Mechanical Ventilation: State of the Art

A micro-scale humanized ventilator-on-a-chip to examine the injurious effects of mechanical ventilation

Patients with compromised respiratory function frequently require mechanical ventilation to survive. Unfortunately, non-uniform ventilation of injured lungs generates complex mechanical forces that lead to ventilator induced lung injury (VILI). Although investigators have developed lung-on-a-chip systems to simulate normal respiration, modeling the complex mechanics of VILI as well as the subsequent recovery phase is a challenge. Here we present a novel humanized in vitro ventilator-on-a-chip (VOC) model of the lung microenvironment that simulates the different types of injurious forces generated in the lung during mechanical ventilation. We used transepithelial/endothelial electrical impedance measurements to investigate how individual and simultaneous application of mechanical forces alters real-time changes in barrier integrity during and after injury. We find that compressive stress (i.e. barotrauma) does not significantly alter barrier integrity while over-distention (20% cyclic radial strain, volutrauma) results in decreased barrier integrity that quickly recovers upon removal of mechanical stress. Conversely, surface tension forces generated during airway reopening (atelectrauma), result in a rapid loss of barrier integrity with a delayed recovery relative to volutrauma. Simultaneous application of cyclic stretching (volutrauma) and airway reopening (atelectrauma), indicates that the surface tension forces associated with reopening fluid-occluded lung regions are the primary driver of barrier disruption. Thus, our novel VOC system can monitor the effects of different types of injurious forces on barrier disruption and recovery in real-time and can be used to interogate the biomechanical mechanisms of VILI.

Machine learning-based risk prediction model construction of difficult weaning in ICU patients with mechanical ventilation

In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of difficult weaning contributes to increased ventilator-related complications, prolonged hospitalization duration, and a significant rise in healthcare costs. Therefore, early identification of influencing factors and prediction of patients at risk of difficult weaning can facilitate early intervention and preventive measures. This study aimed to strengthen airway management for ICU patients by constructing a risk prediction model with comprehensive and individualized offline programs based on machine learning techniques. This study involved the collection of data from 487 patients undergoing MV in the ICU, with a total of 36 variables recorded. The dataset was divided into a training set (70% of the data) and a test set (30% of the data). Five machine learning models, namely logistic regression, random forest, support vector machine, light gradient boosting machine, and extreme gradient boosting, were compared to predict the risk of difficult weaning in ICU patients with MV. Significant influencing factors were identified based on the results of these models, and a risk prediction model for ICU patients with MV was established. When evaluating the models using AUC (Area under the Curve of ROC) and Accuracy as performance metrics, the Random Forest algorithm exhibited the best performance among the five machine learning algorithms. The area under the operating characteristic curve for the subjects was 0.805, with an accuracy of 0.748, recall (0.888), specificity (0.767) and F1 score (0.825). This study successfully developed a risk prediction model for ICU patients with MV using a machine learning algorithm. The Random Forest algorithm demonstrated the highest prediction performance. These findings can assist clinicians in accurately assessing the risk of difficult weaning in patients and formulating effective individualized treatment plans. Ultimately, this can help reduce the risk of difficult weaning and improve the quality of life for patients.

Comparative analysis of open and closed tracheal suction systems on mechanical ventilation efficiency in adults: A systematic review and meta-analysis

BACKGROUND

There are two types of suction methods used clinically: closed tracheal suction system (CTSS) and open tracheal suction system (OTSS). However, the safety and efficacy of these two suction systems for patients remain to be analysed.

OBJECTIVE

The objective of this study was to assess the safety and efficacy of OTSSs and CTSSs in adult mechanical ventilation.

METHODS

Computer searches were conducted on PubMed, Web of science, MEDLINE, CINAHL, and Cochrane Library databases. The search spanned from the inception of each database to December 2023. Two researchers independently reviewed and extracted data from the literature. Quality assessment was performed using the Cochrane Manual of Systematic Reviews, and meta-analysis was conducted using RevMan 5.3 software.

RESULTS

A total of 13 articles, involving 2822 patients, were included in the analysis. The meta-analysis showed that the CTSS could reduce the incidence of ventilator-associated pneumonia (odds ratio [OR] = 0.77, 95% confidence interval [CI]: [0.61, 0.98], P = 0.03). However, no significant difference existed in the microbial colonisation rate in the respiratory tract (OR = 1.40, 95% CI: [0.91, 2.15], P = 0.13), mechanical ventilation time (mean difference = -0.33, 95% CI: [-1.43, 0.78], P = 0.56), length of intensive care unit stay (mean difference = 0.23, 95% CI: [-0.90, 1.35], P = 0.69), and mortality (OR = 1.01, 95% CI: [0.84, 1.22], P = 0.89).

CONCLUSION

In comparison to the OTSS, the CTSS proves effective in reducing the incidence of ventilator-associated pneumonia. However, additional high-quality evidence is needed to evaluate respiratory microbial colonisation rates, the duration of mechanical ventilation, length of intensive care unit stay, and mortality.

Physicians' decision making when weaning patients from mechanical ventilation: A qualitative content analysis

BACKGROUND

Weaning from mechanical ventilation is a complex and central intensive care process. This complexity indicates that the challenges of weaning must be explored from different perspectives. Furthermore, physicians' experiences and the factors influencing their decision-making regarding weaning are unclear.

OBJECTIVES

This study aimed to explore and describe the factors influencing physicians' decision-making when weaning patients from invasive mechanical ventilation in Swedish intensive care units (ICUs).

METHODS

This qualitative study used an exploratory and descriptive design with qualitative content analysis. Sixteen physicians from five ICUs across Sweden were purposively included and interviewed regarding their weaning experiences.

FINDINGS

The physicians expressed that prioritising the patient's well-being was evident, and there was agreement that both the physical and mental condition of the patient had a substantial impact on decision-making. Furthermore, there was a lack of agreement on whether patients should be involved in the weaning process and how their resources, needs, and wishes should be included in decision-making. In addition, there were factors not directly linked to the patient but which still influenced decision-making, such as the available resources and teamwork. Sometimes, it was difficult to point out the basis for decisions; in that decisions were made by gut feeling, intuition, or clinical experience.

CONCLUSION

Physicians' decision-making regarding weaning was a dynamic process influenced by several factors. These factors were related to the patient's condition and the structure for weaning. Increased understanding of weaning from the physicians' and ICU teams' perspectives may improve the weaning process by broadening the knowledge about the aspects influencing the decision-making.

Factors that influence critical care nurses' management of sedation for ventilated patients in critical care: A qualitative study

BACKGROUND

Optimising sedation use is key to timely extubation. Whilst sedation protocols may be used to guide critical care nurses' management of sedation, sedation management and decision-making is complex, influenced by multiple factors related to patients' circumstances, intensive care unit design and the workforce.

AIM

To explore (i) critical care nurses' experiences managing sedation in mechanically ventilated patients and (ii) the factors that influence their sedation-related decision-making.

DESIGN

Qualitative descriptive study using semi-structured interviews. Data were analysed using Braun and Clarke's six-step thematic analysis.

SETTING AND PARTICIPANTS

This study was conducted in a 26-bed level 3 accredited ICU, in a private hospital in Melbourne, Australia. The majority of patients are admitted following elective surgery. Critical care nurses, who were permanently employed as a registered nurse, worked at least 16 h per week, and cared for ventilated patients, were invited to participate.

FINDINGS

Thirteen critical care nurses participated. Initially, participants suggested their experiences managing sedation were linked to local unit policy and learning. Further exploration revealed that experiences were synonymous with descriptors of factors influencing sedation decision-making according to three themes: (i) Learning from past experiences, (ii) Situational awareness and (iii) Prioritising safety. Nurses relied on their cumulative knowledge from prior experiences to guide decision-making. Situational awareness about other emergent priorities in the unit, staffing and skill-mix were important factors in guiding sedation decision-making. Safety of patients and staff was essential, at times overriding goals to reduce sedation.

CONCLUSION

Sedation decision making cannot be considered in isolation. Rather, sedation decision making must take into account outcomes of patient assessment, emergent priorities, unit and staffing factors and safety concerns.

IMPLICATIONS FOR CLINICAL PRACTICE

Opportunities for ongoing education are essential to promote nurses' situational awareness of other emergent unit priorities, staffing and skill-mix, in addition to evidence-based sedation management and decision making.

