Pulmonary response prediction through personalized basis functions in a virtual patient model.

BACKGROUND AND OBJECTIVE: Recruitment maneuvers with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveoli collapse. However, determining a safe, effective, and patient-specific PEEP is not standardized, and this more optimal PEEP level evolves with patient condition, requiring personalised monitoring and care approaches to maintain optimal ventilation settings. METHODS: This research examines 3 physiologically relevant basis function sets (exponential, parabolic, cumulative) to enable better prediction of elastance evolution for a virtual patient or digital twin model of MV lung mechanics, including novel elements to model and predict distension elastance. Prediction accuracy and robustness are validated against recruitment maneuver data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12 cmH2O) and 14 pressure-controlled ventilation (PCV) patients at 4 different baseline PEEP levels (6 to 12 cmH2O), yielding 623 and 294 prediction cases, respectively. Predictions were made up to 12 cmH2O of added PEEP ahead, covering 6 × 2 cmH2O PEEP steps. RESULTS: The 3 basis function sets yield median absolute peak inspiratory pressure (PIP) prediction error of 1.63 cmH2O for VCV patients, and median peak inspiratory volume (PIV) prediction error of 0.028 L for PCV patients. The exponential basis function set yields a better trade-off of overall performance across VCV and PCV prediction than parabolic and cumulative basis function sets from other studies. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2 = 0.90-0.95. CONCLUSIONS: The results demonstrate recruitment mechanics are best captured by an exponential basis function across different mechanical ventilation modes, matching physiological expectations, and accurately capture, for the first time, distension mechanics to within 5-10 % accuracy. Enabling the risk of lung injury to be predicted before changing ventilator settings. The overall outcomes significantly extend and more fully validate this digital twin or virtual mechanical ventilation patient model.

Serial electrical impedance tomography course in different treatment groups; The MaastrICCht cohort.

PURPOSE: To describe the effect of dexamethasone and tocilizumab on regional lung mechanics over admission in all mechanically ventilated COVID-19 patients. MATERIALS AND METHODS: Dynamic compliance, alveolar overdistension and collapse were serially determined using electric impedance tomography (EIT). Patients were categorized into three groups; no anti-inflammatory therapy, dexamethasone therapy, dexamethasone + tocilizumab therapy. The EIT variables were (I) visualized using polynomial regression, (II) evaluated throughout admission using linear mixed-effects models, and (III) average respiratory variables were compared. RESULTS: Visual inspection of EIT variables showed a pattern of decreasing dynamic compliance. Overall, optimal set PEEP was lower in the dexamethasone group (-1.4 cmH2O, -2.6; -0.2). Clinically applied PEEP was lower in the dexamethasone and dexamethasone + tocilizumab group (-1.5 cmH2O, -2.6; -0.2; -2.2 cmH2O, -5.1; 0.6). Dynamic compliance, alveolar overdistension, and alveolar collapse at optimal set PEEP did not significantly differ between the three groups. CONCLUSION: Optimal and clinically applied PEEP were lower in the dexamethasone and dexamethasone + tocilizumab groups. The results suggest that the potential beneficial effects of these therapies do not affect lung mechanics favorably. However, this study cannot fully rule out any beneficial effect of anti-inflammatory treatment on pulmonary function due to its observational nature.

Clinical Applicability of Electrical Impedance Tomography in Patient-Tailored Ventilation: A Narrative Review.

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.

Regional peak flow as a novel approach to assess regional pulmonary mechanics by electrical impedance tomography: an observational validation study.

Background: Spontaneous breathing efforts during mechanical ventilation are a widely accepted weaning approach for acute respiratory distress syndrome (ARDS) patients. These efforts can be too vigorous, possibly inflicting lung and diaphragm damage. Higher positive end expiratory pressure (PEEP) levels can be used to lower the magnitude of vigorous breathing efforts. Nevertheless, PEEP titrating tools are lacking in spontaneous mechanical ventilation (SMV). Therefore, the aim is to develop an electrical impedance tomography (EIT) algorithm for quantifying regional lung mechanics independent from a stable plateau pressure phase based on regional peak flow (RPF) by EIT, which is hypothetically applicable in SMV and to validate this algorithm in patients on controlled mechanical ventilation (CMV). Methods: The RPF algorithm quantifies a cumulative overdistension (ODRPF) and collapse (CLRPF) rate and is validated in a prospective cohort of mechanically ventilated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients on CMV. ODRPF and CLRPF are compared with compliance-based cumulative overdistension (ODP500) and collapse (CLP500) rates from the Pulmovista 500 EIT device at multiple PEEP levels (PEEP 10 cmH2O to PEEP 24 cmH2O) in EIT measurements from CMV patients by linear mixed models, Bland-Altman analysis and intraclass correlation coefficient (ICC). Results: Seventy-eight patients were included. Linear mixed models revealed an association between ODRPF and ODP500 of 1.02 (0.98-1.07, P<0.001) and between CLRPF and CLP500 of 0.93 (0.80-1.05, P<0.001). ICC values ranged from 0.78 to 0.86 (P<0.001) for ODRPF and ODP500 and from 0.70 to 0.85 (P<0.001) for CLRPF and CLP500 (PEEP 10 to PEEP 24). The mean bias between ODRPF and ODP500 in these PEEP levels ranged from 0.80% to 4.19% and from -1.31% to 0.13% between CLRPF and CLP500. Conclusions: A RPF approach for quantifying regional lung mechanics showed a moderate to good agreement in coronavirus disease 2019 (COVID-19) related ARDS patients on CMV compared to the compliance-based approach. This, in addition to being independent of a plateau pressure phase, indicates that the RPF approach is a valid method to explore for quantifying regional lung mechanics in SMV.

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.

The global inhomogeneity index assessed by electrical impedance tomography overestimates PEEP requirement in patients with ARDS: an observational study.

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 cmH2O. 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) cmH2O in ARDS compared to 9.8 (±2.5) cmH2O 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.

Virtual patients for mechanical ventilation in the intensive care unit.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

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).

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 inter- and intra- patient 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 cmH2O. Prediction errors for peak inspiratory volume for an increase of 16 cmH2O 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.

Mechanical ventilation variability due to tracheal compression in a patient with a mycotic descending aortic aneurysm: an inverse correlation with blood pressure.

Mycotic aortic aneurysms carry significant morbidity and mortality. In the current report, we present a case of a patient with a mycotic descending aortic aneurysm with contained rupture causing variable compression of the trachea, influenced by a variability in blood pressure. In these patients, blood pressure management is paramount as relative hypertensive periods do not only increase the risk of rupture but can also warrant high ventilation pressures or can potentially result in airway occlusion.

Clinical implementation of electric impedance tomography in the treatment of ARDS: a single centre experience.

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 H2O as clinically relevant. Changes in lung compliance and PaO2/FiO2-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 PaO2/FiO2-ratio. However, this increase was also observed in the group where the PEEP difference was less than 4 cm H2O. 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.

Accuracy of end-tidal CO2 capnometers in post-cardiac surgery patients during controlled mechanical ventilation.

BACKGROUND: The determination of end-tidal carbon dioxide (etCO2) 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 etCO2 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 etCO2 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 etCO2 and PaCO2 was seen in EMMA (3.30), the highest in Evita (3.98). CONCLUSIONS: A good correlation between etCO2 and PaCO2 was shown with the three capnometers in the present study. However, etCO2 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.