Patient-ventilator Asynchronies Research Articles

Oesophageal pressure monitoring in intubated patients by intensive care units' nurses: An educational study.

Trained ICU nurses may perform oesophageal pressure measurements which may help facilitate its implementation in the usual patient care to better assess lung and chest wall mechanics and easily detect patient-ventilator asynchronies. We thus conducted a prospective educational study aiming to assess the ability of ICU nurses to perform reliable oesophageal pressure measurements after a short dedicated training program. All the 11 nurses who completed the program succeeded their practical evaluation (nine (82%) at the first evaluation and two (18%) at their second attempt). These results show that this training program is feasible and that trained ICU nurses can perform accurate oesophageal pressure measurements in mechanically ventilated patients.

Automatic patient-ventilator asynchrony detection framework using objective asynchrony definitions

Patient-ventilator asynchrony is one of the largest challenges in mechanical ventilation and is associated with prolonged ICU stay and increased mortality. The aim of this paper is to automatically detect and classify the different types of patient-ventilator asynchronies during a patient’s breath using the typically available data on commercially available ventilators. This is achieved by a detection and classification framework using an objective definition of asynchrony and a supervised learning approach. The achieved detection performance of the near-real time framework on a clinical dataset is a significant improvement over current clinical practice, therewith and, this framework has the potential to significantly improve the patient comfort and treatment outcomes.

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

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. 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). 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. 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.

Lesión pulmonar autoinducida por el paciente (P-SILI)

The article reviews the scientific literature on Patient Self-Induced Lung Injury (P-SILI) in patients with Acute Respiratory Distress Syndrome (ARDS). It begins with a history since 1985, highlighting events of pulmonary edema related to hyperventilation in diabetic patients and advances in the understanding of pulmonary mechanics. Experimental studies in sheep and humans are detailed that show lung damage due to hyperventilation, highlighting the importance of spontaneous ventilation and alveolar damage. In addition, studies on alveolar hemorrhage post-intense exercise in animals and high-performance athletes are mentioned. The text explores the relationship between Invasive Mechanical Ventilation (IMV) and lung injury, addressing therapeutic strategies such as neuromuscular blockade and the impact of positive end-expiratory pressure (PEEP). The theoretical mechanisms of P-SILI are delved into, highlighting pulmonary overdistension, increased pulmonary perfusion and patient-ventilator asynchronies. The clinical impact of P-SILI, especially in patients with COVID-19 and ARDS, is discussed, and management strategies such as high-flow oxygen therapy are highlighted. The article concludes by highlighting the need for more studies to standardize the monitoring of inspiratory efforts, understand its implication in prognosis and guide appropriate therapy, recognizing that the optimal approach is still the subject of debate.

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

BackgroundPatient–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.MethodsIdentification 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.ResultsThe 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.ConclusionsThe 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.

Eccentric Contractions of the Diaphragm During Mechanical Ventilation.

Diaphragm dysfunction is a highly prevalent phenomenon in patients receiving mechanical ventilation, mainly due to ventilatory over-assistance and the development of diaphragm disuse atrophy. Promoting diaphragm activation whenever possible and facilitating an adequate interaction between the patient and the ventilator is encouraged at the bedside to avoid myotrauma and further lung injury. Eccentric contractions of the diaphragm are defined as muscle activation while muscle fibers are lengthening within the exhalation phase. There is recent evidence that suggests that eccentric activation of the diaphragm is very frequent and may occur during post-inspiratory activity or under different types of patient-ventilator asynchronies, which include ineffective efforts, premature cycling, and reverse triggering. The consequences of this eccentric contraction of the diaphragm may have opposite effects, depending on the level of breathing effort. For instance, during high or excessive effort, eccentric contractions can result in diaphragm dysfunction and injured muscle fibers. Conversely, when eccentric contractions of the diaphragm occur along with low breathing effort, a preserved diaphragm function, better oxygenation, and more aerated lung tissue are observed. Despite this controversial evidence, evaluating the level of breathing effort at the bedside seems crucial and is highly recommended to optimize ventilatory therapy. The impact of eccentric contractions of the diaphragm on the patient's outcome remains to be elucidated.

