Analgesia and Sedation Use During Noninvasive Ventilation for Acute Respiratory Failure.

OBJECTIVES: To describe U.S. practice regarding administration of sedation and analgesia to patients on noninvasive ventilation (NIV) for acute respiratory failure (ARF) and to determine the association of this practice with odds of intubation or death. DESIGN: A retrospective multicenter cohort study. SETTING: A total of 1017 hospitals contributed data between January 2010 and September 2020 to the Premier Healthcare Database, a nationally representative healthcare database in the United States. PATIENTS: Adult (≥ 18 yr) patients admitted to U.S. hospitals requiring NIV for ARF. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We identified 433,357 patients on NIV of whom (26.7% [95% CI] 26.3%-27.0%) received sedation or analgesia. A total of 50,589 patients (11.7%) received opioids only, 40,646 (9.4%) received benzodiazepines only, 20,146 (4.6%) received opioids and benzodiazepines, 1.573 (0.4%) received dexmedetomidine only, and 2,639 (0.6%) received dexmedetomidine in addition to opioid and/or benzodiazepine. Of 433,357 patients receiving NIV, 50,413 (11.6%; 95% CI, 11.5-11.7%) patients underwent invasive mechanical ventilation on hospital days 2-5 or died on hospital days 2-30. Intubation was used in 32,301 patients (7.4%; 95% CI, 7.3-7.6%). Further, death occurred in 24,140 (5.6%; 95% CI, 5.5-5.7%). In multivariable analysis adjusting for relevant covariates, receipt of any medication studied was associated with increased odds of intubation or death. In inverse probability weighting, receipt of any study medication was also associated with increased odds of intubation or death (average treatment effect odds ratio 1.38; 95% CI, 1.35-1.40). CONCLUSIONS: The use of sedation and analgesia during NIV is common. Medication exposure was associated with increased odds of intubation or death. Further investigation is needed to confirm this finding and determine whether any subpopulations are especially harmed by this practice.

Quantifiable identification of flow-limited ventilator dyssynchrony with the deformed lung ventilator model.

BACKGROUND: Ventilator dyssynchrony (VD) can worsen lung injury and is challenging to detect and quantify due to the complex variability in the dyssynchronous breaths. While machine learning (ML) approaches are useful for automating VD detection from the ventilator waveform data, scalable severity quantification and its association with pathogenesis and ventilator mechanics remain challenging. OBJECTIVE: We develop a systematic framework to quantify pathophysiological features observed in ventilator waveform signals such that they can be used to create feature-based severity stratification of VD breaths. METHODS: A mathematical model was developed to represent the pressure and volume waveforms of individual breaths in a feature-based parametric form. Model estimates of respiratory effort strength were used to assess the severity of flow-limited (FL)-VD breaths compared to normal breaths. A total of 93,007 breath waveforms from 13 patients were analyzed. RESULTS: A novel model-defined continuous severity marker was developed and used to estimate breath phenotypes of FL-VD breaths. The phenotypes had a predictive accuracy of over 97% with respect to the previously developed ML-VD identification algorithm. To understand the incidence of FL-VD breaths and their association with the patient state, these phenotypes were further successfully correlated with ventilator-measured parameters and electronic health records. CONCLUSION: This work provides a computational pipeline to identify and quantify the severity of FL-VD breaths and paves the way for a large-scale study of VD causes and effects. This approach has direct application to clinical practice and in meaningful knowledge extraction from the ventilator waveform data.

Flow-Limited and Reverse-Triggered Ventilator Dyssynchrony Are Associated With Increased Tidal and Dynamic Transpulmonary Pressure.

