Non-invasive over-distension measurements: data driven vs model-based.

Clinical measurements offer bedside monitoring aiming to minimise unintended over-distension, but have limitations and cannot be predicted for changes in mechanical ventilation (MV) settings and are only available in certain MV modes. This study introduces a non-invasive, real-time over-distension measurement, which is robust, predictable, and more intuitive than current methods. The proposed over-distension measurement, denoted as OD, is compared with the clinically proven stress index (SI). Correlation is analysed via R2 and Spearman rs. The OD safe range corresponding to the unit-less SI safe range (0.95-1.05) is calibrated by sensitivity and specificity test. Validation is fulfilled with 19 acute respiratory distress syndrome (ARDS) patients data (196 cases), including assessment across ARDS severity. Overall correlation between OD and SI yielded R2 = 0.76 and Spearman rs = 0.89. Correlation is higher considering only moderate and severe ARDS patients. Calibration of OD to SI yields a safe range defined: 0 ≤ OD ≤ 0.8 cmH2O. The proposed OD offers an efficient, general, real-time measurement of patient-specific lung mechanics, which is more intuitive and robust than SI. OD eliminates the limitations of SI in MV mode and its less intuitive lung status value. Finally, OD can be accurately predicted for new ventilator settings via its foundation in a validated predictive personalized lung mechanics model. Therefore, OD offers potential clinical value over current clinical methods.

Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model.

BACKGROUND: Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics. METHODS: Changes in patient-specific lung elastance over a pressure-volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and reconstruct ventilated waveforms unaffected by asynchronous breaths. Asynchrony magnitude can then be quantified using an energy-dissipation metric, Easyn, comparing PV loop area between model-reconstructed and original, altered asynchronous breathing cycles. Performance is evaluated using both test-lung experimental data with a known ground truth and clinical data from four patients with varying levels of asynchrony. RESULTS: Root mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. Easyn clearly matches known asynchrony magnitude for experimental data with RMS errors < 4.1%. Clinical data performance shows 57% breaths having Easyn > 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with Easyn > 10%. Patient 4 has Easyn = 0 for 96% breaths showing accuracy in a case without asynchrony. CONCLUSIONS: Experimental test-lung validation demonstrates the method's reconstruction accuracy and generality in controlled scenarios. Clinical validation matches direct observations of asynchrony in incidence and quantifies magnitude, including cases without asynchrony, validating its robustness and potential efficacy as a clinical real-time asynchrony monitoring tool.

Early individualized positive end-expiratory pressure guided by electrical impedance tomography in acute respiratory distress syndrome: a randomized controlled clinical trial.

BACKGROUND: Individualized positive end-expiratory pressure (PEEP) by electrical impedance tomography (EIT) has potential interest in the optimization of ventilation distribution in acute respiratory distress syndrome (ARDS). The aim of the study was to determine whether early individualized titration of PEEP with EIT improved outcomes in patients with ARDS. METHODS: A total of 117 ARDS patients receiving mechanical ventilation were randomly assigned to EIT group (n = 61, PEEP adjusted based on ventilation distribution) or control group (n = 56, low PEEP/FiO2 table). The primary outcome was 28-day mortality. Secondary and exploratory outcomes were ventilator-free days, length of ICU stay, incidence of pneumothorax and barotrauma, and difference in Sequential Organ Failure Assessment (SOFA) score at day 1 (ΔD1-SOFA) and day 2 (ΔD2-SOFA) compared with baseline. MEASUREMENTS AND MAIN RESULTS: There was no statistical difference in the value of PEEP between the EIT group and control group, but the combination of PEEP and FiO2 was different between groups. In the control group, a significantly positive correlation was found between the PEEP value and the corresponding FiO2 (r = 0.47, p < 0.00001) since a given matched table was used for PEEP settings. Diverse combinations of PEEP and FiO2 were found in the EIT group (r = 0.05, p = 0.68). There was no significant difference in mortality rate (21% vs. 27%, EIT vs. control, p = 0.63), ICU length of stay (13.0 (7.0, 25.0) vs 10.0 (7.0, 14.8), median (25th-75th percentile); p = 0.17), and ventilator-free days at day 28 (14.0 (2.0, 23.0) vs 19.0 (0.0, 24.0), p = 0.55) between the two groups. The incidence of new barotrauma was zero. Compared with control group, significantly lower ΔD1-SOFA and ΔD2-SOFA were found in the EIT group (p < 0.001) in a post hoc comparison. Moreover, the EIT group exhibited a significant decrease of SOFA at day 2 compared with baseline (paired t-test, difference by - 1 (- 3.5, 0), p = 0.001). However, the control group did show a similar decrease (difference by 1 (- 2, 2), p = 0.131). CONCLUSION: Our study showed a 6% absolute decrease in mortality in the EIT group: a statistically non-significant, but clinically non-negligible result. This result along with the showed improvement in organ function might justify further reserach to validate the beneficial effect of individualized EIT-guided PEEP setting on clinical outcomes of patients with ARDS. TRIAL REGISTRATION: ClinicalTrials, NCT02361398. Registered 11 February 2015-prospectively registered, https://clinicaltrials.gov/show/NCT02361398 .

