 470±68 vs. 185±129 mmHg in response to RM, respectively. Subsequently, decremental PEEP titration revealed that Crs peaked at 36 ± 10 vs. 25 ± 5 ml/cm H2O with PEEP of 12 vs. 16 cmH2O, and PaO2/FIO2 peaked at 563 ± 83 vs. 334 ± 148 mm Hg with PEEP of 16 vs. 22 cmH2O in the protective vs. injurious ventilation groups, respectively. The large disparity of recruitability between groups was not reflected in the Crs nor the magnitude of mechanical power present after injurious ventilation, once protective ventilation was resumed. CONCLUSION Addition of transitory injurious ventilation after lung lavage causes prolonged acute lung injury with diffuse alveolar damage and low recruitability yielding high titrated PEEP levels. Mimicking lung mechanical and functional characteristics of ARDS, this porcine model rectifies the constraints of single-hit lavage models and may enhance the translation of experimental research on mechanical ventilation strategies.
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Affiliation(s)
- Emilia Boerger
- Department of Anesthesiology and Intensive Care Medicine CCM/CVK, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13351, Berlin, Germany
| | - Martin Russ
- Department of Anesthesiology and Intensive Care Medicine CCM/CVK, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13351, Berlin, Germany
| | - Philip von Platen
- Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, 52074, Aachen, Germany
| | - Mahdi Taher
- Department of Anesthesiology and Intensive Care Medicine CCM/CVK, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13351, Berlin, Germany
| | - Lea Hinken
- Fritz Stephan GmbH, Kirchstr. 19, 56412, Gackenbach, Germany
| | - Anake Pomprapa
- Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, 52074, Aachen, Germany
| | - Rainer Koebrich
- EKU Elektronik GmbH, Am Sportplatz, 56291, Leiningen, Germany
| | - Frank Konietschke
- Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Jan Adriaan Graw
- Department of Anesthesiology and Intensive Care Medicine CCM/CVK, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13351, Berlin, Germany
| | - Burkhard Lachmann
- Department of Anesthesiology and Intensive Care Medicine CCM/CVK, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13351, Berlin, Germany
| | - Wolfgang Braun
- Fritz Stephan GmbH, Kirchstr. 19, 56412, Gackenbach, Germany
| | - Steffen Leonhardt
- Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, 52074, Aachen, Germany
| | - Philipp A Pickerodt
- Department of Anesthesiology and Intensive Care Medicine CCM/CVK, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13351, Berlin, Germany
| | - Roland C E Francis
- Department of Anesthesiology and Intensive Care Medicine CCM/CVK, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13351, Berlin, Germany.
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12
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Buglowski M, Pfannschmidt V, Becker S, Braun O, Hutten M, Ophelders D, Oprea C, Pattai S, Schoberer M, Stollenwerk A. Closed-Loop Control of Arterial CO 2 in Mechanical Ventilation of Neonates. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4991-4995. [PMID: 36083943 DOI: 10.1109/embc48229.2022.9871185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
During mechanical ventilation of the neonate the main goal is to stabilize respiratory function of the often premature lungs. Ventilating the patient without inflicting harm is then the subordinated next goal. Ideally the arterial partial pressure of CO2 lays within a normocapnic range and fluctuations are kept minimal. By closely monitoring CO2 and controlling ventilation parameters accordingly, CO2 levels in the blood can be managed. We present an approach consisting of a cascaded controller for arterial CO2 by approximating arterial partial pressure PaCO2 from end-tidal PetCO2. As a proof of concept, feasibility of the controller was first evaluated on a mathematical patient model and subsequently in-vivo in lamb experiments. The controller is able to regulate CO2 into a normocapnic range in both setups with satisfactory stationarity within the target range. Estimation of the arterial partial pressure of CO2 remains a critical aspect that needs to be further investigated. Clinical relevance-Closed-loop control of CO2 in mechanical ventilation aims to avoid PaC O2 extremes and to reduce fluctuations. Both are a relevant risk factors especially for neurological complications among preterm newborns.
