Manual and ventilator hyperinflation parameters used by intensive care physiotherapists in Sri Lanka: An online survey.

INTRODUCTION: Hyperinflation is a common procedure to clear secretion, increase lung compliance and enhance oxygenation in mechanically ventilated patients. Hyperinflation can be provided as manual hyperinflation (MHI) or ventilator hyperinflation (VHI), where outcomes depend upon the methods of application. Hence it is crucial to assess the application of techniques employed in Sri Lanka due to observed variations from recommended practices. OBJECTIVE: This study is aimed to evaluate the application and parameters used for MHI and VHI by physiotherapists in intensive care units (ICUs) in Sri Lanka. METHODOLOGY: An online survey was conducted among physiotherapists who are working in ICUs in Sri Lanka using WhatsApp groups and other social media platforms. RESULTS: A total of 96 physiotherapists responded. The survey comprised of three sections to obtain information about socio-demographic data, MHI practices and VHI practices. Most of the respondents (47%) worked in general hospitals and 74% of participants had a bachelor's degree in physiotherapy; 31.3% had 3-6 years of experience; 93.8% used hyperinflation, and 78.9% used MHI. MHI was performed routinely and as needed to treat low oxygen levels, abnormal breath sounds, and per physician orders while avoiding contraindications. Self-inflation bags are frequently used for MHI (40.6%). Only a few participants (26%) used a manometer or tracked PIP. In addition to the supine position, some participants (37.5%) used the side-lying position. Most physiotherapists followed the recommended MHI technique: slow squeeze (57.3%), inspiratory pause (45.8%), and quick release (70.8%). VHI was practised by 19.8%, with medical approval and it was frequently performed by medical staff compared to physiotherapists. Treatment time, number of breaths, and patient positioning varied, and parameters were not well-defined. CONCLUSION: The study found that MHI was not applied with the recommended PIP, and VHI parameters were not identified. The study indicates a need to educate physiotherapists about current VHI and MHI practice guidelines.

Development and performance evaluation of a solenoid valve assisted low-cost ventilator on gas exchange and respiratory mechanics in a porcine model.

INTRODUCTION: During the COVID-19 pandemic, ventilator shortages necessitated the development of new, low-cost ventilator designs. The fundamental requirements of a ventilator include precise gas delivery, rapid adjustments, durability, and user-friendliness, often achieved through solenoid valves. However, few solenoid-valve assisted low-cost ventilator (LCV) designs have been published, and gas exchange evaluation during LCV testing is lacking. This study describes the development and performance evaluation of a solenoid-valve assisted low-cost ventilator (SV-LCV) in vitro and in vivo, focusing on gas exchange and respiratory mechanics. METHODS: The SV-LCV, a fully open ventilator device, was developed with comprehensive hardware and design documentation, utilizing solenoid valves for gas delivery regulation. Lung simulator testing calibrated tidal volumes at specified inspiratory and expiratory times, followed by in vivo testing in a porcine model to compare SV-LCV performance with a conventional ventilator. RESULTS: The SV-LCV closely matched the control ventilator's respiratory profile and gas exchange across all test cycles. Lung simulator testing revealed direct effects of compliance and resistance changes on peak pressures and tidal volumes, with no significant changes in respiratory rate. In vivo testing demonstrated comparable gas exchange parameters between SV-LCV and conventional ventilator across all cycles. Specifically, in cycle 1, the SV-LCV showed arterial blood gas (ABG) results of pH 7.54, PCO2 34.5 mmHg, and PO2 91.7 mmHg, compared to the control ventilator's ABG of pH 7.53, PCO2 37.1 mmHg, and PO2 134 mmHg. Cycle 2 exhibited ABG results of pH 7.53, PCO2 33.6 mmHg, and PO2 84.3 mmHg for SV-LCV, and pH 7.5, PCO2 34.2 mmHg, and PO2 93.5 mmHg for the control ventilator. Similarly, cycle 3 showed ABG results of pH 7.53, PCO2 32.1 mmHg, and PO2 127 mmHg for SV-LCV, and pH 7.5, PCO2 35.5 mmHg, and PO2 91.3 mmHg for the control ventilator. CONCLUSION: The SV-LCV provides similar gas exchange and respiratory mechanic profiles compared to a conventional ventilator. With a streamlined design and performance akin to commercially available ventilators, the SV-LCV presents a viable, readily available, and reliable short-term solution for overcoming ventilator supply shortages during crises.

