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.

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.

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.

Segmenting/Pre-Processing Data from Bone Screw Thread-Stripping Tests.

Bone screws must be appropriately tightened to achieve optimal patient outcomes. If over-torqued, the threads formed in the bone may break, compromising the strength of the fixation; and, if under-torqued, the screw may loosen over time, compromising the stability. Previous work has proposed a model-based system to automatically determine the optimal insertion torque. This system consists of a reverse-modelling step to determine strength, and a forward modelling step to determine maximum torque. These have previously been tested in isolation, however future work must test the combined system. To do so, the data must be segmented and pre-processed. This was done based on specific features of the recorded data. The methodology was tested on 50 screw-insertion data sets across 5 different materials. With the parameters used, all data sets were correctly segmented. This will form a basis for the further processing of the data and validating the combined systemClinical relevance: The system for torque limit determination must be tested in its entirety to properly asses its performance. This paper discusses some of the steps required to pre-process the data to make this assessment. If successful, this system may improve patient outcomes in orthopaedic surgery.

Finite element analysis of the interaction between high-compliant balloon catheters and non-cylindrical vessel structures: towards tactile sensing balloon catheters.

Aiming for sensing balloon catheters which are able to provide intraoperative information of the vessel stiffness and shape, the present study uses finite element analysis (FEA) to evaluate the interaction between high-compliant elastomer balloon catheters with the inner wall of a non-cylindrical-shaped lumen structure. The contact simulations are based on 3D models with varying balloon thicknesses and varying tissue geometries to analyse the resulting balloon and tissue deformation as well as the inflation pressure dependent contact area. The wrinkled tissue structure is modelled by utilizing a two-layer fibre-based Holzapfel-Gasser-Ogden constitutive model and the model parameters are adapted based on available biomechanical data for human urethral vessel samples. The balloon catheter structure is implemented as a high-compliant hyper-elastic silicone material (based on polydimethylsiloxane (PDMS)) with a varying catheter wall thickness between 0.5 and 2.5 µm. Two control parameters are introduced to describe the balloon shape adaption in reaction to a wrinkled vessel wall during the inflation process. Basic semi-quantitative relations are revealed depending on the evolving balloon deformation and contact surface. Based on these relations some general design guidelines for balloon-based sensor catheters are presented. The results of the conducted in-silico study reveal some general interdependencies with respect to the compliance ratio between balloon and tissue and also in respect of the tissue aspect ratio. Further they support the proposed concept of high-compliant balloon catheters equipped for tactile sensing as diagnosis approach in urology and angioplasty.

The influence of gravity on electrical impedance tomography measurements during upper body position change.

Objective: The aim of the study was to examine the influence of gravity on regional ventilation measured by electrical impedance tomography (EIT) with the standard electrode belt position at the 5th intercostal space during tilting from supine to sitting positions. Methods: A total of 30 healthy volunteers were examined prospectively in supine position during quiet tidal breathing. Subsequently, the bed was tilted so that the upper body of the subjects achieved 30, 60 and 90° every 3 min. Regional ventilation distribution and end-expiratory lung impedance (EELI) were monitored with EIT throughout the whole experiment. Absolute tidal volumes were measured with spirometry and the volume-impedance ratio was calculated for each position. Results: The volume-impedance ratio did not differ statistically between the studied body positions but 11 subjects exhibited a large change in ratio at one of the positions (outside 99.3% coverage). In general, ventilation distribution became more heterogeneous and moved towards dorsal regions as the upper body was tilted to 90-degree position. EELI increased and tidal volume decreased. The lung regions identified at various positions differed significantly. Conclusion: Gravity has non-negligible influence on EIT data, as the upper body tilted from supine to sitting positions. The standard electrode belt position might be reconsidered if ventilation distribution is to be compared between supine and sitting positions.

Optimal machine learning methods for prediction of high-flow nasal cannula outcomes using image features from electrical impedance tomography.

BACKGROUND: High-flow nasal cannula (HNFC) is able to provide ventilation support for patients with hypoxic respiratory failure. Early prediction of HFNC outcome is warranted, since failure of HFNC might delay intubation and increase mortality rate. Existing methods require a relatively long period to identify the failure (approximately 12 h) and electrical impedance tomography (EIT) may help identify the patient's respiratory drive during HFNC. OBJECTIVES: This study aimed to investigate a proper machine-learning model to predict HFNC outcomes promptly by EIT image features. METHODS: The Z-score standardization method was adopted to normalize the samples from 43 patients who underwent HFNC and six EIT features were selected as model input variables through the random forest feature selection method. Machine-learning methods including discriminant, ensembles, k-nearest neighbour (KNN), artificial neural network (ANN), support vector machine (SVM), AdaBoost, xgboost, logistic, random forest, bernoulli bayes, gaussian bayes and gradient-boosted decision trees (GBDT) were used to build prediction models with the original data and balanced data proceeded by the synthetic minority oversampling technique. RESULTS: Prior to data balancing, an extremely low specificity (less than 33.33%) as well as a high accuracy in the validation data set were observed in all the methods. After data balancing, the specificity of KNN, xgboost, random forest, GBDT, bernoulli bayes and AdaBoost significantly reduced (p<0.05) while the area under curve did not improve considerably (p>0.05); and the accuracy and recall decreased significantly (p<0.05). CONCLUSIONS: The xgboost method showed better overall performance for balanced EIT image features, which may be considered as the ideal machine learning method for early prediction of HFNC outcomes.

