Patient-ventilator asynchrony, impact on clinical outcomes and effectiveness of interventions: a systematic review and meta-analysis

Optimizing Mechanical Ventilation: A Clinical and Practical Bedside Method for the Identification and Management of Patient-Ventilator Asynchronies in Critical Care

The prompt identification and correction of patient-ventilator asynchronies (PVA) remain a cornerstone for ensuring the quality of respiratory failure treatment and the prevention of further injury to critically ill patients. These disruptions, whether due to over- or under-assistance, have a profound clinical impact not only on the respiratory mechanics and the mortality associated with mechanical ventilation but also on the patient's cardiac output and hemodynamic profile. Strong evidence has demonstrated that these frequently occurring and often underdiagnosed events have significant prognostic value for mechanical ventilation outcomes and are strongly associated with prolonged ICU stays and hospital mortality. Halting the consequences of PVA relies on the correct identification and approach of its underlying causes. However, this often requires advanced knowledge of respiratory physiology and the evaluation of complex ventilator waveforms in patient-ventilator interactions, posing a challenge to intensive care practitioners, in particular, those less experienced. This review aims to outline the most frequent types of PVA and propose a clinical algorithm to provide physicians with a structured approach to assess, accurately diagnose, and correct PVA.

Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

BACKGROUND AND OBJECTIVE

Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-based and machine learning PVA approaches exist, they have variable performance and can miss specific PVA events. This study compares a model and rule-based algorithm with a machine learning PVA method by retrospectively validating both methods using an independent patient cohort.

METHODS

Hysteresis loop analysis (HLA) which is a rule-based method (RBM) and a tri-input convolutional neural network (TCNN) machine learning model are used to classify 7 different types of PVA, including: 1) flow asynchrony; 2) reverse triggering; 3) premature cycling; 4) double triggering; 5) delayed cycling; 6) ineffective efforts; and 7) auto triggering. Class activation mapping (CAM) heatmaps visualise sections of respiratory waveforms the TCNN model uses for decision making, improving result interpretability. Both PVA classification methods were used to classify incidence in an independent retrospective clinical cohort of 11 mechanically ventilated patients for validation and performance comparison.

RESULTS

Self-validation with the training dataset shows overall better HLA performance (accuracy, sensitivity, specificity: 97.5 %, 96.6 %, 98.1 %) compared to the TCNN model (accuracy, sensitivity, specificity: 89.5 %, 98.3 %, 83.9 %). In this study, the TCNN model demonstrates higher sensitivity in detecting PVA, but HLA was better at identifying non-PVA breathing cycles due to its rule-based nature. While the overall AI identified by both classification methods are very similar, the intra-patient distribution of each PVA type varies between HLA and TCNN.

CONCLUSION

The collective findings underscore the efficacy of both HLA and TCNN in PVA detection, indicating the potential for real-time continuous monitoring of PVA. While ML methods such as TCNN demonstrate good PVA identification performance, it is essential to ensure optimal model architecture and diversity in training data before widespread uptake as standard care. Moving forward, further validation and adoption of RBM methods, such as HLA, offers an effective approach to PVA detection while providing clear distinction into the underlying patterns of PVA, better aligning with clinical needs for transparency, explicability, adaptability and reliability of these emerging tools for clinical care.

Detection and quantitative analysis of patient-ventilator interactions in ventilated infants by deep learning networks

BACKGROUND

The study of patient-ventilator interactions (PVI) in mechanically ventilated neonates is limited by the lack of unified PVI definitions and tools to perform large scale analyses.

METHODS

An observational study was conducted in 23 babies randomly selected from 170 neonates who were ventilated with SIPPV-VG, SIMV-VG or PSV-VG mode for at least 12 h. 500 breaths were randomly selected and manually annotated from each recording to train convolutional neural network (CNN) models for PVI classification.

RESULTS

The average asynchrony index (AI) over all recordings was 52.5%. The most frequently occurring PVIs included expiratory work (median: 28.4%, interquartile range: 23.2-40.2%), late cycling (7.6%, 2.8-10.2%), failed triggering (4.6%, 1.2-6.2%) and late triggering (4.4%, 2.8-7.4%). Approximately 25% of breaths with a PVI had two or more PVIs occurring simultaneously. Binary CNN classifiers were developed for PVIs affecting ≥1% of all breaths (n = 7) and they achieved F1 scores of >0.9 on the test set except for early triggering where it was 0.809.

CONCLUSIONS

PVIs occur frequently in neonates undergoing conventional mechanical ventilation with a significant proportion of breaths containing multiple PVIs. We have developed computational models for seven different PVIs to facilitate automated detection and further evaluation of their clinical significance in neonates.