Driving pressure decoded: Precision strategies in adult respiratory distress syndrome management

Mechanical ventilation (MV) is an important strategy for improving the survival of patients with respiratory failure. However, MV is associated with aggravation of lung injury, with ventilator-induced lung injury (VILI) becoming a major concern. Thus, ventilation protection strategies have been developed to minimize complications from MV, with the goal of relieving excessive breathing workload, improving gas exchange, and minimizing VILI. By opting for lower tidal volumes, clinicians seek to strike a balance between providing adequate ventilation to support gas exchange and preventing overdistension of the alveoli, which can contribute to lung injury. Additionally, other factors play a role in optimizing lung protection during MV, including adequate positive end-expiratory pressure levels, to maintain alveolar recruitment and prevent atelectasis as well as careful consideration of plateau pressures to avoid excessive stress on the lung parenchyma.

A risk prediction model for efficient intubation in the emergency department: A 4-year single-center retrospective analysis

Objective

To analyze the risk factors associated with intubated critically ill patients in the emergency department (ED) and develop a prediction model by machine learning algorithms.

Methods

This study was conducted in an academic tertiary hospital in Hangzhou, China. Critically ill patients admitted to the ED were retrospectively analyzed from May 2018 to July 2022. The demographic characteristics, distribution of organ dysfunction, parameters for different organs' examination, and status of mechanical ventilation were recorded. These patients were assigned to the intubation and non-intubation groups according to ventilation support. We used the eXtreme Gradient Boosting (XGBoost) algorithm to develop the prediction model and compared it with other algorithms, such as logistic regression, artificial neural network, and random forest. SHapley Additive exPlanations was used to analyze the risk factors of intubated critically ill patients in the ED.

Results

Of 14,589 critically ill patients, 10,212 comprised the training group and 4377 comprised the test group; 2289 intubated patients were obtained from the electronic medical records. The mean age, mean scores of vital signs, parameters of different organs, and blood oxygen examination results differed significantly between the two groups (p < 0.05). The white blood cell count, international normalized ratio, respiratory rate, and pH are the top four risk factors for intubation in critically ill patients. Based on the risk factors in different predictive models, the XGBoost model showed the highest area under the receiver operating characteristic curve (0.84) for predicting ED intubation.

Conclusions

For critically ill patients in the ED, the proposed model can predict potential intubation based on the risk factors in the clinically predictive model.

Machine Learning-Based Model for Predicting Prolonged Mechanical Ventilation in Patients with Congestive Heart Failure

BACKGROUND

Mechanical ventilation (MV) is widely used to relieve respiratory failure in patients with congestive heart failure (CHF). Prolonged MV (PMV) is associated with a poor prognosis. We aimed to establish a prediction model based on machine learning (ML) algorithms for the early identification of patients with CHF requiring PMV.

METHODS

Twelve commonly used ML algorithms were used to build the prediction model. The least absolute shrinkage and selection operator (LASSO) regression was employed to select the key features. We examined the area under the curve (AUC) statistics to evaluate the prediction performance. Data from another database were used to conduct external validation.

RESULTS

We screened out 10 key features from the initial 65 variables via LASSO regression to improve the practicability of the model. The CatBoost model showed the best performance for predicting PMV among the 12 commonly used ML algorithms, with favorable discrimination (AUC = 0.790) and calibration (Brier score = 0.154). Moreover, hospital mortality could be accurately predicted using the CatBoost model as well (AUC = 0.844). In the external validation, the CatBoost model also showed satisfactory prediction performance (AUC = 0.780), suggesting certain generalizability of the model. Finally, a nomogram with risk classification of PMV was shown in this study.

CONCLUSION

The present study developed and validated a CatBoost model, which could accurately predict PMV in mechanically ventilated patients with CHF. Moreover, this model has a favorable performance in predicting hospital mortality in these patients.

A Predictive Model for Prolonged Mechanical Ventilation After Triple-Branched Stent Graft for Acute Type A Aortic Dissection

INTRODUCTION

The aim of this study is to develop a model for predicting the risk of prolonged mechanical ventilation (PMV) following surgical repair of acute type A aortic dissection (AAAD).

METHODS

We retrospectively collected clinical data from 381 patients with AAAD who underwent emergency surgery. Clinical features variables for predicting postoperative PMV were selected through univariate analysis, least absolute shrinkage and selection operator regression analysis, and multivariate logistic regression analysis. A risk prediction model was established using a nomogram. The model's accuracy and reliability were evaluated using the area under the curve of the receiver operating characteristic curve and the calibration curve. Internal validation of the model was performed using bootstrap resampling. The clinical applicability of the model was assessed using decision curve analysis and clinical impact curve.

RESULTS

Among the 381 patients, 199 patients (52.2%) experienced postoperative PMV. The predictive model exhibited good discriminative ability (area under the curve = 0.827, 95% confidence interval: 0.786-0.868, P < 0.05). The calibration curve confirmed that the predicted outcomes of the model closely approximated the ideal curve, indicating agreement between the predicted and actual results (with an average absolute error of 0.01 based on 1000 bootstrap resampling). The decision curve analysis curve demonstrated that the model has significant clinical value.

CONCLUSIONS

The nomogram model established in this study can be used to predict the risk of postoperative PMV in patients with AAAD. It serves as a practical tool to assist clinicians in adjusting treatment strategies promptly and implementing targeted therapeutic measures.

Laryngeal microsurgery under Transnasal Humidified Rapid Insufflation Ventilatory Exchange

Objective

Since 2015, Transnasal Humidified Rapid Insufflation Ventilatory Exchange (THRIVE) has been used in general anesthesia for preoxygenation or difficult exposure airway management. Its use offers new opportunities in laryngology. THRIVE increases apnea time and frees the access to the upper airway. However, its use may be less stable than orotracheal intubation. The main objective of this work was to evaluate the feasibility of laryngeal microsurgery under THRIVE including using Laser.

Study Design

Retrospective.

Setting

A total of N = 99 patients with laryngeal microsurgery (with or without CO2 laser) under THRIVE were included successively from January 1, 2020 to January 30, 2022.

Method

Medical history, comorbidities, clinical and surgical data were extracted and analyzed. Two groups were constituted regarding the "success" (use of THRIVE along all the procedure) or the "failure" (need for an endotracheal tube) of the use of THRIVE during the procedure.

Results

A failure occurred in N = 15/99 patients (15.2%) mainly due to refractory hypoxia. The odd ratios (OR) for THRIVE failure were: OR = 6.6 [2.9-35] for overweight (BMI >25 kg/m2); OR = 3.8 [1.7-18.7] for ASA score >2; OR = 4.7 [2.3-24.7] for the use of CO2 laser. Elderly patients and patients with pulmonary pathology were not statistically at greater risk of THRIVE failure. No adverse event was described.

Conclusion

This work confirms the feasibility of laryngeal microsurgery under THRIVE, including with CO2 laser. Overweight, ASA >2 and lower fraction of inspired oxygen during CO2 laser use increased the risk for orotracheal intubation.

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.

A Micro-scale Humanized Ventilator-on-a-Chip to Examine the Injurious Effects of Mechanical Ventilation

Patients with compromised respiratory function frequently require mechanical ventilation to survive. Unfortunately, non-uniform ventilation of injured lungs generates complex mechanical forces that lead to ventilator induced lung injury (VILI). Although investigators have developed lung-on-a-chip systems to simulate normal respiration, modeling the complex mechanics of VILI as well as the subsequent recovery phase is a challenge. Here we present a novel humanized in vitro ventilator-on-a-chip (VOC) model of the lung microenvironment that simulates the different types of injurious forces generated in the lung during mechanical ventilation. We used transepithelial/endothelial electrical resistance (TEER) measurements to investigate how individual and simultaneous application of the different mechanical forces alters real-time changes in barrier integrity during and after injury. We find that compressive stress (i.e. barotrauma) does not significantly alter barrier integrity while over-distention (20% cyclic radial strain, volutrauma) results in decreased barrier integrity that quickly recovers upon removal of mechanical stress. Conversely, surface tension forces generated during airway reopening (atelectrauma), result in a rapid loss of barrier integrity with a delayed recovery relative to volutrauma. Simultaneous application of cyclic stretching (volutrauma) and airway reopening (atelectrauma), indicate that the surface tension forces associated with reopening fluid-occluded lung regions is the primary driver of barrier disruption. Thus, our novel VOC system can monitor the effects of different types of injurious forces on barrier disruption and recovery in real-time and can be used to identify the biomechanical mechanisms of VILI.