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.

A review of the hormones involved in the endocrine dysfunctions of polycystic ovary syndrome and their interactions.

Polycystic ovary syndrome (PCOS) affects up to 20% of women but remains poorly understood. It is a heterogeneous condition with many potential comorbidities. This review offers an overview of the dysregulation of the reproductive and metabolic systems associated with PCOS. Review of the literature informed the development of a comprehensive summarizing 'wiring' diagram of PCOS-related features. This review provides a justification for each diagram aspect from the relevant academic literature, and explores the interactions between the hypothalamus, ovarian follicles, adipose tissue, reproductive hormones and other organ systems. The diagram will provide an efficient and useful tool for those researching and treating PCOS to understand the current state of knowledge on the complexity and variability of PCOS.

The calculation of electrical impedance tomography based silent spaces requires individual thorax and lung contours.

Objective.The present study evaluates the influence of different thorax contours (generic versus individual) on the parameter 'silent spaces' computed from electrical impedance tomography (EIT) measurements.Approach.Six patients with acute respiratory distress syndrome were analyzed retrospectively. EIT measurements were performed and the silent spaces were calculated based on (1) patient-specific contours Sind, (2) generic adult male contours SEidorsAand (3) generic neonate contours SEidorsN.Main results.The differences among all studied subjects were 5 ± 6% and 8 ± 7% for Sindversus SEidorsA, Sindversus SEidorsN, respectively (median ± interquartile range). Sindvalues were higher than the generic ones in two patients.Significance.In the present study, we demonstrated the differences in values when the silent spaces were calculated based on different body and organ contours. To our knowledge, this study was the first one showing explicitly that silent spaces calculated with generic thorax and lung contours might lead to results with different locations and values as compared to the calculation with subject-specific models. Interpretations of silent spaces should be proceeded with caution.

Differentiating Phenotypes of Coronavirus Disease-2019 Pneumonia by Electric Impedance Tomography.

Introduction: Coronavirus disease-2019 (COVID-19) pneumonia has different phenotypes. Selecting the patient individualized and optimal respirator settings for the ventilated patient is a challenging process. Electric impedance tomography (EIT) is a real-time, radiation-free functional imaging technique that can aid clinicians in differentiating the "low" (L-) and "high" (H-) phenotypes of COVID-19 pneumonia described previously. Methods: Two patients ("A" and "B") underwent a stepwise positive end-expiratory pressure (PEEP) recruitment by 3 cmH2O of steps from PEEP 10 to 25 and back to 10 cmH2O during a pressure control ventilation of 15 cmH2O. Recruitment maneuvers were performed under continuous EIT recording on a daily basis until patients required controlled ventilation mode. Results: Patients "A" and "B" had a 7- and 12-day long trial, respectively. At the daily baseline, patient "A" had significantly higher compliance: mean ± SD = 53 ± 7 vs. 38 ± 5 ml/cmH2O (p < 0.001) and a significantly higher physiological dead space according to the Bohr-Enghoff equation than patient "B": mean ± SD = 52 ± 4 vs. 45 ± 6% (p = 0.018). Following recruitment maneuvers, patient "A" had a significantly higher cumulative collapse ratio detected by EIT than patient "B": mean ± SD = 0.40 ± 0.08 vs. 0.29 ± 0.08 (p = 0.007). In patient "A," there was a significant linear regression between the cumulative collapse ratios at the end of the recruitment maneuvers (R 2 = 0.824, p = 0.005) by moving forward in days, while not for patient "B" (R 2 = 0.329, p = 0.5). Conclusion: Patient "B" was recognized as H-phenotype with high elastance, low compliance, higher recruitability, and low ventilation-to-perfusion ratio; meanwhile patient "A" was identified as the L-phenotype with low elastance, high compliance, and lower recruitability. Observation by EIT was not just able to differentiate the two phenotypes, but it also could follow the transition from L- to H-type within patient "A." Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT04360837.

Is there a need for individualized adjustment of electrode belt position during EIT-guided titration of positive end-expiratory pressure?