Dyssynchronous diaphragm contractions impair diaphragm function in mechanically ventilated patients.

BACKGROUND: Pre-clinical studies suggest that dyssynchronous diaphragm contractions during mechanical ventilation may cause acute diaphragm dysfunction. We aimed to describe the variability in diaphragm contractile loading conditions during mechanical ventilation and to establish whether dyssynchronous diaphragm contractions are associated with the development of impaired diaphragm dysfunction. METHODS: In patients receiving invasive mechanical ventilation for pneumonia, septic shock, acute respiratory distress syndrome, or acute brain injury, airway flow and pressure and diaphragm electrical activity (Edi) were recorded hourly around the clock for up to 7 days. Dyssynchronous post-inspiratory diaphragm loading was defined based on the duration of neural inspiration after expiratory cycling of the ventilator. Diaphragm function was assessed on a daily basis by neuromuscular coupling (NMC, the ratio of transdiaphragmatic pressure to diaphragm electrical activity). RESULTS: A total of 4508 hourly recordings were collected in 45 patients. Edi was low or absent (≤ 5 µV) in 51% of study hours (median 71 h per patient, interquartile range 39-101 h). Dyssynchronous post-inspiratory loading was present in 13% of study hours (median 7 h per patient, interquartile range 2-22 h). The probability of dyssynchronous post-inspiratory loading was increased with reverse triggering (odds ratio 15, 95% CI 8-35) and premature cycling (odds ratio 8, 95% CI 6-10). The duration and magnitude of dyssynchronous post-inspiratory loading were associated with a progressive decline in diaphragm NMC (p < 0.01 for interaction with time). CONCLUSIONS: Dyssynchronous diaphragm contractions may impair diaphragm function during mechanical ventilation. TRIAL REGISTRATION: MYOTRAUMA, ClinicalTrials.gov NCT03108118. Registered 04 April 2017 (retrospectively registered).

Complex Heart-Lung Ventilator Emergencies in the CICU.

This review aims to enhance the comprehension and management of cardiopulmonary interactions in critically ill patients with cardiovascular disease undergoing mechanical ventilation. Highlighting the significance of maintaining a delicate balance, this article emphasizes the crucial role of adjusting ventilation parameters based on both invasive and noninvasive monitoring. It provides recommendations for the induction and liberation from mechanical ventilation. Special attention is given to the identification of auto-PEEP (positive end-expiratory pressure) and other situations that may impact hemodynamics and patients' outcomes.

Mechanical ventilation: Beyond the basics.

ABSTRACT: Mechanical ventilation is rarely a simple matter. Skill and knowledge are required to operate the ventilator modes, choose the optimal settings, and understand many monitored variables. Supporting the patient safely and effectively is the top priority in providing mechanical ventilation. This article discusses mechanical ventilation in adults.

Key Factors Impacting a Medical Ventilator Supply Chain During the COVID- 19 Pandemic: Lessons for Pandemic Preparedness.

OBJECTIVES: Future pandemics may cause more severe respiratory illness in younger age groups than COVID-19, requiring many more mechanical ventilators. This publication synthesizes the experiences of diverse contributors to Medtronic's mechanical ventilator supply chain during the pandemic, serving as a record of what worked and what didn't, while identifying key factors affecting production ramp-up in this healthcare crisis. METHOD: In-depth, one-on-one interviews (n = 17) were held with key Medtronic personnel and suppliers. Template analysis was used, and interview content was analyzed for signals, initiatives, actions, and outcomes, as well as influencing forces. RESULTS: Key findings revealed many factors limiting ventilator production ramp-up. Supply chain strengths and weaknesses were identified. Political factors played a role in allocating ventilators and also supported production. Commercial considerations were not priority, but economic awareness was essential to support suppliers. Workers were motivated and flexible. Component shortages, space, production processes, and logistics were challenges. Legally based pressures were reported e.g., import and export restrictions. CONCLUSION: Crisis response alone is not enough; preparation is essential. Coordinated international strategies are more effective than individual country responses. Supply chain resilience based on visibility and flexibility is key. This research can help public health planners and the medical device industry prepare for future healthcare crises.