IMPACT

The study of patient-ventilator interactions (PVI) in mechanically ventilated neonates is limited by the lack of unified PVI definitions and tools to perform large scale analyses. By adapting a recent taxonomy of PVI definitions in adults, we have manually annotated neonatal ventilator waveforms to determine prevalence and co-occurrence of neonatal PVIs. We have also developed binary deep learning classifiers for common PVIs to facilitate their automatic detection and quantification.

Knowledge and associated factors of healthcare professionals in detecting patient-ventilator asynchrony using waveform analysis at intensive care units of the federal public hospitals in Addis Ababa, Ethiopia, 2023

BACKGROUND

The interaction between the patient and the ventilator is often disturbed, resulting in patient-ventilator asynchrony (PVA). Asynchrony can lead to respiratory failure, increased artificial ventilation time, prolonged hospitalization, and escalated healthcare costs. Professionals' knowledge regarding waveform analysis has significant implications for improving patient outcomes and minimizing ventilation-related adverse events. Studies investigating the knowledge of healthcare professionals on patient-ventilator asynchrony and its associated factors in the Ethiopian context are limited. Therefore, this study aimed to assess the knowledge of healthcare professionals about using waveform analysis to detect asynchrony.

METHODS

A multicenter cross-sectional study was conducted on 237 healthcare professionals (HCPs) working in the intensive care units (ICUs) of federal public hospitals in Addis Ababa, Ethiopia, from December 2022 to May 2023. The data were collected using a structured and pretested interviewer-administered questionnaire. Then, the collected data were cleaned, coded, and entered into Epi data V-4.2.2 and exported to SPSS V-27 for analysis. After description, associations were analyzed using binary logistic regression. Variables with a P-value of < 0.25 in the bivariable analysis were transferred to the multivariable analysis. Statistical significance was declared using 95% confidence intervals, and the strengths of associations were reported using adjusted odds ratios (AORs).

RESULTS

A total of 237 HCPs participated in the study with a response rate of 100%. Half (49.8%) of the participants were females. The mean age of the participants was 29 years (SD = 3.57). Overall, 10.5% (95% CI: 6.9-15.2) of the participants had good knowledge of detecting PVA using waveform analysis. In the logistic regression, the number of MV-specific trainings and the training site had a statistically significant association with knowledge of HCPs. HCPs who attended more frequent MV training were more likely to have good knowledge than their counterparts [AOR = 6.88 (95% CI: 2.61-15.45)]. Additionally, the odds of good knowledge among professionals who attended offsite training were 2.6 times higher than those among professionals trained onsite [AOR = 2.63 (95% CI: 1.36-7.98)].

CONCLUSION

The knowledge of ICU healthcare professionals about the identification of PVA using waveform analysis is low. In addition, the study also showed that attending offsite MV training and repeated MV training sessions were independently associated with good knowledge. Consequently, the study findings magnify the relevance of providing frequent and specific training sessions focused on waveform analysis to boost the knowledge of HCPs.

Specific Training Improves the Detection and Management of Patient-Ventilator Asynchrony

BACKGROUND

Patient-ventilator asynchrony is common in patients undergoing mechanical ventilation. The proportion of health-care professionals capable of identifying and effectively managing different types of patient-ventilator asynchronies is limited. A few studies have developed specific training programs, but they mainly focused on improving patient-ventilator asynchrony detection without assessing the ability of health-care professionals to determine the possible causes.

METHODS

We conducted a 36-h training program focused on patient-ventilator asynchrony detection and management for health-care professionals from 20 hospitals in Latin America and Spain. The training program included 6 h of a live online lesson during which 120 patient-ventilator asynchrony cases were presented. After the 6-h training lesson, health-care professionals were required to complete a 1-h training session per day for the subsequent 30 d. A 30-question assessment tool was developed and used to assess health-care professionals before training, immediately after the 6-h training lecture, and after the 30 d of training (1-month follow-up).

RESULTS

One hundred sixteen health-care professionals participated in the study. The median (interquartile range) of the total number of correct answers in the pre-training, post-training, and 1-month follow-up were significantly different (12 [8.75-15], 18 [13.75-22], and 18.5 [14-23], respectively). The percentages of correct answers also differed significantly between the time assessments. Study participants significantly improved their performance between pre-training and post-training (P < .001). This performance was maintained after a 1-month follow-up (P = .95) for the questions related to the detection, determination of cause, and management of patient-ventilator asynchrony.