Lung ventilation distribution in patients after traditional full sternotomy and minimally invasive thoracotomy: An observational study.

BACKGROUND: The aim of the study was to examine the post-operative ventilation distribution changes in cardiac surgical patients after traditional full sternotomy (FS) or minimally invasive thoracotomy (MIT). METHODS: A total of 40 patients scheduled for FS with two-lung ventilation or MIT with one-lung ventilation were included. Ventilation distribution was measured with electrical impedance tomography (EIT) at T1, before surgery; T2, after surgery in ICU before weaning; T3, 24 hours after extubation. EIT-based parameters were calculated to assess the ventilation distribution, including the left-to-right lung ratio, ventral-to-dorsal ratio, and the global inhomogeneity index. RESULTS: The global inhomogeneity index increased at T2 and T3 compared to T1 in all patients but only statistically significant in patients with MIT (FS, P = .06; MIT, P < .01). Notable decrease in the dorsal regions (FS) or in the non-ventilated side (MIT) was observed at T2. Ventilation distribution was partially improved at T3 but huge variations of recovery progresses were found in all patients regardless of the surgery types. Subgroup analysis indicated that operation duration was significantly lower in the MIT group (240 ± 40 in FS vs 205 ± 90 minutes in MIT, median ± interquartile range, P < .05) but the incidence of atrial fibrillation/flutter was significantly higher (5% in FS vs 50% in MIT, P < .01). Other exploratory outcomes showed no statistical differences. CONCLUSIONS: Ventilation distribution was impaired after cardiac surgery. The recovery process of ventilation homogeneity was strongly depending on individuals so that MIT was not always superior in this aspect. EIT may help to identify the patients requiring further care after surgery.

Influence of overdistension/recruitment induced by high positive end-expiratory pressure on ventilation-perfusion matching assessed by electrical impedance tomography with saline bolus.