Identifying asynchronies: Delayed triggering

Patient-ventilator asynchronies can occur at any phase throughout the respiratory cycle. Because it has been associated with patient outcomes, it is important to recognize and address these asynchronies. Bedside interpretation of air flow and airway pressure waveforms are helpful for recognizing patient–ventilator asynchronies and optimizing ventilator settings. Patient effort is sensed by either a drop in circuit pressure (pressure trigger) or circuit bias flow (flow trigger). Triggering delay is the time interval between the start of the neural and mechanical inspiration. Triggers must be sensitive enough to recognize patient effort to avoid imposing an additional load but not too sensitive to avoid auto-triggering. Despite improvements in triggering technology, triggering asynchronies continue to occur and are manifest, among others, by delayed triggering. Keywords: asynchrony, patient effort, trigger, delayed triggering

An investigation on the respiratory mechanics of mechanically ventilated patients during spontaneous breathing trials with enhanced low-level pressure support ventilation.

Low-level pressure support ventilation (PSV) is most commonly adopted in spontaneous breathing trials (SBTs), and some have proposed setting the positive end-expiratory pressure (PEEP) to 0cmH2 O in order to shorten the observation time of SBTs. This study aims to investigate the effects of two PSV protocols on the patients' respiratory mechanics. A prospective randomized self-controlled crossover design was adopted in this study, which involved enrolling 30 difficult-to-wean patients who were admitted to the intensive care unit of the First Affiliated Hospital of Guangzhou Medical University between July 2019 and September 2021. Patients were subjected to the S group (pressure support: 8cmH2 O, PEEP: 5cmH2 O) and S1 group (PS: 8cmH2 O, PEEP: 0cmH2 O) for 30 min in a random order, and respiratory mechanics indices were dynamically monitored via a four-lumen multi-functional catheter with an integrated gastric tube. Among the 30 enrolled patients, 27 were successfully weaned. The S group showed higher airway pressure (Paw), intragastric pressure (Pga) and airway pressure-time product (PTP) than the S1 group. The S group also showed a shorter inspiratory trigger delay, (93.80 ± 47.85) versus (137.33 ± 85.66) ms (P = 0.004); and fewer abnormal triggers, (0.97 ± 2.65) versus (2.67 ± 4.48) (P = 0.042) compared with the S1 group. Stratification based on the causes of mechanical ventilation revealed that under the S1 protocol, patients with chronic obstructive pulmonary disease (COPD) had a longer inspiratory trigger delay compared to both post-thoracic surgery (PTS) patients and patients with acute respiratory distress syndrome. Despite providing greater respiratory support, S group led to significant reductions in inspiratory trigger delay and less abnormal triggers compared to S1 group, especially among patients with chronic obstructive pulmonary disease. These findings suggest that the zero PEEP group was more likely to induce a higher number of patient-ventilator asynchronies in difficult-to-wean patients.

Impact on the ability of healthcare professionals to correctly identify patient-ventilator asynchronies of the simultaneous visualization of estimated muscle pressure curves on the ventilator display: a randomized study (Pmus study)

BackgroundPatient-ventilator asynchronies are usually detected by visual inspection of ventilator waveforms but with low sensitivity, even when performed by experts in the field. Recently, estimation of the inspiratory muscle pressure (Pmus) waveforms through artificial intelligence algorithm has been proposed (Magnamed®, São Paulo, Brazil). We hypothesized that the display of these waveforms could help healthcare providers identify patient-ventilator asynchronies.MethodsA prospective single-center randomized study with parallel assignment was conducted to assess whether the display of the estimated Pmus waveform would improve the correct identification of asynchronies in simulated clinical scenarios. The primary outcome was the mean asynchrony detection rate (sensitivity). Physicians and respiratory therapists who work in intensive care units were randomized to control or intervention group. In both groups, participants analyzed pressure and flow waveforms of 49 different scenarios elaborated using the ASL-5000 lung simulator. In the intervention group the estimated Pmus waveform was displayed in addition to pressure and flow waveforms.ResultsA total of 98 participants were included, 49 per group. The sensitivity per participant in identifying asynchronies was significantly higher in the Pmus group (65.8 ± 16.2 vs. 52.94 ± 8.42, p < 0.001). This effect remained when stratifying asynchronies by type.ConclusionsWe showed that the display of the Pmus waveform improved the ability of healthcare professionals to recognize patient-ventilator asynchronies by visual inspection of ventilator tracings. These findings require clinical validation.Trial registration: ClinicalTrials.gov: NTC05144607. Retrospectively registered 3 December 2021.