CONCLUSIONS

A specific 36-h training program significantly improved the ability of health-care professionals to detect patient-ventilator asynchrony, determine the possible causes of patient-ventilator asynchrony, and properly manage different types of patient-ventilator asynchrony.

Critical Care Pharmacist Attitudes and Perceptions of Neuromuscular Blocker Infusions in ARDS

BACKGROUND

Current critical care pharmacist (CCP) practices and perceptions related to neuromuscular infusion (NMBI) use for acute respiratory distress syndrome (ARDS) maybe different with the COVID-19 pandemic and the publication of 2020 NMBI practice guidelines.

OBJECTIVE

To evaluate CCP practices and perceptions regarding NMBI use for patients with moderate-severe ARDS.

METHODS

We developed, tested, and electronically administered a questionnaire (7 parent-, 42 sub-questions) to 409 American College of Clinical Pharmacy (ACCP) Critical Care Practice and Research Network members in 12 geographically diverse states. The questionnaire focused on adults with moderate-severe ARDS (PaO2:FiO2<150) whose causes of dyssynchrony were addressed. Two reminders were sent at 10-day intervals.

RESULTS

Respondents [131/409 (32%)] primarily worked in a medical intensive care unit (ICU) 102 (78%). Compared to COVID-negative(-) ARDS patients, COVID positive(+) ARDS patients were twice as likely to receive a NMBI (34 ± 18 vs.16 ± 17%; P < 0.01). Respondents somewhat/strongly agreed a NMBI should be reserved until after trials of deep sedation (112, 86%) or proning (92, 81%) and that NMBI reduced barotrauma (88, 67%), dyssynchrony (87, 66%), and plateau pressure (79, 60%). Few respondents somewhat/strongly agreed that a NMBI should be initiated at ARDS onset (23, 18%) or that NMBI reduced 90-day mortality (12, 10%). Only 2/14 potential NMBI risks [paralysis awareness (101, 82%) and prolonged muscle weakness (84, 68%)] were frequently reported to be of high/very high concern. Multiple NMBI titration targets were assessed as very/extremely important including arterial pH (109, 88%), dyssynchrony (107, 86%), and PaO2: FiO2 ratio (82, 66%). Train-of-four (55, 44%) and BIS monitoring (36, 29%) were deemed less important. Preferred NMBI discontinuation criteria included absence of dysschrony (84, 69%) and use ≥48 hour (72, 59%).

CONCLUSIONS AND RELEVANCE

Current critical care pharmacists believe NMBI for ARDS patients are best reserved until after trials of deep sedation or proning; unique considerations exist in COVID+ patients. Our results should be considered when ICU NMBI protocols are being developed and bedside decisions regarding NMBI use in ARDS are being formulated.

Automated evaluation of typical patient-ventilator asynchronies based on lung hysteretic responses

BACKGROUND

Patient-ventilator asynchrony is common during mechanical ventilation (MV) in intensive care unit (ICU), leading to worse MV care outcome. Identification of asynchrony is critical for optimizing MV settings to reduce or eliminate asynchrony, whilst current clinical visual inspection of all typical types of asynchronous breaths is difficult and inefficient. Patient asynchronies create a unique pattern of distortions in hysteresis respiratory behaviours presented in pressure-volume (PV) loop.

METHODS

Identification method based on hysteretic lung mechanics and hysteresis loop analysis is proposed to delineate the resulted changes of lung mechanics in PV loop during asynchronous breathing, offering detection of both its incidence and 7 major types. Performance is tested against clinical patient data with comparison to visual inspection conducted by clinical doctors.

RESULTS

The identification sensitivity and specificity of 11 patients with 500 breaths for each patient are above 89.5% and 96.8% for all 7 types, respectively. The average sensitivity and specificity across all cases are 94.6% and 99.3%, indicating a very good accuracy. The comparison of statistical analysis between identification and human inspection yields the essential same clinical judgement on patient asynchrony status for each patient, potentially leading to the same clinical decision for setting adjustment.

CONCLUSIONS

The overall results validate the accuracy and robustness of the identification method for a bedside monitoring, as well as its ability to provide a quantified metric for clinical decision of ventilator setting. Hence, the method shows its potential to assist a more consistent and objective assessment of asynchrony without undermining the efficacy of the current clinical practice.

Ineffective Effort in Patients With Acute Brain Injury Undergoing Invasive Mechanical Ventilation

BACKGROUND

Ineffective effort (IE) is a frequent patient-ventilator asynchrony in invasive mechanical ventilation. This study aimed to investigate the incidence of IE and to explore its relationship with respiratory drive in subjects with acute brain injury undergoing invasive mechanical ventilation.