BACKGROUND: High positive end-expiratory pressures (PEEP) may induce overdistension/recruitment and affect ventilation-perfusion matching (VQMatch) in mechanically ventilated patients. This study aimed to investigate the association between PEEP-induced lung overdistension/recruitment and VQMatch by electrical impedance tomography (EIT). METHODS: The study was conducted prospectively on 30 adult mechanically ventilated patients: 18/30 with ARDS and 12/30 with high risk for ARDS. EIT measurements were performed at zero end-expiratory pressures (ZEEP) and subsequently at high (12-15 cmH2O) PEEP. The number of overdistended pixels over the number of recruited pixels (O/R ratio) was calculated, and the patients were divided into low O/R (O/R ratio < 15%) and high O/R groups (O/R ratio ≥ 15%). The global inhomogeneity (GI) index was calculated to evaluate the ventilation distribution. Lung perfusion image was calculated from the EIT impedance-time curves caused by 10 ml 10% NaCl injection during a respiratory pause (> 8 s). DeadSpace%, Shunt%, and VQMatch% were calculated based on lung EIT perfusion and ventilation images. RESULTS: Increasing PEEP resulted in recruitment mainly in dorsal regions and overdistension mainly in ventral regions. ΔVQMatch% (VQMatch% at high PEEP minus that at ZEEP) was significantly correlated with recruited pixels (r = 0.468, P = 0.009), overdistended pixels (r = - 0.666, P < 0.001), O/R ratio (r = - 0.686, P < 0.001), and ΔSpO2 (r = 0.440, P = 0.015). Patients in the low O/R ratio group (14/30) had significantly higher Shunt% and lower VQMatch% than those in the high O/R ratio group (16/30) at ZEEP but not at high PEEP. Comparable DeadSpace% was found in both groups. A high PEEP caused a significant improvement of VQMatch%, DeadSpace%, Shunt%, and GI in the low O/R ratio group, but not in the high O/R ratio group. Using O/R ratio of 15% resulted in a sensitivity of 81% and a specificity of 100% for an increase of VQMatch% > 20% in response to high PEEP. CONCLUSIONS: Change of ventilation-perfusion matching was associated with regional overdistention and recruitment induced by PEEP. A low O/R ratio induced by high PEEP might indicate a more homogeneous ventilation and improvement of VQMatch. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04081155 . Registered on 9 September 2019-retrospectively registered.

Measuring lung mechanics of expiratory tidal breathing with non-invasive breath occlusion.

BACKGROUND AND OBJECTIVE: Lung mechanics measurements provide clinically useful information about disease progression and lung health. Currently, there are no commonly practiced methods to non-invasively measure both resistive and elastic lung mechanics during tidal breathing, preventing the important information provided by lung mechanics from being utilised. This study presents a novel method to easily assess lung mechanics of spontaneously breathing subjects using a dynamic elastance, single-compartment lung model. METHODS: A spirometer with a built-in shutter was used to occlude expiration during tidal breathing, creating exponentially decaying flow when the shutter re-opened. The lung mechanics measured were respiratory system elastance and resistance, separated from the exponentially decaying flow, and interrupter resistance calculated at shutter closure. Progressively increasing resistance was added to the spirometer mouthpiece to simulate upper airway obstruction. The lung mechanics of 17 healthy subjects were successfully measured through spirometry. RESULTS: N = 17 (8 female, 9 male) healthy subjects were recruited. Measured decay rates ranged from 5 to 42/s, subjects with large variation of decay rates showed higher muscular breathing effort. Lung elastance measurements ranged from 3.9 to 21.2 cmH[Formula: see text]O/L, with no clear trend between change in elastance and added resistance. Resistance calculated from decay rate and elastance ranged from 0.15 to 1.95 cmH[Formula: see text]Os/L. These very small resistance values are due to the airflow measured originating from low-resistance areas in the centre of airways. Occlusion resistance measurements were as expected for healthy subjects, and increased as expected as resistance was added. CONCLUSIONS: This test was able to identify reasonable dynamic lung elastance and occlusion resistance values, providing new insight into expiratory breathing effort. Clinically, this lung function test could impact current practice. It does not require high levels of cooperation from the subject, allowing a wider cohort of patients to be assessed more easily. Additionally, this test can be simply implemented in a small standalone device, or with standard lung function testing equipment.

The incidence and interpretation of large differences in EIT-based measures for PEEP titration in ARDS patients.