Obstructive respiratory disease simulation device

Respiratory disease is a major contributor to healthcare costs, as well as increasing morbidity and early mortality. The device presented is used to simulate the effects of Chronic Obstructive Pulmonary Disease (COPD) in healthy people. The intended use is to provide data equivalent to COPD data measured from those who are ill for initial validation of respiratory mechanics models. It would thus eliminate the need to test unhealthy and/or fragile subjects, or the need for invasive or costly equipment based test methods. The device is used in conjunction with an open-access venturi-based flow sensor, to measure pressure, flow, and breath tidal volume. The device simulates the pressure and flow profiles of a person who has COPD including the non-linear increased resistance to end-exhalation and gas trapping. To achieve this non-linearity, a combination of high and low resistance outlets is used. Thus, the simulator allows the collection of patient-specific COPD-like breathing data in a non-invasive manner from healthy subjects. The device is low-cost with the majority of the parts 3D printed using a Prusa mini 3D printer and PLA filament.

Effect of Dexmedetomidine on Oxygen and Intrapulmonary Shunt (Qs/Qt) During One-Lung Ventilation in Pediatric Surgery: A Randomized Controlled Trial

Background One-lung ventilation (OLV) is a common ventilation technique used during thoracic surgery. It can cause serious complications in children, and hypoxic pulmonary vasoconstriction (HPV) is a protective mechanism against the resulting hypoxia. Dexmedetomidine does not affect HPV, so we will investigate its impact on the partial pressure of oxygen in arterial blood (PaO2) and pulmonary shunt fraction (Qs/Qt). Methods Children who underwent OLV were divided into two equal groups. The Dex group received 0.4 μg/kg/h of dexmedetomidine intravenously. The placebo group received normal saline. Two blood samples were taken to analyze arterial and central venous blood gasses during four time periods: T1, 10 minutes after anesthesia; T2, 10 minutes after OLV; T3, 60 minutes after OLV; and T4, 20 minutes after the end of OLV. Heart rate, mean arterial pressure (MAP), PaO2, Qs/Qt, and peak inspiratory pressure (PIP) values were recorded at these time points. Results Regarding heart rate, the Dex group remained relatively stable, whereas the placebo group showed a slight increase in T3 and T4. Concerning MAP, the Dex group had a reduction at T1 compared with the placebo group and remained similar for other points. PaO2 decreased with OLV. However, the Dex group consistently maintained higher PaO2 values than the placebo, especially in T3 and T4. Concerning Qs/Qt, the Dex group maintained lower time values than the placebo group at OLV. Regarding PIP, the Dex group had significantly lower T2 and T3 than the placebo group. Conclusion Administration of dexmedetomidine in children with OLV improves PaO2 and reduces pulmonary shunt fraction (Qs/Qt), thereby improving oxygen transport. It reduces the maximum PIP values, thereby reducing pressure-related complications.

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.

Predictive values of ultrasonic diaphragm thickening fraction combined with integrative weaning index in weaning patients with mechanical ventilation: a retrospective study

OBJECTIVE

We aimed to explore the predictive values of ultrasonic diaphragm thickening fraction (DTF) combined with integrative weaning index (IWI) in weaning patients with mechanical ventilation.

METHODS

Patients with mechanical ventilation who received oral endotracheal intubation from September 2020 to September 2021 were included in this retrospective study. Before the start of the spontaneous breathing test (SBT), IWI was calculated according to the blood gas analysis parameters and parameters read in volume control mode. After the start of SBT, DTF was calculated according to the end-expiratory thickness and end-inspiratory thickness of the right diaphragm. The receiver operating curve (ROC) was used to evaluate the predictive value of DTF and IWI for successful weaning, and the sensitivity and specificity were calculated according to the best critical value.

RESULTS

The sensitivity, specificity, and best cutoff value of DTF to predict successful weaning was 0.772, 0.727, and 0.293, respectively, and the area under the curve (AUC) was 0.72 (95%CI 0.59-0.86, p = 0.003). The sensitivity, specificity, and best cutoff value of IWI to predict successful weaning was 0.614, 0.909, 53.00, respectively, and AUC was 0.82 (95%CI 0.72-0.91, p < 0.001). The sensitivity, specificity, and best cutoff value of the combination of DTF and IWI to predict successful weaning was 0.614, 0.909, 17.848, respectively, and AUC was 0.84 (95%CI 0.75-0.93, p < 0.001).

CONCLUSION

DTF and IWI can guide the selection of weaning, while DTF combined with IWI can improve the effect of weaning prediction and provide support for patients' weaning safety.

Mechanical Ventilation, Past, Present, and Future

Mechanical ventilation (MV) has played a crucial role in the medical field, particularly in anesthesia and in critical care medicine (CCM) settings. MV has evolved significantly since its inception over 70 years ago and the future promises even more advanced technology. In the past, ventilation was provided manually, intermittently, and it was primarily used for resuscitation or as a last resort for patients with severe respiratory or cardiovascular failure. The earliest MV machines for prolonged ventilatory support and oxygenation were large and cumbersome. They required a significant amount of skills and expertise to operate. These early devices had limited capabilities, battery, power, safety features, alarms, and therefore these often caused harm to patients. Moreover, the physiology of MV was modified when mechanical ventilators moved from negative pressure to positive pressure mechanisms. Monitoring systems were also very limited and therefore the risks related to MV support were difficult to quantify, predict and timely detect for individual patients who were necessarily young with few comorbidities. Technology and devices designed to use tracheostomies versus endotracheal intubation evolved in the last century too and these are currently much more reliable. In the present, positive pressure MV is more sophisticated and widely used for extensive period of time. Modern ventilators use mostly positive pressure systems and are much smaller, more portable than their predecessors, and they are much easier to operate. They can also be programmed to provide different levels of support based on evolving physiological concepts allowing lung-protective ventilation. Monitoring systems are more sophisticated and knowledge related to the physiology of MV is improved. Patients are also more complex and elderly compared to the past. MV experts are informed about risks related to prolonged or aggressive ventilation modalities and settings. One of the most significant advances in MV has been protective lung ventilation, diaphragm protective ventilation including noninvasive ventilation (NIV). Health care professionals are familiar with the use of MV and in many countries, respiratory therapists have been trained for the exclusive purpose of providing safe and professional respiratory support to critically ill patients. Analgo-sedation drugs and techniques are improved, and more sedative drugs are available and this has an impact on recovery, weaning, and overall patients' outcome. Looking toward the future, MV is likely to continue to evolve and improve alongside monitoring techniques and sedatives. There is increasing precision in monitoring global "patient-ventilator" interactions: structure and analysis (asynchrony, desynchrony, etc). One area of development is the use of artificial intelligence (AI) in ventilator technology. AI can be used to monitor patients in real-time, and it can predict when a patient is likely to experience respiratory distress. This allows medical professionals to intervene before a crisis occurs, improving patient outcomes and reducing the need for emergency intervention. This specific area of development is intended as "personalized ventilation." It involves tailoring the ventilator settings to the individual patient, based on their physiology and the specific condition they are being treated for. This approach has the potential to improve patient outcomes by optimizing ventilation and reducing the risk of harm. In conclusion, MV has come a long way since its inception, and it continues to play a critical role in anesthesia and in CCM settings. Advances in technology have made MV safer, more effective, affordable, and more widely available. As technology continues to improve, more advanced and personalized MV will become available, leading to better patients' outcomes and quality of life for those in need.

4-octyl itaconate ameliorates ventilator-induced lung injury

Ventilator-induced lung injury (VILI) disturbs the disordered immune system and causes persistent inflammatory damage. 4-octyl itaconate (OI) is a synthetic cell-permeable itaconate derivative with antioxidant and anti-inflammatory effects. In this study, we assessed whether OI protects against VILI. OI was intraperitoneally injected for three days before mechanical ventilation (MV; 20 ml/kg at 70 breaths/min) for 2 h. Mouse lung vascular endothelial cells (MLVECs) were pretreated with OI (62.5, 125, and 250 μM) prior to cyclic stretch for 4 h. We found that OI attenuated VILI and inflammatory response. OI also increased superoxide dismutase, nuclear factor E2-related factor 2, and heme oxygenase-1 levels, and decreased reactive oxygen species and malondialdehyde levels. Furthermore, OI inhibited the expression of NLR family pyrin domain-containing 3 (NLRP3), caspase-1 p20, apoptosis-associated speck-like protein containing a CARD, and N-terminal fragment of gasdermin D. Therefore, OI attenuates VILI, potentially by suppressing oxidative stress and NLRP3 activation.

