Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques.

BACKGROUND: Flow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery does not fully meet the patients' ventilatory demand due to an insufficient airflow and/or a high inspiratory effort, and it is usually identified by visual inspection of airway pressure waveform. Clinical diagnosis is cumbersome and prone to underdiagnosis, being an opportunity for artificial intelligence. Our objective is to develop a supervised artificial intelligence algorithm for identifying airway pressure deformation during square-flow assisted ventilation and patient-triggered breaths. METHODS: Multicenter, observational study. Adult critically ill patients under mechanical ventilation > 24 h on square-flow assisted ventilation were included. As the reference, 5 intensive care experts classified airway pressure deformation severity. Convolutional neural network and recurrent neural network models were trained and evaluated using accuracy, precision, recall and F1 score. In a subgroup of patients with esophageal pressure measurement (ΔPes), we analyzed the association between the intensity of the inspiratory effort and the airway pressure deformation. RESULTS: 6428 breaths from 28 patients were analyzed, 42% were classified as having normal-mild, 23% moderate, and 34% severe airway pressure deformation. The accuracy of recurrent neural network algorithm and convolutional neural network were 87.9% [87.6-88.3], and 86.8% [86.6-87.4], respectively. Double triggering appeared in 8.8% of breaths, always in the presence of severe airway pressure deformation. The subgroup analysis demonstrated that 74.4% of breaths classified as severe airway pressure deformation had a ΔPes > 10 cmH2O and 37.2% a ΔPes > 15 cmH2O. CONCLUSIONS: Recurrent neural network model appears excellent to identify airway pressure deformation due to flow starvation. It could be used as a real-time, 24-h bedside monitoring tool to minimize unrecognized periods of inappropriate patient-ventilator interaction.

Setting positive end-expiratory pressure: does the 'best compliance' concept really work?

PURPOSE OF REVIEW: Determining the optimal positive end-expiratory pressure (PEEP) setting remains a central yet debated issue in the management of acute respiratory distress syndrome (ARDS).The 'best compliance' strategy set the PEEP to coincide with the peak respiratory system compliance (or 2 cmH 2 O higher) during a decremental PEEP trial, but evidence is conflicting. RECENT FINDINGS: The physiological rationale that best compliance is always representative of functional residual capacity and recruitment has raised serious concerns about its efficacy and safety, due to its association with increased 28-day all-cause mortality in a randomized clinical trial in ARDS patients.Moreover, compliance measurement was shown to underestimate the effects of overdistension, and neglect intra-tidal recruitment, airway closure, and the interaction between lung and chest wall mechanics, especially in obese patients. In response to these concerns, alternative approaches such as recruitment-to-inflation ratio, the nitrogen wash-in/wash-out technique, and electrical impedance tomography (EIT) are gaining attention to assess recruitment and overdistention more reliably and precisely. SUMMARY: The traditional 'best compliance' strategy for determining optimal PEEP settings in ARDS carries risks and overlooks some key physiological aspects. The advent of new technologies and methods presents more reliable strategies to assess recruitment and overdistention, facilitating personalized approaches to PEEP optimization.

Objective and subjective cognition in survivors of COVID-19 one year after ICU discharge: the role of demographic, clinical, and emotional factors.

BACKGROUND: Intensive Care Unit (ICU) COVID-19 survivors may present long-term cognitive and emotional difficulties after hospital discharge. This study aims to characterize the neuropsychological dysfunction of COVID-19 survivors 12 months after ICU discharge, and to study whether the use of a measure of perceived cognitive deficit allows the detection of objective cognitive impairment. We also explore the relationship between demographic, clinical and emotional factors, and both objective and subjective cognitive deficits. METHODS: Critically ill COVID-19 survivors from two medical ICUs underwent cognitive and emotional assessment one year after discharge. The perception of cognitive deficit and emotional state was screened through self-rated questionnaires (Perceived Deficits Questionnaire, Hospital Anxiety and Depression Scale and Davidson Trauma Scale), and a comprehensive neuropsychological evaluation was carried out. Demographic and clinical data from ICU admission were collected retrospectively. RESULTS: Out of eighty participants included in the final analysis, 31.3% were women, 61.3% received mechanical ventilation and the median age of patients was 60.73 years. Objective cognitive impairment was observed in 30% of COVID-19 survivors. The worst performance was detected in executive functions, processing speed and recognition memory. Almost one in three patients manifested cognitive complaints, and 22.5%, 26.3% and 27.5% reported anxiety, depression and post-traumatic stress disorder (PTSD) symptoms, respectively. No significant differences were found in the perception of cognitive deficit between patients with and without objective cognitive impairment. Gender and PTSD symptomatology were significantly associated with perceived cognitive deficit, and cognitive reserve with objective cognitive impairment. CONCLUSIONS: One-third of COVID-19 survivors suffered objective cognitive impairment with a frontal-subcortical dysfunction 12 months after ICU discharge. Emotional disturbances and perceived cognitive deficits were common. Female gender and PTSD symptoms emerged as predictive factors for perceiving worse cognitive performance. Cognitive reserve emerged as a protective factor for objective cognitive functioning. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04422444; June 9, 2021.

