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Muñoz J, Ruíz-Cacho R, Fernández-Araujo NJ, Candela A, Visedo LC, Muñoz-Visedo J. Artificial intelligence in the management of patient-ventilator asynchronies: A scoping review. Heart Lung 2025; 73:139-152. [PMID: 40412305 DOI: 10.1016/j.hrtlng.2025.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2025] [Revised: 04/23/2025] [Accepted: 05/13/2025] [Indexed: 05/27/2025]
Abstract
BACKGROUND Patient-ventilator asynchronies (PVAs) are frequent complications in mechanically ventilated patients, contributing to adverse outcomes such as ventilator-induced lung injury, prolonged mechanical ventilation, and increased mortality. Artificial intelligence (AI) has emerged as a promising tool for enhancing PVA detection, prediction, and optimization. Despite its growing potential, the full scope of AI applications in this field and persistent gaps in evidence remain inadequately explored. OBJECTIVE This scoping review examines the breadth of AI-based approaches for managing PVAs, identifying key methodologies, evaluating research trends, and highlighting limitations in the current literature. METHODS A comprehensive search was conducted in PubMed, Embase, Science Direct, IEEE Xplore, and the Cochrane Library without time restrictions. Extracted data included study objectives, AI methodologies, patient populations, performance metrics, and clinical outcomes. The findings were synthesized into thematic categories to map advancements and research gaps. RESULTS Twenty-six studies were identified that applied AI techniques to detect, predict, or optimize PVAs. The included studies employed a range of AI methodologies, including convolutional neural networks, long short-term memory networks, and hybrid algorithms. These models demonstrated high predictive performance, with accuracy ranging from 87 % to 99 % and AUROC values exceeding 0.98 for detecting complex asynchronous events. AI systems also showed promise in predicting weaning success and optimizing ventilatory settings through real-time patient-specific adjustments. However, challenges such as reliance on single-center datasets, inconsistencies in calibration, and limited implementation of explainable AI frameworks restrict their clinical applicability. CONCLUSIONS AI holds transformative potential in managing PVAs by enabling real-time detection, improved weaning prediction, and personalized ventilatory strategies. However, significant challenges remain, particularly the need for multi-center validation, standardized reporting protocols, and randomized controlled trials to evaluate clinical efficacy. Addressing these gaps is essential for integrating AI into routine critical care practice and transitioning from theoretical models to practical, real-world applications in intensive care units.
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Affiliation(s)
- Javier Muñoz
- ICU. Hospital General Universitario Gregorio Marañón, Madrid, Spain.
| | - Rocío Ruíz-Cacho
- ICU. Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | | | - Alberto Candela
- ICU. Hospital General Universitario Gregorio Marañón, Madrid, Spain
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Morinishi K, Itagaki T, Akimoto Y, Chikata Y, Oto J. Effects of Trigger Algorithms on Trigger Performance and Patient-Ventilator Synchrony. Respir Care 2025. [PMID: 40329919 DOI: 10.1089/respcare.12694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2025]
Abstract
Background: Patient-ventilator synchrony is essential for successful patient-triggered ventilation. This study compared the ability of a trigger algorithm, based on detailed analysis of flow changes (IntelliSync+, Hamilton Medical), to trigger patient breaths with conventional algorithms. Methods: Three models with different lung mechanics (normal, ARDS, and COPD) at 3 severities were simulated with a lung model ventilated in pressure control continuous mandatory ventilation or pressure control continuous spontaneous ventilation (PC-CSV). Inspiratory pressure above PEEP was set at 15 cm H2O and PEEP at 5 cm H2O. Inspiratory trigger was selected from IntelliSync+ (IS+insp), flow trigger (1- 5 L/min), or pressure trigger (-1 to -5 cm H2O). In PC-CSV, expiratory trigger was set at IntelliSync+ (IS+exp) or cycling criteria (5%, 25%, and 40% for ARDS, normal, and COPD, respectively). Measurements were performed with and without leak (50% inspiratory tidal volume). Five breaths per condition were collected to calculate trigger delay time and asynchronous events. Results: For pressure trigger, none of the conditions resulted in 3 successfully triggered consecutive breaths. Overall trigger delay time was significantly longer with flow trigger than with IS+insp in normal (99 vs 81 ms without leak, P < .001; 98 vs 80 ms with leak, P < .001) and ARDS models (334 vs 223 ms without leak, P < .001; 320 vs 236 ms with leak, P = .02). Across all conditions, ineffective efforts occurred more frequently with flow trigger than with IS+insp (7.3% vs 1.5% without leak, P = .01; 10.8% vs 3.0% with leak, P = .01). In PC-CSV, overall cycling delay time with IS+exp was equivalent or longer compared with cycling criteria. Conclusions: In this lung model study, IS+insp demonstrated similar trigger time and fewer ineffective efforts compared with flow trigger even in simulated respiratory conditions, whereas cycling delay time was unaffected by IS+exp because of large variations between conditions.
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Affiliation(s)
- Keisuke Morinishi
- Mr. Morinishi and Mr. Chikata are affiliated with Division of Clinical Engineering, Tokushima University Hospital, Tokushima, Japan
| | - Taiga Itagaki
- Dr. Itagaki is affiliated with Emergency and Disaster Medicine, Tokushima University Hospital, Tokushima, Japan
| | - Yusuke Akimoto
- Dr. Akimoto is affiliated with Emergency Department, Tokushima Prefectural Miyoshi Hospital, Miyoshi, Japan
- Drs. Akimoto and Oto are affiliated with Emergency and Critical Care Medicine, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
| | - Yusuke Chikata
- Mr. Morinishi and Mr. Chikata are affiliated with Division of Clinical Engineering, Tokushima University Hospital, Tokushima, Japan
| | - Jun Oto
- Drs. Akimoto and Oto are affiliated with Emergency and Critical Care Medicine, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
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Del Bono MR, Damiani LF, Plotnikow GA, Consalvo S, Di Salvo E, Murias G. Ineffective respiratory efforts and their potential consequences during mechanical ventilation. Med Intensiva 2025; 49:502133. [PMID: 39919955 DOI: 10.1016/j.medine.2025.502133] [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] [Received: 03/21/2024] [Revised: 09/30/2024] [Accepted: 10/02/2024] [Indexed: 02/09/2025]
Abstract
The implementation of invasive mechanical ventilation (IMV) in critically ill patients involves two crucial moments: the total control phase, affected among other things by the use of analgesics and sedatives, and the transition phase to spontaneous ventilation, which seeks to shorten IMV times and where optimizing patient-ventilator interaction is one of the main challenges. Ineffective inspiratory efforts (IEE) arise when there is no coordination between patient effort and ventilator support. IIE are common in different ventilatory modes and are associated with worse clinical outcomes: dyspnea, increased sedation requirements, increased IMV days and longer intensive care unit (ICU) and hospital stay. These are manifested graphically as an abrupt decrease in expiratory flow, being more frequent during expiration. However, and taking into consideration that it is still unknown whether this association is causal or rather a marker of disease severity, recognizing the potential physiological consequences, reviewing diagnostic methods and implementing detection and treatment strategies that can limit them, seems reasonable.
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Affiliation(s)
- Mauro Robertino Del Bono
- Servicio de Rehabilitación, Unidad de Cuidados Intensivos, Hospital Británico de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina.
| | - Luis Felipe Damiani
- Departamento de Ciencias de la Salud, Carrera de Kinesiología, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Gustavo Adrián Plotnikow
- Servicio de Rehabilitación, Unidad de Cuidados Intensivos, Hospital Británico de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina; Facultad de Medicina y Ciencias de la Salud, Universidad Abierta Interamericana, Ciudad Autónoma de Buenos Aires, Argentina
| | - Sebastián Consalvo
- Unidad de Cuidados Intensivos, Hospital Británico de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
| | - Emanuel Di Salvo
- Servicio de Rehabilitación, Unidad de Cuidados Intensivos, Hospital Británico de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
| | - Gastón Murias
- Unidad de Cuidados Intensivos, Hospital Británico de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
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Yoon B, Blokpoel R, Ibn Hadj Hassine C, Ito Y, Albert K, Aczon M, Kneyber MCJ, Emeriaud G, Khemani RG. An overview of patient-ventilator asynchrony in children. Expert Rev Respir Med 2025; 19:435-447. [PMID: 40163381 DOI: 10.1080/17476348.2025.2487165] [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] [Received: 12/10/2024] [Revised: 03/19/2025] [Accepted: 03/27/2025] [Indexed: 04/02/2025]
Abstract
INTRODUCTION Mechanically ventilated children often have patient-ventilator asynchrony (PVA). When a ventilated patient has spontaneous effort, the ventilator attempts to synchronize with the patient, but PVA represents a mismatch between patient respiratory effort and ventilator delivered breaths. AREAS COVERED This review will focus on subtypes of patient ventilator asynchrony, methods to detect or measure PVA, risk factors for and characteristics of patients with PVA subtypes, potential clinical implications, treatment or prevention strategies, and future areas for research. Throughout this review, we will provide pediatric specific considerations. EXPERT OPINION PVA in pediatric patients supported by mechanical ventilation occurs frequently and is understudied. Pediatric patients have unique physiologic and pathophysiologic characteristics which affect PVA. While recognition of PVA and its subtypes is important for bedside clinicians, the clinical implications and risks versus benefits of treatment targeted at reducing PVA remain unknown. Future research should focus on harmonizing PVA terminology, refinement of automated detection technologies, determining which forms of PVA are harmful, and development of PVA-specific ventilator interventions.
