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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

[A theoretical study on a method for estimating dynamic intrinsic positive end-expiratory pressure in invasive mechanical ventilation].

OBJECTIVE: To explore a simple method for measuring the dynamic intrinsic positive end-expiratory pressure (PEEPi) during invasive mechanical ventilation. METHODS: A 60-year-old male patient was admitted to the critical care medicine department of Dongying People's Hospital in September 2020. He underwent invasive mechanical ventilation treatment for respiratory failure due to head and chest trauma, and incomplete expiratory flow occurred during the treatment. The expiratory flow-time curve of this patient was served as the research object. The expiratory flow-time curve of the patient was observed, the start time of exhalation was taken as T0, the time before the initiation of inspiratory action (inspiratory force) was taken as T1, and the time when expiratory flow was reduced to zero by inspiratory drive (inspiratory force continued) was taken as T2. Taking T1 as the starting point, the follow-up tracing line was drawn according to the evolution trending of the natural expiratory curve before the T1 point, until the expiratory flow reached to 0, which was called T3 point. According to the time phase, the intrapulmonary pressure at the time just from expiratory to inspiratory (T1 point) was called PEEPi1. When the expiratory flow was reduced to 0 (T2 point), the intrapulmonary pressure with the inhaling power being removed hypothetically was called PEEPi2. And it was equal to positive end-expiratory pressure (PEEP) set in the ventilator at T3 point. The area under the expiratory flow-time curve (expiratory volume) between T0 and T1 was called S1. And it was S2 between T0 and T2, S3 between T0 and T3. After sedation, in the volume controlled ventilation mode, approximately one-third of the tidal volume was selected, and the static compliance of patient's respiratory system called "C" was measured using the inspiratory pause method. PEEPi1 and PEEP2 were calculated according to the formula "C = ΔV/ΔP". Here, ΔV was the change in alveolar volume during a certain period of time, and ΔP represented the change in intrapulmonary pressure during the same time period. This estimation method had obtained a National Invention Patent of China (ZL 2020 1 0391736.1). RESULTS: (1) PEEPi1: according to the formula "C = ΔV/ΔP", the expiratory volume span from T1 to T3 was "S3-S1", and the intrapulmonary pressure decreased span was "PEEPi1-PEEP". So, C = (S3-S1)/(PEEPi1-PEEP), PEEPi1 = PEEP+(S3-S1)/C. (2)PEEPi2: the expiratory volume span from T2 to T3 was "S3-S2", and the intrapulmonary pressure decreased span was "PEEPi2-PEEP". So, C = (S3-S2)/(PEEPi2-PEEP), PEEPi2 = PEEP+(S3-S2)/C. CONCLUSIONS: For patients with incomplete expiratory during invasive mechanical ventilation, the expiratory flow-time curve extension method can theoretically be used to estimate the dynamic PEEPi in real time.

Development of a Prognostic Scoring System for Tracheostomized Patients Requiring Prolonged Ventilator Care: A Ten-Year Experience in a University-Affiliated Tertiary Hospital.

Background and Objectives: This study aimed to assess the value of a novel prognostic model, based on clinical variables, comorbidities, and demographic characteristics, to predict long-term prognosis in patients who received mechanical ventilation (MV) for over 14 days and who underwent a tracheostomy during the first 14 days of MV. Materials and Methods: Data were obtained from 278 patients (66.2% male; median age: 71 years) who underwent a tracheostomy within the first 14 days of MV from February 2011 to February 2021. Factors predicting 1-year mortality after the initiation of MV were identified by binary logistic regression analysis. The resulting prognostic model, known as the tracheostomy-ProVent score, was computed by assigning points to variables based on their respective ß-coefficients. Results: The overall 1-year mortality rate was 64.7%. Six factors were identified as prognostic indicators: platelet count < 150 × 103/µL, PaO2/FiO2 < 200 mmHg, body mass index (BMI) < 23.0 kg/m2, albumin concentration < 2.8 g/dL on day 14 of MV, chronic cardiovascular diseases, and immunocompromised status at admission. The tracheostomy-ProVent score exhibited acceptable discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.786 (95% confidence interval: 0.733-0.833, p < 0.001) and acceptable calibration (Hosmer-Lemeshow chi-square: 2.753, df: 8, p = 0.949). Based on the maximum Youden index, the cut-off value for predicting mortality was set at ≥2, with a sensitivity of 67.4% and a specificity of 76.3%. Conclusions: The tracheostomy-ProVent score is a good predictive tool for estimating 1-year mortality in tracheostomized patients undergoing MV for >14 days. This comprehensive model integrates clinical variables and comorbidities, enhancing the precision of long-term prognosis in these patients.