Self-regulated learning microanalysis feedback for developing clinical skills: A pilot study

INTRODUCTION

The development of clinical skills requires the appropriate use of self-regulated learning (SRL). Students' use of key SRL processes as they perform a clinical skill can be identified by SRL microanalysis and used to provide feedback. SRL-microanalysis feedback only on students' key SRL processes has not been previously researched for developing clinical skills. The aim of this study was to investigate whether SRL-microanalysis feedback only on students' key SRL processes can improve both their use of SRL and their clinical skill performance.

METHODS

Twenty-three final year medical students with no experience in the clinical skill required for mechanical ventilation participated in this study. Key SRL processes and clinical skill performance were measured before and after SRL microanalysis feedback.

RESULTS

Overall, we found an improvement in the key SRL processes of planning and monitoring of performance, with a significant difference in monitoring. We also found an increase in students' clinical skill performance.

DISCUSSION

This study, which is the first in clinical skills, demonstrated that SRL microanalysis feedback only on key SRL processes can improve both students' SRL and their clinical skill performance. studies are recommended with a great number of students and across a variety of clinical skills.

A physiological model of phrenic nerve excitation by electrical stimulation

Mechanical ventilation is essential in intensive care treatment but leads to diaphragmatic atrophy, which in turn contributes to prolonged weaning and increased mortality. One approach to prevent diaphragmatic atrophy while achieving pulmonary ventilation is electrical stimulation of the phrenic nerve. To automize phrenic nerve stimulation resulting in lung protective tidal volumes with lowest possible currents, mathematical models are required. Nerve stimulation models are often complex, so many parameters have to be identified prior to implementation. This paper presents a novel, simplified approach to model phrenic nerve excitation to obtain an individualized patient model using a few data points. The latter is based on the idea that nerve fibers are excited when the electric field exceeds a threshold. The effect of the geometry parameter on the model output was analyzed, and the model was validated with measurement data from a pig trial (RMSE in between 0.44 × 10-2and 1.64 × 10-2for parameterized models). The modeled phrenic nerve excitation behaved similarly to the measured tidal volumes, and thus could be used to develop automated phrenic nerve stimulation systems for lung protective ventilation.

Development and validation of a nomogram for predicting mechanical ventilation need among acutely intoxicated patients with impaired consciousness

BACKGROUND

A considerable portion of acutely intoxicated patients is presented with impaired consciousness. Early identification of those patients who require advanced medical care, such as mechanical ventilation (MV), can improve their prognosis.

METHODS

This study included 330 acutely intoxicated patients who were presented with impaired consciousness and admitted to Tanta University Poison Control Center, Egypt, in the period from January 2021 to December 2023. Patients were enrolled in derivation (257 patients) and validation (73 patients) cohorts. Patients' data were analyzed to develop and validate a predictive nomogram to determine the probability of MV need in acutely intoxicated patients.

RESULTS

Significant predictors for MV need were mean arterial blood pressure (OR = 0.96, p = .014), PaO2 (OR = 0.96, p = .001), pH (OR = 0.00, p < . 001), and glucose/potassium ratio (OR = 1.59, p = .030). These four parameters were used to formulate a bedside nomogram. Receiver-operating characteristic (ROC) analysis for the proposed nomogram shows that area under the curve (AUC) = 95.7%, accuracy = 93.4%, sensitivity = 88.9%, and specificity = 95.1%. The internal validation for the developed nomogram was assessed using a bootstrapping method and calibration curve. Regarding external validation, AUCs for the developed nomogram probability was 96.5%, and for predicted probability using the developed nomogram was 97.8%.

CONCLUSION

The current study provides a validated nomogram that could be used as a reliable tool for the accurate prediction of MV need among acutely intoxicated patients with impaired consciousness. It could assist in the early identification of patients who will require MV, especially in low-income countries with limited resources.

Early application of awake extracorporeal membrane oxygenation in pneumocystis jirovecii pneumonia complicated with severe acute respiratory distress syndrome: a case report

Introduction

Patients suffering from severe acute respiratory distress syndrome (ARDS) are usually treated with mechanical ventilation. Extracorporeal membrane oxygenation (ECMO) has traditionally been considered a life-saving therapy and was reserved as a last resort when other treatment options were exhausted. However, this report outlines our successful initial experience with early implementation of awake venovenous extracorporeal membrane oxygenation (VV-ECMO) in a case of pneumocystis jirovecii pneumonia complicated by severe acute respiratory distress syndrome (ARDS), offering a promising new approach for recovery.

Case presentation

We present a case report of the effective application of awake VV-ECMO in a 29 years-old man with severe ARDS caused by pneumocystis jirovecii pneumonia. The patient initially received antibiotic treatment and non-invasive ventilation (NIV) for respiratory distress, but these interventions failed to improve the worsening dyspnea that occurred in the patient. Following the combined antifungal therapy, high-flow nasal cannula (HFNC) oxygen therapy, and VV-ECMO for a duration of 7 days, the patient's symptoms improved, showing relief.

Conclusion

Awake VV-ECMO proved to be an effective treatment for critically ill patients with ARDS, avoiding the need for invasive mechanical ventilation. However, increased clinical evidence is needed to verify whether awake ECMO could be widely used in severe ARDS caused by other diseases or conditions.

COVID-19-associated lung weakness (CALW): Systematic review and meta-analysis

OBJECTIVES

To assess mortality and different clinical factors derived from the development of atraumatic pneumothorax (PNX) and/or pneumomediastinum (PNMD) in critically ill patients as a consequence of COVID-19-associated lung weakness (CALW).

DESIGN

Systematic review with meta-analysis.

SETTING

Intensive Care Unit (ICU).

PARTICIPANTS

Original research evaluating patients, with or without the need for protective invasive mechanical ventilation (IMV), with a diagnosis of COVID-19, who developed atraumatic PNX or PNMD on admission or during hospital stay.

INTERVENTIONS

Data of interest were obtained from each article and analyzed and assessed by the Newcastle-Ottawa Scale. The risk of the variables of interest was assessed with data derived from studies including patients who developed atraumatic PNX or PNMD.

MAIN VARIABLES OF INTEREST

Mortality, mean ICU stay and mean PaO2/FiO2 at diagnosis.

RESULTS

Information was collected from 12 longitudinal studies. Data from a total of 4901 patients were included in the meta-analysis. A total of 1629 patients had an episode of atraumatic PNX and 253 patients had an episode of atraumatic PNMD. Despite the finding of significantly strong associations, the great heterogeneity between studies implies that the interpretation of results should be made with caution.

CONCLUSIONS

Mortality among COVID-19 patients was higher in those who developed atraumatic PNX and/or PNMD compared to those who did not. The mean PaO2/FiO2 index was lower in patients who developed atraumatic PNX and/or PNMD. We propose grouping these cases under the term COVID-19-associated lung weakness (CALW).

Comparison of the timed inspiratory effort index with the T-piece trial as a decision-making tool for extubation: a randomized controlled non-inferiority trial

The "timed inspiratory effort" (TIE) index, a new predictor of weaning outcome, normalizes the maximal inspiratory pressure with the time required to reach this value up to 60 s, incorporating the time domain into the assessment of inspiratory muscle function. The objective of this study was to determine whether the TIE predicts successful extubation at a similar rate as the T-piece trial with less time required. A non-inferiority randomized controlled trial was performed with ICU subjects eligible for weaning. The participants were allocated to the TIE or the T-piece groups. The primary outcome was successful weaning, and the main secondary outcome was ICU mortality. Eighty participants of each group were included in the final analysis. Time from the start of a successful test to effective extubation was significantly lower in the TIE group than in the T-piece group, 15 (10 to 24) vs 55 (40 to 75) min, P<0.001. In the Kaplan-Meier analysis, no significant differences were found in successful weaning (79.5 vs 82.5%, P=0.268) or survival rate (62.9 vs 53.8%, P=0.210) between the TIE and T-piece groups at the 30th day. In this preliminary study, the TIE index was not inferior to the T-piece trial as a decision-making tool for extubation and allowed a reduction in the decision time.

Fibrotic extracellular vesicles contribute to mechanical ventilation-induced pulmonary fibrosis development by activating lung fibroblasts via JNK signalling pathway: an experimental study

Recent research has revealed that mechanical ventilation (MV) could initiate ventilator-induced lung injury along with the initiation of the process of pulmonary fibrosis (PF), leading to MV-induced PF (MVPF). However, the underlying mechanism remains unclear. This study aimed to explore the role of MV-induced extracellular vesicles (MV-EVs) and the c-Jun N-terminal kinase (JNK) signalling pathway in the pathogenesis of MVPF in vivo and in vitro. The process of MV is accompanied by the secretion of MV-EVs, which could induce lung fibroblast activation. Furthermore, single-cell RNA-sequencing analysis revealed that the JNK pathway in lung fibroblasts was activated after MV initiation. Inhibiting the JNK pathway could both restrain MV-EV-induced lung fibroblast activation in vitro or reduce the severity of MVPF in vivo. In conclusion, this study demonstrated that MV-EVs contribute to MVPF progression by activating lung fibroblasts via the JNK signalling pathway and that inhibiting the secretion of EV and the activation of the JNK signalling pathway is a promising strategy for treating MVPF.