OBJECTIVES: Ventilator dyssynchrony may be associated with increased delivered tidal volumes (V t s) and dynamic transpulmonary pressure (ΔP L,dyn ), surrogate markers of lung stress and strain, despite low V t ventilation. However, it is unknown which types of ventilator dyssynchrony are most likely to increase these metrics or if specific ventilation or sedation strategies can mitigate this potential. DESIGN: A prospective cohort analysis to delineate the association between ten types of breaths and delivered V t , ΔP L,dyn , and transpulmonary mechanical energy. SETTING: Patients admitted to the medical ICU. PATIENTS: Over 580,000 breaths from 35 patients with acute respiratory distress syndrome (ARDS) or ARDS risk factors. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patients received continuous esophageal manometry. Ventilator dyssynchrony was identified using a machine learning algorithm. Mixed-effect models predicted V t , ΔP L,dyn , and transpulmonary mechanical energy for each type of ventilator dyssynchrony while controlling for repeated measures. Finally, we described how V t , positive end-expiratory pressure (PEEP), and sedation (Richmond Agitation-Sedation Scale) strategies modify ventilator dyssynchrony's association with these surrogate markers of lung stress and strain. Double-triggered breaths were associated with the most significant increase in V t , ΔP L,dyn , and transpulmonary mechanical energy. However, flow-limited, early reverse-triggered, and early ventilator-terminated breaths were also associated with significant increases in V t , ΔP L,dyn , and energy. The potential of a ventilator dyssynchrony type to increase V t , ΔP L,dyn , or energy clustered similarly. Increasing set V t may be associated with a disproportionate increase in high-volume and high-energy ventilation from double-triggered breaths, but PEEP and sedation do not clinically modify the interaction between ventilator dyssynchrony and surrogate markers of lung stress and strain. CONCLUSIONS: Double-triggered, flow-limited, early reverse-triggered, and early ventilator-terminated breaths are associated with increases in V t , ΔP L,dyn , and energy. As flow-limited breaths are more than twice as common as double-triggered breaths, further work is needed to determine the interaction of ventilator dyssynchrony frequency to cause clinically meaningful changes in patient outcomes.

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.

Differential effects of two-hit models of acute and ventilator-induced lung injury on lung structure, function, and inflammation.

Acute respiratory distress syndrome (ARDS) and acute lung injury have a diverse spectrum of causative factors including sepsis, aspiration of gastric contents, and near drowning. Clinical management of severe lung injury typically includes mechanical ventilation to maintain gas exchange which can lead to ventilator-induced lung injury (VILI). The cause of respiratory failure is acknowledged to affect the degree of lung inflammation, changes in lung structure, and the mechanical function of the injured lung. However, these differential effects of injury and the role of etiology in the structure-function relationship are not fully understood. To address this knowledge gap we caused lung injury with intratracheal hydrochloric acid (HCL) or endotoxin (LPS) 2 days prior to ventilation or with an injurious lavage (LAV) immediately prior to ventilation. These injury groups were then ventilated with high inspiratory pressures and positive end expiratory pressure (PEEP) = 0 cmH2O to cause VILI and model the clinical course of ARDS followed by supportive ventilation. The effects of injury were quantified using invasive lung function measurements recorded during PEEP ladders where the end-expiratory pressure was increased from 0 to 15 cm H2O and decreased back to 0 cmH2O in steps of 3 cmH2O. Design-based stereology was used to quantify the parenchymal structure of lungs air-inflated to 2, 5, and 10 cmH2O. Pro-inflammatory gene expression was measured with real-time quantitative polymerase chain reaction and alveolocapillary leak was estimated by measuring bronchoalveolar lavage protein content. The LAV group had small, stiff lungs that were recruitable at higher pressures, but did not demonstrate substantial inflammation. The LPS group showed septal swelling and high pro-inflammatory gene expression that was exacerbated by VILI. Despite widespread alveolar collapse, elastance in LPS was only modestly elevated above healthy mice (CTL) and there was no evidence of recruitability. The HCL group showed increased elastance and some recruitability, although to a lesser degree than LAV. Pro-inflammatory gene expression was elevated, but less than LPS, and the airspace dimensions were reduced. Taken together, those data highlight how different modes of injury, in combination with a 2nd hit of VILI, yield markedly different effects.

Protocol and statistical analysis plan for the PREOXI trial of preoxygenation with noninvasive ventilation vs oxygen mask.

Background: Hypoxemia is a common and life-threatening complication during emergency tracheal intubation of critically ill adults. The administration of supplemental oxygen prior to the procedure ("preoxygenation") decreases the risk of hypoxemia during intubation. Research Question: Whether preoxygenation with noninvasive ventilation prevents hypoxemia during tracheal intubation of critically ill adults, compared to preoxygenation with oxygen mask, remains uncertain. Study Design and Methods: The PRagmatic trial Examining OXygenation prior to Intubation (PREOXI) is a prospective, multicenter, non-blinded randomized comparative effectiveness trial being conducted in 7 emergency departments and 17 intensive care units across the United States. The trial compares preoxygenation with noninvasive ventilation versus oxygen mask among 1300 critically ill adults undergoing emergency tracheal intubation. Eligible patients are randomized in a 1:1 ratio to receive either noninvasive ventilation or an oxygen mask prior to induction. The primary outcome is the incidence of hypoxemia, defined as a peripheral oxygen saturation <85% between induction and 2 minutes after intubation. The secondary outcome is the lowest oxygen saturation between induction and 2 minutes after intubation. Enrollment began on 10 March 2022 and is expected to conclude in 2023. Interpretation: The PREOXI trial will provide important data on the effectiveness of noninvasive ventilation and oxygen mask preoxygenation for the prevention of hypoxemia during emergency tracheal intubation. Specifying the protocol and statistical analysis plan prior to the conclusion of enrollment increases the rigor, reproducibility, and interpretability of the trial. Clinical trial registration number: NCT05267652.