Positive end-expiratory pressure (PEEP) can be titrated by electrical impedance tomography (EIT). The aim of the present study was to examine the performance of different EIT measures during PEEP trials with the aim of identifying "optimum" PEEP and to provide possible interpretations of largely diverging results. After recruitment (maximum plateau pressure 35 cmH2O), decremental PEEP trial with steps of 2 cmH2O and duration of 2 min per step was performed. Ventilation gain and loss, the global inhomogeneity (GI) index, trend of end-expiratory lung impedance (EELI) and regional compliance (Creg) for estimation of overdistension and collapse were calculated. Largely diverging results of PEEP selection among the measures were defined as differences ≥ 4 PEEP steps (i.e. ≥ 8 cmH2O). In 30 ARDS patients we examined so far, 3 patients showed significant differences in PEEP selections. Overdistension and collapse estimation based on Creg tended to select lower PEEP while the GI index and EELI trend suggested higher PEEP settings. Regional inspiration times were heterogeneous indicating that the assumption of a uniform driving pressure in the calculation of Creg may not be valid. Judging by the predominant ventilation distribution in the most dependent regions, these patients were non-recruitable with the applied recruitment method or pressure levels. The existence of differences in the recommended PEEP among the analyzed EIT measures might be an indicator of non-recruitable lungs and heterogeneous airway resistances. In these extreme cases, the largely diverging results may prompt the attending clinician to develop individual ventilation strategies.Clinical Trial Registration Registration number NCT03112512, https://clinicaltrials.gov/ Registered 13 April 2017.

Optimising mechanical ventilation through model-based methods and automation.

Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload. Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.

Identification of regional overdistension, recruitment and cyclic alveolar collapse with electrical impedance tomography in an experimental ARDS model.

BACKGROUND: Information on regional ventilation distribution in mechanically ventilated patients is important to develop lung protective ventilation strategies. In the present prospective animal study, we introduce an electrical impedance tomography (EIT)-based method to classify lungs into normally ventilated, overinflated, tidally recruited/derecruited and recruited regions. METHODS: Acute respiratory distress syndrome (ARDS) was introduced with repeated bronchoalveolar lavage in ten healthy male pigs until the ratio of arterial partial pressure of oxygen and fraction of inspired oxygen (PaO2/FiO2) decreased to less than 100 mmHg and remained stable for 30 minutes. Stepwise positive end-expiratory pressure (PEEP) increments were performed from 0 cmH2O to 30 cmH2O with 3 cmH2O increase every 5 minutes. Respiratory system compliance (Crs), blood gases and hemodynamics were measured at the same time. Lung regions at end-expiration and during tidal breathing were identified in EIT images. RESULTS: Overinflated regions contain air at end-expiration but they are not or are only minimally ventilated. Recruited regions compared to reference PEEP level contain air at end-expiration of arbitrary PEEP level but not at that of reference PEEP level. Tidally recruited/derecruited regions are not represented in lung regions at end-expiration but are ventilated during tidal breathing. The results coincided with measurements of blood gases. The coefficient for correlation between the number of recruited pixels and PaO2/FiO2 was 0.89 ± 0.12 (p = 0.02). CONCLUSION: The proposed novel EIT-based method provides information on overinflation, recruitment and cyclic alveolar collapse at the bedside, which may improve the ventilation strategies used.

On the practical identifiability of a two-parameter model of pulmonary gas exchange.