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Affiliation(s)
- Benjamin Yoon
- Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Robert Blokpoel
- Department of Paediatrics, Division of Paediatric Intensive Care, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Chatila Ibn Hadj Hassine
- Pediatric Intensive Care Unit, CHU Sainte Justine, Universite ́ de Montre ́al, Montreal, Quebec C, Canada
| | - Yukie Ito
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Los Angeles, Los Angeles, CA, USA
| | - Kevin Albert
- Pediatric Intensive Care Unit, CHU Sainte Justine, Universite ́ de Montre ́al, Montreal, Quebec C, Canada
| | - Melissa Aczon
- Laura P. and Leland K. Whittier Virtual Pediatric Intensive Care Unit, Department of Anesthesiology Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Martin C J Kneyber
- Department of Paediatrics, Division of Paediatric Intensive Care, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Critical Care, Anaesthesiology, Peri-Operative Medicine and Emergency Medicine (CAPE), University of Groningen, Groningen, The Netherlands
| | - Guillaume Emeriaud
- Pediatric Intensive Care Unit, CHU Sainte Justine, Universite ́ de Montre ́al, Montreal, Quebec C, Canada
| | - Robinder G Khemani
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Los Angeles, Los Angeles, CA, USA
- Department of Pediatrics, University of Southern California, Los Angeles, CA, USA
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Rietveld TP, van der Ster BJP, Schoe A, Endeman H, Balakirev A, Kozlova D, Gommers DAMPJ, Jonkman AH. Let's get in sync: current standing and future of AI-based detection of patient-ventilator asynchrony. Intensive Care Med Exp 2025; 13:39. [PMID: 40119215 PMCID: PMC11928342 DOI: 10.1186/s40635-025-00746-8] [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: 01/21/2025] [Accepted: 03/06/2025] [Indexed: 03/24/2025] Open
Abstract
BACKGROUND Patient-ventilator asynchrony (PVA) is a mismatch between the patient's respiratory drive/effort and the ventilator breath delivery. It occurs frequently in mechanically ventilated patients and has been associated with adverse events and increased duration of ventilation. Identifying PVA through visual inspection of ventilator waveforms is highly challenging and time-consuming. Automated PVA detection using Artificial Intelligence (AI) has been increasingly studied, potentially offering real-time monitoring at the bedside. In this review, we discuss advances in automatic detection of PVA, focusing on developments of the last 15 years. RESULTS Nineteen studies were identified. Multiple forms of AI have been used for the automated detection of PVA, including rule-based algorithms, machine learning and deep learning. Three licensed algorithms are currently reported. Results of algorithms are generally promising (average reported sensitivity, specificity and accuracy of 0.80, 0.93 and 0.92, respectively), but most algorithms are only available offline, can detect a small subset of PVAs (focusing mostly on ineffective effort and double trigger asynchronies), or remain in the development or validation stage (84% (16/19 of the reviewed studies)). Moreover, only in 58% (11/19) of the studies a reference method for monitoring patient's breathing effort was available. To move from bench to bedside implementation, data quality should be improved and algorithms that can detect multiple PVAs should be externally validated, incorporating measures for breathing effort as ground truth. Last, prospective integration and model testing/finetuning in different ICU settings is key. CONCLUSIONS AI-based techniques for automated PVA detection are increasingly studied and show potential. For widespread implementation to succeed, several steps, including external validation and (near) real-time employment, should be considered. Then, automated PVA detection could aid in monitoring and mitigating PVAs, to eventually optimize personalized mechanical ventilation, improve clinical outcomes and reduce clinician's workload.
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Affiliation(s)
- Thijs P Rietveld
- Adult Intensive Care, Erasmus Medical Center, Dr. Molewaterplein 40, Rotterdam, The Netherlands
| | - Björn J P van der Ster
- Adult Intensive Care, Erasmus Medical Center, Dr. Molewaterplein 40, Rotterdam, The Netherlands
| | - Abraham Schoe
- Intensive Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Henrik Endeman
- Adult Intensive Care, Erasmus Medical Center, Dr. Molewaterplein 40, Rotterdam, The Netherlands
- Intensive Care, OLVG, Amsterdam, The Netherlands
| | | | | | | | - Annemijn H Jonkman
- Adult Intensive Care, Erasmus Medical Center, Dr. Molewaterplein 40, Rotterdam, The Netherlands.
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Yu X, Yan J, Ruan L, Luo M, Che B, Deng L, Luo Y. Development and performance assessment of a novel scroll compressor-based oxygen generator integrated ventilator. Sci Rep 2025; 15:9844. [PMID: 40118954 PMCID: PMC11928624 DOI: 10.1038/s41598-025-94363-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2024] [Accepted: 03/13/2025] [Indexed: 03/24/2025] Open
Abstract
Current ventilators rely on wall outlets or cylinders for oxygen supply, which limits their continuous use in the field or emergencies. In this study, we proposed a ventilator prototype that can achieve stand-alone oxygenated respiratory support, by designing and integrating a high-performance oxygen generator, and optimizing the control strategies of the whole system. Based on the designed oil-free scroll compressor and pressure swing adsorption (PSA) system, we first realized a mobile high-flow oxygen generator, which achieved an output flow greater than 17 L/min with an oxygen concentration of 93% ± 3%. The ventilator was also designed to synchronize with the respiratory state, to optimize the trigger performance for the pressure support of early inspiration, and reduce the gas supply in the late inspiratory phase to avoid pressure overshoot in the early expiratory phase. The respiratory synchronization of the integrated ventilator was estimated by the recorded chest movement of the subjects. Satisfactory respiratory synchronization was realized with an inspiratory trigger delay (ITD) time of less than 200 ms and sound respiratory waveform tracking. By regulating the PSA strategy, the oxygen generation and utilization efficiencies could be further improved. Ultimately, under the setting of inspiratory positive airway pressure (IPAP) at 10 cmH2O, and expiratory positive airway pressure (EPAP) at 4 cmH2O, we achieved non-invasive ventilation with a maximum oxygen concentration of 58% ± 1.75%. In conclusion, the proposed oxygen generator integrated ventilator could provide reliable oxygenated respiratory support in emergencies, such as on-site first aid, patient transport, and military field environments.
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Affiliation(s)
- Xiaokang Yu
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518000, Guangdong, China
| | - Jing Yan
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518000, Guangdong, China
| | - Lijun Ruan
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518000, Guangdong, China
| | - Mingzhi Luo
- Institute of Biomedical Engineering and Health Sciences, Changzhou University, Changzhou, 213000, Jiangsu, China
| | - Bo Che
- Institute of Biomedical Engineering and Health Sciences, Changzhou University, Changzhou, 213000, Jiangsu, China
| | - Linhong Deng
- Institute of Biomedical Engineering and Health Sciences, Changzhou University, Changzhou, 213000, Jiangsu, China.
| | - Yuxi Luo
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, 518000, Guangdong, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, Sun Yat-Sen University, Shenzhen, 518000, Guangdong, China.
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Costa V, Cidade JP, Medeiros I, Póvoa P. Optimizing Mechanical Ventilation: A Clinical and Practical Bedside Method for the Identification and Management of Patient-Ventilator Asynchronies in Critical Care. J Clin Med 2025; 14:214. [PMID: 39797296 PMCID: PMC11721790 DOI: 10.3390/jcm14010214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2024] [Revised: 12/29/2024] [Accepted: 12/31/2024] [Indexed: 01/13/2025] Open
Abstract
The prompt identification and correction of patient-ventilator asynchronies (PVA) remain a cornerstone for ensuring the quality of respiratory failure treatment and the prevention of further injury to critically ill patients. These disruptions, whether due to over- or under-assistance, have a profound clinical impact not only on the respiratory mechanics and the mortality associated with mechanical ventilation but also on the patient's cardiac output and hemodynamic profile. Strong evidence has demonstrated that these frequently occurring and often underdiagnosed events have significant prognostic value for mechanical ventilation outcomes and are strongly associated with prolonged ICU stays and hospital mortality. Halting the consequences of PVA relies on the correct identification and approach of its underlying causes. However, this often requires advanced knowledge of respiratory physiology and the evaluation of complex ventilator waveforms in patient-ventilator interactions, posing a challenge to intensive care practitioners, in particular, those less experienced. This review aims to outline the most frequent types of PVA and propose a clinical algorithm to provide physicians with a structured approach to assess, accurately diagnose, and correct PVA.
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Affiliation(s)
- Vasco Costa
- Department of Critical Care Medicine, Hospital de São Francisco Xavier, Unidade Local de Saúde Lisboa Ocidental (ULSLO), Estrada Forte do Alto Duque, 1449-005 Lisbon, Portugal; (J.P.C.); (I.M.); (P.P.)
- NOVA Medical School, New University of Lisbon, 1169-056 Lisbon, Portugal
| | - José Pedro Cidade
- Department of Critical Care Medicine, Hospital de São Francisco Xavier, Unidade Local de Saúde Lisboa Ocidental (ULSLO), Estrada Forte do Alto Duque, 1449-005 Lisbon, Portugal; (J.P.C.); (I.M.); (P.P.)
- NOVA Medical School, New University of Lisbon, 1169-056 Lisbon, Portugal
| | - Inês Medeiros
- Department of Critical Care Medicine, Hospital de São Francisco Xavier, Unidade Local de Saúde Lisboa Ocidental (ULSLO), Estrada Forte do Alto Duque, 1449-005 Lisbon, Portugal; (J.P.C.); (I.M.); (P.P.)
| | - Pedro Póvoa
- Department of Critical Care Medicine, Hospital de São Francisco Xavier, Unidade Local de Saúde Lisboa Ocidental (ULSLO), Estrada Forte do Alto Duque, 1449-005 Lisbon, Portugal; (J.P.C.); (I.M.); (P.P.)
