The Effect of Lower Tidal Volume Ventilation Facilitated by Extracorporeal Carbon Dioxide Removal Compared With Conventional Lung Protective Ventilation on Cardiac Function.

OBJECTIVES: Lower tidal volume ventilation (targeting 3 mL/kg predicted body weight, PBW) facilitated by extracorporeal carbon dioxide removal (ECCO2R) has been investigated as a potential therapy for acute hypoxemic respiratory failure (AHRF) in the pRotective vEntilation with veno-venouS lung assisT in respiratory failure (REST) trial. We investigated the effect of this strategy on cardiac function, and in particular the right ventricle. DESIGN: Substudy of the REST trial. SETTING: Nine U.K. ICUs. PATIENTS: Patients with AHRF (Pao2/Fio2 < 150 mm Hg [20 kPa]). INTERVENTION: Transthoracic echocardiography and N-terminal pro-B-type natriuretic peptide (NT-proBNP) measurements were collected at baseline and postrandomization in patients randomized to ECCO2R or usual care. MEASUREMENTS: The primary outcome measures were a difference in tricuspid annular plane systolic excursion (TAPSE) on postrandomization echocardiogram and difference in NT-proBNP postrandomization. RESULTS: There were 21 patients included in the echocardiography cohort (ECCO2R, n = 13; usual care, n = 8). Patient characteristics were similar in both groups at baseline. Median (interquartile range) tidal volumes were lower in the ECCO2R group compared with the usual care group postrandomization; 3.6 (3.1-4.2) mL/kg PBW versus 5.2 (4.9-5.7) mL/kg PBW, respectively (p = 0.01). There was no difference in the primary outcome measure of mean (sd) TAPSE in the ECCO2R and usual care groups postrandomization; 21.3 (5.4) mm versus 20.1 (3.2) mm, respectively (p = 0.60). There were 75 patients included in the NT-proBNP cohort (ECCO2R, n = 36; usual care, n = 39). Patient characteristics were similar in both groups at baseline. Median (interquartile range [IQR]) tidal volumes were lower in the ECCO2R group than the usual care group postrandomization; 3.8 (3.3-4.2) mL/kg PBW versus 6.7 (5.8-8.1) mL/kg PBW, respectively (p < 0.0001). There was no difference in median (IQR) NT-proBNP postrandomization; 1121 (241-5370) pg/mL versus 1393 (723-4332) pg/mL in the ECCO2R and usual care groups, respectively (p = 0.30). CONCLUSIONS: In patients with AHRF, a reduction in tidal volume facilitated by ECCO2R, did not modify cardiac function.

Simvastatin in Critically Ill Patients with Covid-19.

BACKGROUND: The efficacy of simvastatin in critically ill patients with coronavirus disease 2019 (Covid-19) is unclear. METHODS: In an ongoing international, multifactorial, adaptive platform, randomized, controlled trial, we evaluated simvastatin (80 mg daily) as compared with no statin (control) in critically ill patients with Covid-19 who were not receiving statins at baseline. The primary outcome was respiratory and cardiovascular organ support-free days, assessed on an ordinal scale combining in-hospital death (assigned a value of -1) and days free of organ support through day 21 in survivors; the analyis used a Bayesian hierarchical ordinal model. The adaptive design included prespecified statistical stopping criteria for superiority (>99% posterior probability that the odds ratio was >1) and futility (>95% posterior probability that the odds ratio was <1.2). RESULTS: Enrollment began on October 28, 2020. On January 8, 2023, enrollment was closed on the basis of a low anticipated likelihood that prespecified stopping criteria would be met as Covid-19 cases decreased. The final analysis included 2684 critically ill patients. The median number of organ support-free days was 11 (interquartile range, -1 to 17) in the simvastatin group and 7 (interquartile range, -1 to 16) in the control group; the posterior median adjusted odds ratio was 1.15 (95% credible interval, 0.98 to 1.34) for simvastatin as compared with control, yielding a 95.9% posterior probability of superiority. At 90 days, the hazard ratio for survival was 1.12 (95% credible interval, 0.95 to 1.32), yielding a 91.9% posterior probability of superiority of simvastatin. The results of secondary analyses were consistent with those of the primary analysis. Serious adverse events, such as elevated levels of liver enzymes and creatine kinase, were reported more frequently with simvastatin than with control. CONCLUSIONS: Although recruitment was stopped because cases had decreased, among critically ill patients with Covid-19, simvastatin did not meet the prespecified criteria for superiority to control. (REMAP-CAP ClinicalTrials.gov number, NCT02735707.).