METHODS

We retrospectively analyzed a clinical database that assessed patient-ventilator asynchrony in subjects with acute brain injury. IE was identified based on airway pressure, flow, and esophageal pressure waveforms collected at 15-min intervals 4 times daily. At the end of each data set recording, airway-occlusion pressure (P0.1) was determined by the airway occlusion test. IE index was calculated to indicate the severity of IE. The incidence of IE in different types of brain injuries as well as its relationship with P0.1 was determined.

RESULTS

We analyzed 852 data sets of 71 subjects with P0.1 measured and undergoing mechanical ventilation for at least 3 d after enrollment. IE was detected in 688 (80.8%) data sets, with a median index of 2.2% (interquartile range 0.4-13.1). Severe IE (IE index ≥ 10%) was detected in 246 (28.9%) data sets. The post craniotomy for brain tumor and the stroke groups had higher median IE index and lower P0.1 compared with the traumatic brain injury group (2.6% [0.7-9.7] vs 2.7% [0.3-21] vs 1.2% [0.1-8.5], P = .002; 1.4 [1-2] cm H2O vs 1.5 [1-2.2] cm H2O vs 1.8 [1.1-2.8] cm H2O, P = .001). Low respiratory drive (P0.1 < 1.14 cm H2O) was independently associated with severe IE in the expiratory phase (IEE) even after adjusting for confounding factors by logistic regression analysis (odds ratio 5.18 [95% CI 2.69-10], P < .001).

CONCLUSIONS

IE was very common in subjects with acute brain injury. Low respiratory drive was independently associated with severe IEE.

The oesophageal balloon for respiratory monitoring in ventilated patients: updated clinical review and practical aspects

There is a well-recognised importance for personalising mechanical ventilation settings to protect the lungs and the diaphragm for each individual patient. Measurement of oesophageal pressure (P oes) as an estimate of pleural pressure allows assessment of partitioned respiratory mechanics and quantification of lung stress, which helps our understanding of the patient's respiratory physiology and could guide individualisation of ventilator settings. Oesophageal manometry also allows breathing effort quantification, which could contribute to improving settings during assisted ventilation and mechanical ventilation weaning. In parallel with technological improvements, P oes monitoring is now available for daily clinical practice. This review provides a fundamental understanding of the relevant physiological concepts that can be assessed using P oes measurements, both during spontaneous breathing and mechanical ventilation. We also present a practical approach for implementing oesophageal manometry at the bedside. While more clinical data are awaited to confirm the benefits of P oes-guided mechanical ventilation and to determine optimal targets under different conditions, we discuss potential practical approaches, including positive end-expiratory pressure setting in controlled ventilation and assessment of inspiratory effort during assisted modes.

Fundamental concepts and the latest evidence for esophageal pressure monitoring

Transpulmonary pressure is an essential physiologic concept as it reflects the true pressure across the alveoli, and is a more precise marker for lung stress. To calculate transpulmonary pressure, one needs an estimate of both alveolar pressure and pleural pressure. Airway pressure during conditions of no flow is the most widely accepted surrogate for alveolar pressure, while esophageal pressure remains the most widely measured surrogate marker for pleural pressure. This review will cover important concepts and clinical applications for esophageal manometry, with a particular focus on how to use the information from esophageal manometry to adjust or titrate ventilator support. The most widely used method for measuring esophageal pressure uses an esophageal balloon catheter, although these measurements can be affected by the volume of air in the balloon. Therefore, when using balloon catheters, it is important to calibrate the balloon to ensure the most appropriate volume of air, and we discuss several methods which have been proposed for balloon calibration. In addition, esophageal balloon catheters only estimate the pleural pressure over a certain area within the thoracic cavity, which has resulted in a debate regarding how to interpret these measurements. We discuss both direct and elastance-based methods to estimate transpulmonary pressure, and how they may be applied for clinical practice. Finally, we discuss a number of applications for esophageal manometry and review many of the clinical studies published to date which have used esophageal pressure. These include the use of esophageal pressure to assess lung and chest wall compliance individually which can provide individualized information for patients with acute respiratory failure in terms of setting PEEP, or limiting inspiratory pressure. In addition, esophageal pressure has been used to estimate effort of breathing which has application for ventilator weaning, detection of upper airway obstruction after extubation, and detection of patient and mechanical ventilator asynchrony.