- NOVA Medical School, New University of Lisbon, 1169-056 Lisbon, Portugal
- Center for Clinical Epidemiology and Research Unit of Clinical Epidemiology, OUH Odense University Hospital, DK-5230 Odense, Denmark
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Ang CYS, Chiew YS, Wang X, Ooi EH, Cove ME, Chen Y, Zhou C, Chase JG. Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method? COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108323. [PMID: 39029417 DOI: 10.1016/j.cmpb.2024.108323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 06/27/2024] [Accepted: 07/10/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND AND OBJECTIVE Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-based and machine learning PVA approaches exist, they have variable performance and can miss specific PVA events. This study compares a model and rule-based algorithm with a machine learning PVA method by retrospectively validating both methods using an independent patient cohort. METHODS Hysteresis loop analysis (HLA) which is a rule-based method (RBM) and a tri-input convolutional neural network (TCNN) machine learning model are used to classify 7 different types of PVA, including: 1) flow asynchrony; 2) reverse triggering; 3) premature cycling; 4) double triggering; 5) delayed cycling; 6) ineffective efforts; and 7) auto triggering. Class activation mapping (CAM) heatmaps visualise sections of respiratory waveforms the TCNN model uses for decision making, improving result interpretability. Both PVA classification methods were used to classify incidence in an independent retrospective clinical cohort of 11 mechanically ventilated patients for validation and performance comparison. RESULTS Self-validation with the training dataset shows overall better HLA performance (accuracy, sensitivity, specificity: 97.5 %, 96.6 %, 98.1 %) compared to the TCNN model (accuracy, sensitivity, specificity: 89.5 %, 98.3 %, 83.9 %). In this study, the TCNN model demonstrates higher sensitivity in detecting PVA, but HLA was better at identifying non-PVA breathing cycles due to its rule-based nature. While the overall AI identified by both classification methods are very similar, the intra-patient distribution of each PVA type varies between HLA and TCNN. CONCLUSION The collective findings underscore the efficacy of both HLA and TCNN in PVA detection, indicating the potential for real-time continuous monitoring of PVA. While ML methods such as TCNN demonstrate good PVA identification performance, it is essential to ensure optimal model architecture and diversity in training data before widespread uptake as standard care. Moving forward, further validation and adoption of RBM methods, such as HLA, offers an effective approach to PVA detection while providing clear distinction into the underlying patterns of PVA, better aligning with clinical needs for transparency, explicability, adaptability and reliability of these emerging tools for clinical care.
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Affiliation(s)
| | - Yeong Shiong Chiew
- School of Engineering, Monash University Malaysia, Selangor, Malaysia; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.
| | - Xin Wang
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | - Ean Hin Ooi
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | - Matthew E Cove
- Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Health System, Singapore
| | - Yuhong Chen
- Intensive Care Unit, the Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China
| | - Cong Zhou
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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Chen Y, Zhang K, Zhou C, Chase JG, Hu Z. Automated evaluation of typical patient-ventilator asynchronies based on lung hysteretic responses. Biomed Eng Online 2023; 22:102. [PMID: 37875890 PMCID: PMC10598979 DOI: 10.1186/s12938-023-01165-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/16/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Patient-ventilator asynchrony is common during mechanical ventilation (MV) in intensive care unit (ICU), leading to worse MV care outcome. Identification of asynchrony is critical for optimizing MV settings to reduce or eliminate asynchrony, whilst current clinical visual inspection of all typical types of asynchronous breaths is difficult and inefficient. Patient asynchronies create a unique pattern of distortions in hysteresis respiratory behaviours presented in pressure-volume (PV) loop. METHODS Identification method based on hysteretic lung mechanics and hysteresis loop analysis is proposed to delineate the resulted changes of lung mechanics in PV loop during asynchronous breathing, offering detection of both its incidence and 7 major types. Performance is tested against clinical patient data with comparison to visual inspection conducted by clinical doctors. RESULTS The identification sensitivity and specificity of 11 patients with 500 breaths for each patient are above 89.5% and 96.8% for all 7 types, respectively. The average sensitivity and specificity across all cases are 94.6% and 99.3%, indicating a very good accuracy. The comparison of statistical analysis between identification and human inspection yields the essential same clinical judgement on patient asynchrony status for each patient, potentially leading to the same clinical decision for setting adjustment. CONCLUSIONS The overall results validate the accuracy and robustness of the identification method for a bedside monitoring, as well as its ability to provide a quantified metric for clinical decision of ventilator setting. Hence, the method shows its potential to assist a more consistent and objective assessment of asynchrony without undermining the efficacy of the current clinical practice.
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Affiliation(s)
- Yuhong Chen
- Intensive Care Unit, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Kun Zhang
- Intensive Care Unit, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Cong Zhou
- Department of Mechanical Engineering & Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.
- Taicang Yangtze River Delta Research Institute, Suzhou, China.
| | - J Geoffrey Chase
- Department of Mechanical Engineering & Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Zhenjie Hu
- Intensive Care Unit, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
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10
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Qie XJ, Liu ZH, Guo LM. Evaluation of progressive early rehabilitation training mode in intensive care unit patients with mechanical ventilation. World J Clin Cases 2022; 10:8152-8160. [PMID: 36159546 PMCID: PMC9403689 DOI: 10.12998/wjcc.v10.i23.8152] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 05/27/2022] [Accepted: 06/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Mechanical ventilation is a common resuscitation method in the intensive care unit (ICU). Unfortunately, this treatment process prolongs the ICU stay of patients with an increased incidence of delirium, which ultimately affects the prognosis.
AIM To evaluate the effect of progressive early rehabilitation training on treatment and prognosis of patients with mechanical ventilation in ICU.
METHODS The convenience sampling method selected 190 patients with mechanical ventilation admitted to the Fourth Hospital of Hebei Medical University from March 2020 to March 2021. According to the random number table method, they were divided into the control and intervention groups. The control group received routine nursing and rehabilitation measures, whereas the intervention group received progressive early rehabilitation training. In addition, the incidence and duration of delirium were compared for the two groups along with mechanical ventilation time, ICU hospitalization time, functional independence measure (FIM) score, Barthel index, and the incidence of complications (deep venous thrombosis, pressure sores, and acquired muscle weakness).
RESULTS In the intervention group, the incidence of delirium was significantly lower than in the control group (28% vs 52%, P < 0.001). In the intervention group, the duration of delirium, mechanical ventilation time, and ICU stay were shorter than in the control group (P < 0.001). The FIM and Barthel index scores were significantly higher in the intervention group than the control group (P < 0.001). The total incidence of complications in the intervention group was 3.15%, which was lower than 17.89% in the control group (P < 0.001).
CONCLUSION Progressive early rehabilitation training reduced the incidence of delirium and complications in ICU patients with mechanical ventilation, which improved prognosis and quality of life.
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Affiliation(s)
- Xiao-Jing Qie
- Department of Cardiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, Hebei Province, China
| | - Zhi-Hong Liu
- Department of Intensive Care Unit, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, Hebei Province, China
| | - Li-Min Guo
- Department of Cardiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, Hebei Province, China
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Mojoli F, Pozzi M, Orlando A, Bianchi IM, Arisi E, Iotti GA, Braschi A, Brochard L. Timing of inspiratory muscle activity detected from airway pressure and flow during pressure support ventilation: the waveform method. Crit Care 2022; 26:32. [PMID: 35094707 PMCID: PMC8802480 DOI: 10.1186/s13054-022-03895-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 01/11/2022] [Indexed: 11/10/2022] Open
Abstract
Background Whether respiratory efforts and their timing can be reliably detected during pressure support ventilation using standard ventilator waveforms is unclear. This would give the opportunity to assess and improve patient–ventilator interaction without the need of special equipment.
Methods In 16 patients under invasive pressure support ventilation, flow and pressure waveforms were obtained from proximal sensors and analyzed by three trained physicians and one resident to assess patient’s spontaneous activity. A systematic method (the waveform method) based on explicit rules was adopted. Esophageal pressure tracings were analyzed independently and used as reference. Breaths were classified as assisted or auto-triggered, double-triggered or ineffective. For assisted breaths, trigger delay, early and late cycling (minor asynchronies) were diagnosed. The percentage of breaths with major asynchronies (asynchrony index) and total asynchrony time were computed. Results Out of 4426 analyzed breaths, 94.1% (70.4–99.4) were assisted, 0.0% (0.0–0.2) auto-triggered and 5.8% (0.4–29.6) ineffective. Asynchrony index was 5.9% (0.6–29.6). Total asynchrony time represented 22.4% (16.3–30.1) of recording time and was mainly due to minor asynchronies. Applying the waveform method resulted in an inter-operator agreement of 0.99 (0.98–0.99); 99.5% of efforts were detected on waveforms and agreement with the reference in detecting major asynchronies was 0.99 (0.98–0.99). Timing of respiratory efforts was accurately detected on waveforms: AUC for trigger delay, cycling delay and early cycling was 0.865 (0.853–0.876), 0.903 (0.892–0.914) and 0.983 (0.970–0.991), respectively. Conclusions Ventilator waveforms can be used alone to reliably assess patient’s spontaneous activity and patient–ventilator interaction provided that a systematic method is adopted. Supplementary Information The online version contains supplementary material available at 10.1186/s13054-022-03895-4.
