Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

ACCURACY OF END-TIDAL CO2 CAPNOMETERS IN POST-CARDIAC SURGERY PATIENTS DURING CONTROLLED MECHANICAL VENTILATION

Background: The determination of end-tidal carbon dioxide (etCO(2)) is very helpful in cardiac resuscitation for confirmation and monitoring of endotracheal tube placement and as an indicator of return of circulation and effectiveness of chest compressions. There is now also widespread use of capnometry on-site at emergency and trauma fields. Objective: We studied the accuracy and correlation of three capnometers (EMMA, Medtronic, and Evita) with partial pressure of arterial CO2 (PaCO2) measurements. Methods: The three capnometers were placed in-line in the ventilator tubing of the patient. Forty sedated and mechanically ventilated post-cardiac surgery patients were studied. Twenty consecutive etCO(2) values were collected simultaneously from all three monitors while drawing an arterial blood sample. Paired sample t-test and Pearson correlation were used to compare the capnometers and their correlation with PaCO2. Results: The correlation of etCO(2) measurements between all three capnometers was good (Emma vs. Evita: 0.874, Emma vs. Medtronic: 0.949, Evita vs. Medtronic: 0.878). The correlation of PaCO2 with the Evita is the lowest (0.671) as compared to the EMMA (0.693) and the Medtronic (0.727). The lowest dispersion of the difference between etCO(2) and PaCO2 was seen in EMMA (3.30), the highest in Evita (3.98). Conclusions: A good correlation between etCO(2) and PaCO2 was shown with the three capnometers in the present study. However, etCO(2) measurements were not valid to estimate PaCO2 in these patients. Therefore, capnometry cannot be used to replace serial blood gas analyses completely, but may be a good cardiopulmonary trend monitor and alerting system in catastrophic events

Prediction of lung mechanics throughout recruitment maneuvers in pressure-controlled ventilation

Mechanical ventilation (MV) is a core therapy in the intensive care unit (ICU). Some patients rely on MV to support breathing. However, it is a difficult therapy to optimise, where interand intrapatient variability leads to significantly increased risk of lung damage. Excessive volume and/or pressure can cause volutrauma or barotrauma, resulting in increased length of time on ventilation, length of stay, cost and mortality. Virtual patient modelling has changed care in other areas of ICU medicine, enabling more personalized and optimal care, and have emerged for volume-controlled MV. This research extends this MV virtual patient model into the increasingly more commonly used pressure-controlled MV mode. The simulation methods are extended to use pressure, instead of both volume and flow, as the known input, increasing the output variables to be predicted (flow and its integral, volume). The model and methods are validated using data from N = 14 pressure-control ventilated patients during recruitment maneuvers, with n = 558 prediction tests over changes of PEEP ranging from 2 to 16 cmH(2)O. Prediction errors for peak inspiratory volume for an increase of 16 cmH(2)O were 80 [30 - 140] mL (15.9 [8.4 - 31.0]%), with RMS fitting errors of 0.05 [0.03 - 0.12] L. These results show very good prediction accuracy able to guide personalised MV care. (C) 2020 Elsevier B.V. All rights reserved.</p

Prediction and estimation of pulmonary response and elastance evolution for volume-controlled and pressure-controlled ventilation

Mechanical ventilation (MV) is a core treatment for patients suffering from respiratory disease and failure. However, MV settings are not standardized due to significant inter- and intra- patient variability in response to care, leading to variability in outcome. There is thus a need to personalize MV settings. This research significantly extends a single compartment lung mechanics model with physiologically relevant basis functions, and uses it to identify patient-specific lung mechanics and predict response to changes in MV settings. Nonlinear evolution of pulmonary elastance over positive end expiratory pressure (PEEP) is modelled by a newly proposed, physiologically relevant and simplified compensatory function to enable prediction of pulmonary response for both volume-controlled ventilation (VCV) and pressure-controlled ventilation (PCV), and identified as patientspecific using each patient's data at a baseline PEEP. Predictions at higher PEEP levels test the validity of the proposed models based on errors in predicted peak inspiratory pressure (PIP) in two VCV trials and volume (PIV) in one PCV trial. A total of 210 prediction cases over 36 patients (22 VCV; 14 PCV) yielded absolute predicted PIP errors within 1.0 cmH2O (2.3%) and 3.3 cmH2O (7.3%) for 90% cases in VCV, while predicted PIV errors are within 0.073 L (16.8%) for 85% cases in PCV. In conclusion, a novel deterministic virtual patient model is presented, able to offer accurate prediction of pulmonary response across a wide range of PEEP changes for the two main MV modes used clinically, enabling predictive decision support in real-time to safely personalize and optimize care

Minimal Lung Mechanics Basis-functions for a Mechanical Ventilation Virtual Patient

