Automated detection and quantification of reverse triggering effort under mechanical ventilation

Reverse triggering (RT) is a dyssynchrony defined by a respiratory muscle contraction following a passive mechanical insufflation. It is potentially harmful for the lung and the diaphragm, but its detection is challenging. Magnitude of effort generated by RT is currently unknown. Our objective was to validate supervised methods for automatic detection of RT using only airway pressure (Paw) and flow. A secondary objective was to describe the magnitude of the efforts generated during RT. We developed algorithms for detection of RT using Paw and flow waveforms. Experts having Paw, flow and esophageal pressure (Pes) assessed automatic detection accuracy by comparison against visual assessment. Muscular pressure (Pmus) was measured from Pes during RT, triggered breaths and ineffective efforts. Tracings from 20 hypoxemic patients were used (mean age 65 ± 12 years, 65% male, ICU survival 75%). RT was present in 24% of the breaths ranging from 0 (patients paralyzed or in pressure support ventilation) to 93.3%. Automatic detection accuracy was 95.5%: sensitivity 83.1%, specificity 99.4%, positive predictive value 97.6%, negative predictive value 95.0% and kappa index of 0.87. Pmus of RT ranged from 1.3 to 36.8 cmH0, with a median of 8.7 cmH0. RT with breath stacking had the highest levels of Pmus, and RTs with no breath stacking were of similar magnitude than pressure support breaths. An automated detection tool using airway pressure and flow can diagnose reverse triggering with excellent accuracy. RT generates a median Pmus of 9 cmHO with important variability between and within patients. BEARDS, NCT03447288

Asynchronies during mechanical ventilation are associated with mortality

This study aimed to assess the prevalence and time course of asynchronies during mechanical ventilation (MV). Prospective, noninterventional observational study of 50 patients admitted to intensive care unit (ICU) beds equipped with Better Care (TM) software throughout MV. The software distinguished ventilatory modes and detected ineffective inspiratory efforts during expiration (IEE), double-triggering, aborted inspirations, and short and prolonged cycling to compute the asynchrony index (AI) for each hour. We analyzed 7,027 h of MV comprising 8,731,981 breaths. Asynchronies were detected in all patients and in all ventilator modes. The median AI was 3.41 % [IQR 1.95-5.77]; the most common asynchrony overall and in each mode was IEE [2.38 % (IQR 1.36-3.61)]. Asynchronies were less frequent from 12 pm to 6 am [1.69 % (IQR 0.47-4.78)]. In the hours where more than 90 % of breaths were machine-triggered, the median AI decreased, but asynchronies were still present. When we compared patients with AI > 10 vs AI a parts per thousand currency sign 10 %, we found similar reintubation and tracheostomy rates but higher ICU and hospital mortality and a trend toward longer duration of MV in patients with an AI above the cutoff. Asynchronies are common throughout MV, occurring in all MV modes, and more frequently during the daytime. Further studies should determine whether asynchronies are a marker for or a cause of mortality

Considerations for an Optimal EAdi Threshold for Automated Detection of Ineffective Efforts

Overall, it seems that increasing the threshold of EAdi would decrease the false-negative rate, improving the sensitivity of any given automated detection software and keeping a good specificity. We believe that, according to our reassessed results, an EAdi .2 mV could be suitable for this purpose. In addition, as Jonkman and colleagues mentioned, the removal of cardiac electrical activity is technically challenging, particularly when the signal:noise ratio of the crural diaphragm electromyography signal is low. In this scenario, we hypothesized that the automatic detection of true ineffective efforts from EAdi will be improved by using a personalized adaptive threshold for each patient considering the signal:noise ratio of the diaphragm electromyography signal. Interestingly, nonlinear methods less sensitive to ECG interference based on sample entropy algorithms (4) could be used to reduce the delay on the neural onset when an ECG peak matches at the beginning of the breath

Predicting Patient-ventilator Asynchronies with Hidden Markov Models

Abstract In mechanical ventilation, it is paramount to ensure the patient’s ventilatory demand is met while minimizing asynchronies. We aimed to develop a model to predict the likelihood of asynchronies occurring. We analyzed 10,409,357 breaths from 51 critically ill patients who underwent mechanical ventilation >24 h. Patients were continuously monitored and common asynchronies were identified and regularly indexed. Based on discrete time-series data representing the total count of asynchronies, we defined four states or levels of risk of asynchronies, z1 (very-low-risk) – z4 (very-high-risk). A Poisson hidden Markov model was used to predict the probability of each level of risk occurring in the next period. Long periods with very few asynchronous events, and consequently very-low-risk, were more likely than periods with many events (state z4). States were persistent; large shifts of states were uncommon and most switches were to neighbouring states. Thus, patients entering states with a high number of asynchronies were very likely to continue in that state, which may have serious implications. This novel approach to dealing with patient-ventilator asynchrony is a first step in developing smart alarms to alert professionals to patients entering high-risk states so they can consider actions to improve patient-ventilator interaction

