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.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: Tracheostomy did not affect patient-ventilator asynchronies or respiratory mechanics within 24 h before and after the procedure.

Automated detection and quantification of reverse triggering effort under mechanical ventilation.

BACKGROUND: 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. METHODS: 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. RESULTS: 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 cmH20, with a median of 8.7 cmH20. RT with breath stacking had the highest levels of Pmus, and RTs with no breath stacking were of similar magnitude than pressure support breaths. CONCLUSION: An automated detection tool using airway pressure and flow can diagnose reverse triggering with excellent accuracy. RT generates a median Pmus of 9 cmH2O with important variability between and within patients. TRIAL REGISTRATION: BEARDS, NCT03447288.

Effects of sedatives and opioids on trigger and cycling asynchronies throughout mechanical ventilation: an observational study in a large dataset from critically ill patients.

BACKGROUND: In critically ill patients, poor patient-ventilator interaction may worsen outcomes. Although sedatives are often administered to improve comfort and facilitate ventilation, they can be deleterious. Whether opioids improve asynchronies with fewer negative effects is unknown. We hypothesized that opioids alone would improve asynchronies and result in more wakeful patients than sedatives alone or sedatives-plus-opioids. METHODS: This prospective multicenter observational trial enrolled critically ill adults mechanically ventilated (MV) > 24 h. We compared asynchronies and sedation depth in patients receiving sedatives, opioids, or both. We recorded sedation level and doses of sedatives and opioids. BetterCare™ software continuously registered ineffective inspiratory efforts during expiration (IEE), double cycling (DC), and asynchrony index (AI) as well as MV modes. All variables were averaged per day. We used linear mixed-effects models to analyze the relationships between asynchronies, sedation level, and sedative and opioid doses. RESULTS: In 79 patients, 14,166,469 breaths were recorded during 579 days of MV. Overall asynchronies were not significantly different in days classified as sedatives-only, opioids-only, and sedatives-plus-opioids and were more prevalent in days classified as no-drugs than in those classified as sedatives-plus-opioids, irrespective of the ventilatory mode. Sedative doses were associated with sedation level and with reduced DC (p < 0.0001) in sedatives-only days. However, on days classified as sedatives-plus-opioids, higher sedative doses and deeper sedation had more IEE (p < 0.0001) and higher AI (p = 0.0004). Opioid dosing was inversely associated with overall asynchronies (p < 0.001) without worsening sedation levels into morbid ranges. CONCLUSIONS: Sedatives, whether alone or combined with opioids, do not result in better patient-ventilator interaction than opioids alone, in any ventilatory mode. Higher opioid dose (alone or with sedatives) was associated with lower AI without depressing consciousness. Higher sedative doses administered alone were associated only with less DC. TRIAL REGISTRATION: ClinicalTrial.gov, NCT03451461.

Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities.

BACKGROUND: Mechanical ventilation is common in critically ill patients. This life-saving treatment can cause complications and is also associated with long-term sequelae. Patient-ventilator asynchronies are frequent but underdiagnosed, and they have been associated with worse outcomes. MAIN BODY: Asynchronies occur when ventilator assistance does not match the patient's demand. Ventilatory overassistance or underassistance translates to different types of asynchronies with different effects on patients. Underassistance can result in an excessive load on respiratory muscles, air hunger, or lung injury due to excessive tidal volumes. Overassistance can result in lower patient inspiratory drive and can lead to reverse triggering, which can also worsen lung injury. Identifying the type of asynchrony and its causes is crucial for effective treatment. Mechanical ventilation and asynchronies can affect hemodynamics. An increase in intrathoracic pressure during ventilation modifies ventricular preload and afterload of ventricles, thereby affecting cardiac output and hemodynamic status. Ineffective efforts can decrease intrathoracic pressure, but double cycling can increase it. Thus, asynchronies can lower the predictive accuracy of some hemodynamic parameters of fluid responsiveness. New research is also exploring the psychological effects of asynchronies. Anxiety and depression are common in survivors of critical illness long after discharge. Patients on mechanical ventilation feel anxiety, fear, agony, and insecurity, which can worsen in the presence of asynchronies. Asynchronies have been associated with worse overall prognosis, but the direct causal relation between poor patient-ventilator interaction and worse outcomes has yet to be clearly demonstrated. Critical care patients generate huge volumes of data that are vastly underexploited. New monitoring systems can analyze waveforms together with other inputs, helping us to detect, analyze, and even predict asynchronies. Big data approaches promise to help us understand asynchronies better and improve their diagnosis and management. CONCLUSIONS: Although our understanding of asynchronies has increased in recent years, many questions remain to be answered. Evolving concepts in asynchronies, lung crosstalk with other organs, and the difficulties of data management make more efforts necessary in this field.

Predicting Patient-ventilator Asynchronies with Hidden Markov Models.

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.

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.

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

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: NCT02078206.

Automatic detection of ventilatory modes during invasive mechanical ventilation.

BACKGROUND: Expert systems can help alleviate problems related to the shortage of human resources in critical care, offering expert advice in complex situations. Expert systems use contextual information to provide advice to staff. In mechanical ventilation, it is crucial for an expert system to be able to determine the ventilatory mode in use. Different manufacturers have assigned different names to similar or even identical ventilatory modes so an expert system should be able to detect the ventilatory mode. The aim of this study is to evaluate the accuracy of an algorithm to detect the ventilatory mode in use. METHODS: We compared the results of a two-step algorithm designed to identify seven ventilatory modes. The algorithm was built into a software platform (BetterCare® system, Better Care SL; Barcelona, Spain) that acquires ventilatory signals through the data port of mechanical ventilators. The sample analyzed compared data from consecutive adult patients who underwent >24 h of mechanical ventilation in intensive care units (ICUs) at two hospitals. We used Cohen's kappa statistics to analyze the agreement between the results obtained with the algorithm and those recorded by ICU staff. RESULTS: We analyzed 486 records from 73 patients. The algorithm correctly labeled the ventilatory mode in 433 (89 %). We found an unweighted Cohen's kappa index of 84.5 % [CI (95 %) = (80.5 %: 88.4 %)]. CONCLUSIONS: The computerized algorithm can reliably identify ventilatory mode.