Cerebral Autoregulation Indices Are Not Interchangeable in Patients With Sepsis.

Introduction: Dynamic cerebral autoregulation (dCA) is frequently altered in patients with sepsis and may be associated with sepsis-associated brain dysfunction. However, the optimal index to quantify dCA in patients with sepsis is currently unknown. Objective: To assess the agreement between two validated dCA indices in patients with sepsis. Methods: Retrospective analysis of prospectively collected data in patients with sepsis; those with acute or chronic intracranial disease, arrhythmias, mechanical cardiac support, or history of supra-aortic vascular disease were excluded. Transcranial Doppler was performed on the right or left middle cerebral artery (MCA) with a 2-MHz probe, and MCA blood flow velocity (FV) and arterial pressure (BP) signals were simultaneously recorded. We calculated two indices of dCA: the mean flow index (Mxa), which is the Pearson correlation coefficient between BP and FV (MATLAB, MathWorks), and the autoregulation index (ARI), which is the transfer function analysis of spontaneous fluctuations in BP and FV (custom-written FORTRAN code). Impaired dCA was defined as Mxa >0.3 or ARI ≤ 4. The agreement between the two indices was assessed by Cohen's kappa coefficient. Results: We included 95 patients (age 64 ± 13 years old; male 74%); ARI was 4.38 [2.83-6.04] and Mxa was 0.32 [0.14-0.59], respectively. There was no correlation between ARI and Mxa (r = -0.08; p = 0.39). dCA was altered in 40 (42%) patients according to ARI and in 50 (53%) patients according to Mxa. ARI and Mxa were concordant in classifying 23 (24%) patients as having impaired dCA and 28 (29%) patients as having intact dCA. Cohen's kappa coefficient was 0.08, suggesting poor agreement. ARI was altered more frequently in patients on mechanical ventilation than others (27/52, 52% vs. 13/43, 30%, p = 0.04), whereas Mxa did not differ between those two groups. On the contrary, Mxa was altered more frequently in patients receiving sedatives than others (23/34, 68% vs. 27/61, 44%, p = 0.03), whereas ARI did not differ between these two groups. Conclusions: Agreement between ARI and Mxa in assessing dCA in patients with sepsis was poor. The identification of specific factors influencing the dCA analysis might lead to a better selection of the adequate cerebral autoregulation (CAR) index in critically ill patients with sepsis.

Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation.

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients' readiness, there is still around 15-20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation -being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Cognitive phenotypes 1 month after ICU discharge in mechanically ventilated patients: a prospective observational cohort study.

BACKGROUND: ICU patients undergoing invasive mechanical ventilation experience cognitive decline associated with their critical illness and its management. The early detection of different cognitive phenotypes might reveal the involvement of diverse pathophysiological mechanisms and help to clarify the role of the precipitating and predisposing factors. Our main objective is to identify cognitive phenotypes in critically ill survivors 1 month after ICU discharge using an unsupervised machine learning method, and to contrast them with the classical approach of cognitive impairment assessment. For descriptive purposes, precipitating and predisposing factors for cognitive impairment were explored. METHODS: A total of 156 mechanically ventilated critically ill patients from two medical/surgical ICUs were prospectively studied. Patients with previous cognitive impairment, neurological or psychiatric diagnosis were excluded. Clinical variables were registered during ICU stay, and 100 patients were cognitively assessed 1 month after ICU discharge. The unsupervised machine learning K-means clustering algorithm was applied to detect cognitive phenotypes. Exploratory analyses were used to study precipitating and predisposing factors for cognitive impairment. RESULTS: K-means testing identified three clusters (K) of patients with different cognitive phenotypes: K1 (n = 13), severe cognitive impairment in speed of processing (92%) and executive function (85%); K2 (n = 33), moderate-to-severe deficits in learning-memory (55%), memory retrieval (67%), speed of processing (36.4%) and executive function (33.3%); and K3 (n = 46), normal cognitive profile in 89% of patients. Using the classical approach, moderate-to-severe cognitive decline was recorded in 47% of patients, while the K-means method accurately classified 85.9%. The descriptive analysis showed significant differences in days (p = 0.016) and doses (p = 0.039) with opioid treatment in K1 vs. K2 and K3. In K2, there were more women, patients were older and had more comorbidities (p = 0.001) than in K1 or K3. Cognitive reserve was significantly (p = 0.001) higher in K3 than in K1 or K2. CONCLUSION: One month after ICU discharge, three groups of patients with different cognitive phenotypes were identified through an unsupervised machine learning method. This novel approach improved the classical classification of cognitive impairment in ICU survivors. In the exploratory analysis, gender, age and the level of cognitive reserve emerged as relevant predisposing factors for cognitive impairment in ICU patients. TRIAL REGISTRATION: ClinicalTrials.gov Identifier:NCT02390024; March 17,2015.