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See KC, Sahagun J, Cove M, Sum CL, Garcia B, Chanco D, Misanes S, Abastillas E, Taculod J. Managing patient-ventilator asynchrony with a twice-daily screening protocol: A retrospective cohort study. Aust Crit Care 2021; 34:539-546. [PMID: 33632607 DOI: 10.1016/j.aucc.2020.11.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 09/26/2020] [Accepted: 11/01/2020] [Indexed: 11/27/2022] Open
Abstract
PURPOSE Severe patient-ventilator asynchrony (PVA) might be associated with prolonged mechanical ventilation and mortality. It is unknown if systematic screening and application of conventional methods for PVA management can modify these outcomes. We therefore constructed a twice-daily bedside PVA screening and management protocol and investigated its effect on patient outcomes. MATERIALS AND METHODS A retrospective cohort study of patients who were intubated in the emergency department and directly admitted to the medical intensive care unit (ICU). In phase 1 (6 months; August 2016 to January 2017), patients received usual care comprising lung protective ventilation and moderate analgesia/sedation. In phase 2 (6 months; February 2017 to July 2017), patients were additionally managed with a PVA protocol on ICU admission and twice daily (7 am, 7 pm). RESULTS A total of 280 patients (160 in phase 1, 120 in phase 2) were studied (age = 64.5 ± 21.4 years, 107 women [38.2%], Acute Physiology and Chronic Health Evaluation II score = 27.1 ± 8.5, 271 [96.8%] on volume assist-control ventilation initially). Phase 2 patients had lower hospital mortality than phase 1 patients (20.0% versus 34.4%, respectively, P = 0.011), even after adjustment for age and Acute Physiology and Chronic Health Evaluation II scores (odds ratio = 0.46, 95% confidence interval = 0.25-0.84). CONCLUSIONS Application of a bedside PVA protocol for mechanically ventilated patients on ICU admission and twice daily was associated with decreased hospital mortality. There was however no association with sedation-free days or mechanical ventilation-free days through day 28 or length of hospital stay.
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Affiliation(s)
- Kay Choong See
- Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Health System, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Juliet Sahagun
- Division of Critical Care - Respiratory Therapy, National University Hospital, Singapore.
| | - Matthew Cove
- Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Health System, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Chew Lai Sum
- Department of Nursing, National University Hospital, Singapore.
| | - Bimbo Garcia
- Division of Critical Care - Respiratory Therapy, National University Hospital, Singapore.
| | - David Chanco
- Division of Critical Care - Respiratory Therapy, National University Hospital, Singapore.
| | - Sherill Misanes
- Division of Critical Care - Respiratory Therapy, National University Hospital, Singapore.
| | - Emily Abastillas
- Division of Critical Care - Respiratory Therapy, National University Hospital, Singapore.
| | - Juvel Taculod
- Division of Critical Care - Respiratory Therapy, National University Hospital, Singapore.
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Albaiceta GM, Brochard L, Dos Santos CC, Fernández R, Georgopoulos D, Girard T, Jubran A, López-Aguilar J, Mancebo J, Pelosi P, Skrobik Y, Thille AW, Wilcox ME, Blanch L. The central nervous system during lung injury and mechanical ventilation: a narrative review. Br J Anaesth 2021; 127:648-659. [PMID: 34340836 DOI: 10.1016/j.bja.2021.05.038] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/03/2021] [Accepted: 05/24/2021] [Indexed: 11/26/2022] Open
Abstract
Mechanical ventilation induces a number of systemic responses for which the brain plays an essential role. During the last decade, substantial evidence has emerged showing that the brain modifies pulmonary responses to physical and biological stimuli by various mechanisms, including the modulation of neuroinflammatory reflexes and the onset of abnormal breathing patterns. Afferent signals and circulating factors from injured peripheral tissues, including the lung, can induce neuronal reprogramming, potentially contributing to neurocognitive dysfunction and psychological alterations seen in critically ill patients. These impairments are ubiquitous in the presence of positive pressure ventilation. This narrative review summarises current evidence of lung-brain crosstalk in patients receiving mechanical ventilation and describes the clinical implications of this crosstalk. Further, it proposes directions for future research ranging from identifying mechanisms of multiorgan failure to mitigating long-term sequelae after critical illness.
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Affiliation(s)
- Guillermo M Albaiceta
- Unidad de Cuidados Intensivos Cardiológicos, Hospital Universitario Central de Asturias, Oviedo, Spain; Departamento de Biología Funcional, Instituto Universitario de Oncología del Principado de Asturias, Universidad de Oviedo, Oviedo, Spain; Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain; Centro de Investigación Biomédica en Red-Enfermedades Respiratorias (CIBER)-Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.
| | - Laurent Brochard
- Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada; Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada
| | - Claudia C Dos Santos
- Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada; Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada
| | - Rafael Fernández
- Centro de Investigación Biomédica en Red-Enfermedades Respiratorias (CIBER)-Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Critical Care Department, Althaia Xarxa Assistencial Universitaria de Manresa, Universitat Internacional de Catalunya, Manresa, Spain
| | - Dimitris Georgopoulos
- Intensive Care Medicine Department, University Hospital of Heraklion, School of Medicine, University of Crete, Heraklion, Crete, Greece
| | - Timothy Girard
- Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Amal Jubran
- Division of Pulmonary and Critical Care Medicine, Hines VA Hospital, Hines, IL, USA; Loyola University of Chicago, Stritch School of Medicine, Maywood, IL, USA
| | - Josefina López-Aguilar
- Centro de Investigación Biomédica en Red-Enfermedades Respiratorias (CIBER)-Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Jordi Mancebo
- Servei Medicina Intensiva, University Hospital Sant Pau, Barcelona, Spain
| | - Paolo Pelosi
- Department of Surgical Sciences and Integrated Diagnostics, University of Genoa, Genoa, Italy; Anesthesia and Intensive Care, San Martino Policlinico Hospital, IRCCS for Oncology and Neurosciences, Genoa, Italy
| | - Yoanna Skrobik
- Department of Medicine, McGill University, Regroupement de Soins Critiques Respiratoires, Réseau de Soins Respiratoires FRQS, Montreal, QC, Canada
| | - Arnaud W Thille
- CHU de Poitiers, Médecine Intensive Réanimation, Poitiers, France; INSERM CIC 1402 ALIVE, Université de Poitiers, Poitiers, France
| | - Mary E Wilcox
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada; Department of Medicine, Division of Respirology (Critical Care Medicine), University Health Network, Toronto, ON, Canada
| | - Lluis Blanch
- Centro de Investigación Biomédica en Red-Enfermedades Respiratorias (CIBER)-Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain
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Etiology, incidence, and outcomes of patient-ventilator asynchrony in critically-ill patients undergoing invasive mechanical ventilation. Sci Rep 2021; 11:12390. [PMID: 34117278 PMCID: PMC8196026 DOI: 10.1038/s41598-021-90013-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 04/30/2021] [Indexed: 02/05/2023] Open
Abstract
Patient-ventilator asynchrony (PVA) is commonly encountered during mechanical ventilation of critically ill patients. Estimates of PVA incidence vary widely. Type, risk factors, and consequences of PVA remain unclear. We aimed to measure the incidence and identify types of PVA, characterize risk factors for development, and explore the relationship between PVA and outcome among critically ill, mechanically ventilated adult patients admitted to medical, surgical, and medical-surgical intensive care units in a large academic institution staffed with varying provider training background. A single center, retrospective cohort study of all adult critically ill patients undergoing invasive mechanical ventilation for ≥ 12 h. A total of 676 patients who underwent 696 episodes of mechanical ventilation were included. Overall PVA occurred in 170 (24%) episodes. Double triggering 92(13%) was most common, followed by flow starvation 73(10%). A history of smoking, and pneumonia, sepsis, or ARDS were risk factors for overall PVA and double triggering (all P < 0.05). Compared with volume targeted ventilation, pressure targeted ventilation decreased the occurrence of events (all P < 0.01). During volume controlled synchronized intermittent mandatory ventilation and pressure targeted ventilation, ventilator settings were associated with the incidence of overall PVA. The number of overall PVA, as well as double triggering and flow starvation specifically, were associated with worse outcomes and fewer hospital-free days (all P < 0.01). Double triggering and flow starvation are the most common PVA among critically ill, mechanically ventilated patients. Overall incidence as well as double triggering and flow starvation PVA specifically, portend worse outcome.
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Alqahtani JS. Patient–ventilator asynchrony in Saudi Arabia: Where we stand? World J Crit Care Med 2021; 10:58-60. [PMID: 34046311 PMCID: PMC8131934 DOI: 10.5492/wjccm.v10.i3.58] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 01/13/2021] [Accepted: 03/08/2021] [Indexed: 02/06/2023] Open
Abstract
Patient–ventilator asynchrony in Saudi Arabia practices is common, and more emphasis on how to mitigate such a clinical problem is needed. This letter is intended to shed the light on the current national evidence of patient–ventilator asynchrony and how to step ahead for better patients' ventilation management.