BACKGROUND: Successful application of mechanical ventilation as a life-saving therapy implies appropriate ventilator settings. Decision making is based on clinicians' knowledge, but can be enhanced by mathematical models that determine the individual patient state by calculating parameters that are not directly measurable. Evaluation of models may support the clinician to reach a defined treatment goal. Bedside applicability of mathematical models for decision support requires a robust identification of the model parameters with a minimum of measuring effort. The influence of appropriate data selection on the identification of a two-parameter model of pulmonary gas exchange was analyzed. METHODS: The model considers a shunt as well as ventilation-perfusion-mismatch to simulate a variety of pathologic pulmonary gas exchange states, i.e. different severities of pulmonary impairment. Synthetic patient data were generated by model simulation. To incorporate more realistic effects of measurement errors, the simulated data were corrupted with additive noise. In addition, real patient data retrieved from a patient data management system were used retrospectively to confirm the obtained findings. The model was identified to a wide range of different FiO 2 settings. Just one single measurement was used for parameter identification. Subsequently prediction performance was obtained by comparing the identified model predicted oxygen level in arterial blood either to exact data taken from simulations or patients measurements. RESULTS: Structural identifiability of the model using one single measurement for the identification process could be demonstrated. Minimum prediction error of blood oxygenation depends on blood gas level at the time of system identification i.e. the measurement situation. For severe pulmonary impairment, higher FiO 2 settings were required to achieve a better prediction capability compared to less impaired pulmonary states. Plausibility analysis with real patient data could confirm this finding. DISCUSSION AND CONCLUSIONS: Dependent on patients' pulmonary state, the influence of ventilator settings (here FiO 2) on model identification of the gas exchange model could be demonstrated. To maximize prediction accuracy i.e. to find the best individualized model with as few data as possible, best ranges of FiO 2-settings for parameter identification were obtained. A less effort identification process, which depends on the pulmonary state, can be deduced from the results of this identifiability analysis.

Simulating physiological interactions in a hybrid system of mathematical models.

Mathematical models can be deployed to simulate physiological processes of the human organism. Exploiting these simulations, reactions of a patient to changes in the therapy regime can be predicted. Based on these predictions, medical decision support systems (MDSS) can help in optimizing medical therapy. An MDSS designed to support mechanical ventilation in critically ill patients should not only consider respiratory mechanics but should also consider other systems of the human organism such as gas exchange or blood circulation. A specially designed framework allows combining three model families (respiratory mechanics, cardiovascular dynamics and gas exchange) to predict the outcome of a therapy setting. Elements of the three model families are dynamically combined to form a complex model system with interacting submodels. Tests revealed that complex model combinations are not computationally feasible. In most patients, cardiovascular physiology could be simulated by simplified models decreasing computational costs. Thus, a simplified cardiovascular model that is able to reproduce basic physiological behavior is introduced. This model purely consists of difference equations and does not require special algorithms to be solved numerically. The model is based on a beat-to-beat model which has been extended to react to intrathoracic pressure levels that are present during mechanical ventilation. The introduced reaction to intrathoracic pressure levels as found during mechanical ventilation has been tuned to mimic the behavior of a complex 19-compartment model. Tests revealed that the model is able to represent general system behavior comparable to the 19-compartment model closely. Blood pressures were calculated with a maximum deviation of 1.8 % in systolic pressure and 3.5 % in diastolic pressure, leading to a simulation error of 0.3 % in cardiac output. The gas exchange submodel being reactive to changes in cardiac output showed a resulting deviation of less than 0.1 %. Therefore, the proposed model is usable in combinations where cardiovascular simulation does not have to be detailed. Computing costs have been decreased dramatically by a factor 186 compared to a model combination employing the 19-compartment model.

Individual thorax geometry reduces position and size differences in reconstructed images of electrical impedance tomography.

BACKGROUND: Due to the ill-posed problem, the electrical impedance within the thorax cannot be exactly reconstructed. OBJECTIVE: The aim of our study was to prove that reconstruction with individual thorax geometry improved the quality of EIT (electrical impedance tomography) images. METHODS: Seven mechanically ventilated patients with acute respiratory distress syndrome were examined by EIT. The thorax contours were determined from routine computed tomography (CT) images based on automatic threshold filtering. EIT raw data was reconstructed offline with (1) back-projection with circular forward model; (2) GREIT reconstruction method with circular forward model and (3) GREIT with individual thorax geometry. The resulting EIT images were compared to rescaled CT images. The distance between the lung contour and the thorax contour was calculated for each method and the differences to that in CT were denoted as position differences. Shape differences was defined as the ratio of thorax (or lungs) size in EIT and that in rescaled CT. RESULTS: Method (3) has the smallest position differences (6.6 ± 2.8, 5.3 ± 3.3, 2.3 ± 1.4 in pixel, for each reconstruction method respectively; mean ± SD). The thorax and lungs sizes in the transformed CT images were 514 ± 73 and 177 ± 39. Shape differences of thorax were 1.81 ± 0.26, 1.81 ± 0.26, 1.10 ± 0.12 and that of lungs were 1.69 ± 0.45, 1.52 ± 0.45, 1.34 ± 0.35 for each method respectively. CONCLUSION: The reconstructed images using the GREIT method with individual thorax geometry were more realistic. Improvement of EIT image quality may foster the acceptance of EIT in routine clinical use.

Image-Based 3D Reconstruction in Laparoscopy: A Review Focusing on the Quantitative Evaluation by Applying the Reconstruction Error.

Image-based 3D reconstruction enables laparoscopic applications as image-guided navigation and (autonomous) robot-assisted interventions, which require a high accuracy. The review's purpose is to present the accuracy of different techniques to label the most promising. A systematic literature search with PubMed and google scholar from 2015 to 2023 was applied by following the framework of "Review articles: purpose, process, and structure". Articles were considered when presenting a quantitative evaluation (root mean squared error and mean absolute error) of the reconstruction error (Euclidean distance between real and reconstructed surface). The search provides 995 articles, which were reduced to 48 articles after applying exclusion criteria. From these, a reconstruction error data set could be generated for the techniques of stereo vision, Shape-from-Motion, Simultaneous Localization and Mapping, deep-learning, and structured light. The reconstruction error varies from below one millimeter to higher than ten millimeters-with deep-learning and Simultaneous Localization and Mapping delivering the best results under intraoperative conditions. The high variance emerges from different experimental conditions. In conclusion, submillimeter accuracy is challenging, but promising image-based 3D reconstruction techniques could be identified. For future research, we recommend computing the reconstruction error for comparison purposes and use ex/in vivo organs as reference objects for realistic experiments.

Respiratory monitoring dataset, with rapid expiratory occlusions, over increasing positive airway pressure ventilation.

Resting breathing data was collected from 80 smokers, vapers, asthmatics, and otherwise healthy people in the low-risk clinical unit at the University of Canterbury. Subjects were asked to breathe normally through a full-face mask connected to a Fisher and Paykel Healthcare SleepStyle SPSCAA CPAP device. PEEP (Positive End-Expiratory Pressure) support was increased from 4 to 12 cmH2O in 0.5 cmH2O increments. Data was also collected during resting breathing at ZEEP (0 cmH2O) before and after the PEEP trial. The trial was conducted under University of Canterbury Human Research Ethics Committee consent (Ref: HREC 2023/04/LR-PS). Data was collected by and Dräeger PulmoVista 500 EIT machine and a custom Venturi-based pressure and flow sensor device connected in series with the CPAP and full-face mask. The outlined dataset includes pressure, flow, volume, dynamic circumference (thoracic and abdominal, and cross-sectional aeration. Subject demographic data was self-reported using a questionnaire given prior to the trial.

Pulmonary function testing dataset of pressure and flow, dynamic circumference, heart rate, and aeration monitoring.

Respiratory data was collected from 20 subjects, with an even sex distribution, in the low-risk clinical unit at the University of Canterbury. Ethical consent for this trial was granted by the University of Canterbury Human Research Ethics Committee (Ref: HREC 2023/30/LR-PS). Respiratory data were collected, for each subject, over three tests consisting of: 1) increasing set PEEP from a starting point of ZEEP using a CPAP machine; 2) test 1 repeated with two simulated apnoea's (breath holds) at each set PEEP; and 3) three forced expiratory manoeuvres at ZEEP. Data were collected using a custom pressure and flow sensor device, ECG, PPG, Garmin HRM Dual heartrate belt, and a Dräeger PulmoVista 500 Electrical Impedance Tomography (EIT) machine. Subject demographic data was also collected prior to the trial, in a questionnaire, with measurement equipment available. These data aim to inform the development of pulmonary mechanics models and titration algorithms.