To report on our clinical experience using EIT in individualized PEEP titration in ARDS. Using EIT assessment, we optimized PEEP settings in 39 ARDS patients. The EIT PEEP settings were compared with ...the physicians’ PEEP settings and the PEEP settings according to the ARDS network. We defined a PEEP difference equal to or greater than 4 cm H
2
O as clinically relevant. Changes in lung compliance and PaO
2
/FiO
2
-ratio were compared in patients with EIT-based PEEP adjustments and in patients with unaltered PEEP. In 28% of the patients, the difference in EIT-based PEEP and physician-PEEP was clinically relevant; in 36%, EIT-based PEEP and physician-PEEP were equal. The EIT-based PEEP disagreed with the PEEP settings according to the ARDS network. Adjusting PEEP based upon EIT led to a rapid increase in lung compliance and PaO
2
/FiO
2
-ratio. However, this increase was also observed in the group where the PEEP difference was less than 4 cm H
2
O. We hypothesize that this can be attributed to the alveolar recruitment during the PEEP trial. EIT based individual PEEP setting appears to be a promising method to optimize PEEP in ARDS patients. The clinical impact, however, remains to be established.
Early death in sepsis occurs frequently; however, specific causes are largely unknown. An autopsy can contribute to ascertain causes of death. The objective of the study was to determine ...discrepancies in clinical diagnosis and postmortem findings in septic intensive care unit (ICU) patients deceased within 48 h after ICU admission. All septic ICU patients who deceased within 48 h after ICU admission were identified and included. Four intensivists determined the clinical cause of death by medical record review. An autopsy was performed within 24 h of death. Clinical diagnosis and postmortem findings were compared and classified as autopsy-identified missed clinical diagnoses and autopsy-refuted diagnoses. Class I and II missed major diagnoses using the Goldman criteria were scored. Between 2012 and 2017, 1107 septic patients were admitted to ICU. Of these, 344 patients (31%) died, of which 97 patients (28%) deceased within 48 h. In 32 (33%) early deceased patients, an autopsy was agreed. There were 26 autopsy-identified missed clinical diagnoses found, mostly myocardial infarction (
n
= 4) and pneumonia (
n
= 4). In four patients (13%), a class I discrepancy was found. In fourteen patients (42%), a class II discrepancy was found. In conclusion, an autopsy is an important diagnostic tool that can identify definite causes of death. These diagnoses deviate from diagnoses established during admission in early deceased sepsis patients.
We validated a Deep Embedded Clustering (DEC) model and its adaptation for integrating mixed datatypes (in this study, numerical and categorical variables). Deep Embedded Clustering (DEC) is a ...promising technique capable of managing extensive sets of variables and non-linear relationships. Nevertheless, DEC cannot adequately handle mixed datatypes. Therefore, we adapted DEC by replacing the autoencoder with an X-shaped variational autoencoder (XVAE) and optimising hyperparameters for cluster stability. We call this model "X-DEC". We compared DEC and X-DEC by reproducing a previous study that used DEC to identify clusters in a population of intensive care patients. We assessed internal validity based on cluster stability on the development dataset. Since generalisability of clustering models has insufficiently been validated on external populations, we assessed external validity by investigating cluster generalisability onto an external validation dataset. We concluded that both DEC and X-DEC resulted in clinically recognisable and generalisable clusters, but X-DEC produced much more stable clusters.
Abstract
Background
Electrical impedance tomography (EIT) visualises alveolar overdistension and alveolar collapse and enables optimisation of ventilator settings by using the best balance between ...alveolar overdistension and collapse (ODCL). Besides, the global inhomogeneity index (GI), measured by EIT, may also be of added value in determining PEEP. Optimal PEEP is often determined based on the best dynamic compliance without EIT at the bedside. This study aimed to assess the effect of a PEEP trial on ODCL, GI and dynamic compliance in patients with and without ARDS. Secondly, PEEP levels from “optimal PEEP” approaches by ODCL, GI and dynamic compliance are compared.
Methods
In 2015–2016, we included patients with ARDS using postoperative cardiothoracic surgery patients as a reference group. A PEEP trial was performed with four consecutive incremental followed by four decremental PEEP steps of 2 cmH
2
O. Primary outcomes at each step were GI, ODCL and best dynamic compliance. In addition, the agreement between ODCL, GI, and dynamic compliance was determined for the individual patient.