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Affiliation(s)
- Jaber S Alqahtani
- UCL Respiratory, University College London, London WC1E 6BT, United Kingdom
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam 34313, Saudi Arabia
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Marini JJ, Crooke PS, Gattinoni L. Intra-cycle power: is the flow profile a neglected component of lung protection? Intensive Care Med 2021; 47:609-611. [PMID: 33797574 PMCID: PMC8017116 DOI: 10.1007/s00134-021-06375-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 02/21/2021] [Indexed: 01/28/2023]
Affiliation(s)
- John J Marini
- Pulmonary and Critical Care Medicine, University of Minnesota and Regions Hospital, MS 11203B, 640 Jackson St., Saint Paul, MN, 55101, USA.
| | - Philip S Crooke
- Department of Mathematics, Vanderbilt University, Nashville, TN, USA
| | - Luciano Gattinoni
- Department of Anesthesiology, Intensive Care and Emergency Medicine, Medical University of Göttingen, Göttingen, Germany
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Patient–Ventilator Interaction Testing Using the Electromechanical Lung Simulator xPULM™ during V/A-C and PSV Ventilation Mode. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11093745] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
During mechanical ventilation, a disparity between flow, pressure and volume demands of the patient and the assistance delivered by the mechanical ventilator often occurs. This paper introduces an alternative approach of simulating and evaluating patient–ventilator interactions with high fidelity using the electromechanical lung simulator xPULM™. The xPULM™ approximates respiratory activities of a patient during alternating phases of spontaneous breathing and apnea intervals while connected to a mechanical ventilator. Focusing on different triggering events, volume assist-control (V/A-C) and pressure support ventilation (PSV) modes were chosen to test patient–ventilator interactions. In V/A-C mode, a double-triggering was detected every third breathing cycle, leading to an asynchrony index of 16.67%, which is classified as severe. This asynchrony causes a significant increase of peak inspiratory pressure (7.96 ± 6.38 vs. 11.09 ± 0.49 cmH2O, p < 0.01)) and peak expiratory flow (−25.57 ± 8.93 vs. 32.90 ± 0.54 L/min, p < 0.01) when compared to synchronous phases of the breathing simulation. Additionally, events of premature cycling were observed during PSV mode. In this mode, the peak delivered volume during simulated spontaneous breathing phases increased significantly (917.09 ± 45.74 vs. 468.40 ± 31.79 mL, p < 0.01) compared to apnea phases. Various dynamic clinical situations can be approximated using this approach and thereby could help to identify undesired patient–ventilation interactions in the future. Rapidly manufactured ventilator systems could also be tested using this approach.
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González-Seguel F, Camus-Molina A, Jasmén A, Molina J, Pérez-Araos R, Graf J. Respiratory Support Adjustments and Monitoring of Mechanically Ventilated Patients Performing Early Mobilization: A Scoping Review. Crit Care Explor 2021; 3:e0407. [PMID: 33912837 PMCID: PMC8078339 DOI: 10.1097/cce.0000000000000407] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
This scoping review is aimed to summarize current knowledge on respiratory support adjustments and monitoring of metabolic and respiratory variables in mechanically ventilated adult patients performing early mobilization. DATA SOURCES Eight electronic databases were searched from inception to February 2021, using a predefined search strategy. STUDY SELECTION Two blinded reviewers performed document selection by title, abstract, and full text according to the following criteria: mechanically ventilated adult patients performing any mobilization intervention, respiratory support adjustments, and/or monitoring of metabolic/respiratory real-time variables. DATA EXTRACTION Four physiotherapists extracted relevant information using a prespecified template. DATA SYNTHESIS From 1,208 references screened, 35 documents were selected for analysis, where 20 (57%) were published between 2016 and 2020. Respiratory support settings (ventilatory modes or respiratory variables) were reported in 21 documents (60%). Reported modes were assisted (n = 11) and assist-control (n = 9). Adjustment of variables and modes were identified in only seven documents (20%). The most frequent respiratory variable was the Fio2, and only four studies modified the level of ventilatory support. Mechanical ventilator brand/model used was not specified in 26 documents (74%). Monitoring of respiratory, metabolic, and both variables were reported in 22 documents (63%), four documents (11%) and 10 documents (29%), respectively. These variables were reported to assess the physiologic response (n = 21) or safety (n = 13). Monitored variables were mostly respiratory rate (n = 26), pulse oximetry (n = 22), and oxygen consumption (n = 9). Remarkably, no study assessed the work of breathing or effort during mobilization. CONCLUSIONS Little information on respiratory support adjustments during mobilization of mechanically ventilated patients was identified. Monitoring of metabolic and respiratory variables is also scant. More studies on the effects of adjustments of the level/mode of ventilatory support on exercise performance and respiratory muscle activity monitoring for safe and efficient implementation of early mobilization in mechanically ventilated patients are needed.
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Affiliation(s)
- Felipe González-Seguel
- Servicio de Medicina Física y Rehabilitación, Departamento de Medicina Interna, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
- Carrera de Kinesiología, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
- Departamento de Paciente Crítico, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Agustín Camus-Molina
- Servicio de Medicina Física y Rehabilitación, Departamento de Medicina Interna, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
- Carrera de Kinesiología, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
- Departamento de Paciente Crítico, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Anita Jasmén
- Bibliotecas Biomédicas, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Jorge Molina
- Carrera de Kinesiología, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Rodrigo Pérez-Araos
- Carrera de Kinesiología, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
- Departamento de Paciente Crítico, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Jerónimo Graf
- Departamento de Paciente Crítico, Facultad de Medicina, Clínica Alemana Universidad del Desarrollo, Santiago, Chile
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Zhang Z, Liu J, Xi J, Gong Y, Zeng L, Ma P. Derivation and Validation of an Ensemble Model for the Prediction of Agitation in Mechanically Ventilated Patients Maintained Under Light Sedation. Crit Care Med 2021; 49:e279-e290. [PMID: 33470778 DOI: 10.1097/ccm.0000000000004821] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Light sedation is recommended over deep sedation for invasive mechanical ventilation to improve clinical outcome but may increase the risk of agitation. This study aimed to develop and prospectively validate an ensemble machine learning model for the prediction of agitation on a daily basis. DESIGN Variables collected in the early morning were used to develop an ensemble model by aggregating four machine learning algorithms including support vector machines, C5.0, adaptive boosting with classification trees, and extreme gradient boosting with classification trees, to predict the occurrence of agitation in the subsequent 24 hours. SETTING The training dataset was prospectively collected in 95 ICUs from 80 Chinese hospitals on May 11, 2016, and the validation dataset was collected in 20 out of these 95 ICUs on December 16, 2019. PATIENTS Invasive mechanical ventilation patients who were maintained under light sedation for 24 hours prior to the study day and who were to be maintained at the same sedation level for the next 24 hours. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS A total of 578 invasive mechanical ventilation patients from 95 ICUs in 80 Chinese hospitals, including 459 in the training dataset and 119 in the validation dataset, were enrolled. Agitation was observed in 36% (270/578) of the invasive mechanical ventilation patients. The stepwise regression model showed that higher body temperature (odds ratio for 1°C increase: 5.29; 95% CI, 3.70-7.84; p < 0.001), greater minute ventilation (odds ratio for 1 L/min increase: 1.15; 95% CI, 1.02-1.30; p = 0.019), higher Richmond Agitation-Sedation Scale (odds ratio for 1-point increase: 2.43; 95% CI, 1.92-3.16; p < 0.001), and days on invasive mechanical ventilation (odds ratio for 1-d increase: 0.95; 95% CI, 0.93-0.98; p = 0.001) were independently associated with agitation in the subsequent 24 hours. In the validation dataset, the ensemble model showed good discrimination (area under the receiver operating characteristic curve, 0.918; 95% CI, 0.866-0.969) and calibration (Hosmer-Lemeshow test p = 0.459) in predicting the occurrence of agitation within 24 hours. CONCLUSIONS This study developed an ensemble model for the prediction of agitation in invasive mechanical ventilation patients under light sedation. The model showed good calibration and discrimination in an independent dataset.
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Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingtao Liu
- SICU, The 8th Medical Center of General Hospital of Chinese People's Liberation Army, Beijing, People's Republic of China
| | - Jingjing Xi
- Department of Critical Care Medicine, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yichun Gong
- SICU, The 8th Medical Center of General Hospital of Chinese People's Liberation Army, Beijing, People's Republic of China
| | - Lin Zeng
- Research Center of Clinical Epidemiology, The Third Hospital of Peking University, Beijing, China
| | - Penglin Ma
- SICU, The 8th Medical Center of General Hospital of Chinese People's Liberation Army, Beijing, People's Republic of China
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Abstract
PURPOSE OF REVIEW Complications of mechanical ventilation, such as ventilator-induced lung injury (VILI) and ventilator-induced diaphragmatic dysfunction (VIDD), adversely affect the outcome of critically ill patients. Although mostly studied during control ventilation, it is increasingly appreciated that VILI and VIDD also occur during assisted ventilation. Hence, current research focuses on identifying ways to monitor and deliver protective ventilation in assisted modes. This review describes the operating principles of proportional modes of assist, their implications for lung and diaphragm protective ventilation, and the supporting clinical data. RECENT FINDINGS Proportional modes of assist, proportional assist ventilation, PAV, and neurally adjusted ventilatory assist, NAVA, deliver a pressure assist that is proportional to the patient's effort, enabling ventilation to be better controlled by the patient's brain. This control underlies the potential of proportional modes to avoid over-assist and under-assist, improve patient--ventilator interaction, and provide protective ventilation. Indeed, in clinical studies, proportional modes have been associated with reduced asynchronies, enhanced diaphragmatic recovery, and limitation of excessive tidal volume. Additionally, proportional modes facilitate better monitoring of the delivery of protective assisted ventilation. SUMMARY Physiological rationale and clinical data suggest a potential role for proportional modes of assist in providing and monitoring lung and diaphragm protective ventilation.