Analysis of risk factors of prolonged mechanical ventilation in patients with severe burn injury

BACKGROUND

Mechanical ventilation is an essential means of life support for patients with severe burns. However, prolonged mechanical ventilation (PMV) increases the incidence of complications and length of hospital stay. Therefore, studying the risk factors of mechanical ventilation duration is of great significance for reducing the duration of mechanical ventilation, reducing related complications, and improving the success rate of severe burn treatment.

METHOD

This study was a retrospective study of patients with burns ≥30% of the area admitted to the BICU of Guangzhou Red Cross Hospital affiliated with Jinan University from January 2016 to January 2023 who were mechanically ventilated. Patients were classified into the prolonged mechanical ventilation group if they were mechanically ventilated for ≥21 days. Then, independent risk factors for prolonged mechanical ventilation were determined by logistic regression analysis of the collected data.

RESULT

Of all the 112 enrolled patients, 79 had prolonged mechanical ventilation, with an incidence of 70.5%. Logistic regression analysis revealed that including abbreviated burn severity index (ABSI%) (P < 0.001), moderate and severe inhalation injury (P = 0.005, P = 0.044), albumin (P = 0.032), lactic acid (P < 0.001) were independent risk factors for prolonged mechanical ventilation. In addition, ventilator-related complications were 44% in the PMV group and 21% in the non-PMV group.

CONCLUSION

ABSI%, inhalation injury, albumin, and lactic acid on admission are the risk factors for PMV in severe burn patients. In addition, ventilator-related complications were higher in group PMV than in group non-PMV in our study.

Oral candidiasis in patients hospitalised in the intensive care unit: Diagnosis through clinical and cytopathological examinations

OBJECTIVE

To evaluate the prevalence and clinical aspects of oral candidiasis in patients hospitalised in the intensive care unit.

METHODS

This is a longitudinal and prospective study that included 48 participants hospitalised in the intensive care unit. Sociodemographic data, presence of systemic disorders, use of medications, laboratory tests, cause of hospital admission, type of breathing, and length of hospital stay were obtained from medical records. Oral clinical inspection and cytopathological examinations were performed on all participants. The diagnosis of clinical candidiasis was based on the presence of clinical alterations together with positive cytopathological examination results. The diagnosis of subclinical candidiasis was based on the absence of clinical lesions and a positive cytopathological examination. The absence of oral candidiasis was considered when the participant did not present oral lesions and had a negative cytopathological examination.

RESULTS

Clinical candidiasis was present in 18.8% of the 48 participants, and 45.8% of them had the subclinical form. Levels of urea (P = 0.005), creatinine (P = 0.009), haemoglobin (P = 0.009), haematocrit (P = 0.011), bands (P = 0.024), international normalised ratio (INR; P = 0.034), types of breathing (P = 0.017), length of hospital stay (P = 0.037), and outcome (P = 0.014) demonstrated statistically significant differences between the groups with and without oral candidiasis.

CONCLUSIONS

Clinical and subclinical forms of oral candidiasis are frequent in intensive care unit patients. Levels of urea, creatinine, haemoglobin, haematocrit, bands, INR, type of breathing, length of hospital stay, and outcome can be associated with the presence of candidiasis.

Accuracy of the estimations of respiratory mechanics using an expiratory time constant in passive and active breathing conditions: a bench study

BACKGROUND

Respiratory mechanics monitoring provides useful information for guiding mechanical ventilation, but many measuring methods are inappropriate for awake patients. This study aimed to evaluate the accuracy of dynamic mechanics estimation using expiratory time constant (RC_exp) calculation during noninvasive pressure support ventilation (PSV) with air leak in different lung models.

METHODS

A Respironics V60 ventilator was connected to an active breathing simulator for modeling five profiles: normal adult, restrictive, mildly and severely obstructive, and mixed obstructive/restrictive. Inspiratory pressure support was adjusted to maintain tidal volumes (V_T), achieving 5.0, 7.0, and 10.0 ml/kg body weight. PEEP was set at 5 cmH₂O, and the back-up rate was 10 bpm. Measurements were conducted at system leaks of 25-28 L/min. RC_exp was estimated from the ratio at 75% exhaled V_T and flow rate, which was then used to determine respiratory system compliance (C_rs) and airway resistance (R_aw).

RESULTS

In non-obstructive conditions (R_aw ≤ 10 cmH₂O/L/s), the C_rs was overestimated in the PSV mode. Peak inspiratory and expiratory flow and V_T increased with PS levels, as calculated C_rs decreased. In passive breathing, the difference of C_rs between different V_T was no significant. Underestimations of inspiratory resistance and expiratory resistance were observed at V_T of 5.0 ml/kg. The difference was minimal at V_T of 7.0 ml/kg. During non-invasive PSV, the estimation of airway resistance with the RC_exp method was accurately at V_T of 7.0 ml/kg.

CONCLUSIONS

The difference between the calculated C_rs and the preset value was influenced by the volume, status and inspiratory effort in spontaneously breathing.

[COVID-19-associated lung weakness (CALW): Systematic review and meta-analysis]

Objectives

To assess mortality and different clinical factors derived from the development of atraumatic pneumothorax (PNX) and/or pneumomediastinum (PNMD) in critically ill patients as a consequence of COVID-19-associated lung weakness (CALW).

Design

Systematic review with meta-analysis.

Setting

Intensive care unit (ICU).

Participants

Original research evaluating patients, with or without the need for protective invasive mechanical ventilation (IMV), with a diagnosis of COVID-19 who had developed atraumatic PNX or PNMD on admission or during their hospital stay.

Interventions

Data of interest were obtained from each article and analysed and assessed by the Newcastle-Ottawa Scale. The risk of the variables of interest was assessed by data derived from studies including patients who developed atraumatic PNX or PNMD.

Main variables of interest

Mortality, mean ICU length of stay and mean PaO₂/FiO₂ at diagnosis.

Results

Data were collected from 12 longitudinal studies. Data from a total of 4,901 patients were included in the meta-analysis. A total of 1,629 patients had an episode of atraumatic PNX and 253 patients had an episode of atraumatic PNMD. Despite finding significantly strong associations, the high heterogeneity between studies means that interpretation of the results should be made with caution.

Conclusions

Mortality of COVID-19 patients was higher in those who developed atraumatic PNX and/or PNMD compared to those who did not. The mean PaO2/FiO2 index was lower in patients who developed atraumatic PNX and/or PNMD. We propose to group these cases under the term CAPD.

Emerging Applications of Extracardiac Ultrasound in Critically Ill Cardiac Patients

Point-of-care ultrasound has evolved as an invaluable diagnostic modality and procedural guidance tool in the care of critically ill cardiac patients. Beyond focused cardiac ultrasound, additional extracardiac ultrasound modalities may provide important information at the bedside. In addition to new uses of existing modalities, such as pulsed-wave Doppler ultrasound, the development of new applications is fostered by the implementation of additional features in mid-range ultrasound machines commonly acquired for intensive care units, such as tissue elastography, speckle tracking, and contrast-enhanced ultrasound quantification software. This review explores several areas in which ultrasound imaging technology may transform care in the future. First, we review how lung ultrasound in mechanically ventilated patients can enable the personalization of ventilator parameters and help to liberate them from mechanical ventilation. Second, we review the role of venous Doppler in the assessment of organ congestion and how tissue elastography may complement this application. Finally, we explore how contrast-enhanced ultrasound could be used to assess changes in organ perfusion.

