Clusters of Double Triggering Impact Clinical Outcomes: Insights From the EPIdemiology of Patient-Ventilator aSYNChrony (EPISYNC) Cohort Study

Expiratory Muscle Activity Counteracts Positive End-Expiratory Pressure and Is Associated with Fentanyl Dose in Patients with Acute Respiratory Distress Syndrome

Rationale: Hypoxemia during mechanical ventilation might be worsened by expiratory muscle activity, which reduces end-expiratory lung volume through lung collapse. A proposed mechanism of benefit of neuromuscular blockade in acute respiratory distress syndrome (ARDS) is the abolition of expiratory efforts. This may contribute to the restoration of lung volumes. The prevalence of this phenomenon, however, is unknown. Objectives: To investigate the incidence and amount of end-expiratory lung impedance (EELI) increase after the administration of neuromuscular blocking agents (NMBAs), clinical factors associated with this phenomenon, its impact on regional lung ventilation, and any association with changes in pleural pressure. Methods: We included mechanically ventilated patients with ARDS monitored with electrical impedance tomography (EIT) who received NMBAs in one of two centers. We measured changes in EELI, a surrogate for end-expiratory lung volume, before and after NMBA administration. In an additional 10 patients, we investigated the characteristic signatures of expiratory muscle activity depicted by EIT and esophageal catheters simultaneously. Clinical factors associated with EELI changes were assessed. Measurements and Main Results: We included 46 patients, half of whom showed an increase in EELI of >10% of the corresponding Vt (46.2%; IQR, 23.9-60.9%). The degree of EELI increase correlated positively with fentanyl dosage and negatively with changes in end-expiratory pleural pressures. This suggests that expiratory muscle activity might exert strong counter-effects against positive end-expiratory pressure that are possibly aggravated by fentanyl. Conclusions: Administration of NMBAs during EIT monitoring revealed activity of expiratory muscles in half of patients with ARDS. The resultant increase in EELI had a dose-response relationship with fentanyl dosage. This suggests a potential side effect of fentanyl during protective ventilation.

The Development, Optimization, and Validation of Four Different Machine Learning Algorithms to Identify Ventilator Dyssynchrony

Invasive mechanical ventilation can worsen lung injury. Ventilator dyssynchrony (VD) may propagate ventilator-induced lung injury (VILI) and is challenging to detect and systematically monitor because each patient takes approximately 25,000 breaths a day yet some types of VD are rare, accounting for less than 1% of all breaths. Therefore, we sought to develop and validate accurate machine learning (ML) algorithms to detect multiple types of VD by leveraging esophageal pressure waveform data to quantify patient effort with airway pressure, flow, and volume data generated during mechanical ventilation, building a computational pipeline to facilitate the study of VD.

MATERIALS AND METHODS

We collected ventilator waveform and esophageal pressure data from 30 patients admitted to the ICU. Esophageal pressure allows the measurement of transpulmonary pressure and patient effort. Waveform data were cleaned, features considered essential to VD detection were calculated, and a set of 10,000 breaths were manually labeled. Four ML algorithms were trained to classify each type of VD: logistic regression, support vector classification, random forest, and XGBoost.

RESULTS

We trained ML models to detect different families and seven types of VD with high sensitivity (>90% and >80%, respectively). Three types of VD remained difficult for ML to classify because of their rarity and lack of sample size. XGBoost classified breaths with increased specificity compared to other ML algorithms.

DISCUSSION

We developed ML models to detect multiple types of VD accurately. The ability to accurately detect multiple VD types addresses one of the significant limitations in understanding the role of VD in affecting patient outcomes.

CONCLUSION

ML models identify multiple types of VD by utilizing esophageal pressure data and airway pressure, flow, and volume waveforms. The development of such computational pipelines will facilitate the identification of VD in a scalable fashion, allowing for the systematic study of VD and its impact on patient outcomes.