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13
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Lohse A, von Platen P, Benner CF, Leonhardt S, Walter M, Deininger MM, Ziles D, Seemann T, Breuer T. Identification of the Tidal Volume Response to Pulse Amplitudes of Phrenic Nerve Stimulation Using Gaussian Process Regression. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:135-138. [PMID: 36085952 DOI: 10.1109/embc48229.2022.9871563] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
While mechanical ventilation (MV) can lead to ventilator-induced diaphragmatic atrophy due to diaphragm inactivity, electrical phrenic nerve stimulation (PNS) can keep the diaphragm active and therefore prevent diaphragmatic weakness. To quantify the effectivity of PNS, an identification experiment during PNS is presented, and its data is used in Gaussian process regression (GPR) of the tidal volume based on the constant voltage amplitude of the stimulation pulses. The measurements were split into training data of variable size and test data for cross validation. For variable training sizes and different PNS settings, the GPR had a root mean square deviation (RMSD) between 0.39 and 0.91 mL/kg. An identification experiment as short as one and a half minutes was able to characteristically capture the relationship between tidal volume and voltage amplitude. The proposed method needs to be validated in further experiments.
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14
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von Platen P, Hallmann A, Lohse A, Leonhardt S, Walter M. Fuzzy-Based Expert Supervision System for Feedback Controlled Oxygenation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:962-965. [PMID: 36083941 DOI: 10.1109/embc48229.2022.9871166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Supervision of mechanical ventilation is currently still performed by clinical staff. With the increasing level of automation in the intensive care unit, automatic supervision is becoming necessary. We present a fuzzy-based expert supervision system applicable to automatic feedback control of oxygenation. An adaptive fuzzy limit checking and trend detection algorithm was implemented. A knowledge-based fuzzy logic system combines these outputs into a final score, which subsequently triggers alarms if a critical event is registered. The system was evaluated against annotated experimental data. An accuracy of 83 percent and a precision of 95 percent were achieved. The automatic detection of critical events during feedback control of oxygenation provides an additional layer of safety and assists in alerting clinicians in the case of abnormal behavior of the system. Clinical relevance - Automatic supervision is a necessary feature of physiological feedback systems to make them safer and more reliable in the future.
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15
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Evaluation of a Proportional-Integral-Derivative Controller for Hemorrhage Resuscitation Using a Hardware-in-Loop Test Platform. J Pers Med 2022; 12:jpm12060979. [PMID: 35743762 PMCID: PMC9224865 DOI: 10.3390/jpm12060979] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 12/04/2022] Open
Abstract
Hemorrhage is a leading cause of preventable death in trauma, which can often be avoided with proper fluid resuscitation. Fluid administration can be cognitive-demanding for medical personnel as the rates and volumes must be personalized to the trauma due to variations in injury severity and overall fluid responsiveness. Thus, automated fluid administration systems are ideal to simplify hemorrhagic shock resuscitation if properly designed for a wide range of hemorrhage scenarios. Here, we highlight the development of a proportional–integral–derivative (PID) controller using a hardware-in-loop test platform. The controller relies only on an input data stream of arterial pressure and a target pressure; the PID controller then outputs infusion rates to stabilize the subject. To evaluate PID controller performance with more than 10 controller metrics, the hardware-in-loop platform allowed for 11 different trauma-relevant hemorrhage scenarios for the controller to resuscitate against. Overall, the two controller configurations performed uniquely for the scenarios, with one reaching the target quicker but often overshooting, while the other rarely overshot the target but failed to reach the target during severe hemorrhage. In conclusion, PID controllers have the potential to simplify hemorrhage resuscitation if properly designed and evaluated, which can be accomplished with the test platform shown here.