Results
Twenty-eight ARDS and 17 postoperative cardiothoracic surgery patients were included. The mean optimal PEEP, according to best compliance, was 10.3 (±2.9) cmH
2
O in ARDS compared to 9.8 (±2.5) cmH
2
O in cardiothoracic surgery patients. Optimal PEEP according to ODCL was 10.9 (±2.5) in ARDS and 9.6 (±1.6) in cardiothoracic surgery patients. Optimal PEEP according to GI was 17.1 (±3.9) in ARDS compared to 14.2 (±3.4) in cardiothoracic surgery patients.
Conclusions
Currently, no golden standard to titrate PEEP is available. We showed that when using the GI, PEEP requirements are higher compared to ODCL and best dynamic compliance during a PEEP trial in patients with and without ARDS.
Abstract
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 PaO
2
/FiO
2
-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) cmH
2
O vs. 11.3 (SD 3.4) cmH
2
O, (
p
= 0.475), for ICU survivors and non-survivors. At day 1–3, compliance was ~ 55 mL/cmH
2
O vs. ~ 45 mL/cmH
2
O in survivors vs. non-survivors. The intersection of overdistension and collapse curves appeared similar at a PEEP of ~ 12–13 cmH
2
O. At day 4–6 compliance changed to ~ 50 mL/cmH
2
O vs. ~ 38 mL/cmH
2
O. At day 7 and beyond, compliance was ~ 38 mL/cmH
2
O with the intersection at a PEEP of ~ 9 cmH
2
O vs. ~ 25 mL/cmH
2
O with overdistension intersecting at collapse curves at a PEEP of ~ 7 cmH
2
O. 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.
•Clinical studies have shown excessive pressure and volume in mechanical ventilation leads to unintended lung injury.•Development and validation of a predictive in-silico models or digital clones is ...presented.•These models and methods are built on well-validated nonlinear mechanics models.•This model very accurately predicts peak volumes and pressures in multiple common ventilation modes.•The model very accurately predicts the volume retained when raising PEEP.•Clinical use of virtual patients would improve clinician confidence and patient safety in MV care.
Mechanical ventilation (MV) is a core intensive care unit (ICU) therapy. Significant inter- and intra- patient variability in lung mechanics and condition makes managing MV difficult. Accurate prediction of patient-specific response to changes in MV settings would enable optimised, personalised, and more productive care, improving outcomes and reducing cost. This study develops a generalised digital clone model, or in-silico virtual patient, to accurately predict lung mechanics in response to changes in MV.
An identifiable, nonlinear hysteresis loop model (HLM) captures patient-specific lung dynamics identified from measured ventilator data. Identification and creation of the virtual patient model is fully automated using the hysteresis loop analysis (HLA) method to identify lung elastances from clinical data. Performance is evaluated using clinical data from 18 volume-control (VC) and 14 pressure-control (PC) ventilated patients who underwent step-wise recruitment maneuvers.
Patient-specific virtual patient models accurately predict lung response for changes in PEEP up to 12 cmH2O for both volume and pressure control cohorts. R2 values for predicting peak inspiration pressure (PIP) and additional retained lung volume, Vfrc in VC, are R2=0.86 and R2=0.90 for 106 predictions over 18 patients. For 14 PC patients and 84 predictions, predicting peak inspiratory volume (PIV) and Vfrc yield R2=0.86 and R2=0.83. Absolute PIP, PIV and Vfrc errors are relatively small.
Overall results validate the accuracy and versatility of the virtual patient model for capturing and predicting nonlinear changes in patient-specific lung mechanics. Accurate response prediction enables mechanically and physiologically relevant virtual patients to guide personalised and optimised MV therapy.
Electrical Impedance Tomography (EIT) is a non-invasive bedside imaging technique that provides real-time lung ventilation information on critically ill patients. EIT can potentially become a ...valuable tool for optimising mechanical ventilation, especially in patients with acute respiratory distress syndrome (ARDS). In addition, EIT has been shown to improve the understanding of ventilation distribution and lung aeration, which can help tailor ventilatory strategies according to patient needs. Evidence from critically ill patients shows that EIT can reduce the duration of mechanical ventilation and prevent lung injury due to overdistension or collapse. EIT can also identify the presence of lung collapse or recruitment during a recruitment manoeuvre, which may guide further therapy. Despite its potential benefits, EIT has not yet been widely used in clinical practice. This may, in part, be due to the challenges associated with its implementation, including the need for specialised equipment and trained personnel and further validation of its usefulness in clinical settings. Nevertheless, ongoing research focuses on improving mechanical ventilation and clinical outcomes in critically ill patients.
