Single-arm, first-in-human feasibility study results for an ultra-low-cost insulin pump.

BACKGROUND: Use of Continuous Subcutaneous Insulin Infusion (CSII) has been shown to improve glycemic outcomes in Type 1 Diabetes (T1D), but high costs limit accessibility. To address this issue, an inter-operable, open-source Ultra-Low-Cost Insulin Pump (ULCIP) was developed and previously shown to demonstrate comparable delivery accuracy to commercial models in standardised laboratory tests. This study aims to evaluate the updated ULCIP in-vivo, assessing its viability as an affordable alternative for those who cannot afford commercially available devices. METHODS: This first-in-human feasibility study recruited six participants with T1D. During a nine-hour inpatient stay, participants used the ULCIP under clinical supervision. Venous glucose, insulin, and ß-Hydroxybutyrate were monitored to assess device performance. RESULTS: Participants displayed expected blood glucose and blood insulin levels in response to programmed basal and bolus insulin dosing. One participant developed mild ketosis, which was treated and did not recur when a new pump reservoir was placed. All other participants maintained ß-Hydroxybutyrate < 0.6 mmol/L throughout. CONCLUSION: The ULCIP safely delivered insulin therapy to users in a supervised inpatient environment. Future work should focus on correcting a pump hardware issue identified in this trial and extending device capabilities for use in closed loop control. Longer-term outpatient studies are warranted. TRIAL REGISTRATION: The trial was prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12623001288617) on the 11 December 2023.

Non-Invasive Assessment of Abdominal/Diaphragmatic and Thoracic/Intercostal Spontaneous Breathing Contributions.

(1) Background: Technically, a simple, inexpensive, and non-invasive method of ascertaining volume changes in thoracic and abdominal cavities are required to expedite the development and validation of pulmonary mechanics models. Clinically, this measure enables the real-time monitoring of muscular recruitment patterns and breathing effort. Thus, it has the potential, for example, to help differentiate between respiratory disease and dysfunctional breathing, which otherwise can present with similar symptoms such as breath rate. Current automatic methods of measuring chest expansion are invasive, intrusive, and/or difficult to conduct in conjunction with pulmonary function testing (spontaneous breathing pressure and flow measurements). (2) Methods: A tape measure and rotary encoder band system developed by the authors was used to directly measure changes in thoracic and abdominal circumferences without the calibration required for analogous strain-gauge-based or image processing solutions. (3) Results: Using scaling factors from the literature allowed for the conversion of thoracic and abdominal motion to lung volume, combining motion measurements correlated to flow-based measured tidal volume (normalised by subject weight) with R2 = 0.79 in data from 29 healthy adult subjects during panting, normal, and deep breathing at 0 cmH2O (ZEEP), 4 cmH2O, and 8 cmH2O PEEP (positive end-expiratory pressure). However, the correlation for individual subjects is substantially higher, indicating size and other physiological differences should be accounted for in scaling. The pattern of abdominal and chest expansion was captured, allowing for the analysis of muscular recruitment patterns over different breathing modes and the differentiation of active and passive modes. (4) Conclusions: The method and measuring device(s) enable the validation of patient-specific lung mechanics models and accurately elucidate diaphragmatic-driven volume changes due to intercostal/chest-wall muscular recruitment and elastic recoil.

Preserving multi-dimensional information: A hypersphere method for parameter space analysis.