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16
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Hemodynamic events during en route critical care for patients with traumatic brain injury. J Trauma Acute Care Surg 2022; 93:S41-S48. [PMID: 35444151 DOI: 10.1097/ta.0000000000003654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Exposure to stressors of flight may increase risk of secondary insults among critically injured combat wounded with traumatic brain injury (TBI). The primary objective of this study was to describe the prevalence of hemodynamic events by phase of transport among patients with TBI transported by Critical Care Air Transport Teams (CCATT). METHODS We performed a secondary analysis of a retrospective cohort of 477 adults with moderate to severe TBI, who required transport by CCATT to Germany from multiple hospitals in the Middle East between January 2007 and May 2014. We abstracted clinical data from handwritten CCATT medical records. Hemodynamic events included systolic blood pressure (SBP) <100 mmHg and cerebral perfusion pressure (CPP) <60 mmHg. We calculated the proportion of patients experiencing hemodynamic events for each phase of flight. RESULTS We analyzed 404 subjects after exclusions for catastrophic brain injury (n = 39) and missing timestamps (n = 34). Subjects had high injury severity scores (median 29, IQR 21-35) and a median flight time of 423 minutes (IQR 392.5-442.5 minutes). The median of documented in-flight vital signs was 8 measurements (IQR 6.5 to 8 measurements). Documented SBP in-flight events occurred in 3% of subjects during ascent, 7.9% during early flight, 7.7% during late flight, and 2.2% during descent, with an overall in-flight prevalence of 13.9%. Among patients with intracranial pressure monitoring (n = 120), documented CPP events occurred in 5% of subjects during ascent, 23% during early flight, 17% during late flight, and 5.8% during descent, with an overall in-flight prevalence of 30.8%. CONCLUSION Documented hemodynamic events occurred during each phase of flight in severely injured combat wounded with TBI, and episodic documentation likely underestimated the actual in-flight frequency of secondary insults. LEVEL OF EVIDENCE Prognostic, level III.
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17
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Snider EJ, Vega SJ, Ross E, Berard D, Hernandez-Torres SI, Salinas J, Boice EN. Supervisory Algorithm for Autonomous Hemodynamic Management Systems. SENSORS (BASEL, SWITZERLAND) 2022; 22:529. [PMID: 35062489 PMCID: PMC8780453 DOI: 10.3390/s22020529] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/07/2022] [Accepted: 01/09/2022] [Indexed: 02/04/2023]
Abstract
Future military conflicts will require new solutions to manage combat casualties. The use of automated medical systems can potentially address this need by streamlining and augmenting the delivery of medical care in both emergency and combat trauma environments. However, in many situations, these systems may need to operate in conjunction with other autonomous and semi-autonomous devices. Management of complex patients may require multiple automated systems operating simultaneously and potentially competing with each other. Supervisory controllers capable of harmonizing multiple closed-loop systems are thus essential before multiple automated medical systems can be deployed in managing complex medical situations. The objective for this study was to develop a Supervisory Algorithm for Casualty Management (SACM) that manages decisions and interplay between two automated systems designed for management of hemorrhage control and resuscitation: an automatic extremity tourniquet system and an adaptive resuscitation controller. SACM monitors the required physiological inputs for both systems and synchronizes each respective system as needed. We present a series of trauma experiments carried out in a physiologically relevant benchtop circulatory system in which SACM must recognize extremity or internal hemorrhage, activate the corresponding algorithm to apply a tourniquet, and then resuscitate back to the target pressure setpoint. SACM continues monitoring after the initial stabilization so that additional medical changes can be quickly identified and addressed, essential to extending automation algorithms past initial trauma resuscitation into extended monitoring. Overall, SACM is an important step in transitioning automated medical systems into emergency and combat trauma situations. Future work will address further interplay between these systems and integrate additional medical systems.
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Affiliation(s)
- Eric J. Snider
- Engineering, Technology, and Automation Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.J.V.); (D.B.); (S.I.H.-T.); (J.S.)
| | - Saul J. Vega
- Engineering, Technology, and Automation Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.J.V.); (D.B.); (S.I.H.-T.); (J.S.)
| | - Evan Ross
- Blood and Shock Resuscitation Group, United States Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
| | - David Berard
- Engineering, Technology, and Automation Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.J.V.); (D.B.); (S.I.H.-T.); (J.S.)
| | - Sofia I. Hernandez-Torres
- Engineering, Technology, and Automation Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.J.V.); (D.B.); (S.I.H.-T.); (J.S.)
| | - Jose Salinas
- Engineering, Technology, and Automation Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.J.V.); (D.B.); (S.I.H.-T.); (J.S.)
| | - Emily N. Boice
- Engineering, Technology, and Automation Group, U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA; (E.J.S.); (S.J.V.); (D.B.); (S.I.H.-T.); (J.S.)