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Ge H, Duan K, Wang J, Jiang L, Zhang L, Zhou Y, Fang L, Heunks LMA, Pan Q, Zhang Z. Risk Factors for Patient-Ventilator Asynchrony and Its Impact on Clinical Outcomes: Analytics Based on Deep Learning Algorithm. Front Med (Lausanne) 2020; 7:597406. [PMID: 33324663 PMCID: PMC7724969 DOI: 10.3389/fmed.2020.597406] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/16/2020] [Indexed: 02/05/2023] Open
Abstract
Background and objectives: Patient-ventilator asynchronies (PVAs) are common in mechanically ventilated patients. However, the epidemiology of PVAs and its impact on clinical outcome remains controversial. The current study aims to evaluate the epidemiology and risk factors of PVAs and their impact on clinical outcomes using big data analytics. Methods: The study was conducted in a tertiary care hospital; all patients with mechanical ventilation from June to December 2019 were included for analysis. Negative binomial regression and distributed lag non-linear models (DLNM) were used to explore risk factors for PVAs. PVAs were included as a time-varying covariate into Cox regression models to investigate its influence on the hazard of mortality and ventilator-associated events (VAEs). Results: A total of 146 patients involving 50,124 h and 51,451,138 respiratory cycles were analyzed. The overall mortality rate was 15.6%. Double triggering was less likely to occur during day hours (RR: 0.88; 95% CI: 0.85-0.90; p < 0.001) and occurred most frequently in pressure control ventilation (PCV) mode (median: 3; IQR: 1-9 per hour). Ineffective effort was more likely to occur during day time (RR: 1.09; 95% CI: 1.05-1.13; p < 0.001), and occurred most frequently in PSV mode (median: 8; IQR: 2-29 per hour). The effect of sedatives and analgesics showed temporal patterns in DLNM. PVAs were not associated mortality and VAE in Cox regression models with time-varying covariates. Conclusions: Our study showed that counts of PVAs were significantly influenced by time of the day, ventilation mode, ventilation settings (e.g., tidal volume and plateau pressure), and sedatives and analgesics. However, PVAs were not associated with the hazard of VAE or mortality after adjusting for protective ventilation strategies such as tidal volume, plateau pressure, and positive end expiratory pressure (PEEP).
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Affiliation(s)
- Huiqing Ge
- Department of Respiratory Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Regional Medical Center for National Institute of Respiratory Diseases, Bethesda, MD, United States
| | - Kailiang Duan
- Department of Respiratory Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jimei Wang
- Department of Respiratory Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Liuqing Jiang
- Department of Respiratory Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lingwei Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Yuhan Zhou
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Luping Fang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Leo M. A. Heunks
- Department of Intensive Care Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Qing Pan
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Alqahtani JS, AlAhmari MD, Alshamrani KH, Alshehri AM, Althumayri MA, Ghazwani AA, AlAmoudi AO, Alsomali A, Alenazi MH, AlZahrani YR, Alqahtani AS, AlRabeeah SM, Arabi YM. Patient-Ventilator Asynchrony in Critical Care Settings: National Outcomes of Ventilator Waveform Analysis. Heart Lung 2020; 49:630-636. [PMID: 32362397 DOI: 10.1016/j.hrtlng.2020.04.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 03/31/2020] [Accepted: 04/02/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Patient-ventilator asynchrony (PVA) is a prevalent and often underrecognized problem in mechanically ventilated patients. Ventilator waveform analysis is a noninvasive and reliable means of detecting PVAs, but the use of this tool has not been broadly studied. METHODS Our observational analysis leveraged a validated evaluation tool to assess the ability of critical care practitioners (CCPs) to detect different PVA types as presented in three videos. This tool consisted of three videos of common PVAs (i.e., double-triggering, auto-triggering, and ineffective triggering). Data were collected via an evaluation sheet distributed to 39 hospitals among the various CCPs, including respiratory therapists (RTs), nurses, and physicians. RESULTS A total of 411 CCPs were assessed; of these, only 41 (10.2%) correctly identified the three PVA types, while 92 (22.4%) correctly detected two types and 174 (42.3%) correctly detected one; 25.3% did not recognize any PVA. There were statistically significant differences between trained and untrained CCPs in terms of recognition (three PVAs, p < 0.001; two PVAs, p = 0.001). The majority of CCPs who identified one or zero PVAs were untrained, and such differences among groups were statistically significant (one PVA, p = 0.001; zero PVAs, p = 0.004). Female gender and prior training on ventilator waveforms were found to increase the odds of identifying more than two PVAs correctly, with odds ratios (ORs) (95% confidence intervals [CIs]) of 1.93 (1.07-3.49) and 5.41 (3.26-8.98), respectively. Profession, experience, and hospital characteristics were not found to correlate with increased odds of detecting PVAs; this association generally held after applying a regression model on the RT profession, with the ORs (95% CIs) of prior training (2.89 [1.28-6.51]) and female gender (2.49 [1.15-5.39]) showing the increased odds of detecting two or more PVAs. CONCLUSION Common PVAs detection were found low in critical care settings, with about 25% of PVA going undetected by CCPs. Female gender and prior training on ventilator graphics were the only significant predictive factors among CCPs and RTs in correctly identifying PVAs. There is an urgent need to establish teaching and training programs, policies, and guidelines vis-à-vis the early detection and management of PVAs in mechanically ventilated patients, so as to improve their outcomes.
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Affiliation(s)
- Jaber S Alqahtani
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia; UCL Respiratory, University College London, London, UK.
| | - Mohammed D AlAhmari
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia; Chief Executive Officer (CEO), Rural Healthcare Networks, Eastren Province Health Cluster, Saudi Arabia
| | - Khalid H Alshamrani
- Department of Respiratory Care, Prince Sultan Military Medical City, Riyadh, Saudi Arabia
| | - Abdullah M Alshehri
- Department of Respiratory Care, Prince Sultan Military Medical City, Riyadh, Saudi Arabia
| | - Mashhour A Althumayri
- Department of Respiratory Care, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Abdullah A Ghazwani
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Asma O AlAmoudi
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Amal Alsomali
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Meshal H Alenazi
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Yousef R AlZahrani
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Abdullah S Alqahtani
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Saad M AlRabeeah
- Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Yaseen M Arabi
- College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia; Intensive Care Department, Ministry of the National Guard, Health Affairs, Riyadh, Saudi Arabia
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24
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Martos-Benítez FD, Domínguez-Valdés Y, Burgos-Aragüez D, Larrondo-Muguercia H, Orama-Requejo V, Lara-Ponce KX, González-Martínez I. Outcomes of ventilatory asynchrony in patients with inspiratory effort. Rev Bras Ter Intensiva 2020; 32:284-294. [PMID: 32667451 PMCID: PMC7405741 DOI: 10.5935/0103-507x.20200045] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 02/04/2020] [Indexed: 01/21/2023] Open
Abstract
Objective To identify the relationship of patient-ventilator asynchrony with the level of sedation and hemogasometric and clinical results. Methods This was a prospective study of 122 patients admitted to the intensive care unit who underwent > 24 hours of invasive mechanical ventilation with inspiratory effort. In the first 7 days of ventilation, patient-ventilator asynchrony was evaluated daily for 30 minutes. Severe patient-ventilator asynchrony was defined as an asynchrony index > 10%. Results A total of 339,652 respiratory cycles were evaluated in 504 observations. The mean asynchrony index was 37.8% (standard deviation 14.1 - 61.5%). The prevalence of severe patient-ventilator asynchrony was 46.6%. The most frequent patient-ventilator asynchronies were ineffective trigger (13.3%), autotrigger (15.3%), insufficient flow (13.5%), and delayed cycling (13.7%). Severe patient-ventilator asynchrony was related to the level of sedation (ineffective trigger: p = 0.020; insufficient flow: p = 0.016; premature cycling: p = 0.023) and the use of midazolam (p = 0.020). Severe patient-ventilator asynchrony was also associated with hemogasometric changes. The persistence of severe patient-ventilator asynchrony was an independent risk factor for failure of the spontaneous breathing test, ventilation time, ventilator-associated pneumonia, organ dysfunction, mortality in the intensive care unit, and length of stay in the intensive care unit. Conclusion Patient-ventilator asynchrony is a frequent disorder in critically ill patients with inspiratory effort. The patient’s interaction with the ventilator should be optimized to improve hemogasometric parameters and clinical results. Further studies are required to confirm these results.
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Affiliation(s)
- Frank Daniel Martos-Benítez
- Unidad de Cuidados Intensivos - 8B, Hospital Clínico Quirúrgico "Hermanos Ameijeiras", Universidad de Ciencias Médicas de La Habana, La Habana, Cuba
| | - Yairén Domínguez-Valdés
- Unidad de Cuidados Intensivos - 8B, Hospital Clínico Quirúrgico "Hermanos Ameijeiras", Universidad de Ciencias Médicas de La Habana, La Habana, Cuba
| | - Dailé Burgos-Aragüez
- Unidad de Cuidados Intensivos - 8B, Hospital Clínico Quirúrgico "Hermanos Ameijeiras", Universidad de Ciencias Médicas de La Habana, La Habana, Cuba
| | - Hilev Larrondo-Muguercia
- Unidad de Cuidados Intensivos - 8B, Hospital Clínico Quirúrgico "Hermanos Ameijeiras", Universidad de Ciencias Médicas de La Habana, La Habana, Cuba
| | - Versis Orama-Requejo
- Unidad de Cuidados Intensivos - 8B, Hospital Clínico Quirúrgico "Hermanos Ameijeiras", Universidad de Ciencias Médicas de La Habana, La Habana, Cuba
| | - Karla Ximena Lara-Ponce
- Unidad de Cuidados Intensivos - 8B, Hospital Clínico Quirúrgico "Hermanos Ameijeiras", Universidad de Ciencias Médicas de La Habana, La Habana, Cuba
| | - Iraida González-Martínez
- Unidad de Cuidados Intensivos, Hospital Universitario "Dr. Miguel Enríquez", Universidad de Ciencias Médicas de La Habana, La Habana, Cuba
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25
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Grotberg JC, Wang BR, Eakin R, Co IN. Cardiogenic Auto-Triggering as a Consequence of Hemoperitoneum. Chest 2020; 158:e1-e3. [PMID: 32654733 DOI: 10.1016/j.chest.2020.03.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 03/12/2020] [Indexed: 10/23/2022] Open
Abstract
A 70-year-old woman presented with hemorrhagic shock secondary to hemoperitoneum following a paracentesis. On hospital day 3, she developed respiratory alkalosis and increased respiratory rates observed on the ventilator despite no spontaneous inspiratory effort. Converting to pressure support mode uncovered a cardiogenic oscillatory flow that had been auto-triggering the ventilator. This cardiogenic auto-triggering resolved with large-volume paracentesis. Cardiogenic auto-triggering leads to patient-ventilator dyssynchrony, respiratory alkalosis, lung distension, and difficulty with weaning from the ventilator, and it may be unrecognized in ICUs.