Association between Substance Misuse and Outcomes in Critically III Patients with Pneumonia.

Rationale: In patients with pneumonia requiring intensive care unit (ICU) admission, alcohol misuse is associated with increased mortality, but the relationship between other commonly misused substances and mortality is unknown. Objectives: We sought to establish whether alcohol misuse, cannabis misuse, opioid misuse, stimulant misuse, or misuse of more than one of these substances was associated with differences in mortality among ICU patients with pneumonia. Methods: This was a retrospective cohort study of hospitals participating in the Premier Healthcare Database between 2010 and 2017. Patients were included if they had a primary or secondary diagnosis of pneumonia and received antibiotics or antivirals within 1 day of admission. Substance misuse related to alcohol, cannabis, stimulants, and opioids, or more than one substance, were identified from the International Classification of Diseases (Ninth and Tenth Editions). The associations between substance misuse and in-hospital mortality were the primary outcomes of interest. Secondary outcomes included the measured associations between substance misuse disorders and mechanical ventilation, as well as vasopressor and continuous paralytic administration. Analyses were conducted with multivariable mixed-effects logistic regression modeling adjusting for age, comorbidities, and hospital characteristics. Results: A total of 167,095 ICU patients met inclusion criteria for pneumonia. Misuse of alcohol was present in 5.0%, cannabis misuse in 0.6%, opioid misuse in 1.5%, stimulant misuse in 0.6%, and misuse of more than one substance in 1.2%. No evidence of substance misuse was found in 91.1% of patients. In unadjusted analyses, alcohol misuse was associated with increased in-hospital mortality (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.06-1.19), whereas opioid misuse was associated with decreased in-hospital mortality (OR, 0.46; 95% CI, 0.39-0.53) compared with no substance misuse. These findings persisted in adjusted analyses. Although cannabis, stimulant, and more than one substance misuse (a majority of which were alcohol in combination with another substance) were associated with lower odds for in-hospital mortality in unadjusted analyses, these relationships were not consistently present after adjustment. Conclusions: In this study of ICU patients hospitalized with severe pneumonia, substance misuse subtypes were associated with different effects on mortality. Although administrative data can provide epidemiologic insight regarding substance misuse and pneumonia outcomes, biases inherent to these data should be considered when interpreting results.

Hypothesis-driven modeling of the human lung-ventilator system: A characterization tool for Acute Respiratory Distress Syndrome research.

Mechanical ventilation is an essential tool in the management of Acute Respiratory Distress Syndrome (ARDS), but it exposes patients to the risk of ventilator-induced lung injury (VILI). The human lung-ventilator system (LVS) involves the interaction of complex anatomy with a mechanical apparatus, which limits the ability of process-based models to provide individualized clinical support. This work proposes a hypothesis-driven strategy for LVS modeling in which robust personalization is achieved using a pre-defined parameter basis in a non-physiological model. Model inversion, here via windowed data assimilation, forges observed waveforms into interpretable parameter values that characterize the data rather than quantifying physiological processes. Accurate, model-based inference on human-ventilator data indicates model flexibility and utility over a variety of breath types, including those from dyssynchronous LVSs. Estimated parameters generate static characterizations of the data that are 50%-70% more accurate than breath-wise single-compartment model estimates. They also retain sufficient information to distinguish between the types of breath they represent. However, the fidelity and interpretability of model characterizations are tied to parameter definitions and model resolution. These additional factors must be considered in conjunction with the objectives of specific applications, such as identifying and tracking the development of human VILI.

Extracorporeal Membrane Oxygenation for Refractory Asthma Exacerbations With Respiratory Failure.

BACKGROUND: Asthma exacerbations with respiratory failure (AERF) are associated with hospital mortality of 7% to 15%. Extracorporeal membrane oxygenation (ECMO) has been used as a salvage therapy for refractory AERF, but controlled studies showing its association with mortality have not been performed. RESEARCH QUESTION: Is treatment with ECMO associated with lower mortality in refractory AERF compared with standard care? STUDY DESIGN AND METHODS: This is a retrospective, epidemiologic, observational cohort study using a national, administrative data set from 2010 to 2020 that includes 25% of US hospitalizations. People were included if they were admitted to an ECMO-capable hospital with an asthma exacerbation, and were treated with short-acting bronchodilators, systemic corticosteroids, and invasive ventilation. People were excluded for age < 18 years, no ICU stay, nonasthma chronic lung disease, COVID-19, or multiple admissions. The main exposure was ECMO vs No ECMO. The primary outcome was hospital mortality. Key secondary outcomes were ICU length of stay (LOS), hospital LOS, time receiving invasive ventilation, and total hospital costs. RESULTS: The study analyzed 13,714 patients with AERF, including 127 with ECMO and 13,587 with No ECMO. ECMO was associated with reduced mortality in the covariate-adjusted (OR, 0.33; 95% CI, 0.17-0.64; P = .001), propensity score-adjusted (OR, 0.36; 95% CI, 0.16-0.81; P = .01), and propensity score-matched models (OR, 0.48; 95% CI, 0.24-0.98; P = .04) vs No ECMO. Sensitivity analyses showed that mortality reduction related to ECMO ranged from OR 0.34 to 0.61. ECMO was also associated with increased hospital costs in all three models (P < .0001 for all) vs No ECMO, but not with decreased ICU LOS, hospital LOS, or time receiving invasive ventilation. INTERPRETATION: ECMO was associated with lower mortality and higher hospital costs, suggesting that it may be an important salvage therapy for refractory AERF following confirmatory clinical trials.

Identifying low-dimensional trajectories of mechanically-ventilated patient systems: Empirical phenotypes of joint patient+care processes to enhance temporal analysis in ARDS research.

Mechanically ventilated patients generate waveform data that corresponds to patient interaction with unnatural forcing. This breath information includes both patient and apparatus sources, imbuing data with broad heterogeneity resulting from ventilator settings, patient efforts, patient-ventilator dyssynchronies, injuries, and other clinical therapies. Lung-protective ventilator settings outlined in respiratory care protocols lack personalization, and the connections between clinical outcomes and injuries resulting from mechanical ventilation remain poorly understood. Intra- and inter-patient heterogeneity and the volume of data comprising lung-ventilator system (LVS) observations limit broader and longer-time analysis of such systems. This work presents a computational pipeline for resolving LVS systems by tracking the evolution of data-conditioned model parameters and ventilator information. For individuals, the method presents LVS trajectory in a manageable way through low-dimensional representation of phenotypic breath waveforms. More general phenotypes across patients are also developed by aggregating patient-personalized estimates with additional normalization. The effectiveness of this process is demonstrated through application to multi-day observational series of 35 patients, which reveals the complexity of changes in the LVS over time. Considerable variations in breath behavior independent of the ventilator are revealed, suggesting the need to incorporate care factors such as patient sedation and posture in future analysis. The pipeline also identifies structural similarity in pressure-volume (pV) loop characterizations at the cohort level. The design invites active learning to incorporate clinical practitioner expertise into various methodological stages and algorithm choices.

A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms.

Motivated by a desire to understand pulmonary physiology, scientists have developed physiological lung models of varying complexity. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present challenges for modeling efforts. This is because the real-world pressure and volume signals may be too complex for simple models to capture, and while complex models tend not to be estimable with clinical data, limiting clinical utility. To address this gap, in this manuscript we developed a new damaged-informed lung ventilator (DILV) model. This approach relies on mathematizing ventilator pressure and volume waveforms, including lung physiology, mechanical ventilation, and their interaction. The model begins with nominal waveforms and adds limited, clinically relevant, hypothesis-driven features to the waveform corresponding to pulmonary pathophysiology, patient-ventilator interaction, and ventilator settings. The DILV model parameters uniquely and reliably recapitulate these features while having enough flexibility to reproduce commonly observed variability in clinical (human) and laboratory (mouse) waveform data. We evaluate the proof-in-principle capabilities of our modeling approach by estimating 399 breaths collected for differently damaged lungs for tightly controlled measurements in mice and uncontrolled human intensive care unit data in the absence and presence of ventilator dyssynchrony. The cumulative value of mean squares error for the DILV model is, on average, ≈12 times less than the single compartment lung model for all the waveforms considered. Moreover, changes in the estimated parameters correctly correlate with known measures of lung physiology, including lung compliance as a baseline evaluation. Our long-term goal is to use the DILV model for clinical monitoring and research studies by providing high fidelity estimates of lung state and sources of VILI with an end goal of improving management of VILI and acute respiratory distress syndrome.

Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study.

OBJECTIVE: To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team. MATERIALS AND METHODS: We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. RESULTS: The prospective cohort included 27 296 encounters, of which 1358 (5.0%) were positive for SARS-CoV-2, 4494 (16.5%) required intensive care unit care, 1480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. DISCUSSION: Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction. CONCLUSION: We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.

Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study.

BACKGROUND: The SARS-CoV-2 virus has infected millions of people, overwhelming critical care resources in some regions. Many plans for rationing critical care resources during crises are based on the Sequential Organ Failure Assessment (SOFA) score. The COVID-19 pandemic created an emergent need to develop and validate a novel electronic health record (EHR)-computable tool to predict mortality. RESEARCH QUESTIONS: To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon SOFA. STUDY DESIGN AND METHODS: We conducted a prospective cohort study of a regional health system with 12 hospitals in Colorado between March 2020 and July 2020. All patients >14 years old hospitalized during the study period without a do not resuscitate order were included. Patients were stratified by the diagnosis of COVID-19. From this cohort, we developed and validated a model using stacked generalization to predict mortality using data widely available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. RESULTS: We prospectively analyzed 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) included intensive care unit (ICU)-level care, 1,480 (5.4%) included invasive mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted overall mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted overall mortality with AUROC 0.94. In the subset of patients with COVID-19, we predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. INTERPRETATION: We developed and validated an accurate, in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model, that improved upon SOFA. TAKE HOME POINTS: Study Question: Can we improve upon the SOFA score for real-time mortality prediction during the COVID-19 pandemic by leveraging electronic health record (EHR) data?Results: We rapidly developed and implemented a novel yet SOFA-anchored mortality model across 12 hospitals and conducted a prospective cohort study of 27,296 adult hospitalizations, 1,358 (5.0%) of which were positive for SARS-CoV-2. The Charlson Comorbidity Index and SOFA scores predicted all-cause mortality with AUROCs of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94.Interpretation: A novel EHR-based mortality score can be rapidly implemented to better predict patient outcomes during an evolving pandemic.

Ventilator dyssynchrony - Detection, pathophysiology, and clinical relevance: A Narrative review.

Mortality associated with the acute respiratory distress syndrome remains unacceptably high due in part to ventilator-induced lung injury (VILI). Ventilator dyssynchrony is defined as the inappropriate timing and delivery of a mechanical breath in response to patient effort and may cause VILI. Such deleterious patient-ventilator interactions have recently been termed patient self-inflicted lung injury. This narrative review outlines the detection and frequency of several different types of ventilator dyssynchrony, delineates the different mechanisms by which ventilator dyssynchrony may propagate VILI, and reviews the potential clinical impact of ventilator dyssynchrony. Until recently, identifying ventilator dyssynchrony required the manual interpretation of ventilator pressure and flow waveforms. However, computerized interpretation of ventilator waive forms can detect ventilator dyssynchrony with an area under the receiver operating curve of >0.80. Using such algorithms, ventilator dyssynchrony occurs in 3%-34% of all breaths, depending on the patient population. Moreover, two types of ventilator dyssynchrony, double-triggered and flow-limited breaths, are associated with the more frequent delivery of large tidal volumes >10 mL/kg when compared with synchronous breaths (54% [95% confidence interval (CI), 47%-61%] and 11% [95% CI, 7%-15%]) compared with 0.9% (95% CI, 0.0%-1.9%), suggesting a role in propagating VILI. Finally, a recent study associated frequent dyssynchrony-defined as >10% of all breaths-with an increase in hospital mortality (67 vs. 23%, P = 0.04). However, the clinical significance of ventilator dyssynchrony remains an area of active investigation and more research is needed to guide optimal ventilator dyssynchrony management.

Myorelaxants in ARDS patients.

Neuromuscular blocking agents (NMBAs) inhibit patient-initiated active breath and the risk of high tidal volumes and consequent high transpulmonary pressure swings, and minimize patient/ ventilator asynchrony in acute respiratory distress syndrome (ARDS). Minimization of volutrauma and ventilator-induced lung injury (VILI) results in a lower incidence of barotrauma, improved oxygenation and a decrease in circulating proinflammatory markers. Recent randomized clinical trials did not reveal harmful muscular effects during a short course of NMBAs. The use of NMBAs should be considered during the early phase of severe ARDS for patients to facilitate lung protective ventilation or prone positioning only after optimising mechanical ventilation and sedation. The use of NMBAs should be integrated in a global strategy including the reduction of tidal volume, the rational use of PEEP, prone positioning and the use of a ventilatory mode allowing spontaneous ventilation as soon as possible. Partial neuromuscular blockade should be evaluated in future trials.

Clinical informatics accelerates health system adaptation to the COVID-19 pandemic: examples from Colorado.

OBJECTIVE: Large health systems responding to the coronavirus disease 2019 (COVID-19) pandemic face a broad range of challenges; we describe 14 examples of innovative and effective informatics interventions. MATERIALS AND METHODS: A team of 30 physician and 17 nurse informaticists with an electronic health record (EHR) and associated informatics tools. RESULTS: To meet the demands posed by the influx of patients with COVID-19 into the health system, the team built solutions to accomplish the following goals: 1) train physicians and nurses quickly to manage a potential surge of hospital patients; 2) build and adjust interactive visual pathways to guide decisions; 3) scale up video visits and teach best-practice communication; 4) use tablets and remote monitors to improve in-hospital and posthospital patient connections; 5) allow hundreds of physicians to build rapid consensus; 6) improve the use of advance care planning; 7) keep clinicians aware of patients' changing COVID-19 status; 8) connect nurses and families in new ways; 9) semi-automate Crisis Standards of Care; and 10) predict future hospitalizations. DISCUSSION: During the onset of the COVID-19 pandemic, the UCHealth Joint Informatics Group applied a strategy of "practical informatics" to rapidly translate critical leadership decisions into understandable guidance and effective tools for patient care. CONCLUSION: Informatics-trained physicians and nurses drew upon their trusted relationships with multiple teams within the organization to create practical solutions for onboarding, clinical decision-making, telehealth, and predictive analytics.

Patterns of utilization and effects of hospital-specific factors on physical, occupational, and speech therapy for critically ill patients with acute respiratory failure in the USA: results of a 5-year sample.

BACKGROUND: Timely initiation of physical, occupational, and speech therapy in critically ill patients is crucial to reduce morbidity and improve outcomes. Over a 5-year time interval, we sought to determine the utilization of these rehabilitation therapies in the USA. METHODS: We performed a retrospective cohort study utilizing a large, national administrative database including ICU patients from 591 hospitals. Patients over 18 years of age with acute respiratory failure requiring invasive mechanical ventilation within the first 2 days of hospitalization and for a duration of at least 48 h were included. RESULTS: A total of 264,137 patients received invasive mechanical ventilation for a median of 4.0 [2.0-8.0] days. Overall, patients spent a median of 5.0 [3.0-10.0] days in the ICU and 10.0 [7.0-16.0] days in the hospital. During their hospitalization, 66.5%, 41.0%, and 33.2% (95% CI = 66.3-66.7%, 40.8-41.2%, 33.0-33.4%, respectively) received physical, occupational, and speech therapy. While on mechanical ventilation, 36.2%, 29.7%, and 29.9% (95% CI = 36.0-36.4%, 29.5-29.9%, 29.7-30.1%) received physical, occupational, and speech therapy. In patients receiving therapy, their first physical therapy session occurred on hospital day 5 [3.0-8.0] and hospital day 6 [4.0-10.0] for occupational and speech therapy. Of all patients, 28.6% (95% CI = 28.4-28.8%) did not receive physical, occupational, or speech therapy during their hospitalization. In a multivariate analysis, patients cared for in the Midwest and at teaching hospitals were more likely to receive physical, occupational, and speech therapy (all P < 0.05). Of patients with identical covariates receiving therapy, there was a median of 61%, 187%, and 70% greater odds of receiving physical, occupational, and speech therapy, respectively, at one randomly selected hospital compared with another (median odds ratio 1.61, 2.87, 1.70, respectively). CONCLUSIONS: Physical, occupational, and speech therapy are not routinely delivered to critically ill patients, particularly while on mechanical ventilation in the USA. The utilization of these therapies varies according to insurance coverage, geography, and hospital teaching status, and at a hospital level.