Monitoring Systems in Home Ventilation.

Non-invasive ventilation (NIV) is commonly used at home for patient with nocturnal hypoventilation caused by a chronic respiratory failure. Monitoring NIV is required to optimize the ventilator settings when the lung condition changes over time, and to detect common problems such as unintentional leaks, upper airway obstructions, and patient-ventilator asynchronies. This review describes the accuracy and limitations of the data recorded by the ventilator. To efficiently interpret this huge amount of data, clinician assess the daily use and regularity of NIV utilization, the unintentional leaks and their repartition along the NIV session, the apnea-hypopnea index and the flow waveform, and the patient-ventilator synchrony. Nocturnal recordings of gas exchanges are also required to detect nocturnal alveolar hypoventilation. This review describes the indication, validity criteria, and interpretation of nocturnal oximetry and transcutaneous capnography. Polygraphy and polysomnography are indicated in specific cases to characterize upper airway obstruction. Telemonitoring of the ventilator is a useful tool that should be integrated in the monitoring strategy. The technical solution, information, and limitations are discussed. In conclusion, a basic monitoring package is recommended for all patients complemented by advanced monitoring for specific cases.

Identifying asynchronies: work shifting and double triggering

Mechanical ventilation supports the work of breathing, improves gas exchange, and unloads the respiratory muscles, all of which require good synchronization between the patient and the ventilator. Asynchronies occur when the ventilator’s breath delivery does not match the patient’s neural ventilatory pattern or is inadequate to meet the patient’s flow demand. Patient–ventilator asynchrony can be easily detected by observing the patients in those extreme situations in which they fight the ventilator; nevertheless, the vast majority of asynchronies occur without major clinical signs and go undetected or corrected without measuring patient's respiratory effort (either esophageal pressure or electrical activity of the diaphragm). Synchrony problems are common, occurring in perhaps as many as 25% of patients receiving invasive ventilation and up to 80% of patients receiving noninvasive ventilation. In this concise review, we describe work shifting and double triggering asynchronies. Keywords: Patient-ventilator asynchronies, work shifting, double triggering

Impact of a Mechanical Ventilation Curriculum on Respiratory Therapist Recognition of Patient-Ventilator Asynchrony.

Respiratory therapists (RTs) play a crucial role in managing mechanically ventilated patients, such as addressing patient-ventilator asynchronies that may contribute to patient harm. Waveform analysis is integral to the evaluation of patient-ventilator asynchronies; despite this, no published studies have assessed the ability of practicing RTs to interpret ventilator waveform abnormalities. The study took place between June 2017-February 2019. Eighty-six RTs from 2 academic medical centers enrolled in a one-day mechanical ventilation course. The scores of 79 first-time attendees were included in the analysis. Prior to and following the course, RTs were asked to identify abnormalities on a 5-question, multiple-choice ventilator waveform exam. They were also asked to provide a self-assessment of their ventilator management skills on a 1 (complete novice) to 5 (expert) scale. Initial scores were low but improved after one day of ventilator instruction (19.4 ± 17.1 vs 29.6 ± 19.0, P < .001). No significant difference was noted in mean confidence levels between the pre- and post-course assessments (3.8 ± 0.9 vs 3.8 ± 1.0, P = .56). RTs with fewer years of clinical experience (0-10 y) had a statistically significant improvement in their post-course test scores relative to their pre-course scores (0-5 y: 12.5 ± 10.1 to 46.0 ± 10.8, P < .001; 6-10 y: 18.7 ± 15.8 to 32.1 ± 16.7, P = .02), whereas those with > 11 y of clinical experience did not (11-20 y: 22.4 ± 15.5 to 27.4 ± 19.0, P = .44; 21+ y: 19.6 ± 22.1 to 15.3 ± 13.8, P = .50). RTs may benefit from additional training in ventilator waveform interpretation, especially early in their clinical training. More work is needed to determine the optimal length and content of a mechanical ventilation curriculum for RTs.