Background: Physiological modelling often involves models described by large numbers of variables and significant volumes of clinical data. Mathematical interpretation of such models frequently necessitates analysing data points in high-dimensional spaces. Existing algorithms for analysing high-dimensional points either lose important dimensionality or do not describe the full position of points. Hence, there is a need for an algorithm which preserves this information. Methods: The most-distant uncovered point (MDUP) hypersphere method is a binary classification approach which defines a collection of equidistant N-dimensional points as the union of hyperspheres. The method iteratively generates hyperspheres at the most distant point in the interest region not yet contained within any hypersphere, until the entire region of interest is defined by the union of all generated hyperspheres. This method is tested on a 7-dimensional space with up to 35.8 million points representing feasible and infeasible spaces of model parameters for a clinically validated cardiovascular system model. Results: For different numbers of input points, the MDUP hypersphere method tends to generate large spheres away from the boundary of feasible and infeasible points, but generates the greatest number of relatively much smaller spheres around the boundary of the region of interest to fill this space. Runtime scales quadratically, in part because the current MDUP implementation is not parallelised. Conclusions: The MDUP hypersphere method can define points in a space of any dimension using only a collection of centre points and associated radii, making the results easily interpretable. It can identify large continuous regions, and in many cases capture the general structure of a region in only a relative few hyperspheres. The MDUP method also shows promise for initialising optimisation algorithm starting conditions within pre-defined feasible regions of model parameter spaces, which could improve model identifiability and the quality of optimisation results.

Obstructive respiratory disease simulation device.

Respiratory disease is a major contributor to healthcare costs, as well as increasing morbidity and early mortality. The device presented is used to simulate the effects of Chronic Obstructive Pulmonary Disease (COPD) in healthy people. The intended use is to provide data equivalent to COPD data measured from those who are ill for initial validation of respiratory mechanics models. It would thus eliminate the need to test unhealthy and/or fragile subjects, or the need for invasive or costly equipment based test methods. The device is used in conjunction with an open-access venturi-based flow sensor, to measure pressure, flow, and breath tidal volume. The device simulates the pressure and flow profiles of a person who has COPD including the non-linear increased resistance to end-exhalation and gas trapping. To achieve this non-linearity, a combination of high and low resistance outlets is used. Thus, the simulator allows the collection of patient-specific COPD-like breathing data in a non-invasive manner from healthy subjects. The device is low-cost with the majority of the parts 3D printed using a Prusa mini 3D printer and PLA filament.

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.

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.

Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

BACKGROUND AND OBJECTIVE: Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-based and machine learning PVA approaches exist, they have variable performance and can miss specific PVA events. This study compares a model and rule-based algorithm with a machine learning PVA method by retrospectively validating both methods using an independent patient cohort. METHODS: Hysteresis loop analysis (HLA) which is a rule-based method (RBM) and a tri-input convolutional neural network (TCNN) machine learning model are used to classify 7 different types of PVA, including: 1) flow asynchrony; 2) reverse triggering; 3) premature cycling; 4) double triggering; 5) delayed cycling; 6) ineffective efforts; and 7) auto triggering. Class activation mapping (CAM) heatmaps visualise sections of respiratory waveforms the TCNN model uses for decision making, improving result interpretability. Both PVA classification methods were used to classify incidence in an independent retrospective clinical cohort of 11 mechanically ventilated patients for validation and performance comparison. RESULTS: Self-validation with the training dataset shows overall better HLA performance (accuracy, sensitivity, specificity: 97.5 %, 96.6 %, 98.1 %) compared to the TCNN model (accuracy, sensitivity, specificity: 89.5 %, 98.3 %, 83.9 %). In this study, the TCNN model demonstrates higher sensitivity in detecting PVA, but HLA was better at identifying non-PVA breathing cycles due to its rule-based nature. While the overall AI identified by both classification methods are very similar, the intra-patient distribution of each PVA type varies between HLA and TCNN. CONCLUSION: The collective findings underscore the efficacy of both HLA and TCNN in PVA detection, indicating the potential for real-time continuous monitoring of PVA. While ML methods such as TCNN demonstrate good PVA identification performance, it is essential to ensure optimal model architecture and diversity in training data before widespread uptake as standard care. Moving forward, further validation and adoption of RBM methods, such as HLA, offers an effective approach to PVA detection while providing clear distinction into the underlying patterns of PVA, better aligning with clinical needs for transparency, explicability, adaptability and reliability of these emerging tools for clinical care.

Simulated obstructive respiratory disease dataset over increasing positive end-expiratory pressure.