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18
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AIM in Anesthesiology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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19
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Buehler PK, Herling A, Bienefeld N, Klinzing S, Wegner S, Wendel Garcia PD, Karbach M, Lohmeyer Q, Schaubmayr E, Schuepbach RA, Hofmaenner DA. Differing Visual Behavior Between Inexperienced and Experienced Critical Care Nurses While Using a Closed-Loop Ventilation System-A Prospective Observational Study. Front Med (Lausanne) 2021; 8:681321. [PMID: 34568356 PMCID: PMC8455837 DOI: 10.3389/fmed.2021.681321] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 08/19/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Closed-loop ventilation modes are increasingly being used in intensive care units to ensure more automaticity. Little is known about the visual behavior of health professionals using these ventilation modes. The aim of this study was to analyze gaze patterns of intensive care nurses while ventilating a patient in the closed-loop mode with Intellivent adaptive support ventilation® (I-ASV) and to compare inexperienced with experienced nurses. Materials and Methods: Intensive care nurses underwent eye-tracking during daily care of a patient ventilated in the closed-loop ventilation mode. Five specific areas of interest were predefined (ventilator settings, ventilation curves, numeric values, oxygenation Intellivent, ventilation Intellivent). The main independent variable and primary outcome was dwell time. Secondary outcomes were revisits, average fixation time, first fixation and fixation count on areas of interest in a targeted tracking-time of 60 min. Gaze patterns were compared between I-ASV inexperienced (n = 12) and experienced (n = 16) nurses. Results: In total, 28 participants were included. Overall, dwell time was longer for ventilator settings and numeric values compared to the other areas of interest. Similar results could be obtained for the secondary outcomes. Visual fixation of oxygenation Intellivent and ventilation Intellivent was low. However, dwell time, average fixation time and first fixation on oxygenation Intellivent were longer in experienced compared to inexperienced intensive care nurses. Discussion: Gaze patterns of intensive care nurses were mainly focused on numeric values and settings. Areas of interest related to traditional mechanical ventilation retain high significance for intensive care nurses, despite use of closed-loop mode. More visual attention to oxygenation Intellivent and ventilation Intellivent in experienced nurses implies more routine and familiarity with closed-loop modes in this group. The findings imply the need for constant training and education with new tools in critical care, especially for inexperienced professionals.
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Affiliation(s)
- Philipp K Buehler
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Anique Herling
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Nadine Bienefeld
- Department of Management, Technology, and Economics, Work & Organizational Psychology, ETH Zurich, Zurich, Switzerland
| | - Stephanie Klinzing
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Stephan Wegner
- Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland
| | | | - Michael Karbach
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Quentin Lohmeyer
- Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland
| | - Elisabeth Schaubmayr
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Reto A Schuepbach
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Daniel A Hofmaenner
- Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
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20
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Botta M, Wenstedt EFE, Tsonas AM, Buiteman-Kruizinga LA, van Meenen DMP, Korsten HHM, Horn J, Paulus F, Bindels AGJH, Schultz MJ, De Bie AJR. Effectiveness, safety and efficacy of INTELLiVENT-adaptive support ventilation, a closed-loop ventilation mode for use in ICU patients - a systematic review. Expert Rev Respir Med 2021; 15:1403-1413. [PMID: 34047244 DOI: 10.1080/17476348.2021.1933450] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Introduction: INTELLiVENT-Adaptive Support Ventilation (INTELLiVENT-ASV), an advanced closed-loop ventilation mode for use in intensive care unit (ICU) patients, is equipped with algorithms that automatically adjust settings on the basis of physiologic signals and patient's activity. Here we describe its effectiveness, safety, and efficacy in various types of ICU patients.Areas covered: A systematic search conducted in MEDLINE, EMBASE, the Cochrane Central register of Controlled Trials (CENTRAL), and in Google Scholar identified 10 randomized clinical trials.Expert opinion: Studies suggest INTELLiVENT-ASV to be an effective automated mode with regard to the titrations of tidal volume, airway pressure, and oxygen. INTELLiVENT-ASV is as safe as conventional modes. However, thus far studies have not shown INTELLiVENT-ASV to be superior to conventional modes with regard to duration of ventilation and other patient-centered outcomes. Future studies are needed to test its efficacy.