Identifying asynchronies: Ineffective effort

Mechanical ventilation is a common issue in critically ill patients. It is a lifesaving treatment but also can cause some complications. Patient-ventilator asynchronies are frequent but are often underdiagnosed and they are a serious problem that is associated with worse clinical outcomes. Asynchrony occurs when there is a mismatch between the ventilator setting and the patient´s demand or breath delivery timing. There are a variety of asynchronies between the patient’s respiratory efforts and the programed ventilatory setting. Ineffective effort is a kind of asynchrony of the trigger variable. It occurs when the patient’s inspiratory effort fails to trigger a ventilator breath. Ineffective inspiratory efforts are a great problem in patient-ventilator interaction, and they are the most common type of asynchrony.

Patient-Ventilator Synchrony in Neurally-Adjusted Ventilatory Assist and Variable Pressure Support Ventilation.

Neurally-adjusted ventilatory assist (NAVA) improves patient-ventilator synchrony and reduces the risk of respiratory over-assistance. Variable pressure support ventilation (PSV) is a recently introduced mode of assisted ventilation that has also shown reduction in patient-ventilator asynchronies. We hypothesized that NAVA would reduce patient-ventilator asynchronies and inspiratory effort compared to variable PSV because breathing variability was intrinsically determined by the patient and not by the ventilator. This study aimed to evaluate patient-ventilator asynchronies and inspiratory effort pressure-time product (PTP) between NAVA and variable PSV in subjects with mild ARDS. After 24 h of controlled mechanical ventilation, subjects (PaO2 /FIO2 200-300 and PEEP level < 10 cm H2O) were randomized in sequence 1:1 by using a web-based encrypted platform and assigned to NAVA or variable PSV groups. Both modes of ventilation were consecutively kept for 24 h unless there were clinical changes. The primary aim of this study was to evaluate differences in asynchrony index (AI) between variable PSV and NAVA. Our secondary aims were to evaluate the coefficient of variation (CV) of breathing patterns and inspiratory effort between the groups. Thirteen subjects were randomized in the NAVA group and 13 subjects in the variable PSV group. AI over time and minute PTP (PTPmin) were not different between NAVA and variable PSV groups (AI t0P = .52, AI t12P = .27, AI t24P = .12; and PTPmin-t0P = .60, PTPmin-t12P = .57, PTPmin-t24P = .85, respectively). CV for tidal volume (VT) and pressure support (PS) was lower in variable PSV group over time compared with NAVA group (P < .05). In this randomized controlled trial including subjects with mild ARDS, NAVA and variable PSV had comparable effects on patient-ventilator synchronies and PTP. However, variable PSV reduced the variability of VT and PS when compared with NAVA.

The Impact of a Training Intervention on Detection of Patient-Ventilator Asynchronies in Nursing Students.

Background and aim of the work:Patient-ventilator asynchronies (PVA) are associated to negative outcomes for patients: increased respiratory work, mechanical ventilation time and ICU length of stay, and mortality. Some studies described the positive impact of a training intervention on the knowledge and attitudes of nurses in detecting PVA. The aim of this study was to evaluate the efficacy of a training intervention focused on detection of PVA.Methods:A before-after design on a single group of nursing students (University “L. Vanvitelli” in Naples) was used. The training intervention about detection of the correct respiratory waveform through graphic monitoring was conducted in a single edition live course of 2 hours, for 25 participants. Measurements of correct detection of PVA through specific competency assessment tool (closed-ended question) were performed before (T0), immediately after (T1) and at 1 month (T2) from the training intervention between January and February 2020.Results:19 Nurse students completed the training. A total of 50 questionnaires were distributed (T0; n=19; T1; n=19; T2; n=12). PVA were correctly detected in 67.5% (77) of cases. Statistically significant difference There were differences in trainees’ performance between T0 and T1 [77,2% (CI 95%: 68,7% - 85,8%; p=0.001)] and between T0 and T2 [75% (CI 95%: 65,3% - 84,7%; p=0.001]. No significant difference was recorded between T1 and T2 (p=0.83).Conclusions:Nursing students increased their performance on analysis of the graphic monitoring of the respiratory waveforms and detection of asynchronies after a basic training intervention. These skills were retained after 1 month. (www.actabiomedica.it)

Short-Term Effects of Pressure Controlled Versus Volume Controlled Noninvasive Ventilation in Subjects With Amyotrophic Lateral Sclerosis.