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Affiliation(s)
- John C Grotberg
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI.
| | - Bonnie R Wang
- Department of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI
| | - Richard Eakin
- Department of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI
| | - Ivan N Co
- Department of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI
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26
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Zhang L, Mao K, Duan K, Fang S, Lu Y, Gong Q, Lu F, Jiang Y, Jiang L, Fang W, Zhou X, Wang J, Fang L, Ge H, Pan Q. Detection of patient-ventilator asynchrony from mechanical ventilation waveforms using a two-layer long short-term memory neural network. Comput Biol Med 2020; 120:103721. [PMID: 32250853 DOI: 10.1016/j.compbiomed.2020.103721] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 03/17/2020] [Accepted: 03/21/2020] [Indexed: 01/27/2023]
Abstract
BACKGROUND AND OBJECTIVE Mismatch between invasive mechanical ventilation and the requirements of patients results in patient-ventilator asynchrony (PVA), which is associated with a series of adverse clinical outcomes. Although the efficiency of the available approaches for automatically detecting various types of PVA from the ventilator waveforms is unsatisfactory, the feasibility of powerful deep learning approaches in addressing this problem has not been investigated. METHODS We propose a 2-layer long short-term memory (LSTM) network to detect two most frequently encountered types of PVA, namely, double triggering (DT) and ineffective inspiratory effort during expiration (IEE), on two datasets. The performance of the networks is evaluated first using cross-validation on the combined dataset, and then using a cross testing scheme, in which the LSTM networks are established on one dataset and tested on the other. RESULTS Compared with the reported rule-based algorithms and the machine learning models, the proposed 2-layer LSTM network exhibits the best overall performance, with the F1 scores of 0.983 and 0.979 for DT and IEE detection, respectively, on the combined dataset. Furthermore, it outperforms the other approaches in cross testing. CONCLUSIONS The findings suggest that LSTM is an excellent technique for accurate recognition of PVA in clinics. Such a technique can help detect and correct PVA for a better patient ventilator interaction.
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Affiliation(s)
- Lingwei Zhang
- College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou, 310023, China
| | - Kedong Mao
- College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou, 310023, China
| | - Kailiang Duan
- Department of Respiratory Care, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Qingchun East Rd. 3, Hangzhou, 310016, China
| | - Siqi Fang
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, CB3 OWA, UK
| | - Yunfei Lu
- College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou, 310023, China
| | - Qiang Gong
- College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou, 310023, China
| | - Fei Lu
- College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou, 310023, China
| | - Ye Jiang
- Department of Respiratory Care, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Qingchun East Rd. 3, Hangzhou, 310016, China
| | - Liuqing Jiang
- Department of Respiratory Care, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Qingchun East Rd. 3, Hangzhou, 310016, China
| | - Wenyao Fang
- Department of Respiratory Care, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Qingchun East Rd. 3, Hangzhou, 310016, China
| | - Xiaolin Zhou
- Department of Respiratory Care, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Qingchun East Rd. 3, Hangzhou, 310016, China
| | - Jimei Wang
- Department of Respiratory Care, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Qingchun East Rd. 3, Hangzhou, 310016, China
| | - Luping Fang
- College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou, 310023, China
| | - Huiqing Ge
- Department of Respiratory Care, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Qingchun East Rd. 3, Hangzhou, 310016, China.
| | - Qing Pan
- College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou, 310023, China.
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27
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Pavone M, Verrillo E, Onofri A, Caggiano S, Cutrera R. Ventilators and Ventilatory Modalities. Front Pediatr 2020; 8:500. [PMID: 32984212 PMCID: PMC7492667 DOI: 10.3389/fped.2020.00500] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 07/15/2020] [Indexed: 11/13/2022] Open
Abstract
Non-invasive ventilation is increasingly used in children for acute and chronic respiratory failure. Ventilators available for clinical use have different levels of complexity, and clinicians need to know in detail their characteristics, setting variables, and performances. A wide range of ventilators are currently used in non-invasive ventilation including bi-level ventilators, intermediate ventilators, and critical care ventilators. Simple or advanced continuous positive airway pressure devices are also available. Differences between ventilators may have implications on the development of asynchronies and air leaks and may be associated with discomfort and poor patient tolerance. Although pressure-targeted (controlled) mode is preferable in children because of barotrauma concerns, volume-targeted (controlled) ventilators are also available. Pressure support ventilation represents the most used non-invasive ventilation mode, as it is more physiological. The newest ventilators allow the clinicians to use the hybrid modes that combine the advantages of volume- and pressure-targeted (controlled) ventilation while limiting their drawbacks. The use of in-built software may help clinicians to optimize the ventilator setting as well as to objectively monitor patient adherence to the treatment. The present review aims to help the clinician with the choice of the ventilator and its ventilation modalities to ensure a successful non-invasive ventilation program.
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Affiliation(s)
- Martino Pavone
- Pediatric Pulmonology & Respiratory Intermediate Care Unit, Sleep and Long Term Ventilation Unit, Academic Department of Pediatrics (DPUO), Pediatric Hospital "Bambino Gesù Research Institute, Rome, Italy
| | - Elisabetta Verrillo
- Pediatric Pulmonology & Respiratory Intermediate Care Unit, Sleep and Long Term Ventilation Unit, Academic Department of Pediatrics (DPUO), Pediatric Hospital "Bambino Gesù Research Institute, Rome, Italy
| | - Alessandro Onofri
- Pediatric Pulmonology & Respiratory Intermediate Care Unit, Sleep and Long Term Ventilation Unit, Academic Department of Pediatrics (DPUO), Pediatric Hospital "Bambino Gesù Research Institute, Rome, Italy
| | - Serena Caggiano
- Pediatric Pulmonology & Respiratory Intermediate Care Unit, Sleep and Long Term Ventilation Unit, Academic Department of Pediatrics (DPUO), Pediatric Hospital "Bambino Gesù Research Institute, Rome, Italy
| | - Renato Cutrera
- Pediatric Pulmonology & Respiratory Intermediate Care Unit, Sleep and Long Term Ventilation Unit, Academic Department of Pediatrics (DPUO), Pediatric Hospital "Bambino Gesù Research Institute, Rome, Italy
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de Haro C, Ochagavia A, López-Aguilar J, Fernandez-Gonzalo S, Navarra-Ventura G, Magrans R, Montanyà J, Blanch L. Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities. Intensive Care Med Exp 2019; 7:43. [PMID: 31346799 PMCID: PMC6658621 DOI: 10.1186/s40635-019-0234-5] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 03/07/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Mechanical ventilation is common in critically ill patients. This life-saving treatment can cause complications and is also associated with long-term sequelae. Patient-ventilator asynchronies are frequent but underdiagnosed, and they have been associated with worse outcomes. MAIN BODY Asynchronies occur when ventilator assistance does not match the patient's demand. Ventilatory overassistance or underassistance translates to different types of asynchronies with different effects on patients. Underassistance can result in an excessive load on respiratory muscles, air hunger, or lung injury due to excessive tidal volumes. Overassistance can result in lower patient inspiratory drive and can lead to reverse triggering, which can also worsen lung injury. Identifying the type of asynchrony and its causes is crucial for effective treatment. Mechanical ventilation and asynchronies can affect hemodynamics. An increase in intrathoracic pressure during ventilation modifies ventricular preload and afterload of ventricles, thereby affecting cardiac output and hemodynamic status. Ineffective efforts can decrease intrathoracic pressure, but double cycling can increase it. Thus, asynchronies can lower the predictive accuracy of some hemodynamic parameters of fluid responsiveness. New research is also exploring the psychological effects of asynchronies. Anxiety and depression are common in survivors of critical illness long after discharge. Patients on mechanical ventilation feel anxiety, fear, agony, and insecurity, which can worsen in the presence of asynchronies. Asynchronies have been associated with worse overall prognosis, but the direct causal relation between poor patient-ventilator interaction and worse outcomes has yet to be clearly demonstrated. Critical care patients generate huge volumes of data that are vastly underexploited. New monitoring systems can analyze waveforms together with other inputs, helping us to detect, analyze, and even predict asynchronies. Big data approaches promise to help us understand asynchronies better and improve their diagnosis and management. CONCLUSIONS Although our understanding of asynchronies has increased in recent years, many questions remain to be answered. Evolving concepts in asynchronies, lung crosstalk with other organs, and the difficulties of data management make more efforts necessary in this field.