Mechanical ventilation (MV) is used in the intensive care unit (ICU) to treat patients with respiratory failure. However, MV settings are not standardized due to significant inter- and intra- patient variability in response to care, leading to variability in care, outcome, and cost. There is thus a need to personalize MV. This research extends a single compartment lung mechanics model with physiologically relevant basis functions, to identify patient-specific lung mechanics and predict response to changes in MV care. The nonlinear evolution of pulmonary elastance as positive-end-expiratory pressure (PEEP) changes is captured by a physiologically relevant, simplified compensatory equation as a function of PEEP and pressure identification error at the baseline PEEP level. It allows both patient-specific and general prediction of lung elastance of higher PEEP. The prediction outcome is validated with data from two volume-controlled ventilation (VCV) trials and one pressure-controlled ventilation (PCV) trial, where the biggest PEEP prediction interval is a clinically unrealistic 20cmH(2)O, comprising 210 prediction cases over 36 patients (22 VCV; 14 PCV). Predicted absolute peak inspiratory pressure (PIP) errors are within 1.0cmH(2)O and 3.3cmH(2)O for 90% cases in the two VCV trials, while predicted peak inspiratory tidal volume (PIV) errors are within 0.073L for 85% cases in studied PCV trial. The model presented provides a highly accurate, predictive virtual patient model across multiple MV modes and delivery methods, and over clinically unrealistically large changes. Low computational cost, and fast, easy parameterization enable model-based, predictive decision support in real-time to safely personalize and optimize MV care. Copyright (C) 2021 The Authors

Pulmonary pathophysiology development of COVID-19 assessed by serial Electrical Impedance Tomography in the MaastrICCht cohort

Patients with SARS-CoV-2 infection present with different lung compliance and progression of disease differs. Measures of lung mechanics in SARS-CoV-2 patients may unravel different pathophysiologic mechanisms during mechanical ventilation. The objective of this prospective observational study is to describe whether Electrical Impedance Tomography (EIT) guided positive end-expiratory pressure (PEEP) levels unravel changes in EIT-derived parameters over time and whether the changes differ between survivors and non-survivors. Serial EIT-measurements of alveolar overdistension, collapse, and compliance change in ventilated SARS-CoV-2 patients were analysed. In 80 out of 94 patients, we took 283 EIT measurements (93 from day 1-3 after intubation, 66 from day 4-6, and 124 from day 7 and beyond). Fifty-one patients (64%) survived the ICU. At admission mean PaO2/FiO2-ratio was 184.3 (SD 61.4) vs. 151.3 (SD 54.4) mmHg, (p = 0.017) and PEEP was 11.8 (SD 2.8) cmH2O vs. 11.3 (SD 3.4) cmH2O, (p = 0.475), for ICU survivors and non-survivors. At day 1-3, compliance was ~ 55 mL/cmH2O vs. ~ 45 mL/cmH2O in survivors vs. non-survivors. The intersection of overdistension and collapse curves appeared similar at a PEEP of ~ 12-13 cmH2O. At day 4-6 compliance changed to ~ 50 mL/cmH2O vs. ~ 38 mL/cmH2O. At day 7 and beyond, compliance was ~ 38 mL/cmH2O with the intersection at a PEEP of ~ 9 cmH2O vs. ~ 25 mL/cmH2O with overdistension intersecting at collapse curves at a PEEP of ~ 7 cmH2O. Surviving SARS-CoV-2 patients show more favourable EIT-derived parameters and a higher compliance compared to non-survivors over time. This knowledge is valuable for discovering the different groups

Serial measurements in COVID-19-induced acute respiratory disease to unravel heterogeneity of the disease course:design of the Maastricht Intensive Care COVID cohort (MaastrICCht)

Introduction The course of the disease in SARS-CoV-2 infection in mechanically ventilated patients is unknown. To unravel the clinical heterogeneity of the SARS-CoV-2 infection in these patients, we designed the prospective observational Maastricht Intensive Care COVID cohort (MaastrICCht). We incorporated serial measurements that harbour aetiological, diagnostic and predictive information. The study aims to investigate the heterogeneity of the natural course of critically ill patients with a SARS-CoV-2 infection.Methods and analysis Mechanically ventilated patients admitted to the intensive care with a SARS-CoV-2 infection will be included. We will collect clinical variables, vital parameters, laboratory variables, mechanical ventilator settings, chest electrical impedance tomography, ECGs, echocardiography as well as other imaging modalities to assess heterogeneity of the course of a SARS-CoV-2 infection in critically ill patients. The MaastrICCht is also designed to foster various other studies and registries and intends to create an open-source database for investigators. Therefore, a major part of the data collection is aligned with an existing national intensive care data registry and two international COVID-19 data collection initiatives. Additionally, we create a flexible design, so that additional measures can be added during the ongoing study based on new knowledge obtained from the rapidly growing body of evidence. The spread of the COVID-19 pandemic requires the swift implementation of observational research to unravel heterogeneity of the natural course of the disease of SARS-CoV-2 infection in mechanically ventilated patients. Our study design is expected to enhance aetiological, diagnostic and prognostic understanding of the disease. This paper describes the design of the MaastrICCht.Ethics and dissemination Ethical approval has been obtained from the medical ethics committee (Medisch Ethische Toetsingscommissie 2020-1565/3 00 523) of the Maastricht University Medical Centre+ (Maastricht UMC+), which will be performed based on the Declaration of Helsinki. During the pandemic, the board of directors of Maastricht UMC+ adopted a policy to inform patients and ask their consent to use the collected data and to store serum samples for COVID-19 research purposes. All study documentation will be stored securely for fifteen years after recruitment of the last patient. The results will be published in peer-reviewed academic journals, with a preference for open access journals, while particularly considering deposition of the manuscripts on a preprint server early.Trial registration number The Netherlands Trial Register (NL8613)