Comparison of the effects of pressure controlled and volume controlled noninvasive ventilations (NIV) has usually been limited to the degree of improvement in blood gases. We compared sleep quality, abnormal respiratory events, and patient-ventilator asynchronies during administration of pressure controlled continuous mandatory ventilation (PC-CMV) and volume controlled continuous mandatory ventilation (VC-CMV) in subjects with amyotrophic lateral sclerosis naive to NIV after titration aimed at maximally improving nocturnal arterial blood gases. A crossover evaluation of PC-CMV and VC-CMV was performed in 27 subjects with amyotrophic lateral sclerosis. After baseline polysomnography, ventilators were set in random order so as to warrant similar and satisfactory oxygen saturation and transcutaneous [Formula: see text] in both NIV modalities during day and night. Soon after titration, polysomnography was repeated during administration of each type of NIV. With respect to the baseline night, non-rapid eye movement 3, and rapid eye movement sleep stages increased, and the arousal index decreased during PC-CMV (P = .005, P = .02, and P = .01, PC-CMV vs VC-CMV, respectively) but not during VC-CMV. The arousal index during NIV was correlated to the peak pressure delivered by the ventilators (ρ = 0.47, P < .001). Few abnormal respiratory events were observed in both NIV modes. Patient-ventilator asynchronies were more frequent during VC-CMV (median [IQR] 20.8 [0.0 - 22.0] vs 31.8 [30.1 - 34.0] no./h, PC-CMV vs VC-CMV; P = .002). Twenty-one subjects declared that they preferred PC-CMV therapy. In the short term, PC-CMV may be a preferred NIV modality to VC-CMV for patients with amyotrophic lateral sclerosis, even when both NIV modes are similarly effective in the correction of hypoventilation. Evaluation of the effectiveness of NIV should not be limited to the assessment of blood gas correction.

Sleep increases leaks and asynchronies during home noninvasive ventilation: a polysomnographic study.

In patients treated with noninvasive ventilation, sleep-related breathing changes can modify patient-ventilator interactions, which could reduce its effectiveness. The aim of this prospective observational study was to determine the impact of sleep/wake state on leaks, upper airway obstructive events, and asynchronies in patients treated by long-term noninvasive ventilation. Stable patients adapted to noninvasive ventilation were considered for nocturnal polysomnography. Unintentional leaks, upper airway obstructive events, and asynchronies were compared between sleep and awake periods. Twenty-eight patients were enrolled. Underlying diagnoses were neuromuscular disease (n = 11), chest wall disease (n = 8), and obesity-hypoventilation (n = 9). Leaks were more frequent in sleep than in awake periods, with a median of 10% (interquartile range [IQR], 0%-75%) vs 1% (IQR, 0%-9%) of time (P < .001), respectively. During sleep, asynchronies with and without associated leak affected 27% of breaths (IQR, 16%-39%) compared with non-leak-related asynchronies that were recorded in 8% (IQR, 3%-25%) of breaths (P < .001). Asynchronies affecting more than 10% of total breaths were more frequent in sleep (25 patients, 89%) than in awake time (8 patients, 29%; P = .25). Eleven patients (39%) presented with 5 or more upper airway obstructive events without reduction in ventilatory drive per hour of sleep. In patients adapted to home noninvasive ventilation, leaks, asynchronies, and upper airway obstructive events are frequent during the night and are concentrated in sleep periods. Asynchronies are often associated with leaks. These findings may have clinical implications considering that in patients with low sleep efficiency respiratory events could be underestimated if sleep is not evaluated. Martí S, Ferré A, Sampol G, etal. Sleep increases leaks and asynchronies during home noninvasive ventilation: a polysomnographic study. J Clin Sleep Med. 2022;18(1):225-233.

Longitudinal Changes in Patient-Ventilator Asynchronies and Respiratory System Mechanics Before and After Tracheostomy.