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Affiliation(s)
- Candelaria de Haro
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Spain. .,CIBERES, Instituto de Salud Carlos III, Madrid, Spain.
| | - Ana Ochagavia
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Spain.,CIBERES, Instituto de Salud Carlos III, Madrid, Spain
| | - Josefina López-Aguilar
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Spain.,CIBERES, Instituto de Salud Carlos III, Madrid, Spain
| | - Sol Fernandez-Gonzalo
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Spain.,CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
| | - Guillem Navarra-Ventura
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Spain
| | - Rudys Magrans
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Spain.,CIBERES, Instituto de Salud Carlos III, Madrid, Spain
| | | | - Lluís Blanch
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Spain.,CIBERES, Instituto de Salud Carlos III, Madrid, Spain
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29
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Double and multiple cycling in mechanical ventilation: Complex events with varying clinical effects. Med Intensiva 2019; 44:449-451. [PMID: 31337498 DOI: 10.1016/j.medin.2019.06.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 06/17/2019] [Indexed: 11/21/2022]
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30
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de Haro C, Magrans R, López-Aguilar J, Montanyà J, Lena E, Subirà C, Fernandez-Gonzalo S, Gomà G, Fernández R, Albaiceta GM, Skrobik Y, Lucangelo U, Murias G, Ochagavia A, Kacmarek RM, Rue M, Blanch L. Effects of sedatives and opioids on trigger and cycling asynchronies throughout mechanical ventilation: an observational study in a large dataset from critically ill patients. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2019; 23:245. [PMID: 31277722 PMCID: PMC6612107 DOI: 10.1186/s13054-019-2531-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 06/26/2019] [Indexed: 12/23/2022]
Abstract
Background In critically ill patients, poor patient-ventilator interaction may worsen outcomes. Although sedatives are often administered to improve comfort and facilitate ventilation, they can be deleterious. Whether opioids improve asynchronies with fewer negative effects is unknown. We hypothesized that opioids alone would improve asynchronies and result in more wakeful patients than sedatives alone or sedatives-plus-opioids. Methods This prospective multicenter observational trial enrolled critically ill adults mechanically ventilated (MV) > 24 h. We compared asynchronies and sedation depth in patients receiving sedatives, opioids, or both. We recorded sedation level and doses of sedatives and opioids. BetterCare™ software continuously registered ineffective inspiratory efforts during expiration (IEE), double cycling (DC), and asynchrony index (AI) as well as MV modes. All variables were averaged per day. We used linear mixed-effects models to analyze the relationships between asynchronies, sedation level, and sedative and opioid doses. Results In 79 patients, 14,166,469 breaths were recorded during 579 days of MV. Overall asynchronies were not significantly different in days classified as sedatives-only, opioids-only, and sedatives-plus-opioids and were more prevalent in days classified as no-drugs than in those classified as sedatives-plus-opioids, irrespective of the ventilatory mode. Sedative doses were associated with sedation level and with reduced DC (p < 0.0001) in sedatives-only days. However, on days classified as sedatives-plus-opioids, higher sedative doses and deeper sedation had more IEE (p < 0.0001) and higher AI (p = 0.0004). Opioid dosing was inversely associated with overall asynchronies (p < 0.001) without worsening sedation levels into morbid ranges. Conclusions Sedatives, whether alone or combined with opioids, do not result in better patient-ventilator interaction than opioids alone, in any ventilatory mode. Higher opioid dose (alone or with sedatives) was associated with lower AI without depressing consciousness. Higher sedative doses administered alone were associated only with less DC. Trial registration ClinicalTrial.gov, NCT03451461 Electronic supplementary material The online version of this article (10.1186/s13054-019-2531-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Candelaria de Haro
- Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain. .,Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain. .,CIBERES, Instituto de Salud Carlos III, Madrid, Spain.
| | - Rudys Magrans
- Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain.,CIBERES, Instituto de Salud Carlos III, Madrid, Spain
| | - Josefina López-Aguilar
- Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain.,CIBERES, Instituto de Salud Carlos III, Madrid, Spain
| | | | - Enrico Lena
- Department of Perioperative Medicine, Intensive Care and Emergency, Cattinara Hospital, Trieste University, Trieste, Italy
| | - Carles Subirà
- ICU, Fundació Althaia, Universitat Internacional de Catalunya, Manresa, Spain
| | - Sol Fernandez-Gonzalo
- Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain.,CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
| | - Gemma Gomà
- Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Rafael Fernández
- CIBERES, Instituto de Salud Carlos III, Madrid, Spain.,ICU, Fundació Althaia, Universitat Internacional de Catalunya, Manresa, Spain
| | - Guillermo M Albaiceta
- CIBERES, Instituto de Salud Carlos III, Madrid, Spain.,Unidad de Cuidados Intensivos Cardiológicos, Hospital Universitario Central de Asturias, Oviedo, Spain.,Departamento de Biología Funcional, Instituto Universitario de Oncología del Principado de Asturias, Universidad de Oviedo, Oviedo, Spain
| | - Yoanna Skrobik
- Department of Medicine, McGill University, Montréal, Québec, Canada.,Regroupement des Soins Critiques Respiratoires, Réseau de Santé Respiratoire, Fonds de Recherche du Québec en Santé, Montréal, Québec, Canada
| | - Umberto Lucangelo
- Department of Perioperative Medicine, Intensive Care and Emergency, Cattinara Hospital, Trieste University, Trieste, Italy
| | - Gastón Murias
- Critical Care Department, Hospital Británico, Buenos Aires, Argentina
| | - Ana Ochagavia
- Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain.,CIBERES, Instituto de Salud Carlos III, Madrid, Spain
| | - Robert M Kacmarek
- Department of Respiratory Care, Department of Anesthesiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Montserrat Rue
- Department of Basic Medical Sciences, Universitat de Lleida-IRB Lleida, Lleida, Spain.,Health Services Research Network in Chronic Diseases (REDISSEC), Madrid, Spain
| | - Lluís Blanch
- Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain.,CIBERES, Instituto de Salud Carlos III, Madrid, Spain
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Bulleri E, Fusi C, Bambi S, Pisani L. Patient-ventilator asynchronies: types, outcomes and nursing detection skills. ACTA BIO-MEDICA : ATENEI PARMENSIS 2018; 89:6-18. [PMID: 30539927 PMCID: PMC6502136 DOI: 10.23750/abm.v89i7-s.7737] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 10/05/2018] [Indexed: 01/17/2023]
Abstract
BACKGROUND Mechanical ventilation is often employed as partial ventilatory support where both the patient and the ventilator work together. The ventilator settings should be adjusted to maintain a harmonious patient-ventilator interaction. However, this balance is often altered by many factors able to generate a patient ventilator asynchrony (PVA). The aims of this review were: to identify PVAs, their typologies and classifications; to describe how and to what extent their occurrence can affect the patients' outcomes; to investigate the levels of nursing skill in detecting PVAs. METHODS Literature review performed on Cochrane Library, Medline and CINAHL databases. RESULTS 1610 records were identified; 43 records were included after double blind screening. PVAs have been classified with respect to the phase of the respiratory cycle or based on the circumstance of occurrence. There is agreement on the existence of 7 types of PVAs: ineffective effort, double trigger, premature cycling, delayed cycling, reverse triggering, flow starvation and auto-cycling. PVAs can be identified through the ventilator graphics monitoring of pressure and flow waveforms. The influence on patient outcomes varies greatly among studies but PVAs are mostly associated with poorer outcomes. Adequately trained nurses can learn and retain how to correctly detect PVAs. CONCLUSIONS Since its challenging interpretation and the potential advantages of its implementation, ventilator graphics monitoring can be classified as an advanced competence for ICU nurses. The knowledge and skills to adequately manage PVAs should be provided by specific post-graduate university courses.
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Marchuk Y, Magrans R, Sales B, Montanya J, López-Aguilar J, de Haro C, Gomà G, Subirà C, Fernández R, Kacmarek RM, Blanch L. Predicting Patient-ventilator Asynchronies with Hidden Markov Models. Sci Rep 2018; 8:17614. [PMID: 30514876 PMCID: PMC6279839 DOI: 10.1038/s41598-018-36011-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 11/12/2018] [Indexed: 01/31/2023] Open
Abstract
In mechanical ventilation, it is paramount to ensure the patient's ventilatory demand is met while minimizing asynchronies. We aimed to develop a model to predict the likelihood of asynchronies occurring. We analyzed 10,409,357 breaths from 51 critically ill patients who underwent mechanical ventilation >24 h. Patients were continuously monitored and common asynchronies were identified and regularly indexed. Based on discrete time-series data representing the total count of asynchronies, we defined four states or levels of risk of asynchronies, z1 (very-low-risk) - z4 (very-high-risk). A Poisson hidden Markov model was used to predict the probability of each level of risk occurring in the next period. Long periods with very few asynchronous events, and consequently very-low-risk, were more likely than periods with many events (state z4). States were persistent; large shifts of states were uncommon and most switches were to neighbouring states. Thus, patients entering states with a high number of asynchronies were very likely to continue in that state, which may have serious implications. This novel approach to dealing with patient-ventilator asynchrony is a first step in developing smart alarms to alert professionals to patients entering high-risk states so they can consider actions to improve patient-ventilator interaction.
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Affiliation(s)
| | - Rudys Magrans
- Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí, Universitat Autònoma de Barcelona, Sabadell, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain.
| | | | | | - Josefina López-Aguilar
- Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí, Universitat Autònoma de Barcelona, Sabadell, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Candelaria de Haro
- Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí, Universitat Autònoma de Barcelona, Sabadell, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Gemma Gomà
- Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí, Universitat Autònoma de Barcelona, Sabadell, Spain
| | - Carles Subirà
- Intensive Care Unit, Fundació Althaia, Universitat Internacional de Catalunya, Manresa, Spain
| | - Rafael Fernández
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain.,Intensive Care Unit, Fundació Althaia, Universitat Internacional de Catalunya, Manresa, Spain
| | - Robert M Kacmarek
- Department of Respiratory Care, Department of Anesthesiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Lluis Blanch
- Critical Care Center, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí, Universitat Autònoma de Barcelona, Sabadell, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
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