The Wnt/β-catenin pathway regulates inflammation and apoptosis in ventilator-induced lung injury

Ventilator-induced lung injury (VILI) may be caused by incorrect mechanical ventilation (MV), and its progression is mainly related to inflammatory reaction, apoptosis, and oxidative stress. The Wnt/β-catenin pathway can modulate inflammation and apoptosis; however, its role in VILI is unknown. This research aims to explore the role of the Wnt/β-catenin pathway in VILI. VILI models were established using rats and type II alveolar epithelial (ATII) cells. Glycogen synthase kinase 3β (GSK-3β), β-catenin, and cyclin D1 were determined using western blotting and immunofluorescence. Apoptosis of lung tissues was evaluated using TUNEL, flow cytometry, Bax, and Bcl2 protein. Interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) were detected via enzyme-linked immunosorbent assay (ELISA). Lung pathological injury was evaluated through hematoxylin and eosin (H&E) staining. Lung permeability was evaluated by the ratio of dry to wet weight of lung tissue and the total protein level of bronchoalveolar lavage fluid (BALF). The results showed that GSK-3β expression was enhanced and β-catenin expression was diminished in lung tissue under MV. SB216763 increased β-catenin and cyclin D1 expression by inhibiting GSK-3β expression and inhibited the inflammatory response and apoptosis of lung, alleviated pulmonary edema and lung tissue permeability, and significantly mitigated lung injury. However, inhibition of β-catenin expression by MSAB attenuated the anti-inflammatory and antiapoptotic effects of SB216763 in VILI. Overall, the present study demonstrates that the Wnt/β-catenin pathway activation in MV may play an anti-inflammatory and antiapoptotic role, thereby alleviating lung injury and delaying VILI progression, which may be a key point of intervention in VILI.

A Comparative Study on Predication of Appropriate Mechanical Ventilation Mode through Machine Learning Approach

Ventilation mode is one of the most crucial ventilator settings, selected and set by knowledgeable critical care therapists in a critical care unit. The application of a particular ventilation mode must be patient-specific and patient-interactive. The main aim of this study is to provide a detailed outline regarding ventilation mode settings and determine the best machine learning method to create a deployable model for the appropriate selection of ventilation mode on a per breath basis. Per-breath patient data is utilized, preprocessed and finally a data frame is created consisting of five feature columns (inspiratory and expiratory tidal volume, minimum pressure, positive end-expiratory pressure, and previous positive end-expiratory pressure) and one output column (output column consisted of modes to be predicted). The data frame has been split into training and testing datasets with a test size of 30%. Six machine learning algorithms were trained and compared for performance, based on the accuracy, F1 score, sensitivity, and precision. The output shows that the Random-Forest Algorithm was the most precise and accurate in predicting all ventilation modes correctly, out of the all the machine learning algorithms trained. Thus, the Random-Forest machine learning technique can be utilized for predicting optimal ventilation mode setting, if it is properly trained with the help of the most relevant data. Aside from ventilation mode, control parameter settings, alarm settings and other settings may also be adjusted for the mechanical ventilation process utilizing appropriate machine learning, particularly deep learning approaches.

[Invasive mechanical ventilation in patients with solid tumor or hematological malignancy]

Invasive mechanical ventilation in onco-hematology patients has become relatively routine, and is now part and parcel of their care pathway. Nevertheless, specific complications and subsequent therapeutic possibilities require discussion. To a greater extent than with regard to other patient populations, cooperation between specialist and ICU physician is mandatory, the objective being to more comprehensively assess a therapeutic project before or during the period of invasive mechanical ventilation. After an overview of recent results concerning ventilated patients in intensive care, this review aims to describe the specific complications and factors associated with mortality in this population.

The impact of postoperative cognitive impairment on mid-term survival after heart transplantation

BACKGROUND

Heart transplantation is the definitive therapy for patients with end-stage heart failure. Antecedent studies reported that a substantial proportion of heart transplant recipients developed postoperative cognitive impairment in the long term. However, no studies have explored the association between postoperative cognitive impairment and survival after heart transplantation.

METHODS

The data of 43 adult patients who underwent heart transplantation were consecutively enrolled and assessed using the MMSE and MoCA neuropsychological tests. Kaplan-Meier curves and Cox proportional hazards models were used for survival analyses. Primary component analysis was performed to integrate MoCA subtests into the "Attention factor," "Naming factor," and "Orientation factor."

RESULTS

About 30% of the patients were diagnosed with short-term postoperative cognitive impairment. The impairment group was older and had lower baseline cognitive performances, larger LV diameter, worse MMSE decline and higher ratio of significant MoCA decline. Postoperative cognitive impairment was significantly associated with worse survival (P = .028). Multivariate Cox analyses showed that higher postoperative MoCA score was significantly associated with lower mid-term post-transplant mortality (HR = .744 [.584, .949], P = .017), in which "Attention factor" contributed to this association most (HR = .345 [.123, .970], P = .044) rather than "Naming factor" or "Orientation factor." Notably, preoperative cognitive impairment was closely related with postoperative cognitive impairment and also indicated the worse post-transplant survival (P = .015).

CONCLUSION

Postoperative as well as preoperative cognitive impairments were associated with a worse mid-term survival after heart transplantation, indicating that neuropsychological assessments before and after heart transplantation should be routinely performed for heart transplant recipients for better risk stratification.

Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU

Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model's covariate-specific effect on oxygen therapy for more personalized intervention.

Acute Respiratory Distress Syndrome in Pregnancy: Updates in Principles and Practice

Acute respiratory failure occurs in 0.05% to 0.3% of pregnancies and is precipitated by pulmonary and nonpulmonary insults. Acute respiratory distress syndrome (ARDS) is the rapid onset of hypoxemic respiratory failure associated with bilateral pulmonary opacities on chest imaging attributed to noncardiogenic pulmonary edema. The pathophysiological features of ARDS include hypoxemia, diminished lung volumes, and decreased lung compliance. While there is a paucity of data concerning ARDS in the pregnant individual, management principles do not vary significantly between pregnant and nonpregnant patients. The following review will discuss the diagnosis and management of the pregnant patient with ARDS.

Effects of Individualised High Positive End-Expiratory Pressure and Crystalloid Administration on Postoperative Pulmonary Function in Patients Undergoing Robotic-Assisted Radical Prostatectomy: A Prospective Randomised Single-Blinded Pilot Study

OBJECTIVES

Robotic-assisted laparoscopic prostatectomy (RALP) is typically conducted in steep Trendelenburg position (STP). The aim of the study was to evaluate whether crystalloid administration and individual management of positive end-expiratory pressure (PEEP) improve peri- and post-operative pulmonary function in patients undergoing RALP.

DESIGN

Prospective randomised single-centre single-blinded explorative study.

SETTING

Patients were either allocated to a standard PEEP (5 cmH₂O) group or an individualised high PEEP group. Furthermore, each group was divided into a liberal and a restrictive crystalloid group (8 vs. 4 mL/kg/h predicted body weight). Individualised PEEP levels were determined by means of preoperative recruitment manoeuvre and PEEP titration in STP.

PARTICIPANTS

Informed consent was obtained from 98 patients scheduled for elective RALP.

INTERVENTIONS

The following intraoperative parameters were analysed in each of the four study groups: ventilation setting (peak inspiratory pressure [PIP], plateau pressure, driving pressure [P_driv], lung compliance [LC] and mechanical power [MP]) and postoperative pulmonary function (bed-side spirometry). The spirometric parameters Tiffeneau index (FEV₁/FVC ratio) and mean forced expiratory flow (FEF_25-75) were measured pre- and post-operatively. Data are shown as mean ± standard deviation (SD), and groups were compared with ANOVA. A p-value of <0.05 was considered significant.

RESULTS

The two individualised high PEEP groups (mean PEEP 15.5 [±1.71 cmH₂O]) showed intraoperative significantly higher PIP, plateau pressure and MP levels but significantly decreased P_driv and increased LC. On the first and second postoperative day, patients with individualised high PEEP levels had a significantly higher mean Tiffeneau index and FEF_25-75. Perioperative oxygenation and ventilation and postoperative spirometric parameters were not influenced by restrictive or liberal crystalloid infusion in either of the two respective PEEP groups.

CONCLUSIONS

Individualised high PEEP levels (≥14 cmH₂O) during RALP improved intraoperative blood oxygenation and resulted in more lung-protective ventilation. Furthermore, postoperative pulmonary function was improved for up to 48 h after surgery in the sum of the two individualised high PEEP groups. Restrictive crystalloid infusion during RALP seemed to have no effect on peri- and post-operative oxygenation and pulmonary function.