Asynchronies during mechanical ventilation are associated with mortality.

PURPOSE: This study aimed to assess the prevalence and time course of asynchronies during mechanical ventilation (MV). METHODS: Prospective, noninterventional observational study of 50 patients admitted to intensive care unit (ICU) beds equipped with Better Care™ 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. RESULTS: 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 ≤ 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. CONCLUSIONS: 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.

Effect of dynamic random leaks on the monitoring accuracy of home mechanical ventilators: a bench study.

BACKGROUND: So far, the accuracy of tidal volume (VT) and leak measures provided by the built-in software of commercial home ventilators has only been tested using bench linear models with fixed calibrated and continuous leaks. The objective was to assess the reliability of the estimation of tidal volume (VT) and unintentional leaks in a single tubing bench model which introduces random dynamic leaks during inspiratory or expiratory phases. METHODS: The built-in software of four commercial home ventilators and a fifth ventilator-independent ad hoc designed external software tool were tested with two levels of leaks and two different models with excess leaks (inspiration or expiration). The external software analyzed separately the inspiratory and expiratory unintentional leaks. RESULTS: In basal condition, all ventilators but one underestimated tidal volume with values ranging between -1.5 ± 3.3% to -8.7% ± 3.27%. In the model with excess of inspiratory leaks, VT was overestimated by all four commercial software tools, with values ranging from 18.27 ± 7.05% to 35.92 ± 17.7%, whereas the ventilator independent-software gave a smaller difference (3.03 ± 2.6%). Leaks were underestimated by two applications with values of -11.47 ± 6.32 and -5.9 ± 0.52 L/min. With expiratory leaks, VT was overestimated by the software of one ventilator and the ventilator-independent software and significantly underestimated by the other three, with deviations ranging from +10.94 ± 7.1 to -48 ± 23.08%. The four commercial tools tested overestimated unintentional leaks, with values between 2.19 ± 0.85 to 3.08 ± 0.43 L/min. CONCLUSIONS: In a bench model, the presence of unintentional random leaks may be a source of error in the measurement of VT and leaks provided by the software of home ventilators. Analyzing leaks during inspiration and expiration separately may reduce this source of error.

Nurses' detection of ineffective inspiratory efforts during mechanical ventilation.

UNLABELLED: BACKGROUND PATIENT: ventilator dyssynchrony is common and may influence patients' outcomes. Detection of such dyssynchronies relies on careful observation of patients and airway flow and pressure measurements. Given the shortage of specialists, critical care nurses could be trained to identify dyssynchronies. OBJECTIVE: To evaluate the accuracy of specifically trained critical care nurses in detecting ineffective inspiratory efforts during expiration. METHODS: We compared 2 nurses' evaluations of measurements from 1007 breaths in 8 patients with the evaluations of experienced critical care physicians. Sensitivity, specificity, positive predictive value, negative predictive value, and the Cohen κ for interobserver agreement were calculated. RESULTS: For the first nurse, sensitivity was 92.5%, specificity was 98.3%, positive predictive value was 95.4%, negative predictive value was 97.1%, and κ was 0.92 (95% CI, 0.89-0.94). For the second nurse, sensitivity was 98.5%, specificity was 84.7%, positive predictive value was 70.7%, negative predictive value was 99.3%, and κ was 0.74 (95% CI, 0.70-0.78). CONCLUSION: Specifically trained nurses can reliably detect ineffective inspiratory efforts during expiration.

Validation of the Better Care® system to detect ineffective efforts during expiration in mechanically ventilated patients: a pilot study.

PURPOSE: Ineffective respiratory efforts during expiration (IEE) are a problem during mechanical ventilation (MV). The goal of this study is to validate mathematical algorithms that automatically detect IEE in a computerized (Better Care®) system that obtains and processes data from intensive care unit (ICU) ventilators in real time. METHODS: The Better Care® system, integrated with ICU health information systems, synchronizes and processes data from bedside technology. Algorithms were developed to analyze airflow waveforms during expiration to determine IEE. Data from 2,608,800 breaths from eight patients were recorded. From these breaths 1,024 were randomly selected. Five experts independently analyzed the selected breaths and classified them as IEE or not IEE. Better Care® evaluated the same 1,024 breaths and assigned a score to each one. The IEE score cutoff point was determined based on the experts' analysis. The IEE algorithm was subsequently validated using the electrical activity of the diaphragm (EAdi) signal to analyze 9,600 breaths in eight additional patients. RESULTS: Optimal sensitivity and specificity were achieved by setting the cutoff point for IEE by Better Care® at 42%. A score >42% was classified as an IEE with 91.5% sensitivity, 91.7% specificity, 80.3% positive predictive value (PPV), 96.7% negative predictive value (NPV), and 79.7% Kappa index [confidence interval (CI) (95%) = (75.6%; 83.8%)]. Compared with the EAdi, the IEE algorithm had 65.2% sensitivity, 99.3% specificity, 90.8% PPV, 96.5% NPV, and 73.9% Kappa index [CI (95%) = (71.3%; 76.3%)]. CONCLUSIONS: In this pilot, Better Care® classified breaths as IEE in close agreement with experts and the EAdi signal.