Intravenous Vitamin C for Patients Hospitalized With COVID-19: Two Harmonized Randomized Clinical Trials.

Importance: The efficacy of vitamin C for hospitalized patients with COVID-19 is uncertain. Objective: To determine whether vitamin C improves outcomes for patients with COVID-19. Design, Setting, and Participants: Two prospectively harmonized randomized clinical trials enrolled critically ill patients receiving organ support in intensive care units (90 sites) and patients who were not critically ill (40 sites) between July 23, 2020, and July 15, 2022, on 4 continents. Interventions: Patients were randomized to receive vitamin C administered intravenously or control (placebo or no vitamin C) every 6 hours for 96 hours (maximum of 16 doses). Main Outcomes and Measures: The primary outcome was a composite of organ support-free days defined as days alive and free of respiratory and cardiovascular organ support in the intensive care unit up to day 21 and survival to hospital discharge. Values ranged from -1 organ support-free days for patients experiencing in-hospital death to 22 organ support-free days for those who survived without needing organ support. The primary analysis used a bayesian cumulative logistic model. An odds ratio (OR) greater than 1 represented efficacy (improved survival, more organ support-free days, or both), an OR less than 1 represented harm, and an OR less than 1.2 represented futility. Results: Enrollment was terminated after statistical triggers for harm and futility were met. The trials had primary outcome data for 1568 critically ill patients (1037 in the vitamin C group and 531 in the control group; median age, 60 years [IQR, 50-70 years]; 35.9% were female) and 1022 patients who were not critically ill (456 in the vitamin C group and 566 in the control group; median age, 62 years [IQR, 51-72 years]; 39.6% were female). Among critically ill patients, the median number of organ support-free days was 7 (IQR, -1 to 17 days) for the vitamin C group vs 10 (IQR, -1 to 17 days) for the control group (adjusted proportional OR, 0.88 [95% credible interval {CrI}, 0.73 to 1.06]) and the posterior probabilities were 8.6% (efficacy), 91.4% (harm), and 99.9% (futility). Among patients who were not critically ill, the median number of organ support-free days was 22 (IQR, 18 to 22 days) for the vitamin C group vs 22 (IQR, 21 to 22 days) for the control group (adjusted proportional OR, 0.80 [95% CrI, 0.60 to 1.01]) and the posterior probabilities were 2.9% (efficacy), 97.1% (harm), and greater than 99.9% (futility). Among critically ill patients, survival to hospital discharge was 61.9% (642/1037) for the vitamin C group vs 64.6% (343/531) for the control group (adjusted OR, 0.92 [95% CrI, 0.73 to 1.17]) and the posterior probability was 24.0% for efficacy. Among patients who were not critically ill, survival to hospital discharge was 85.1% (388/456) for the vitamin C group vs 86.6% (490/566) for the control group (adjusted OR, 0.86 [95% CrI, 0.61 to 1.17]) and the posterior probability was 17.8% for efficacy. Conclusions and Relevance: In hospitalized patients with COVID-19, vitamin C had low probability of improving the primary composite outcome of organ support-free days and hospital survival. Trial Registration: ClinicalTrials.gov Identifiers: NCT04401150 (LOVIT-COVID) and NCT02735707 (REMAP-CAP).