A New Model of Air-Oxygen Blender for Mechanical Ventilators Using Dynamic Pressure Sensors.

Respiratory diseases are among the leading causes of death globally, with the COVID-19 pandemic serving as a prominent example. Issues such as infections affect a large population and, depending on the mode of transmission, can rapidly spread worldwide, impacting thousands of individuals. These diseases manifest in mild and severe forms, with severely affected patients requiring ventilatory support. The air-oxygen blender is a critical component of mechanical ventilators, responsible for mixing air and oxygen in precise proportions to ensure a constant supply. The most commonly used version of this equipment is the analog model, which faces several challenges. These include a lack of precision in adjustments and the inspiratory fraction of oxygen, as well as gas wastage from cylinders as pressure decreases. The research proposes a blender model utilizing only dynamic pressure sensors to calculate oxygen saturation, based on Bernoulli's equation. The model underwent validation through simulation, revealing a linear relationship between pressures and oxygen saturation up to a mixture outlet pressure of 500 cmH2O. Beyond this value, the relationship begins to exhibit non-linearities. However, these non-linearities can be mitigated through a calibration algorithm that adjusts the mathematical model. This research represents a relevant advancement in the field, addressing the scarcity of work focused on this essential equipment crucial for saving lives.

Inhalation Injury, Respiratory Failure, and Ventilator Support in Acute Burn Care.

Sustaining an inhalation injury increases the risk of severe complications and mortality. Current evidential support to guide treatment of the injury or subsequent complications is lacking, as studies either exclude inhalation injury or design limit inferences that can be made. Conventional ventilator modes are most commonly used, but there is no consensus on optimal strategies. Settings should be customized to patient tolerance and response. Data for pharmacotherapy adjunctive treatments are limited.

Respiratory entrainment related reverse triggering in mechanically ventilated children.

BACKGROUND: The underlying pathophysiological pathways how reverse triggering is being caused are not fully understood. Respiratory entrainment may be one of these mechanisms, but both terms are used interchangeably. We sought to characterize reverse triggering and the relationship with respiratory entrainment among mechanically ventilated children with and without acute lung injury. METHODS: We performed a secondary phyiology analysis of two previously published data sets of invasively mechanically ventilated children < 18 years with and without lung injury mechanically ventilated in a continuous or intermittent mandatory ventilation mode. Ventilator waveforms, electrical activity of the diaphragm measured with surface electromyography and oesophageal tracings were analyzed for entrained and non-entrained reverse triggered breaths. RESULTS: In total 102 measurements (3110 min) from 67 patients (median age 4.9 [1.8 ; 19,1] months) were analyzed. Entrained RT was identified in 12 (12%) and non-entrained RT in 39 (38%) recordings. Breathing variability for entrained RT breaths was lower compared to non-entrained RT breaths. We did not observe breath stacking during entrained RT. Double triggering often occurred during non-entrained RT and led to an increased tidal volume. Patients with respiratory entrainment related RT had a shorter duration of MV and length of PICU stay. CONCLUSIONS: Reverse triggering is not one entity but a clinical spectrum with different mechanisms and consequences. TRIAL REGISTRATION: Not applicable.

Quantifiable identification of flow-limited ventilator dyssynchrony with the deformed lung ventilator model.

BACKGROUND: Ventilator dyssynchrony (VD) can worsen lung injury and is challenging to detect and quantify due to the complex variability in the dyssynchronous breaths. While machine learning (ML) approaches are useful for automating VD detection from the ventilator waveform data, scalable severity quantification and its association with pathogenesis and ventilator mechanics remain challenging. OBJECTIVE: We develop a systematic framework to quantify pathophysiological features observed in ventilator waveform signals such that they can be used to create feature-based severity stratification of VD breaths. METHODS: A mathematical model was developed to represent the pressure and volume waveforms of individual breaths in a feature-based parametric form. Model estimates of respiratory effort strength were used to assess the severity of flow-limited (FL)-VD breaths compared to normal breaths. A total of 93,007 breath waveforms from 13 patients were analyzed. RESULTS: A novel model-defined continuous severity marker was developed and used to estimate breath phenotypes of FL-VD breaths. The phenotypes had a predictive accuracy of over 97% with respect to the previously developed ML-VD identification algorithm. To understand the incidence of FL-VD breaths and their association with the patient state, these phenotypes were further successfully correlated with ventilator-measured parameters and electronic health records. CONCLUSION: This work provides a computational pipeline to identify and quantify the severity of FL-VD breaths and paves the way for a large-scale study of VD causes and effects. This approach has direct application to clinical practice and in meaningful knowledge extraction from the ventilator waveform data.

Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques.

BACKGROUND: Flow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery does not fully meet the patients' ventilatory demand due to an insufficient airflow and/or a high inspiratory effort, and it is usually identified by visual inspection of airway pressure waveform. Clinical diagnosis is cumbersome and prone to underdiagnosis, being an opportunity for artificial intelligence. Our objective is to develop a supervised artificial intelligence algorithm for identifying airway pressure deformation during square-flow assisted ventilation and patient-triggered breaths. METHODS: Multicenter, observational study. Adult critically ill patients under mechanical ventilation > 24 h on square-flow assisted ventilation were included. As the reference, 5 intensive care experts classified airway pressure deformation severity. Convolutional neural network and recurrent neural network models were trained and evaluated using accuracy, precision, recall and F1 score. In a subgroup of patients with esophageal pressure measurement (ΔPes), we analyzed the association between the intensity of the inspiratory effort and the airway pressure deformation. RESULTS: 6428 breaths from 28 patients were analyzed, 42% were classified as having normal-mild, 23% moderate, and 34% severe airway pressure deformation. The accuracy of recurrent neural network algorithm and convolutional neural network were 87.9% [87.6-88.3], and 86.8% [86.6-87.4], respectively. Double triggering appeared in 8.8% of breaths, always in the presence of severe airway pressure deformation. The subgroup analysis demonstrated that 74.4% of breaths classified as severe airway pressure deformation had a ΔPes > 10 cmH2O and 37.2% a ΔPes > 15 cmH2O. CONCLUSIONS: Recurrent neural network model appears excellent to identify airway pressure deformation due to flow starvation. It could be used as a real-time, 24-h bedside monitoring tool to minimize unrecognized periods of inappropriate patient-ventilator interaction.

Effects of hyperbaric oxygen combined cabin ventilator on critically ill patients with liberation difficulty after tracheostomy.

BACKGROUND: Critically ill patients undergoing liberation often encounter various physiological and clinical complexities and challenges. However, whether the combination of hyperbaric oxygen and in-cabin ventilator therapy could offer a comprehensive approach that may simultaneously address respiratory and potentially improve outcomes in this challenging patient population remain unclear. METHODS: This retrospective study involved 148 patients experiencing difficulty in liberation after tracheotomy. Inclusion criteria comprised ongoing mechanical ventilation need, lung inflammation on computed tomography (CT) scans, and Glasgow Coma Scale (GCS) scores of ≤ 9. Exclusion criteria excluded patients with active bleeding, untreated pneumothorax, cerebrospinal fluid leakage, and a heart rate below 50 beats per minute. Following exclusions, 111 cases were treated with hyperbaric oxygen combined cabin ventilator, of which 72 cases were successfully liberated (SL group) and 28 cases (NSL group) were not successfully liberated. The hyperbaric oxygen chamber group received pressurization to 0.20 MPa (2.0 ATA) for 20 min, followed by 60 min of ventilator oxygen inhalation. Successful liberation was determined by a strict process, including subjective and objective criteria, with a prolonged spontaneous breathing trial. GCS assessments were conducted to evaluate consciousness levels, with scores categorized as normal, mildly impaired, moderately impaired, or severely impaired. RESULTS: Patients who underwent treatment exhibited improved GCS, blood gas indicators, and cardiac function indexes. The improvement of GCS, partial pressure of oxygen (PaO2), oxygen saturation of blood (SaO2), oxygenation index (OI) in the SL group was significantly higher than that of the NSL group. However, there was no significant difference in the improvement of left ventricular ejection fraction (LVEF), left ventricular end-systolic volume (LVESV), left ventricular end-diastolic volume (LVEDV), and stroke volume (SV) between the SL group and the NSL group after treatment. CONCLUSIONS: Hyperbaric oxygen combined with in-cabin ventilator therapy effectively enhances respiratory function, cardiopulmonary function, and various indicators of critically ill patients with liberation difficulty after tracheostomy.