Hiccup-like Contractions in Mechanically Ventilated Patients: Individualized Treatment Guided by Transpulmonary Pressure

Hiccups-like contractions, including hiccups, respiratory myoclonus, and diaphragmatic tremor, refer to involuntary, spasmodic, and inspiratory muscle contractions. They have been repeatedly described in mechanically ventilated patients, especially those with central nervous damage. Nevertheless, their effects on patient-ventilator interaction are largely unknown, and even more overlooked is their contribution to lung and diaphragm injury. We describe, for the first time, how the management of hiccup-like contractions was individualized based on esophageal and transpulmonary pressure measurements in three mechanically ventilated patients. The necessity or not of intervention was determined by the effects of these contractions on arterial blood gases, patient-ventilator synchrony, and lung stress. In addition, esophageal pressure permitted the titration of ventilator settings in a patient with hypoxemia and atelectasis secondary to hiccups and in whom sedatives failed to eliminate the contractions and muscle relaxants were contraindicated. This report highlights the importance of esophageal pressure monitoring in the clinical decision making of hiccup-like contractions in mechanically ventilated 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)

BACKGROUND

Patient-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 (P_mus) 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.

METHODS

A prospective single-center randomized study with parallel assignment was conducted to assess whether the display of the estimated P_mus 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 P_mus waveform was displayed in addition to pressure and flow waveforms.

RESULTS

A total of 98 participants were included, 49 per group. The sensitivity per participant in identifying asynchronies was significantly higher in the P_mus group (65.8 ± 16.2 vs. 52.94 ± 8.42, p < 0.001). This effect remained when stratifying asynchronies by type.

CONCLUSIONS

We showed that the display of the P_mus 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.

Simulation-based Assessment to Measure Proficiency in Mechanical Ventilation among Residents

Background

Mechanical ventilation (MV) skills are essential for clinicians caring for critically ill patients, yet few training programs use structured curricula and appropriate assessments. Objective structured clinical exams (OSCEs) have been used to assess clinical competency in many areas, but there are no OSCE models focused on MV.

Objective

To develop and validate a simulation-based assessment (SBA) with an OSCE structure to assess baseline MV competence among residents and identify knowledge gaps.

Methods

We developed an SBA using a lung simulator and a mechanical ventilator, and an OSCE structure, with six clinical scenarios in MV. We included internal medicine residents at the beginning of their rotation in the respiratory intensive care unit (ICU) of a university-affiliated hospital. A subset of residents was also evaluated with a validated multiple-choice exam (MCE) at the beginning and at the end of the ICU rotation. Scores on both assessments were normalized to range from 0 to 10. We used Cronbach’s α coefficient to assess reliability and Spearman correlation to estimate the correlation between the SBA and the MCE.

Results

We included 80 residents, of whom 42 also completed the MCE examinations. The final version of the SBA had 32 items, and the Cronbach’s α coefficient was 0.72 (95% confidence interval [CI], 0.64–0.81). The average SBA score was 6.2 ± 1.3, and performance was variable across items, with 80% correctly adjusting initial ventilatory settings and only 12% correctly identifying asynchrony. The MCE had 24 questions, and the average score was 7.6 ± 2.4 at the beginning of the rotation and 8.2 ± 2.3 at the end of the rotation (increase of 0.6 points; 95% CI, 0.30–0.90; P < 0.001). There was moderate correlation between the SBA and the MCE (rho = 0.41; P = 0.002).

Conclusion

We developed and validated an objective structured assessment on MV using a pulmonary simulator and a mechanical ventilator addressing the main competencies in MV. The performance of residents in the SBA at the beginning of an ICU rotation was lower than the performance in MCE, highlighting the need for greater emphasis on practical skills in MV during residency.

The Effect of Clusters of Double Triggering and Ineffective Efforts in Critically Ill Patients

OBJECTIVES

To characterize clusters of double triggering and ineffective inspiratory efforts throughout mechanical ventilation and investigate their associations with mortality and duration of ICU stay and mechanical ventilation.

DESIGN

Registry-based, real-world study.

BACKGROUND

Asynchronies during invasive mechanical ventilation can occur as isolated events or in clusters and might be related to clinical outcomes.

SUBJECTS

Adults requiring mechanical ventilation greater than 24 hours for whom greater than or equal to 70% of ventilator waveforms were available.

INTERVENTIONS

We identified clusters of double triggering and ineffective inspiratory efforts and determined their power and duration. We used Fine-Gray's competing risk model to analyze their effects on mortality and generalized linear models to analyze their effects on duration of mechanical ventilation and ICU stay.