Virtual patient with temporal evolution for mechanical ventilation trial studies: A stochastic model approach.

BACKGROUND AND OBJECTIVE: Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic data generation in hospital critical care settings. This research presents a realistic, time-varying mechanically ventilated respiratory failure VP profile synthesised using a stochastic model. METHODS: A stochastic model was developed using respiratory elastance (Ers) data from two clinical cohorts and averaged over 30-minute time intervals. The stochastic model was used to generate future Ers data based on current Ers values with added normally distributed random noise. Self-validation of the VPs was performed via Monte Carlo simulation and retrospective Ers profile fitting. A stochastic VP cohort of temporal Ers evolution was synthesised and then compared to an independent retrospective patient cohort data in a virtual trial across several measured patient responses, where similarity of profiles validates the realism of stochastic model generated VP profiles. RESULTS: A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5-10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of Ers profiles. Results of self-validation show the retrospective Ers profiles were able to be recreated accurately with a mean squared error of only 0.099 [0.009-0.790]% for the PC cohort and 0.051 [0.030-0.126]% for the VC cohort. A virtual trial demonstrates the ability of the stochastic VP cohort to capture Ers trends within and beyond the retrospective patient cohort providing cohort-level validation. CONCLUSION: VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation.

Predicting mechanically ventilated patients future respiratory system elastance - A stochastic modelling approach.

BACKGROUND AND OBJECTIVE: Respiratory mechanics of mechanically ventilated patients evolve significantly with time, disease state and mechanical ventilation (MV) treatment. Existing deterministic data prediction methods fail to comprehensively describe the multiple sources of heterogeneity of biological systems. This research presents two respiratory mechanics stochastic models with increased prediction accuracy and range, offering improved clinical utility in MV treatment. METHODS: Two stochastic models (SM2 and SM3) were developed using retrospective patient respiratory elastance (Ers) from two clinical cohorts which were averaged over time intervals of 10 and 30 min respectively. A stochastic model from a previous study (SM1) was used to benchmark performance. The stochastic models were clinically validated on an independent retrospective clinical cohort of 14 patients. Differences in predictive ability were evaluated using the difference in percentile lines and cumulative distribution density (CDD) curves. RESULTS: Clinical validation shows all three models captured more than 98% (median) of future Ers data within the 5th - 95th percentile range. Comparisons of stochastic model percentile lines reported a maximum mean absolute percentage difference of 5.2%. The absolute differences of CDD curves were less than 0.25 in the ranges of 5 < Ers (cmH2O/L) < 85, suggesting similar predictive capabilities within this clinically relevant Ers range. CONCLUSION: The new stochastic models significantly improve prediction, clinical utility, and thus feasibility for synchronisation with clinical interventions. Paired with other MV protocols, the stochastic models developed can potentially form part of decision support systems, providing guided, personalised, and safe MV treatment.

Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol.

BACKGROUND AND OBJECTIVE: Model-based and personalised decision support systems are emerging to guide mechanical ventilation (MV) treatment for respiratory failure patients. However, model-based treatments require resource-intensive clinical trials prior to implementation. This research presents a framework for generating virtual patients for testing model-based decision support, and direct use in MV treatment. METHODS: The virtual MV patient framework consists of 3 stages: 1) Virtual patient generation, 2) Patient-level validation, and 3) Virtual clinical trials. The virtual patients are generated from retrospective MV patient data using a clinically validated respiratory mechanics model whose respiratory parameters (respiratory elastance and resistance) capture patient-specific pulmonary conditions and responses to MV care over time. Patient-level validation compares the predicted responses from the virtual patient to their retrospective results for clinically implemented MV settings and changes to care. Patient-level validated virtual patients create a platform to conduct virtual trials, where the safety of closed-loop model-based protocols can be evaluated. RESULTS: This research creates and presents a virtual patient platform of 100 virtual patients generated from retrospective data. Patient-level validation reported median errors of 3.26% for volume-control and 6.80% for pressure-control ventilation mode. A virtual trial on a model-based protocol demonstrates the potential efficacy of using virtual patients for prospective evaluation and testing of the protocol. CONCLUSION: The virtual patient framework shows the potential to safely and rapidly design, develop, and optimise new model-based MV decision support systems and protocols using clinically validated models and computer simulation, which could ultimately improve patient care and outcomes in MV.