Autism and neurodevelopmental disability risks in children with tracheostomies and ventilators.

BACKGROUND/OBJECTIVE: Infants who survive prematurity and other critical illnesses and require continued invasive mechanical ventilation (IMV) postdischarge (at home) are at high risk of developmental delays and disabilities. Studies of extremely preterm cohorts (<28-week gestation) demonstrate rates of 25% for intellectual disability (ID) and 7% for autism spectrum disorder (ASD). Rates of ASD and ID in children with IMV are unknown. This study aimed to determine neurodevelopmental disability risk in a cohort of children with postdischarge IMV. DESIGN/METHODS: A consecutive series of children with IMV were assessed 1 month, 6 months, and 1 year after discharge. Cognitive, social, and communicative domains were assessed by a Developmental and Behavioral Pediatrician using (1) clinical adaptive test/clinical linguistic and auditory milestone scale (CAT/CLAMS) of the capute scales; (2) pediatric evaluation of disability inventory computer adaptive test (PEDI-CAT); and (3) modified checklist for autism in toddlers, revised (MCHAT-R). Red flag signs and symptoms of ASD using DSM-V criteria were noted. Longitudinal testing was reviewed. Expert consensus impressions of evolving ASD and/or ID were determined. RESULTS: Eighteen children were followed for 1 year; at 1 year, the median age (range) was 23 (17-42) months. Children were 44% male, 33% non-Hispanic White, 39% non-Hispanic Black, and 28% Hispanic. Fifteen (83%) children were prematurity survivors. Median (range) developmental quotients (DQs): full-scale DQ 59 (11-86), CAT DQ 66.5 (8-96), and CLAMS DQ 49.5 (13-100). Twelve (67%) children were highly suspicious for ASD and/or evolving ID. CONCLUSIONS/SIGNIFICANCE: This cohort of children with at-home IMV demonstrates a higher risk of ASD and ID than prior premature cohorts. Larger investigations with longer follow-up are needed.

Re-Evaluating Cross-Contamination: Additional Trials on Co-Ventilation.

BACKGROUND: Medical equipment can become scarce in disaster scenarios. Prior work has reported that four sheep could be ventilated together on a single ventilator. Others found that this maneuver is possible when needed, but no one has yet investigated whether cross-contamination occurs in co-ventilated individuals. OBJECTIVE: Our goal was to investigate whether an infection could spread between co-ventilated individuals. METHODS: Four 2-L anesthesia bags were connected to a sterilized ventilator circuit that used heat and moisture exchange filters and bacterial and viral filters, as would be expected in this dire scenario. Serratia marcescens was inoculated into "lung" no. 1. After running for 24 h, each lung and three additional points in the circuit were cultured to see whether S. marcescens had spread. These cultures were examined at 24 and 48 h to assess for cross-contamination. This entire procedure was performed three times. RESULTS: S. marcescens was not found in lung no. 2, 3, or 4 or the three additional sites on the expiratory limb at 24 and 48 h in all three trials. CONCLUSIONS: Cross-contamination does not occur within 24 h using the described ventilator circuit configuration.

Machine learning algorithm for ventilator mode selection, pressure and volume control.