MEASUREMENTS AND MAIN RESULTS

We analyzed 58,625,796 breaths from 180 patients. All patients had clusters (mean/d, 8.2 [5.4-10.6]; mean power, 54.5 [29.6-111.4]; mean duration, 20.3 min [12.2-34.9 min]). Clusters were less frequent during the first 48 hours (5.5 [2.5-10] vs 7.6 [4.4-9.9] in the remaining period [p = 0.027]). Total number of clusters/d was positively associated with the probability of being discharged alive considering the total period of mechanical ventilation (p = 0.001). Power and duration were similar in the two periods. Power was associated with the probability of being discharged dead (p = 0.03), longer mechanical ventilation (p < 0.001), and longer ICU stay (p = 0.035); cluster duration was associated with longer ICU stay (p = 0.027).

CONCLUSIONS

Clusters of double triggering and ineffective inspiratory efforts are common. Although higher numbers of clusters might indicate better chances of survival, clusters with greater power and duration indicate a risk of worse clinical outcomes.

Pressure-flow breath representation eases asynchrony identification in mechanically ventilated patients

Breathing asynchronies are mismatches between the requests of mechanically ventilated subjects and the support provided by mechanical ventilators. The most widespread technique in identifying these pathological conditions is the visual analysis of the intra-tracheal pressure and flow time-trends. This work considers a recently introduced pressure-flow representation technique and investigates whether it can help nurses in the early detection of anomalies that can represent asynchronies. Twenty subjects—ten Intensive Care Unit (ICU) nurses and ten persons inexperienced in medical practice—were asked to find asynchronies in 200 breaths pre-labeled by three experts. The new representation increases significantly the detection capability of the subjects—average sensitivity soared from 0.622 to 0.905—while decreasing the classification time—from 1107.0 to 567.1 s on average—at the price of a not statistically significant rise in the number of wrong identifications—specificity average descended from 0.589 to 0.52. Moreover, the differences in experience between the nurse group and the inexperienced group do not affect the sensitivity, specificity, or classification times. The pressure-flow diagram significantly increases sensitivity and decreases the response time of early asynchrony detection performed by nurses. Moreover, the data suggest that operator experience does not affect the identification results. This outcome leads us to believe that, in emergency contexts with a shortage of nurses, intensive care nurses can be supplemented, for the sole identification of possible respiratory asynchronies, by inexperienced staff.

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

Background

Patient–ventilator asynchrony (PVA) is a common problem in patients undergoing invasive mechanical ventilation (MV) in the intensive care unit (ICU), and may accelerate lung injury and diaphragm mis-contraction. The impact of PVA on clinical outcomes has not been systematically evaluated. Effective interventions (except for closed-loop ventilation) for reducing PVA are not well established.

Methods

We performed a systematic review and meta-analysis to investigate the impact of PVA on clinical outcomes in patients undergoing MV (Part A) and the effectiveness of interventions for patients undergoing MV except for closed-loop ventilation (Part B). We searched the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, ClinicalTrials.gov, and WHO-ICTRP until August 2020. In Part A, we defined asynchrony index (AI) ≥ 10 or ineffective triggering index (ITI) ≥ 10 as high PVA. We compared patients having high PVA with those having low PVA.

Results

Eight studies in Part A and eight trials in Part B fulfilled the eligibility criteria. In Part A, five studies were related to the AI and three studies were related to the ITI. High PVA may be associated with longer duration of mechanical ventilation (mean difference, 5.16 days; 95% confidence interval [CI], 2.38 to 7.94; n = 8; certainty of evidence [CoE], low), higher ICU mortality (odds ratio [OR], 2.73; 95% CI 1.76 to 4.24; n = 6; CoE, low), and higher hospital mortality (OR, 1.94; 95% CI 1.14 to 3.30; n = 5; CoE, low). In Part B, interventions involving MV mode, tidal volume, and pressure-support level were associated with reduced PVA. Sedation protocol, sedation depth, and sedation with dexmedetomidine rather than propofol were also associated with reduced PVA.

Conclusions

PVA may be associated with longer MV duration, higher ICU mortality, and higher hospital mortality. Physicians may consider monitoring PVA and adjusting ventilator settings and sedatives to reduce PVA. Further studies with adjustment for confounding factors are warranted to determine the impact of PVA on clinical outcomes.

Trial registration protocols.io (URL: https://www.protocols.io/view/the-impact-of-patient-ventilator-asynchrony-in-adu-bsqtndwn, 08/27/2020).

Supplementary Information

The online version contains supplementary material available at 10.1186/s40560-021-00565-5.