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.

Severity scores should incorporate limitations on life support for predicting mortality / Las puntuaciones de intensidad deberían incorporar las limitaciones del soporte vital para predecir la mortalidad

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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.

Effect of Pressure Support vs T-Piece Ventilation Strategies During Spontaneous Breathing Trials on Successful Extubation Among Patients Receiving Mechanical Ventilation: A Randomized Clinical Trial.

Importance: Daily spontaneous breathing trials (SBTs) are the best approach to determine whether patients are ready for disconnection from mechanical ventilation, but mode and duration of SBT remain controversial. Objective: To evaluate the effect of an SBT consisting of 30 minutes of pressure support ventilation (an approach that is less demanding for patients) vs an SBT consisting of 2 hours of T-piece ventilation (an approach that is more demanding for patients) on rates of successful extubation. Design, Setting, and Participants: Randomized clinical trial conducted from January 2016 to April 2017 among 1153 adults deemed ready for weaning after at least 24 hours of mechanical ventilation at 18 intensive care units in Spain. Follow-up ended in July 2017. Interventions: Patients were randomized to undergo a 2-hour T-piece SBT (n = 578) or a 30-minute SBT with 8-cm H2O pressure support ventilation (n = 557). Main Outcome and Measures: The primary outcome was successful extubation (remaining free of mechanical ventilation 72 hours after first SBT). Secondary outcomes were reintubation among patients extubated after SBT; intensive care unit and hospital lengths of stay; and hospital and 90-day mortality. Results: Among 1153 patients who were randomized (mean age, 62.2 [SD, 15.7] years; 428 [37.1%] women), 1018 (88.3%) completed the trial. Successful extubation occurred in 473 patients (82.3%) in the pressure support ventilation group and 428 patients (74.0%) in the T-piece group (difference, 8.2%; 95% CI, 3.4%-13.0%; P = .001). Among secondary outcomes, for the pressure support ventilation group vs the T-piece group, respectively, reintubation was 11.1% vs 11.9% (difference, -0.8%; 95% CI, -4.8% to 3.1%; P = .63), median intensive care unit length of stay was 9 days vs 10 days (mean difference, -0.3 days; 95% CI, -1.7 to 1.1 days; P = .69), median hospital length of stay was 24 days vs 24 days (mean difference, 1.3 days; 95% CI, -2.2 to 4.9 days; P = .45), hospital mortality was 10.4% vs 14.9% (difference, -4.4%; 95% CI, -8.3% to -0.6%; P = .02), and 90-day mortality was 13.2% vs 17.3% (difference, -4.1% [95% CI, -8.2% to 0.01%; P = .04]; hazard ratio, 0.74 [95% CI, 0.55-0.99]). Conclusions and Relevance: Among patients receiving mechanical ventilation, a spontaneous breathing trial consisting of 30 minutes of pressure support ventilation, compared with 2 hours of T-piece ventilation, led to significantly higher rates of successful extubation. These findings support the use of a shorter, less demanding ventilation strategy for spontaneous breathing trials. Trial Registration: ClinicalTrials.gov Identifier: NCT02620358.

Proportional assist ventilation feasibility in the early stage of respiratory failure: a prospective randomized multicenter trial.