Mechanical ventilation techniques are vital for preserving individuals with a serious condition lives in the prolonged hospitalization unit. Nevertheless, an imbalance amid the hospitalized people demands and the respiratory structure could cause to inconsistencies in the patient's inhalation. To tackle this problem, this study presents an Iterative Learning PID Controller (ILC-PID), a unique current cycle feedback type controller that helps in gaining the correct pressure and volume. The paper also offers a clear and complete examination of the primarily efficient neural approach for generating optimal inhalation strategies. Moreover, machine learning-based classifiers are used to evaluate the precision and performance of the ILC-PID controller. These classifiers able to forecast and choose the perfect type for various inhalation modes, eliminating the likelihood that patients will require mechanical ventilation. In pressure control, the suggested accurate neural categorization exhibited an average accuracy rate of 88.2% in continuous positive airway pressure (CPAP) mode and 91.7% in proportional assist ventilation (PAV) mode while comparing with the other classifiers like ensemble classifier has reduced accuracy rate of 69.5% in CPAP mode and also 71.7% in PAV mode. An average accuracy of 78.9% rate in other classifiers compared to neutral network in CPAP. The neural model had an typical range of 81.6% in CPAP mode and 84.59% in PAV mode for 20 cm H2O of volume created by the neural network classifier in the volume investigation. Compared to the other classifiers, an average of 72.17% was in CPAP mode, and 77.83% was in PAV mode in volume control. Different approaches, such as decision trees, optimizable Bayes trees, naive Bayes trees, nearest neighbour trees, and an ensemble of trees, were also evaluated regarding the accuracy by confusion matrix concept, training duration, specificity, sensitivity, and F1 score.

Effect of Ventilator Settings on Mechanical Power During Simulated Mechanical Ventilation of Patients With ARDS.

BACKGROUND: In recent years, mechanical power (MP) has emerged as an important concept that can significantly impact outcomes from mechanical ventilation. Several individual components of ventilatory support such as tidal volume (VT), breathing frequency, and PEEP have been shown to contribute to the extent of MP delivered from a mechanical ventilator to patients in respiratory distress/failure. The aim of this study was to identify which common individual setting of mechanical ventilation is more efficient in maintaining safe and protective levels of MP using different modes of ventilation in simulated subjects with ARDS. METHODS: We used an interactive mathematical model of ventilator output during volume control ventilation (VCV) with either constant inspiratory flow (VCV-CF) or descending ramp inspiratory flow, as well as pressure control ventilation (PCV). MP values were determined for simulated subjects with mild, moderate, and severe ARDS; and whenever MP > 17 J/min, VT, breathing frequency, or PEEP was manipulated independently to bring back MP to ≤ 17 J/min. Finally, the optimum VT-breathing frequency combinations for MP = 17 J/min were determined with all 3 modes of ventilation. RESULTS: VCV-CF always resulted in the lowest MPs while PCV resulted in highest MPs. Reductions in VT were the most efficient for maintaining safer and protective MP. At targeted MPs of 17 J/min and maximized minute ventilation, the optimum VT-breathing frequency combinations were 250-350 mL for VT and 32-35 breaths/min for breathing frequency in mild ARDS, 200-350 mL for VT and 34-40 breaths/min for breathing frequency in moderate ARDS, and 200-300 mL for VT and 37-45 breaths/min for breathing frequency for severe ARDS. CONCLUSIONS: VCV-CF resulted in the lowest MP. VT was the most efficient for maintaining safe and protective MP in a mathematical simulation of subjects with ARDS. In the context of maintaining low and safe MPs, ventilatory strategies with lower-than-normal VT and higher-than-normal breathing frequency will need to be implemented in patients with ARDS.

[A Review on Automatic Detection Algorithm for Patient-Ventilator Asynchrony during Mechanical Ventilation].

This study summarizes the application of automatic recognition technologies for patient-ventilator asynchrony (PVA) during mechanical ventilation. In the early stages, the method of setting rules and thresholds relied on manual interpretation of ventilator parameters and waveforms. While these methods were intuitive and easy to operate, they were relatively sensitive in threshold setting and rule selection and could not adapt well to minor changes in patient status. Subsequently, machine learning and deep learning technologies began to emerge and develop. These technologies automatically extract and learn data characteristics through algorithms, making PVA detection more robust and universal. Among them, logistic regression, support vector machines, random forest, hidden Markov models, convolutional autoencoders, long short-term memory networks, one-dimensional convolutional neural networks, etc., have all been successfully used for PVA recognition. Despite the significant advancements in feature extraction through deep learning methods, their demand for labelled data is high, potentially consuming significant medical resources. Therefore, the combination of reinforcement learning and self-supervised learning may be a viable solution. In addition, most algorithm validations are based on a single dataset, so the need for cross-dataset validation in the future will be an important and challenging direction for development.