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Affiliation(s)
- M Botta
- Department of Intensive Care, Amsterdam University Medical Centers, Location 'AMC', Amsterdam, The Netherlands
| | - E F E Wenstedt
- Department of Intensive Care, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - A M Tsonas
- Department of Intensive Care, Amsterdam University Medical Centers, Location 'AMC', Amsterdam, The Netherlands
| | - L A Buiteman-Kruizinga
- Department of Intensive Care, Amsterdam University Medical Centers, Location 'AMC', Amsterdam, The Netherlands.,Department of Intensive Care, Reinier de Graaf Hospital, Delft, The Netherlands
| | - D M P van Meenen
- Department of Intensive Care, Amsterdam University Medical Centers, Location 'AMC', Amsterdam, The Netherlands
| | - H H M Korsten
- Department of Intensive Care, Catharina Hospital Eindhoven, Eindhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - J Horn
- Department of Intensive Care, Amsterdam University Medical Centers, Location 'AMC', Amsterdam, The Netherlands.,Amsterdam Neuroscience, Amsterdam UMC Research Institute, Amsterdam, The Netherlands
| | - F Paulus
- Department of Intensive Care, Amsterdam University Medical Centers, Location 'AMC', Amsterdam, The Netherlands.,Faculty of Health, ACHIEVE, Centre of Applied Research, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands
| | - A G J H Bindels
- Department of Intensive Care, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - M J Schultz
- Department of Intensive Care, Amsterdam University Medical Centers, Location 'AMC', Amsterdam, The Netherlands.,Mahidol-Oxford Tropical Research Unit, Mahidol University, Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand.,Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - A J R De Bie
- Department of Intensive Care, Catharina Hospital Eindhoven, Eindhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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21
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Hu X, Xie F, Wang K, Gu H, Mo G, Wen R, Zhao Y, Yang Q, Möller K, Zhao Z, Xie L. Scoring System to Evaluate the Performance of ICU Ventilators in the Pandemic of COVID-19: A Lung Model Study. Front Med (Lausanne) 2021; 8:663608. [PMID: 34336879 PMCID: PMC8316635 DOI: 10.3389/fmed.2021.663608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 06/14/2021] [Indexed: 01/10/2023] Open
Abstract
Ventilators in the intensive care units (ICU) are life-support devices that help physicians to gain additional time to cure the patients. The aim of the study was to establish a scoring system to evaluate the ventilator performance in the context of COVID-19. The scoring system was established by weighting the ventilator performance on five different aspects: the stability of pressurization, response to leaks alteration, performance of reaction, volume delivery, and accuracy in oxygen delivery. The weighting factors were determined with analytic hierarchy process (AHP). Survey was sent out to 66 clinical and mechanical experts. The scoring system was built based on 54 valid replies. A total of 12 commercially available ICU ventilators providing non-invasive ventilation were evaluated using the novel scoring system. A total of eight ICU ventilators with non-invasive ventilation mode and four dedicated non-invasive ventilators were tested according to the scoring system. Four COVID-19 phenotypes were simulated using the ASL5000 lung simulator, namely (1) increased airway resistance (IR) (10 cm H2O/L/s), (2) low compliance (LC) (compliance of 20 ml/cmH2O), (3) low compliance plus increased respiratory effort (LCIE) (respiratory rate of 40 and inspiratory effort of 10 cmH2O), (4) high compliance (HC) (compliance of 50 ml/cmH2O). All of the ventilators were set to three combinations of pressure support and positive end-expiratory pressure levels. The data were collected at baseline and at three customized leak levels. Significant inaccuracies and variations in performance between different non-invasive ventilators were observed, especially in the aspect of leaks alteration, oxygen and volume delivery. Some ventilators have stable performance in different simulated phenotypes whereas the others have over 10% scoring differences. It is feasible to use the proposed scoring system to evaluate the ventilator performance. In the COVID-19 pandemic, clinicians should be aware of possible strengths and weaknesses of ventilators.