Quantification of respiratory effort magnitude in spontaneous breathing patients using Convolutional Autoencoders.

BACKGROUND: Spontaneous breathing (SB) effort during mechanical ventilation (MV) is an important metric of respiratory drive. However, SB effort varies due to a variety of factors, including evolving pathology and sedation levels. Therefore, assessment of SB efforts needs to be continuous and non-invasive. This is important to prevent both over- and under-assistance with MV. In this study, a machine learning model, Convolutional Autoencoder (CAE) is developed to quantify the magnitude of SB effort using only bedside MV airway pressure and flow waveform. METHOD: The CAE model was trained using 12,170,655 simulated SB flow and normal flow data (NB). The paired SB and NB flow data were simulated using a Gaussian Effort Model (GEM) with 5 basis functions. When the CAE model is given a SB flow input, it is capable of predicting a corresponding NB flow for the SB flow input. The magnitude of SB effort (SBEMag) is then quantified as the difference between the SB and NB flows. The CAE model was used to evaluate the SBEMag of 9 pressure control/ support datasets. Results were validated using a mean squared error (MSE) fitting between clinical and training SB flows. RESULTS: The CAE model was able to produce NB flows from the clinical SB flows with the median SBEMag of the 9 datasets being 25.39% [IQR: 21.87-25.57%]. The absolute error in SBEMag using MSE validation yields a median of 4.77% [IQR: 3.77-8.56%] amongst the cohort. This shows the ability of the GEM to capture the intrinsic details present in SB flow waveforms. Analysis also shows both intra-patient and inter-patient variability in SBEMag. CONCLUSION: A Convolutional Autoencoder model was developed with simulated SB and NB flow data and is capable of quantifying the magnitude of patient spontaneous breathing effort. This provides potential application for real-time monitoring of patient respiratory drive for better management of patient-ventilator interaction.

Protocol conception for safe selection of mechanical ventilation settings for respiratory failure Patients.

BACKGROUND AND OBJECTIVE: Mechanical ventilation is the primary form of care provided to respiratory failure patients. Limited guidelines and conflicting results from major clinical trials means selection of mechanical ventilation settings relies heavily on clinician experience and intuition. Determining optimal mechanical ventilation settings is therefore difficult, where non-optimal mechanical ventilation can be deleterious. To overcome these difficulties, this research proposes a model-based method to manage the wide range of possible mechanical ventilation settings, while also considering patient-specific conditions and responses. METHODS: This study shows the design and development of the "VENT" protocol, which integrates the single compartment linear lung model with clinical recommendations from landmark studies, to aid clinical decision-making in selecting mechanical ventilation settings. Using retrospective breath data from a cohort of 24 patients, 3,566 and 2,447 clinically implemented VC and PC settings were extracted respectively. Using this data, a VENT protocol application case study and clinical comparison is performed, and the prediction accuracy of the VENT protocol is validated against actual measured outcomes of pressure and volume. RESULTS: The study shows the VENT protocols' potential use in narrowing an overwhelming number of possible mechanical ventilation setting combinations by up to 99.9%. The comparison with retrospective clinical data showed that only 33% and 45% of clinician settings were approved by the VENT protocol. The unapproved settings were mainly due to exceeding clinical recommended settings. When utilising the single compartment model in the VENT protocol for forecasting peak pressures and tidal volumes, median [IQR] prediction error values of 0.75 [0.31 - 1.83] cmH2O and 0.55 [0.19 - 1.20] mL/kg were obtained. CONCLUSIONS: Comparing the proposed protocol with retrospective clinically implemented settings shows the protocol can prevent harmful mechanical ventilation setting combinations for which clinicians would be otherwise unaware. The VENT protocol warrants a more detailed clinical study to validate its potential usefulness in a clinical setting.