This was a pilot study to analyze the effects of tracheostomy on patient-ventilator asynchronies and respiratory system mechanics. Data were extracted from an ongoing prospective, real-world database that stores continuous output from ventilators and bedside monitors. Twenty adult subjects were on mechanical ventilation and were tracheostomized during an ICU stay: 55% were admitted to the ICU for respiratory failure and 35% for neurologic conditions; the median duration of mechanical ventilation before tracheostomy was 12 d; and the median duration of mechanical ventilation was 16 d. We compared patient-ventilator asynchronies (the overall asynchrony index and the rates of specific asynchronies) and respiratory system mechanics (respiratory-system compliance and airway resistance) during the 24 h before tracheostomy versus the 24 h after tracheostomy. We analyzed possible differences in these variables among the subjects who underwent surgical versus percutaneous tracheostomy. To compare longitudinal changes in the variables, we used linear mixed-effects models for repeated measures along time in different observation periods. A total of 920 h of mechanical ventilation were analyzed. Respiratory mechanics and asynchronies did not differ significantly between the 24-h periods before and after tracheostomy: compliance of the respiratory system median (IQR) (47.9 [41.3 - 54.6] mL/cm H2O vs 47.6 [40.9 - 54.3] mL/cm H2O; P = .94), airway resistance (9.3 [7.5 - 11.1] cm H2O/L/s vs 7.0 [5.2 - 8.8] cm H2O/L/s; P = .07), asynchrony index (2.0% [1.1 - 3.6%] vs 4.1% [2.3 - 7.6%]; P = .09), ineffective expiratory efforts (0.9% [0.4 - 1.8%] vs 2.2% [1.0 - 4.4%]; P = .08), double cycling (0.5% [0.3 - 1.0%] vs 0.9% [0.5 - 1.9%]; P = .24), and percentage of air trapping (7.6% [4.2 - 13.8%] vs 10.6% [5.9 - 19.2%]; P = .43). No differences in respiratory mechanics or patient-ventilator asynchronies were observed between percutaneous and surgical procedures. Tracheostomy did not affect patient-ventilator asynchronies or respiratory mechanics within 24 h before and after the procedure.

Risk Factors for Patient-Ventilator Asynchrony and Its Impact on Clinical Outcomes: Analytics Based on Deep Learning Algorithm.

Background and objectives: Patient–ventilator asynchronies (PVAs) are common in mechanically ventilated patients. However, the epidemiology of PVAs and its impact on clinical outcome remains controversial. The current study aims to evaluate the epidemiology and risk factors of PVAs and their impact on clinical outcomes using big data analytics.Methods: The study was conducted in a tertiary care hospital; all patients with mechanical ventilation from June to December 2019 were included for analysis. Negative binomial regression and distributed lag non-linear models (DLNM) were used to explore risk factors for PVAs. PVAs were included as a time-varying covariate into Cox regression models to investigate its influence on the hazard of mortality and ventilator-associated events (VAEs).Results: A total of 146 patients involving 50,124 h and 51,451,138 respiratory cycles were analyzed. The overall mortality rate was 15.6%. Double triggering was less likely to occur during day hours (RR: 0.88; 95% CI: 0.85–0.90; p < 0.001) and occurred most frequently in pressure control ventilation (PCV) mode (median: 3; IQR: 1–9 per hour). Ineffective effort was more likely to occur during day time (RR: 1.09; 95% CI: 1.05–1.13; p < 0.001), and occurred most frequently in PSV mode (median: 8; IQR: 2–29 per hour). The effect of sedatives and analgesics showed temporal patterns in DLNM. PVAs were not associated mortality and VAE in Cox regression models with time-varying covariates.Conclusions: Our study showed that counts of PVAs were significantly influenced by time of the day, ventilation mode, ventilation settings (e.g., tidal volume and plateau pressure), and sedatives and analgesics. However, PVAs were not associated with the hazard of VAE or mortality after adjusting for protective ventilation strategies such as tidal volume, plateau pressure, and positive end expiratory pressure (PEEP).

Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation

Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm’s performance was compared versus the gold standard (the ventilator’s waveform recordings for CP-VI were scored visually by three experts; Fleiss’ kappa = 0.90 (0.87–0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient’s own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78–0.86) and 0.78 (0.78–0.85), and accuracies of 0.93 (0.89–0.93) and 0.89 (0.89–0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications.