Persistent shortcomings of invasive positive pressure ventilation make it less than an ideal intervention. Over the course of more than seven decades, clinical experience and scientific investigation have helped define its range of hazards and limitations. Apart from compromised airway clearance and lower airway contamination imposed by endotracheal intubation, the primary hazards inherent to positive pressure ventilation may be considered in three broad categories: hemodynamic impairment, potential for ventilation-induced lung injury, and impairment of the respiratory muscle pump. To optimize care delivery, it is crucial for monitoring and machine outputs to integrate information with the potential to impact the underlying requirements of the patient and/or responses of the cardiopulmonary system to ventilatory interventions. Trending analysis, timely interventions, and closer communication with the caregiver would limit adverse clinical trajectories. Judging from the rapid progress of recent years, we are encouraged to think that insights from physiologic research and emerging technological capability may eventually address important aspects of current deficiencies.

BACKGROUND: Despite the significant need for mechanical ventilation in- and out-of-hospital, mechanical ventilators remain inaccessible in many instances because of cost or size constraints. Mechanical ventilation is especially critical in trauma scenarios, but the impractical size and weight of standard mechanical ventilators restrict first responders from carrying them in medical aid bags, leading to reliance on imprecise manual bag-mask ventilation. This is particularly important in combat-related injury, where airway compromise and respiratory failure are leading causes of preventable death, but medics are left without necessary mechanical ventilation. To address the serious gaps in mechanical ventilation accessibility, we are developing an Autonomous, Modular, and Portable Ventilation platform (AMP-Vent) suitable for austere environments, prolonged critical care, surgical applications, mass casualty incidents, and stockpiling. The core system is remarkably compact, weighing <2.3 kg, and can fit inside a shoebox (23.4 cm × 17.8 cm × 10.7 cm). Notably, this device is 65% lighter than standard transport ventilators and astoundingly 96% lighter than typical intensive care unit ventilators. Beyond its exceptional portability, AMP-Vent can be manufactured at less than one-tenth the cost of conventional ventilators. Despite its reduced size and cost, the system\'s functionality is uncompromised. The core system is equipped with closed-loop sensors and advanced modes of ventilation (pressure-control, volume-control, and synchronized intermittent mandatory ventilation), enabling quality care in a portable form factor. The current prototype has undergone preliminary preclinical testing and optimization through trials using a breathing simulator (ASL 5000) and in a large animal model (swine). This report aims to introduce a novel ventilation system and substantiate its promising performance through evidence gathered from preclinical studies.
METHODS: Lung simulator testing was performed using the ASL 5000, in accordance with table 201.105 \"pressure-control inflation-type testing\" from ISO 80601-2-12:2020. Following simulations, AMP-Vent was tested in healthy 10-kg female domestic piglets. The Children\'s Hospital of Philadelphia Institutional Animal Care and Use Committee approved all animal procedures. Swine received 4-min blocks of alternating ventilation, where AMP-Vent and a conventional anesthesia ventilator (GE AISYS CS2) were used to titrate to varied end-tidal carbon dioxide (EtCO2) goals with the initial ventilator switching for each ascending target (35, 40, 45, 50, 55 mmHg).
RESULTS: During ASL 5000 simulations, AMP-Vent exhibited consistent performance under varied conditions, maintaining a coefficient of variation of 2% or less within each test. In a large animal study, AMP-Vent maintained EtCO2 and SpO2 targets with comparable performance to a conventional anesthesia ventilator (GE AISYS CS2). Furthermore, the comparison of minute ventilation (Ve) distributions between the conventional anesthesia ventilator and AMP-Vent at several EtCO2 goals (35, 40, 45, 50, and 55 mmHg) revealed no statistically significant differences (p = 0.46 using the Kruskal-Wallis rank sum test).
CONCLUSIONS: Preclinical results from this study highlight AMP-Vent\'s core functionality and consistent performance across varied scenarios. AMP-Vent sets a benchmark for portability with its remarkably compact design, positioning it to revolutionize trauma care in previously inaccessible medical scenarios.

BACKGROUND: The need for remote ventilator control has been highlighted by the COVID-19 Public Health Emergency. Remote ventilator control from outside a patient\'s room can improve response time to patient needs, protect health care workers, and reduce personal protective equipment (PPE) consumption. Extending remote control to distant locations can expand the capabilities of frontline health care workers by delivering specialized clinical expertise to the point of care, which is much needed in diverse health care settings, such as tele-critical care and military medicine. However, the safety and effectiveness of remote ventilator control can be affected by many risk factors, including communication failures and network disruptions. Consensus safety requirements and test methods are needed to assess the resilience and safety of remote ventilator control under communication failures and network disruptions.
METHODS: We designed two test methods to assess the robustness, usability, and safety of a remote ventilator control prototype system jointly developed by Nihon Kohden OrangeMed, Inc. and DocBox, Inc. (\"the NK-DocBox system\") to control the operation of an NKV-550 critical care ventilator under communication failures and network disruptions. First, the robustness of the NKV-550 ventilator was tested using a remote-control application developed on OpenICE - an open-source medical device interoperability platform - to transmit customized high-frequency and erroneous remote-control commands that could be caused by communication failures in a real-world environment. The second method utilized a network emulator to create different types and severity of network quality of service (QoS) degradation, including bandwidth throttling, network delay and jitter, packet drop and reordering, and bit errors, in the NKV-DocBox system to quantitatively assess the impact on system usability and safety.
RESULTS: The NKV-550 ventilator operated as expected when remote-control commands arrived as fast as once per second. It ignored erroneous commands attempting to adjust invalid ventilation parameters. When facing commands that set the ventilation mode and parameters to invalid values, it reset the ventilation mode or parameters to default values, the safety implication of which may merit further evaluation. When any network QoS attribute (except for packet reordering) started to degrade, the NK-DocBox System experienced interference to its remote-control function, such as delays in the transmission of ventilator data and remote-control commands within the system. When the network QoS was worse than 500 ms network delay, 100 ms network jitter, 1% data drop rate, 12 Mbps minimal bandwidth, or 1e-6 bit error rate, the system became unsafe to use. For example, ventilator waveforms visualized on the remote-control application demonstrated freezes, out-of-synchronization, and moving backwards; and the connection between the ventilator and the remote-control application became unstable.
CONCLUSIONS: The presented test methods confirmed the robustness of the NKV-550 ventilator against high-frequency and erroneous remote control, quantified the impact of network disruptions on the usability, reliability, and safety of the NK-DocBox system and identified the minimum network QoS requirements for it to function safely. These generalizable test methods can be customized to evaluate other remote ventilator control technologies and remote control of other types of medical devices against communication failures and network disruptions.

Invasive devices are routinely used in the care of critically ill patients. Although they are often essential components of patient care, devices such as intravascular catheters, endotracheal tubes, and ventilators are a common source of complications in the intensive care unit. Critical care practitioners who use these devices need to use strategies for risk reduction and understand approaches to management when adverse events occur. This review discusses the identification, prevention, and management of complications of vascular, airway, and mechanical support devices commonly used in the intensive care unit.

BACKGROUND: Mechanical power of ventilation, a summary parameter reflecting the energy transferred from the ventilator to the respiratory system, has associations with outcomes. INTELLiVENT-Adaptive Support Ventilation is an automated ventilation mode that changes ventilator settings according to algorithms that target a low work-and force of breathing. The study aims to compare mechanical power between automated ventilation by means of INTELLiVENT-Adaptive Support Ventilation and conventional ventilation in critically ill patients.
METHODS: International, multicenter, randomized crossover clinical trial in patients that were expected to need invasive ventilation > 24 hours. Patients were randomly assigned to start with a 3-hour period of automated ventilation or conventional ventilation after which the alternate ventilation mode was selected. The primary outcome was mechanical power in passive and active patients; secondary outcomes included key ventilator settings and ventilatory parameters that affect mechanical power.
RESULTS: A total of 96 patients were randomized. Median mechanical power was not different between automated and conventional ventilation (15.8 [11.5-21.0] versus 16.1 [10.9-22.6] J/min; mean difference -0.44 (95%-CI -1.17 to 0.29) J/min; P = 0.24). Subgroup analyses showed that mechanical power was lower with automated ventilation in passive patients, 16.9 [12.5-22.1] versus 19.0 [14.1-25.0] J/min; mean difference -1.76 (95%-CI -2.47 to -10.34J/min; P < 0.01), and not in active patients (14.6 [11.0-20.3] vs 14.1 [10.1-21.3] J/min; mean difference 0.81 (95%-CI -2.13 to 0.49) J/min; P = 0.23).
CONCLUSIONS: In this cohort of unselected critically ill invasively ventilated patients, automated ventilation by means of INTELLiVENT-Adaptive Support Ventilation did not reduce mechanical power. A reduction in mechanical power was only seen in passive patients.
BACKGROUND: Clinicaltrials.gov (study identifier NCT04827927), April 1, 2021.
UNASSIGNED: https://clinicaltrials.gov/study/NCT04827927?term=intellipower&rank=1.

Given the important role of patient-ventilator assessments in ensuring the safety and efficacy of mechanical ventilation, a team of respiratory therapists and a librarian used Grading of Recommendations, Assessment, Development, and Evaluation methodology to make the following recommendations: (1) We recommend assessment of plateau pressure to ensure lung-protective ventilator settings (strong recommendation, high certainty); (2) We recommend an assessment of tidal volume (VT) to ensure lung-protective ventilation (4-8 mL/kg/predicted body weight) (strong recommendation, high certainty); (3) We recommend documenting VT as mL/kg predicted body weight (strong recommendation, high certainty); (4) We recommend an assessment of PEEP and auto-PEEP (strong recommendation, high certainty); (5) We suggest assessing driving pressure to prevent ventilator-induced injury (conditional recommendation, low certainty); (6) We suggest assessing FIO2 to ensure normoxemia (conditional recommendation, very low certainty); (7) We suggest telemonitoring to supplement direct bedside assessment in settings with limited resources (conditional recommendation, low certainty); (8) We suggest direct bedside assessment rather than telemonitoring when resources are adequate (conditional recommendation, low certainty); (9) We suggest assessing adequate humidification for patients receiving noninvasive ventilation (NIV) and invasive mechanical ventilation (conditional recommendation, very low certainty); (10) We suggest assessing the appropriateness of the humidification device during NIV and invasive mechanical ventilation (conditional recommendation, low certainty); (11) We recommend that the skin surrounding artificial airways and NIV interfaces be assessed (strong recommendation, high certainty); (12) We suggest assessing the dressing used for tracheostomy tubes and NIV interfaces (conditional recommendation, low certainty); (13) We recommend assessing the pressure inside the cuff of artificial airways using a manometer (strong recommendation, high certainty); (14) We recommend that continuous cuff pressure assessment should not be implemented to decrease the risk of ventilator-associated pneumonia (strong recommendation, high certainty); and (15) We suggest assessing the proper placement and securement of artificial airways (conditional recommendation, very low certainty).

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.
CONCLUSIONS: 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.

BACKGROUND: Amid the COVID-19 pandemic, this study delves into ventilator shortages, exploring simple split ventilation (SSV), simple differential ventilation (SDV), and differential multiventilation (DMV). The knowledge gap centers on understanding their performance and safety implications.
OBJECTIVE: Our hypothesis posits that SSV, SDV, and DMV offer solutions to the ventilator crisis. Rigorous testing was anticipated to unveil advantages and limitations, aiding the development of effective ventilation approaches.
UNASSIGNED: Using a specialized test bed, SSV, SDV, and DMV were compared. Simulated lungs in a controlled setting facilitated measurements with sensors. Statistical analysis honed in on parameters like peak inspiratory pressure (PIP) and positive end-expiratory pressure.
RESULTS: Setting target PIP at 15 cm H2O for lung 1 and 12.5 cm H2O for lung 2, SSV revealed a PIP of 15.67 ± 0.2 cm H2O for both lungs, with tidal volume (Vt) at 152.9 ± 9 mL. In SDV, lung 1 had a PIP of 25.69 ± 0.2 cm H2O, lung 2 at 24.73 ± 0.2 cm H2O, and Vts of 464.3 ± 0.9 mL and 453.1 ± 10 mL, respectively. DMV trials showed lung 1\'s PIP at 13.97 ± 0.06 cm H2O, lung 2 at 12.30 ± 0.04 cm H2O, with Vts of 125.8 ± 0.004 mL and 104.4 ± 0.003 mL, respectively.
CONCLUSIONS: This study enriches understanding of ventilator sharing strategy, emphasizing the need for careful selection. DMV, offering individualization while maintaining circuit continuity, stands out. Findings lay the foundation for robust multiplexing strategies, enhancing ventilator management in crises.

OBJECTIVE: To investigate the effects of FLow-controlled EXpiration (FLEX) ventilation expiration time and speed on respiratory and pulmonary mechanics in anesthetized horses in dorsal recumbency.
METHODS: 6 healthy adult research horses.
METHODS: In this randomized crossover experimental study, horses were anesthetized 3 times and were ventilated each time for 60 minutes using conventional volume-controlled ventilation (VCV), linear emptying of the lung over 50% of the expiratory time (FLEX50), or linear emptying of the lung over 100% of the expiratory time (FLEX100) in a randomized order. The primary outcome variables were dynamic compliance (Cdyn), hysteresis, and alveolar dead space. The data was analyzed using two-factor ANOVA. Significance was set to P < .05.
RESULTS: Horses ventilated using FLEX50 and FLEX100 showed significantly higher Cdyn and significantly lower hysteresis values compared to horses ventilated using VCV. Horses ventilated using FLEX50 had significantly lower alveolar dead space compared to horses ventilated using FLEX100 or VCV. Horses ventilated using FLEX100 had significantly lower alveolar dead space compared to VCV horses.
CONCLUSIONS: Our results demonstrate improved Cdyn, hysteresis, and alveolar dead space in horses ventilated with either FLEX50 or FLEX100 relative to traditional VCV. The use of FLEX with a faster exhalation speed (FLEX50) offers additional respiratory advantages.

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