Effect of Convalescent Plasma on Organ Support-Free Days in Critically Ill Patients With COVID-19: A Randomized Clinical Trial.

IMPORTANCE: The evidence for benefit of convalescent plasma for critically ill patients with COVID-19 is inconclusive. OBJECTIVE: To determine whether convalescent plasma would improve outcomes for critically ill adults with COVID-19. DESIGN, SETTING, AND PARTICIPANTS: The ongoing Randomized, Embedded, Multifactorial, Adaptive Platform Trial for Community-Acquired Pneumonia (REMAP-CAP) enrolled and randomized 4763 adults with suspected or confirmed COVID-19 between March 9, 2020, and January 18, 2021, within at least 1 domain; 2011 critically ill adults were randomized to open-label interventions in the immunoglobulin domain at 129 sites in 4 countries. Follow-up ended on April 19, 2021. INTERVENTIONS: The immunoglobulin domain randomized participants to receive 2 units of high-titer, ABO-compatible convalescent plasma (total volume of 550 mL ± 150 mL) within 48 hours of randomization (n = 1084) or no convalescent plasma (n = 916). MAIN OUTCOMES AND MEASURES: The primary ordinal end point was organ support-free days (days alive and free of intensive care unit-based organ support) up to day 21 (range, -1 to 21 days; patients who died were assigned -1 day). The primary analysis was an adjusted bayesian cumulative logistic model. Superiority was defined as the posterior probability of an odds ratio (OR) greater than 1 (threshold for trial conclusion of superiority >99%). Futility was defined as the posterior probability of an OR less than 1.2 (threshold for trial conclusion of futility >95%). An OR greater than 1 represented improved survival, more organ support-free days, or both. The prespecified secondary outcomes included in-hospital survival; 28-day survival; 90-day survival; respiratory support-free days; cardiovascular support-free days; progression to invasive mechanical ventilation, extracorporeal mechanical oxygenation, or death; intensive care unit length of stay; hospital length of stay; World Health Organization ordinal scale score at day 14; venous thromboembolic events at 90 days; and serious adverse events. RESULTS: Among the 2011 participants who were randomized (median age, 61 [IQR, 52 to 70] years and 645/1998 [32.3%] women), 1990 (99%) completed the trial. The convalescent plasma intervention was stopped after the prespecified criterion for futility was met. The median number of organ support-free days was 0 (IQR, -1 to 16) in the convalescent plasma group and 3 (IQR, -1 to 16) in the no convalescent plasma group. The in-hospital mortality rate was 37.3% (401/1075) for the convalescent plasma group and 38.4% (347/904) for the no convalescent plasma group and the median number of days alive and free of organ support was 14 (IQR, 3 to 18) and 14 (IQR, 7 to 18), respectively. The median-adjusted OR was 0.97 (95% credible interval, 0.83 to 1.15) and the posterior probability of futility (OR <1.2) was 99.4% for the convalescent plasma group compared with the no convalescent plasma group. The treatment effects were consistent across the primary outcome and the 11 secondary outcomes. Serious adverse events were reported in 3.0% (32/1075) of participants in the convalescent plasma group and in 1.3% (12/905) of participants in the no convalescent plasma group. CONCLUSIONS AND RELEVANCE: Among critically ill adults with confirmed COVID-19, treatment with 2 units of high-titer, ABO-compatible convalescent plasma had a low likelihood of providing improvement in the number of organ support-free days. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02735707.

Optimising the timing of renal replacement therapy in acute kidney injury.