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Affiliation(s)
- Xingshuo Hu
- Critical Care, College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Fei Xie
- Critical Care, College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Kaifei Wang
- Critical Care, College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Hongjun Gu
- Critical Care, College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Guoxin Mo
- Critical Care, College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Ruoxuan Wen
- Critical Care, College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Ying Zhao
- Critical Care, College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Qingyun Yang
- Critical Care, College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Knut Möller
- Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Zhanqi Zhao
- Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany.,Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Lixin Xie
- Critical Care, College of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
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22
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Wendel Garcia PD, Hofmaenner DA, Brugger SD, Acevedo CT, Bartussek J, Camen G, Bader PR, Bruellmann G, Kattner J, Ganter C, Schuepbach RA, Buehler PK. Closed-Loop Versus Conventional Mechanical Ventilation in COVID-19 ARDS. J Intensive Care Med 2021; 36:1184-1193. [PMID: 34098803 PMCID: PMC8442133 DOI: 10.1177/08850666211024139] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background: Lung-protective ventilation is key in bridging patients suffering from
COVID-19 acute respiratory distress syndrome (ARDS) to recovery. However,
resource and personnel limitations during pandemics complicate the
implementation of lung-protective protocols. Automated ventilation modes may
prove decisive in these settings enabling higher degrees of lung-protective
ventilation than conventional modes. Method: Prospective study at a Swiss university hospital. Critically ill,
mechanically ventilated COVID-19 ARDS patients were allocated, by
study-blinded coordinating staff, to either closed-loop or conventional
mechanical ventilation, based on mechanical ventilator availability. Primary
outcome was the overall achieved percentage of lung-protective ventilation
in closed-loop versus conventional mechanical ventilation, assessed
minute-by-minute, during the initial 7 days and overall mechanical
ventilation time. Lung-protective ventilation was defined as the combined
target of tidal volume <8 ml per kg of ideal body weight, dynamic driving
pressure <15 cmH2O, peak pressure <30 cmH2O,
peripheral oxygen saturation ≥88% and dynamic mechanical power <17
J/min. Results: Forty COVID-19 ARDS patients, accounting for 1,048,630 minutes (728 days) of
cumulative mechanical ventilation, allocated to either closed-loop (n = 23)
or conventional ventilation (n = 17), presenting with a median
paO2/ FiO2 ratio of 92 [72-147] mmHg and a static
compliance of 18 [11-25] ml/cmH2O, were mechanically ventilated
for 11 [4-25] days and had a 28-day mortality rate of 20%. During the
initial 7 days of mechanical ventilation, patients in the closed-loop group
were ventilated lung-protectively for 65% of the time versus 38% in the
conventional group (Odds Ratio, 1.79; 95% CI, 1.76-1.82; P
< 0.001) and for 45% versus 33% of overall mechanical ventilation time
(Odds Ratio, 1.22; 95% CI, 1.21-1.23; P < 0.001). Conclusion: Among critically ill, mechanically ventilated COVID-19 ARDS patients during
an early highpoint of the pandemic, mechanical ventilation using a
closed-loop mode was associated with a higher degree of lung-protective
ventilation than was conventional mechanical ventilation.