IGF-1 Regulates Skeletal Muscle Degradation and Remolding in Ventilator-Induced Diaphragmatic Dysfunction by Mediating FOXO1 Expression.

BACKGROUND: Mechanical ventilation (MV) sustains life in critically ill patients by providing adequate alveolar ventilation. However, prolonged MV could induce inspiratory muscle atrophy known as ventilator-induced diaphragmatic dysfunction (VIDD). Insulin-like growth factor (IGF)-1 has been proven to play crucial roles in regulating skeletal muscle size and function. Meanwhile, the forkhead box protein O1 (FOXO1) has been linked to muscle atrophy. This study aimed to explore the effect of IGF-1 on muscle degradation and remodeling in VIDD and delved into the association of the underlying mechanism involving FOXO1. METHODS: VIDD models were established by treating rats with MV. Adeno-associated virus (AAV) was used for transfection to construct IGF-1 and/or FOXO1 overexpressed rats. There were four groups in this study: normal rats (NC), normal rats with MV treatment (MV), IGF-1-overexpressed rats with MV treatment (MV+IGF-1), and rats overexpressing both IGF-1 and FOXO1 with MV treatment (MV+IGF-1+FOXO1). Protein levels were measured by western blot or enzyme-linked immunosorbent assay (ELISA), and mRNA levels were detected by real-time reverse transcriptase-polymerase chain reaction (RT-qPCR). IGF-1 and FOXO1 expression were validated by detecting mRNA and protein levels. Diaphragmatic muscle contractility and morphometry were tested using stimulating electrodes in conjunction with hematoxylin and eosin (H&E) staining. Interleukin (IL)-6 and carbonylated protein were used for evaluating muscle atrophy and oxidation, respectively. Protein degradation was determined by troponin-I level and tyrosine release. Apoptosis was assessed using the terminal deoxynucleotidyl transferase-mediated uridine 5'-triphosphate (UTP) nick-end labeling (TUNEL) assay, alongside markers like Bax, B-cell lymphoma 2 (BCL-2), and Cleaved Caspase-3. Atrogin-1, muscle RING finger 1 (MURF1), neuronally expressed developmentally downregulated 4 (NEDD4), muscle ubiquitin ligase of SCF complex in atrophy-1 (MUSA1), and ubiquitinated protein was used to determine proteolysis. Additionally, protein synthesis was measured by assessing the rates of mixed muscle protein (MMP) and myosin heavy chain (MHC). RESULTS: MV treatment caused IGF-1 downregulation (p < 0.01) and FOXO1 upregulation (p < 0.01). The IGF-1 upregulation downregulated FOXO1 in the MV+IGF-1 group (p < 0.001) while IGF-1 and FOXO1 were both upregulated in the MV+IGF-1+FOXO1 group (p < 0.001). The treatment of MV decreased muscle contractility and cross-sectional areas of diaphragm muscle fibers (p < 0.01). Additionally, IL-6, troponin-1, tyrosine release, carbonylated protein, TUNEL positive nuclei, Bax, Cleaved Caspase-3, Atrogin-1, MURF1, neuronally expressed developmentally downregulated 4 (NEDD4), MUSA1, and ubiquitinated protein levels increased significantly in MV group (p < 0.001) while levels of BCL-2, fractional synthetic rate of MMP and MHC, and type I and type II MHC protein mRNA expression decreased in MV group (p < 0.001). All of these alterations were reversed in the MV+IGF-1 group (p < 0.01), while the IGF-1-induced reversion was disrupted in the MV+IGF-1+FOXO1 group (p < 0.01). CONCLUSIONS: IGF-1 may protect diaphragmatic muscles from VIDD-induced structural damage and function loss by downregulating FOXO1. This action suppresses muscle breakdown and facilitates muscle remodeling in diaphragmatic muscles affected by VIDD.

[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.