Objective. The aim of the present study was to evaluate the variation of tidal volume-to-impedance ratio (VT/ZT) during positive end-expiratory pressure (PEEP) titration with electrical impedance tomography (EIT) measurement.Approach. Forty-two patients with acute respiratory distress syndrome were retrospectively analyzed. An incremental and subsequently a decremental PEEP trial were performed with steps of 2 cm H2O and duration of 2 min per step during volume-controlled ventilation with decelerating flow. EIT measurement was conducted in the 5th intercostal space andVTwas recorded simultaneously. The variation ofVT/ZT(RatioV) was defined as the changes in percentage to average ratio per cm H2O PEEP change. A z-score > 1 was considered as a significant variation and an implication that the measurement plane was inadequate.Main results. TheRatioVof 42 patients was 1.29 ± 0.80%·cm H2O-1. A z-score of 1 corresponded to the variation of 2.09%·cm H2O-1. Seven patients (16.7%) had a z-score > 1 and showed either positive or negative correlation between the volume-to-impedance ratio and PEEP.Significance. Electrode placement at 5th intercostal space might not be ideal for every individual during EIT measurement. Evaluation of volume-to-impedance ratio variation is necessary for patients undergoing maneuvers with wide alteration in absolute lung volume.

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.

Regional ventilation distribution in patients with scoliosis assessed by electrical impedance tomography: Is individual thorax shape required?

BACKGROUND: Electrical impedance tomography (EIT) is a non-invasive non-radiological regional lung function measurement. The aim of the study was to examine the feasibility of assessing ventilation distribution with EIT in scoliosis patients using generic and individual thorax shape. METHODS: Eight subjects were measured with EIT before scoliosis surgery. Reconstructions with two different forward models were compared: the generic shape and the individual thorax shapes. Three EIT-based parameters measuring ventilation distribution were calculated: left lung to overall ratio, center of ventilation (CoV), global inhomogeneity index. RESULTS: EIT measurements were successfully conducted in all subjects. No statistical differences were found in the EIT-based parameters using the different reconstruction models. CoV based on the generic shape was significantly correlated to the main Cobb angle (r=-0.84, p < 0.01). CONCLUSION: It was feasible to monitor regional ventilation distribution in scoliosis patients with EIT. Individual thorax shapes might not be required for reliable patient assessment in a clinical setting.

Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model.

BACKGROUND AND OBJECTIVE: Recruitment maneuvers (RMs) with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveolar collapse. However, a suboptimal PEEP could induce undesired injury in lungs by insufficient or excessive breath support. Thus, a predictive model for patient response under PEEP changes could improve clinical care and lower risks. METHODS: This research adds novel elements to a virtual patient model to identify and predict patient-specific lung distension to optimise and personalise care. Model validity and accuracy are validated using data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0-12cmH2O), yielding 623 prediction cases. Predictions were made up to ΔPEEP = 12cmH2O ahead covering 6x2cmH2O PEEP steps. RESULTS: Using the proposed lung distension model, 90% of absolute peak inspiratory pressure (PIP) prediction errors compared to clinical measurement are within 3.95cmH2O, compared with 4.76cmH2O without this distension term. Comparing model-predicted and clinically measured distension had high correlation increasing to R2 = 0.93-0.95 if maximum ΔPEEP ≤ 6cmH2O. Predicted dynamic functional residual capacity (Vfrc) changes as PEEP rises yield 0.013L median prediction error for both prediction groups and overall R2 of 0.84. CONCLUSIONS: Overall results demonstrate nonlinear distension mechanics are accurately captured in virtual lung mechanics patients for mechanical ventilation, for the first time. This result can minimise the risk of lung injury by predicting its potential occurrence of distension before changing ventilator settings. The overall outcomes significantly extend and more fully validate this virtual mechanical ventilation patient model.

Investigating Torque-Speed Relationship of Self-Tapping Screws.

Correctly torquing bone screws is important to prevent fixation failures and ensure positive patient outcomes. It has been proposed that an automatic model-based method may be able to determine the patient-specific material properties of bone, and provide objective and quantitative torquing recommendations. One major part of developing this system is the modelling of the bone-screwing process, and the self-tapping screwing process in general. In this paper, we investigate the relationship between screw insertion torque (Nm) and speed of insertion (RPM). A weak positive correlation was found below approximately 30 RPM. Further research should focus on increasing the precision of the methodology, and this testing must be extended to ex-vivo animal bone testing in addition to the polyurethane foam substitute used here.Clinical relevance: To maximise the accuracy of torque recommendations, the model should account for all important factors. This study investigates and attempts to quantify the relationship between screw insertion speed and torque for later inclusion in modelling if significant.

Geometric Generalization of Self Tapping Screw Insertion Model.

Bone screws are used in orthopaedic procedures to fix implants and stabilise fractures. These procedures require care, as improperly torquing the screws can lead to implant failure or tissue damage, potentially requiring revision surgery or causing further disability. It was proposed that automated torque-limit identification may allow clinical decision support to control the screw torque, and lead to improved patient outcomes. This work extends a previous model of the screw insertion process to model complex thread geometries used for bone screws; consideration was made for the variable material properties and behaviours of bone to allow further tuning in the future. The new model was simulated and compared with the original model. The model was found to be in rough agreement with the earlier model, but was distinct, and could model thread features that the earlier model could not, such as the fillets and curves on the bone screw profile. The new model shows promise in modelling the more advanced thread geometries of bone screws with higher accuracy.Clinical relevance: This work extends a self tapping screw model to support complex thread shapes, as common in bone screws, allowing more accurate modelling of the clinically relevant geometries.