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Affiliation(s)
| | | | - Silvio D Brugger
- Division of Infectious Diseases, 27243University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Claudio T Acevedo
- Division of Infectious Diseases, 27243University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Jan Bartussek
- Institute of Intensive Care Medicine, 27243University Hospital of Zurich, Zurich, Switzerland
| | - Giovanni Camen
- Institute of Intensive Care Medicine, 27243University Hospital of Zurich, Zurich, Switzerland
| | - Patrick Raphael Bader
- Institute of Intensive Care Medicine, 27243University Hospital of Zurich, Zurich, Switzerland
| | - Gregor Bruellmann
- Institute of Intensive Care Medicine, 27243University Hospital of Zurich, Zurich, Switzerland
| | - Johannes Kattner
- Institute of Intensive Care Medicine, 27243University Hospital of Zurich, Zurich, Switzerland
| | - Christoph Ganter
- Institute of Intensive Care Medicine, 27243University Hospital of Zurich, Zurich, Switzerland
| | - Reto Andreas Schuepbach
- Institute of Intensive Care Medicine, 27243University Hospital of Zurich, Zurich, Switzerland
| | - Philipp Karl Buehler
- Institute of Intensive Care Medicine, 27243University Hospital of Zurich, Zurich, Switzerland
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23
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Ossai CI, Wickramasinghe N. Intelligent decision support with machine learning for efficient management of mechanical ventilation in the intensive care unit - A critical overview. Int J Med Inform 2021; 150:104469. [PMID: 33906020 DOI: 10.1016/j.ijmedinf.2021.104469] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 04/16/2021] [Accepted: 04/18/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Effective management of Mechanical Ventilation (MV) is vital for reducing morbidity, mortality, and cost of healthcare. OBJECTIVE This study aims to synthesize evidence for effective MV management through Intelligent decision support (IDS) with Machine Learning (ML). METHOD Databases that include EBSCO, IEEEXplore, Google Scholar, SCOPUS, and the Web of Science were systematically searched to identify studies on IDS for effective MV management regarding Tidal Volume (TV), asynchrony, weaning, and other outcomes such as the risk of Prolonged Mechanical ventilation (PMV). The quality of the articles identified was assessed with a modified Joanna Briggs Institute (JBI) critical appraisal checklist for cross-sessional research. RESULTS A total of 26 articles were identified for the study that has IDS for TV (n = 2, 7.8 %), asynchrony (n = 9, 34.6 %), weaning (n = 12, 46.2 %), and others (n = 3, 11.5 %). It was affirmed that implementing IDS in MV management will enhance seamless ICU patient management following the utilization of various Machine Learning (ML) algorithms in decision support. The studies relied on (n = 14) ML algorithms to predict the TV, asynchrony, weaning, risk of PMV and Positive End-Expiratory Pressure (PEEP) changes of 11-20262 ICU patients records with model inputs ranging from (n = 1) for timeseries analysis of TV to (n = 47) for weaning prediction. CONCLUSIONS The small data size, poor study design, and result reporting, with the heterogeneity of techniques used in the various studies, hampered the development of a unified approach for managing MV efficiency in TV monitoring, asynchrony, and weaning predictions. Notwithstanding, the ensemble model was able to predict TV, asynchrony, and weaning to a higher accuracy than the other algorithms.
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Affiliation(s)
- Chinedu I Ossai
- Faculty of Health, Arts and Design, School of Health Sciences, Department of Health and Medical Sciences, Swinburne University, John street Hawthorn, Victoria, 3122, Australia.
| | - Nilmini Wickramasinghe
- Faculty of Health, Arts and Design, School of Health Sciences, Department of Health and Medical Sciences, Swinburne University, John street Hawthorn, Victoria, 3122, Australia; Epworth Healthcare Australia, Australia.
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24
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Condello I, Rimmaudo F, Speziale G. Closed-loop circuit for reduce oxygen waste on hollow-fiber oxygenators during extracorporeal technologies. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2021; 25:85. [PMID: 33632260 PMCID: PMC7908748 DOI: 10.1186/s13054-021-03514-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 02/18/2021] [Indexed: 11/18/2022]
Affiliation(s)
- Ignazio Condello
- Department of Cardiac Surgery, Perfusion Service, Anthea Hospital, GVM Care and Research, Via Camillo Rosalba 35/37, 70124, Bari, Italy.
| | - Flavio Rimmaudo
- Department of Interventional Cardiology, Anthea Hospital, GVM Care and Research, Bari, Italy
| | - Giuseppe Speziale
- Department of Cardiac Surgery, Perfusion Service, Anthea Hospital, GVM Care and Research, Via Camillo Rosalba 35/37, 70124, Bari, Italy
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25
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Komorowski M, Joosten A. AIM in Anesthesiology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_246-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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26
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Tehrani FT. In regard to P. von Platen et al., “The dawn of physiological closed-loop ventilation—a review”. Crit Care 2020; 24:326. [PMID: 32522248 PMCID: PMC7288473 DOI: 10.1186/s13054-020-03042-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 05/27/2020] [Indexed: 11/24/2022] Open
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