The optimal timing of renal replacement therapy (RRT) in critically ill patients with acute kidney injury (AKI) has been much debated. Over the past five years several studies have provided new guidance for evidence-based decision-making. High-quality evidence now supports an approach of expectant management in critically ill patients with AKI, where RRT may be deferred up to 72 h unless a life-threatening indication develops. Nevertheless, physicians' judgment still plays a central role in identifying appropriate patients for expectant management.

Respiratory Dialysis-A Novel Low Bicarbonate Dialysate to Provide Extracorporeal CO2 Removal.

OBJECTIVES: We designed a novel respiratory dialysis system to remove CO2 from blood in the form of bicarbonate. We aimed to determine if our respiratory dialysis system removes CO2 at rates comparable to low-flow extracorporeal CO2 removal devices (blood flow < 500 mL/min) in a large animal model. DESIGN: Experimental study. SETTING: Animal research laboratory. SUBJECTS: Female Yorkshire pigs. INTERVENTIONS: Five bicarbonate dialysis experiments were performed. Hypercapnia (PCO2 90-100 mm Hg) was established in mechanically ventilated swine by adjusting the tidal volume. Dialysis was then performed with a novel low bicarbonate dialysate. MEASUREMENTS AND MAIN RESULTS: We measured electrolytes, blood gases, and plasma-free hemoglobin in arterial blood, as well as blood entering and exiting the dialyzer. We used a physical-chemical acid-base model to understand the factors influencing blood pH after bicarbonate removal. During dialysis, we removed 101 (±13) mL/min of CO2 (59 mL/min when normalized to venous PCO2 of 45 mm Hg), corresponding to a 29% reduction in PaCO2 (104.0 ± 8.1 vs 74.2 ± 8.4 mm Hg; p < 0.001). Minute ventilation and body temperature were unchanged during dialysis (1.2 ± 0.4 vs 1.1 ± 0.4 L/min; p = 1.0 and 35.3°C ± 0.9 vs 35.2°C ± 0.6; p = 1.0). Arterial pH increased after bicarbonate removal (7.13 ± 0.04 vs 7.21 ± 0.05; p < 0.001) despite no attempt to realkalinize the blood. Our modeling showed that dialysate electrolyte composition, plasma albumin, and plasma total CO2 accurately predict the measured pH of blood exiting the dialyser. However, the final effluent dose exceeded conventional doses, depleting plasma glucose and electrolytes, such as potassium and phosphate. CONCLUSIONS: Bicarbonate dialysis results in CO2 removal at rates comparable with existing low-flow extracorporeal CO2 removal in a large animal model, but the final dialysis dose delivered needs to be reduced before the technique can be used for prolonged periods.

Carbon dioxide removal using low bicarbonate dialysis in rodents.

BACKGROUND: Extracorporeal carbon dioxide removal may be used to manage hypercapnia, but compared to dialysis, it's not widely available. A recent in vitro study showed that dialysis with low bicarbonate dialysates removes CO2. OBJECTIVE: To show that bicarbonate dialysis removes CO2 in an animal model to validate in-vitro findings and quantify the effect on arterial pH. METHODS: Male Sprague-Dawley hypercapnic rats were dialyzed with either a conventional dialysate (PrismasolTM) or a bicarbonate-free dialysate (Bicarb0). The effect of dialysis on standard blood gases and electrolytes was measured. RESULTS: Partial pressure of CO2 and bicarbonate concentration in blood decreased significantly after exposure to Bicarb0 compared to PrismasolTM (filter outflow values 12.8 vs 81.1 mmHg; p < 0.01 for CO2 and 3.5 vs 22.0 mmol/L; p < 0.01 for bicarbonate). Total CO2 content of blood was reduced by 459 mL/L during dialysis with Bicarb0 (filter inflow 546 ± 91 vs filter outflow 87 ± 52 mL/L; p < 0.01), but was not significantly reduced with PrismasolTM. CONCLUSIONS: Bicarbonate dialysis removes CO2 at rates comparable to existing low-flow ECCO2R.