Feasibility and safety of virtual-reality-based early neurocognitive stimulation in critically ill patients

Abstract Background Growing evidence suggests that critical illness often results in significant long-term neurocognitive impairments in one-third of survivors. Although these neurocognitive impairments are long-lasting and devastating for survivors, rehabilitation rarely occurs during or after critical illness. Our aim is to describe an early neurocognitive stimulation intervention based on virtual reality for patients who are critically ill and to present the results of a proof-of-concept study testing the feasibility, safety, and suitability of this intervention. Methods Twenty critically ill adult patients undergoing or having undergone mechanical ventilation for ≥24 h received daily 20-min neurocognitive stimulation sessions when awake and alert during their ICU stay. The difficulty of the exercises included in the sessions progressively increased over successive sessions. Physiological data were recorded before, during, and after each session. Safety was assessed through heart rate, peripheral oxygen saturation, and respiratory rate. Heart rate variability analysis, an indirect measure of autonomic activity sensitive to cognitive demands, was used to assess the efficacy of the exercises in stimulating attention and working memory. Results Patients successfully completed the sessions on most days. No sessions were stopped early for safety concerns, and no adverse events occurred. Heart rate variability analysis showed that the exercises stimulated attention and working memory. Critically ill patients considered the sessions enjoyable and relaxing without being overly fatiguing. Conclusions The results in this proof-of-concept study suggest that a virtual-reality-based neurocognitive intervention is feasible, safe, and tolerable, stimulating cognitive functions and satisfying critically ill patients. Future studies will evaluate the impact of interventions on neurocognitive outcomes. Trial registration Clinical trials.gov identifier: NCT0207820

Double cycling during mechanical ventilation: Frequency, mechanisms, and physiologic implications*

OBJECTIVES: Double cycling generates larger than expected tidal volumes that contribute to lung injury. We analyzed the incidence, mechanisms, and physiologic implications of double cycling during volume- and pressure-targeted mechanical ventilation in critically ill patients. DESIGN: Prospective, observational study. SETTING: Three general ICUs in Spain. PATIENTS: Sixty-seven continuously monitored adult patients undergoing volume control-continuous mandatory ventilation with constant flow, volume control-continuous mandatory ventilation with decelerated flow, or pressure control-continuous mandatory mechanical ventilation for longer than 24 hours. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed 9,251 hours of mechanical ventilation corresponding to 9,694,573 breaths. Double cycling occurred in 0.6%. All patients had double cycling; however, the distribution of double cycling varied over time. The mean percentage (95% CI) of double cycling was higher in pressure control-continuous mandatory ventilation 0.54 (0.34-0.87) than in volume control-continuous mandatory ventilation with constant flow 0.27 (0.19-0.38) or volume control-continuous mandatory ventilation with decelerated flow 0.11 (0.06-0.20). Tidal volume in double-cycled breaths was higher in volume control-continuous mandatory ventilation with constant flow and volume control-continuous mandatory ventilation with decelerated flow than in pressure control-continuous mandatory ventilation. Double-cycled breaths were patient triggered in 65.4% and reverse triggered (diaphragmatic contraction stimulated by a previous passive ventilator breath) in 34.6% of cases; the difference was largest in volume control-continuous mandatory ventilation with decelerated flow (80.7% patient triggered and 19.3% reverse triggered). Peak pressure of the second stacked breath was highest in volume control-continuous mandatory ventilation with constant flow regardless of trigger type. Various physiologic factors, none mutually exclusive, were associated with double cycling. CONCLUSIONS: Double cycling is uncommon but occurs in all patients. Periods without double cycling alternate with periods with clusters of double cycling. The volume of the stacked breaths can double the set tidal volume in volume control-continuous mandatory ventilation with constant flow. Gas delivery must be tailored to neuroventilatory demand because interdependent ventilator setting-related physiologic factors can contribute to double cycling. One third of double-cycled breaths were reverse triggered, suggesting that repeated respiratory muscle activation after time-initiated ventilator breaths occurs more often than expected