GAT107-mediated α7 nicotinic acetylcholine receptor signaling attenuates inflammatory lung injury and mortality in a mouse model of ventilator-associated pneumonia by alleviating macrophage mitochondrial oxidative stress via reducing MnSOD-S-glutathionylation

Supraphysiological concentrations of oxygen (hyperoxia) can compromise host defense and increase susceptibility to bacterial and viral infections, causing ventilator-associated pneumonia (VAP). Compromised host defense and inflammatory lung injury are mediated, in part, by high extracellular concentrations of HMGB1, which can be decreased by GTS-21, a partial agonist of α7 nicotinic acetylcholine receptor (α7nAChR). Here, we report that a novel α7nAChR agonistic positive allosteric modulator (ago-PAM), GAT107, at 3.3 mg/kg, i.p., significantly decreased animal mortality and markers of inflammatory injury in mice exposed to hyperoxia and subsequently infected with Pseudomonas aeruginosa. The incubation of macrophages with 3.3 μM of GAT107 significantly decreased hyperoxia-induced extracellular HMGB1 accumulation and HMGB1-induced macrophage phagocytic dysfunction. Hyperoxia-compromised macrophage function was correlated with impaired mitochondrial membrane integrity, increased superoxide levels, and decreased manganese superoxide dismutase (MnSOD) activity. This compromised MnSOD activity is due to a significant increase in its level of glutathionylation. The incubation of hyperoxic macrophages with 3.3 μM of GAT107 significantly decreases the levels of glutathionylated MnSOD, and restores MnSOD activity and mitochondrial membrane integrity. Thus, GAT107 restored hyperoxia-compromised phagocytic functions by decreasing HMGB1 release, most likely via a mitochondrial-directed pathway. Overall, our results suggest that GAT107 may be a potential treatment to decrease acute inflammatory lung injury by increasing host defense in patients with VAP.

Pressure Prediction on Mechanical Ventilation Control Using Bidirectional Long-Short Term Memory Neural Networks

Life support systems are playing a critical role on keeping a patient alive when admitted in ICU bed. One of the most popular life support system is Mechanical Ventilation which helps a patient to breath when breathing is inadequate to maintain life. Despite its important role during ICU admission, the technology for Mechanical Ventilation hasn't change a lot for several years. In this paper, we developed a model using artificial neural networks, in an attempt to make ventilators more intelligent and personalized to each patient's needs. We used artificial data to train a deep learning model that predicts the correct pressure to be applied on patient's lungs every timepoint within a breath cycle. Our model was evaluated using cross-validation and achieved a Mean Absolute Error of 0.19 and a Mean Absolute Percentage Error of 2%.

Anesthesia Machine and New Modes of Ventilation

Mechanical ventilation is ubiquitous in the operating room. This article explores the anesthesia machine as a ventilator, examining its unique features and differences from ventilators designed for long-term use. It will describe standard and nonstandard modes of ventilation. The reader will develop a more nuanced understanding of how to tailor ventilation and oxygenation strategies based on patient and anesthetic scenarios as well as with the assistance of new technologies.

Toward Evaluating Critical Factors of Extubation Outcome with XCSR-Generated Rules

Predicting the correct timing for extubation is pivotal for critically ill patients with mechanical ventilation support. Evidence suggests that extubation failure occurs in approximately 15–20% of patients, despite their passing of the extubation evaluation, necessitating reintubation. For critically ill patients, reintubation invariably increases mortality risk and medical costs. The numerous parameters that have been proposed for extubation decision-making, which constitute the key predictors of successful extubation, remains unclear. In this study, an extended classifier system capable of processing real-value inputs was proposed to select features of successful extubation. In total, 40 features linked to clinical information and variables acquired during spontaneous breathing trial (SBT) were used as the environmental inputs. According to the number of “don’t care” rules in a population set, Prob_usage, the probability of the feature not being classified as above rules, can be calculated. A total of 228 subjects’ results showed that Prob_usage was higher than 90% for minute ventilation at the 1st, 30th, 60th, and 90th minutes; respiratory rate at the 90th minute; and body weight, indicating that the variance in respiratory parameters during an SBT are critical predictors of successful extubation. The present XCSR model is useful to evaluate critical factors of extubation outcomes. Additionally, the current findings suggest that SBT duration should exceed 90 min, and that clinicians should consider the variance in respiratory variables during an SBT before making extubation decisions.

Prognostic value of the novel P/FPE index to classify ARDS severity: A cohort study

OBJECTIVE

To evaluate the impact of the novel P/FP_E index to classify ARDS severity on mortality of patients with ARDS.

DESIGN

A retrospective cohort study.

SETTING

Twelve-bed medical and surgical intensive care unit from January 2018 to December 2020.

PATIENTS

A total of 217 ARDS patients managed with invasive mechanical ventilation >48h.

INTERVENTIONS

None.

VARIABLES

ARDS severity on day 1 and day 3 was measured based on PaO₂/FiO₂ ratio and P/FP_E index [PaO₂/(FiO₂×PEEP)]. Primary outcome was the hospital mortality.

RESULTS

Hospital mortality rate was 59.9%. Relative to PaO₂/FiO₂ ratio, 31.8% of patients on day 1 and 77.0% on day 3 were reclassified into a different category of ARDS severity by P/FP_E index. The level of PEEP was lower by P/FP_E index-based ARDS severity classification than by using PaO₂/FiO₂ ratio. The performance for predicting mortality of P/FP_E index was superior to PaO₂/FiO₂ ratio in term of AROC (day 1: 0.72 vs. 0.62; day 3: 0.87 vs. 0.68) and CORR (day 1: 0.370 vs. 0.213; day 3: 0.634 vs. 0.301). P/FP_E index improved prediction of risk of death compared to PaO₂/FiO₂ ratio as showed by the qNRI (day 1: 72.0%, p<0.0001; day 3: 132.4%, p<0.0001) and IDI (day 1: 0.09, p<0.0001; day 3: 0.31, p<0.0001).

CONCLUSIONS

Assessment of ARDS severity based on P/FP_E index seems better than PaO₂/FiO₂ ratio for predicting mortality. The value of P/FP_E index for clinical decision-making requires confirmation by randomized controlled trials.

Whole-lung finite-element models for mechanical ventilation and respiratory research applications

Mechanical ventilation has been a vital treatment for Covid-19 patients with respiratory failure. Lungs assisted with mechanical ventilators present a wide variability in their response that strongly depends on air-tissue interactions, which motivates the creation of simulation tools to enhance the design of ventilatory protocols. In this work, we aim to create anatomical computational models of the lungs that predict clinically-relevant respiratory variables. To this end, we formulate a continuum poromechanical framework that seamlessly accounts for the air-tissue interaction in the lung parenchyma. Based on this formulation, we construct anatomical finite-element models of the human lungs from computed-tomography images. We simulate the 3D response of lungs connected to mechanical ventilation, from which we recover physiological parameters of high clinical relevance. In particular, we provide a framework to estimate respiratory-system compliance and resistance from continuum lung dynamic simulations. We further study our computational framework in the simulation of the supersyringe method to construct pressure-volume curves. In addition, we run these simulations using several state-of-the-art lung tissue models to understand how the choice of constitutive models impacts the whole-organ mechanical response. We show that the proposed lung model predicts physiological variables, such as airway pressure, flow and volume, that capture many distinctive features observed in mechanical ventilation and the supersyringe method. We further conclude that some constitutive lung tissue models may not adequately capture the physiological behavior of lungs, as measured in terms of lung respiratory-system compliance. Our findings constitute a proof of concept that finite-element poromechanical models of the lungs can be predictive of clinically-relevant variables in respiratory medicine.

Extracorporeal Life Support for Status Asthmaticus: Early Outcomes in Teens and Young Adults

Extracorporeal life support (ECLS) may be life saving for patients with status asthmaticus (SA), a difficult-to-treat, severe subset of asthma. Contemporary ECLS outcomes for SA in teens and young adults are not well described. The Extracorporeal Life Support Organization (ELSO) Registry was reviewed (2009-2019) for patients (15-35 years) with a primary diagnosis of SA. In-hospital mortality and complications were described. Multivariable logistic regression was used to identify independent risk factors for hospital mortality. Overall, 137 patients, (26 teens and 111 young adults; median age 25 years) were included. Extracorporeal life support utilization for SA sharply increased in 2010, coinciding with increased ECLS utilization overall. Median ECLS duration and length of stay were 97 hours and 11 days, respectively. In-hospital mortality and major complication rates were 10% and 11%, respectively. Nonsurvivors were more likely to have experienced ECLS complications, compared to survivors (86% vs. 42%, p = 0.003). Independent risk factors for in-hospital mortality included pre-ECLS arrest and any renal and/or neurologic complication. Prospective studies designed to evaluate complications and subsequent failure to rescue may help optimize quality improvement efforts.