Patient Self-Inflicted Lung Injury-A Narrative Review of Pathophysiology, Early Recognition, and Management Options

Patient self-inflicted lung injury (P-SILI) is a life-threatening condition arising from excessive respiratory effort and work of breathing in patients with lung injury. The pathophysiology of P-SILI involves factors related to the underlying lung pathology and vigorous respiratory effort. P-SILI might develop both during spontaneous breathing and mechanical ventilation with preserved spontaneous respiratory activity. In spontaneously breathing patients, clinical signs of increased work of breathing and scales developed for early detection of potentially harmful effort might help clinicians prevent unnecessary intubation, while, on the contrary, identifying patients who would benefit from early intubation. In mechanically ventilated patients, several simple non-invasive methods for assessing the inspiratory effort exerted by the respiratory muscles were correlated with respiratory muscle pressure. In patients with signs of injurious respiratory effort, therapy aimed to minimize this problem has been demonstrated to prevent aggravation of lung injury and, therefore, improve the outcome of such patients. In this narrative review, we accumulated the current information on pathophysiology and early detection of vigorous respiratory effort. In addition, we proposed a simple algorithm for prevention and treatment of P-SILI that is easily applicable in clinical practice.

Modern Sedation and Analgesia Strategies in Neurocritical Care

PURPOSE OF REVIEW

Patients with acute neurologic injury require a specialized approach to critical care, particularly with regard to sedation and analgesia. This article reviews the most recent advances in methodology, pharmacology, and best practices of sedation and analgesia for the neurocritical care population.

RECENT FINDINGS

In addition to established agents such as propofol and midazolam, dexmedetomidine and ketamine are two sedative agents that play an increasingly central role, as they have a favorable side effect profile on cerebral hemodynamics and rapid offset can facilitate repeated neurologic exams. Recent evidence suggests that dexmedetomidine is also an effective component when managing delirium. Combined analgo-sedation with low doses of short-acting opiates is a preferred sedation strategy to facilitate neurologic exams as well as patient-ventilator synchrony. Optimal care for patients in the neurocritical care population requires an adaptation of general ICU strategies that incorporates understanding of neurophysiology and the need for close neuromonitoring. Recent data continues to improve care tailored to this population.

Association between ventilatory ratio and ICU mortality in interstitial lung disease patients on mechanical ventilation: A retrospective study

BACKGROUND

Ventilatory ratio (VR) is a simple bedside index of ventilatory efficiency. Interstitial lung disease (ILD) is a diverse group of diseases that causes fibrosis or inflammation of the pulmonary parenchyma, and the main clinical manifestation is hypoxemia. To date, no study has explored ventilation efficiency in patients with ILD.

OBJECTIVES

This study aimed to explore the features of VR in mechanically ventilated patients with ILD and their relationship with intensive care unit (ICU) mortality.

METHODS

In this retrospective analysis, we included mechanically ventilated patients with ILD in the ICU of West China Hospital, Sichuan University, from 2013 to 2021. Demographic data and mechanical ventilation (MV) parameters within 24 h of intubation were collected. The characteristics of VR and their relationships with ICU mortality were also analyzed.

RESULTS

224 patients were included in the final analysis. There were 146 males (53.9%), and the median age was 65 years (interquartile range [IQR]54∼74). The mean value of VR was 2.22, and VR was significantly higher in nonsurvivors than in survivors (1.79 vs 2.32, P < 0.001). A high VR value was an independent risk factor for ICU mortality (odds ratio=1.602, P = 0.038) after adjustment. A high value of VR was associated with a shorter survival time after admission to ICU (hazard ratio=1.485, P = 0.006) CONCLUSIONS: VR in patients with ILD on MV was increased, and the VR of nonsurvivors within 24 h of intubation was higher than that of survivors. The high VR value within 24 h of intubation was an independent risk factor for ICU mortality after adjusting for other factors.

Asynchronies during invasive mechanical ventilation: narrative review and update

Invasive mechanical ventilation is a frequent therapy in critically ill patients in critical care units. To achieve favorable outcomes, patient and ventilator interaction must be adequate. However, many clinical situations could attempt against this principle and generate a mismatch between these two actors. These asynchronies can lead the patient to worst outcomes; because of that is vital to recognize and treat these entities as soon as possible. Early detection and recognition of the different asynchronies could favor the reduction of the days of mechanical ventilation, the days of hospital stay, and in intensive care and improve clinical results.

What is new in respiratory monitoring?

This paper provides a review of a selection of papers published in the Journal of Clinical Monitoring and Computing in 2020 and 2021 highlighting what is new within the field of respiratory monitoring. Selected papers cover work in pulse oximetry monitoring, acoustic monitoring, respiratory system mechanics, monitoring during surgery, electrical impedance tomography, respiratory rate monitoring, lung ultrasound and detection of patient-ventilator asynchrony.