BACKGROUND: Proportional assist ventilation (PAV+) is an assisted ventilator mode usually applied during weaning. We aimed to determine the feasibility of using PAV+ in the early phase of acute respiratory failure compared to volume-assist control ventilation (V-ACV) in order to shorten the length of mechanical ventilation (MV). METHODS: We conducted a prospective randomized trial comparing high-assistance PAV+ (gain 80%) vs. V-ACV in four university hospital Intensive Care Units. Patients were included based on a previous pilot trial. Length of MV was the main objective. Secondary objectives were length of stay (LOS) in ICU/hospital, and ICU/hospital/60-day mortality. Statistics - Mann-Whitney U Test and Fisher's Exact Test. RESULTS: We could not find differences in length of MV or any of the analyzed variables between the 52 patients with PAV+ and 50 patients with V-ACV. The high PAV+ failure rate (42%) was attributed to excessive sedation, high respiratory rate, and high respiratory effort. CONCLUSIONS: The use of high-assistance PAV+ in the early phase of MV does not present benefits compared to V-ACV. The high rate of PAV+ failure reinforces the need for sedative optimization, learning curve, and better patient selection.

Impaired cerebral autoregulation is associated with brain dysfunction in patients with sepsis.

BACKGROUND: Sepsis-associated brain dysfunction (SABD) is associated with high morbidity and mortality. The pathophysiology of SABD is multifactorial. One hypothesis is that impaired cerebral autoregulation (CAR) may result in brain hypoperfusion and neuronal damage leading to SABD. METHODS: We studied 100 adult patients with sepsis (July 2012-March 2017) (age = 62 [52-71] years; Acute Physiology and Chronic Health Evaluation II score on admission = 21 [15-26]). Exclusion criteria were acute or chronic intracranial disease, arrhythmias, extracorporeal membrane oxygenation, and known intra- or extracranial supra-aortic vessel disease. The site of infection was predominantly abdominal (46%) or pulmonary (28%). Transcranial Doppler was performed, insonating the left middle cerebral artery with a 2-MHz probe. Middle cerebral artery blood flow velocity (FV) and arterial blood pressure (ABP) signals were recorded simultaneously; Pearson's correlation coefficient (mean flow index [Mxa]) between ABP and FV was calculated using MATLAB. Impaired CAR was defined as Mxa > 0.3. RESULTS: Mxa was 0.29 [0.05-0.62]. CAR was impaired in 50 patients (50%). In a multiple linear regression analysis, low mean arterial pressure, history of chronic kidney disease and fungal infection were associated with high Mxa. SABD was diagnosed in 57 patients (57%). In a multivariable analysis, altered cerebral autoregulation, mechanical ventilation and history of vascular disease were independent predictors of SABD. CONCLUSIONS: Cerebral autoregulation was altered in half of the patients with sepsis and was associated with the development of SABD. These findings support the concept that cerebral hypoxia could contribute to the development of SABD.

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.

High red blood cell distribution width as a marker of hospital mortality after ICU discharge: a cohort study.

BACKGROUND: High red blood cell distribution width (RDW) is associated with worse outcome in diverse scenarios, including in critical illness. The Sabadell score (SS) predicts in-hospital survival after ICU discharge. We aimed to determine RDW's association with survival after ICU discharge and whether RDW can improve the accuracy of the SS. DESIGN: Retrospective cohort study. Setting: general ICU at a university hospital. PATIENTS: We included all patients discharged to wards from January 2010 to October 2016. METHODS: We analyzed associations between RDW and variables recorded on admission (age, comorbidities, severity score), during the ICU stay (treatments, complications, length of stay (LOS)), and at ICU discharge (SS). The primary outcome was hospital mortality. Statistical analysis included multivariable logistic regression and receiver operating characteristic curve (ROC) analyses. RESULTS: We discharged 3366 patients to wards; median ward LOS was 7 [4-13] days; ward mortality was 5.2%. Mean RDW at ICU discharge was 15.4 ± 2.5%. Ward mortality was higher at each quartile of RDW (0.7%, 2.9%, 7.5%, 10.3%; area under ROC 0.81). A logistic regression model with Sabadell score obtained an excellent accuracy for ward mortality (area under ROC 0.863), and the addition of RDW slightly improved accuracy (AUROC 0.890, p < 0.05). Recursive partitioning demonstrated higher mortality in patients with high RDW at each SS level (1.6% vs. 0.3% in SS0, 9.7% vs. 1.1% in SS1, 21.9% vs. 9.7% in SS2), but not in SS3. CONCLUSION: High RDW is a marker of severity at ICU discharge and improves the accuracy of Sabadell score in predicting ward mortality except in the more extreme SS3.

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.