Palliative Ventilator Withdrawal Practices in an Inpatient Hospice Unit

Background

Palliative ventilator withdrawal (PVW) involves removal of mechanical ventilation in patients not expected to survive to allow a peaceful death. This process traditionally occurs in Intensive Care Units (ICU) and recently has evolved to occur in Inpatient Hospice and Palliative Care Units (IPU).

Objectives

To describe the process and response of patients undergoing PVW in an IPU setting.

Methods

This is a longitudinal observational cohort study of adult patients who underwent PVW in an IPU from January 2021 through March 2022.

Results

Among 25 enrolled subjects, median age was 68 (IQR 62.5-76.5) years and 14 (56%) were females. Median time from PVW to death was 16.8 (IQR 2.6-100) hours. A registered nurse and attending physician were present in all the cases, while a respiratory therapist was present in 20 (80%) and chaplain in 9 (36%) of the cases. Before PVW, opioids and benzodiazepines were administered to 24 (96%) patients. Post PVW, respiratory distress was noted among 16 (64%) patients and medication was given to 15 (60%) patients for respiratory distress. There was a significant association between the presence of respiratory distress and administration of medication within 30 minutes after PVW ( P = .009). The rituals performed during PVW were reciting prayers for 11 (44%), playing music for 8 (32%), and observing silence for 6 (24%) of the patients.

Conclusion

This study describes the PVW practices in an IPU setting where a multidisciplinary team was present during PVW for most of the cases and two-third of the patients undergoing PVW experienced respiratory distress immediately after PVW.

The Significance of Implementing Bilevel Positive Airway Pressure under Cluster Nursing in Improving the Survival Possibility of Patients with Severe Pulmonary Infection Complicated by Respiratory Failure

Objective. To explore the significance of bilevel positive airway pressure (BIPAP) under cluster nursing in improving the survival probability of patients with severe pulmonary infection (SPI) complicated by respiratory failure (RF). Methods. This research included 153 SPI patients complicated by RF ( $\text{SPI}+\text{RF}$ ) admitted between January 2020 and March 2022, including 55 cases in group A who were treated with BIPAP under cluster care during hospitalization, 47 cases in group B receiving invasive continuous mechanical ventilation during hospitalization, and 51 cases in group C treated with BIPAP under routine care during hospitalization. The three cohorts were compared regarding pre- and posttreatment serum inflammatory factors (IFs), blood gas (BG) parameters, heart rate (HR), and respiratory rate (RR). Besides, the cumulative time of ventilator use, successful ventilator weaning rate, mortality, and incidence of adverse events were counted. Finally, patients were scored for their psychological state using the Hamilton Anxiety/Depression Scale (HAMA/HAMD). Results. The posttreatment TNF-α, IL-6, PCT, WBC, and PaCO2 reduced statistically in all the three groups, with the lowest levels found in group A and the highest in group B ( $P<0.05$ ); while PaO2 and SpO2 increased, with the highest values found in group A and the lowest in group B ( $P<0.05$ ). Among the three groups, group A had the shortest duration of ventilator use, the highest successful weaning rate, and the lowest incidence of adverse events ( $P<0.05$ ). Besides, HAMA and HAMD scores were the lowest in group A among the three groups, while those in group B were higher compared with group C ( $P<0.05$ ). Conclusion. The implementation of BIPAP under cluster nursing can effectively reduce inflammatory responses of $\text{SPI}+\text{RF}$ patients, improve their vital signs, and enhance their psychological state, which has extremely high clinical application value.

Perspectives on Current Mechanical Ventilation Use and Training in Pediatric Critical Care Medicine Fellowship Programs in the United States

Mechanical ventilation (MV) is an integral part of the care of the critically ill child, and contemporary MV includes a myriad of sophisticated modalities that should be tailored to specific disease pathology and severity. This requires Pediatric Critical Care Medicine (PCCM) physicians to have comprehensive training and experience with various modes. We sought to assess and describe the current landscape of MV practices and training from PCCM fellowship programs. To describe current MV practices and training in PCCM fellowship programs and assess the different modes of MV to which trainees are exposed, a piloted survey was sent to all 67 American PCCM fellowship program directors (PDs) in 2019. Forty-eight (71.6%) of PCCM PDs responded. Our survey shows that PCCM fellows are not uniformly exposed to the same MV modalities, and PDs' perception of fellows' competency in managing MV is variable. Our results suggest that there is a lack of exposure to the various modes of “conventional” MV, as well as inconsistent education about less-utilized “nonconventional” MV. Additionally, we found that PDs are aware of their fellows' limited exposure to various modalities, as many practice patterns are institution specific. This study identified gaps in MV education and training and advocates for PCCM fellowship programs to identify these deficiencies to equip all trainees with a solid knowledge base that will prepare them to utilize any mode of MV in their future practice.

Diaphragmatic ultrasonography-based rapid shallow breathing index for predicting weaning outcome during a pressure support ventilation spontaneous breathing trial

Background

The optimum timing to wean is crucial to avoid negative outcomes for mechanically ventilated patients. The rapid shallow breathing index (RSBI), a widely used weaning index, has limitations in predicting weaning outcomes. By replacing the tidal volume of the RSBI with diaphragmatic excursion (DE) and diaphragm thickening fraction (DTF) assessed by ultrasonography, we calculated two weaning indices, the diaphragmatic excursion rapid shallow breathing index (DE-RSBI, respiratory rate [RR]/DE) and the diaphragm thickening fraction rapid shallow breathing index (DTF-RSBI, RR/DTF). The aim of this study was to evaluate the predictive values of DTF-RSBI, DE-RSBI and traditional RSBI in weaning failure.

Methods

This prospective observational study included patients undergoing mechanical ventilation (MV) for > 48 h and who were readied for weaning. During a pressure support ventilation (PSV) spontaneous breathing trial (SBT), right hemidiaphragmatic excursion and DTF were measured by bedside ultrasonography as well as RSBI. Weaning failure was defined as: (1) failing the SBT and (2) SBT success but inability to maintain spontaneous breathing for more than 48 h without noninvasive or invasive ventilation. A receiver operator characteristic (ROC) curve was used for analyzing the diagnostic accuracy of RSBI, DE-RSBI, and DTF-RSBI.

Results

Of the 110 patients studied, 37 patients (33.6%) failed weaning. The area under the ROC (AUROC) curves for RSBI, DE-RSBI, and DTF-RSBI for predicting failed weaning were 0.639, 0.813, and 0.859, respectively. The AUROC curves for DE-RSBI and DTF-RSBI were significantly higher than for RSBI (P = 0.004 and P < 0.001, respectively). The best cut-off values for predicting failed weaning were RSBI > 51.2 breaths/min/L, DE-RSBI > 1.38 breaths/min/mm, and DTF-RSBI > 78.1 breaths/min/%.

Conclusions

In this study, two weaning indices determined by bedside ultrasonography, the DE-RSBI (RR/DE) and DTF-RSBI (RR/DTF), were shown to be more accurate than the traditional RSBI (RR/VT) in predicting weaning outcome during a PSV SBT.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02133-5.

Assessment of hemodynamics, blood gases, and lung histopathology of healthy Pig model on two different mechanical ventilators

In response to COVID-19 global crisis and arising from social responsibility, efforts have been exerted to promptly research, develop and manufacture ICU ventilators locally to meet the spike in demand. This study aimed at : Evaluating the safety and performance of a newly developed mechanical ventilator; EZVent compared to a commercial ventilator regarding hemodynamics, arterial blood gases (ABG), lung inflammatory markers, and histopathology in a healthy pig model using three different ventilation modes. Methods: Eight adult male pigs were anesthetized and randomly assigned into two equal groups: Commercial vent and EZVent group, the animals of which were ventilated using a standard commercial ventilator and EZVent, respectively. On every animal, three ventilation modes were tested, each mode for 30 min: CMV-VC, CMV-PC, and CPAP-PS modes. Vital signs, ECG, Lung Mechanics (LM), and ABG were measured before ventilation and after 30 min of ventilation of each mode. After animals' euthanasia, histological examinations of lung samples including morphometric assessment of alveolar edema, alveolar wall thickening, and the mean number of inflammatory cellular infiltrate/cm2 of lung tissue were analyzed. TNF-α and Il-6 expression and localization in lung tissue were assessed by western blot and immunohistochemistry. Results: The vital signs, LM, ABG, morphometric analysis, and histopathological score during the different ventilation modes showed non-significant differences between the study groups. TNF-α and IL-6 were minimally expressed in the bronchiolar epithelium and the alveolar septa. Their increased expression level was insignificant. Conclusion: EZVent is equivalent to the commercial ventilator regarding its safety and efficacy.