The Radiographic Assessment of Lung Edema (RALE) score provides a semi-quantitative measure of pulmonary edema. In patients with acute respiratory distress syndrome (ARDS), the RALE score is ...associated with mortality. In mechanically ventilated patients in the intensive care unit (ICU) with respiratory failure not due to ARDS, a variable degree of lung edema is observed as well. We aimed to evaluate the prognostic value of RALE in mechanically ventilated ICU patients.
Secondary analysis of patients enrolled in the 'Diagnosis of Acute Respiratory Distress Syndrome' (DARTS) project with an available chest X-ray (CXR) at baseline. Where present, additional CXRs at day 1 were analysed. The primary endpoint was 30-day mortality. Outcomes were also stratified for ARDS subgroups (no ARDS, non-COVID-ARDS and COVID-ARDS).
422 patients were included, of which 84 had an additional CXR the following day. Baseline RALE scores were not associated with 30-day mortality in the entire cohort (OR: 1.01, 95% CI: 0.98-1.03,
= 0.66), nor in subgroups of ARDS patients. Early changes in RALE score (baseline to day 1) were only associated with mortality in a subgroup of ARDS patients (OR: 1.21, 95% CI: 1.02-1.51,
= 0.04), after correcting for other known prognostic factors.
The prognostic value of the RALE score cannot be extended to mechanically ventilated ICU patients in general. Only in ARDS patients, early changes in RALE score were associated with mortality.
International guidelines do not recommend a specific probe for assessment of lung aeration using lung ultrasound (LUS). The aim of this study was to assess the concordance between linear and sector ...array probes of a handheld ultrasound device in assessment of lung aeration in invasively ventilated intensive care unit patients. This study included intensive care unit patients who were expected to be ventilated for longer than 24 h. A 12-region LUS exam was performed with a linear and a sector array probe. In each image, the LUS aeration score and number of B-lines were determined. Adding the LUS aeration scores of all regions resulted in a global LUS aeration score. Agreement between the two probes was calculated using intra-class correlation coefficients (ICCs). A total of 30 LUS exams were performed in 19 patients, resulting in a total of 328 pairs of images. Twenty-nine pairs of images were excluded from analysis because the images from the linear probe could not be scored. ICCs calculated for the remaining images revealed good concordance the LUS aeration scores for individual images (ICC = 0.73, 95% confidence interval 0.67–0.78), number of B-lines (ICC = 0.79, 95% confidence interval 0.72–0.83) and global LUS aeration score (ICC = 0.74, 95% confidence interval 0.52–0.87). In conclusion, there is good concordance between linear and sector array probes of a handheld ultrasound device in assessment of lung aeration patterns in mechanically ventilated intensive care unit patients. However, in roughly 10% of the images acquired using the linear probe, the aeration pattern could not be scored.
Molecular pathological pathways leading to multi-organ failure in critical illness are progressively being unravelled. However, attempts to modulate these pathways have not yet improved the clinical ...outcome. Therefore, new targetable mechanisms should be investigated. We hypothesize that increased dicarbonyl stress is such a mechanism. Dicarbonyl stress is the accumulation of dicarbonyl metabolites (i.e., methylglyoxal, glyoxal, and 3-deoxyglucosone) that damages intracellular proteins, modifies extracellular matrix proteins, and alters plasma proteins. Increased dicarbonyl stress has been shown to impair the renal, cardiovascular, and central nervous system function, and possibly also the hepatic and respiratory function. In addition to hyperglycaemia, hypoxia and inflammation can cause increased dicarbonyl stress, and these conditions are prevalent in critical illness. Hypoxia and inflammation have been shown to drive the rapid intracellular accumulation of reactive dicarbonyls, i.e., through reduced glyoxalase-1 activity, which is the key enzyme in the dicarbonyl detoxification enzyme system. In critical illness, hypoxia and inflammation, with or without hyperglycaemia, could thus increase dicarbonyl stress in a way that might contribute to multi-organ failure. Thus, we hypothesize that increased dicarbonyl stress in critical illness, such as sepsis and major trauma, contributes to the development of multi-organ failure. This mechanism has the potential for new therapeutic intervention in critical care.
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