The breathing dataset presented is collected from 20 healthy individuals at the University of Canterbury using a device to simulate the pressure and flow profiles of obstructive pulmonary disease. Specifically, the expiratory non-linear resistance, which generates the characteristic expiratory pressure-flow loop lobe seen in obstructive disease. Ethical consent for the trial was granted by the University of Canterbury Human Research Ethics Committee (Ref: HREC 2022/26/LR). Data was collected using an open-source data collection device connected to a Fisher and Paykel Healthcare SleepStyle SPSCAA CPAP. The trial was conducted at CPAP PEEP levels of 4 and 8 cmH2O, as well as at ZEEP (0 cmH2O) with no CPAP attached. The simulation device was a modular device connected to the expiratory pathway, consisting of a free volume diversion and fixed high resistance outlet. Three simulation levels were selected for testing, achieved by changing the size of the elastic free volume. The intended use of this dataset is for the initial validation and development of respiratory pulmonary mechanics models, using data collected from healthy people with simulated disease prior to clinical testing.

Treatment of Severe Hyperglycemia in Extremely Preterm Infants Using Continuous Subcutaneous Insulin Therapy.

Background: Hyperglycemia in preterm infants is usually treated with adjustment of glucose intake and, if persistent, with continuous insulin infusion. However, hypoglycemia is a well-known complication of iv insulin treatment. The aim of our study was to evaluate the feasibility of continuous subcutaneous insulin infusion (CSII) in extremely preterm infants. Methods and material: Clinical data from 15 extemely premature infants (< 28 weeks of gestation) undergoing CSII treatment for severe hyperglycemia at the NICU were included. Blood glucose levels during CSII as well as the nutritional intake and insulin intake were sampled. Data were analyzed and compared to a control group of very preterm infants receiving iv insulin therapy. Results: Normoglycemia rates were best in the iv insulin-cohort (50.3%; 15.6%). Hypoglycemia was very rare in both groups (0.4%; 0.0%). CSII therapy might require higher insulin doses compared to continuous iv therapy. Discussion: Subcutaneous Insulin therapy in extremely preterm infants is feasible, regarding the prevention of hypoglycemia. However, dose control needs to be improved. Conclusion: The results justify further model validation and clinical trial research to explore a model-based protocol and the use of CSII.

Design, manufacture, and control of a low-cost positive airway pressure device.

Current positive airway pressure devices cost NZ$800-$2500, posing a financial barrier for the estimated 1 billion individuals worldwide with sleep apnea and those researching respiratory diseases. Increasing diagnoses and research interest in the area necessitate a low-cost, easily accessible alternative. Thus, the mePAP, a high-quality, multipurpose, low-cost (∼NZ$250) positive airway pressure device, was designed and prototyped specifically for respiratory disease research, particularly for sleep apnea. The mePAP allows user customization and provides researchers with an affordable tool for testing positive airway pressure algorithms. Unlike typical commercial devices, the mePAP offers adaptability with open-source data collection and easily modifiable software for implementing and analysing different control and diagnostic algorithms. It features three control modes: constant; bilevel; and automatic; and provides pressures from 4 to 20 cmH2O, controlled via a phone app through Wi-Fi, with a mini-sensor added at the mask for increased accuracy. Validation tests showed the mePAP's performance is comparable to a gold-standard Fisher & Paykel device, with extremely similar output pressures. The mePAP's low cost enhances accessibility and equity, allowing researchers to test ventilation algorithms for sleep apnea and other respiratory conditions, with all data openly available for analysis. Its adaptability and multiple applications increase its usability and usefulness across various research and clinical settings.

Diazoxide for Severe or Recurrent Neonatal Hypoglycemia: A Randomized Clinical Trial.

Importance: Neonatal hypoglycemia is an important preventable cause of neurodevelopmental impairment, but there is a paucity of evidence to guide treatment. Objective: To evaluate whether early, low-dose oral diazoxide for severe or recurrent neonatal hypoglycemia reduces time to resolution of hypoglycemia. Design, Setting, and Participants: This 2-arm, placebo-controlled randomized clinical trial was conducted from May 2020 to February 2023 in tertiary neonatal units at 2 New Zealand hospitals. Participants were neonates born at 35 or more weeks' gestation and less than 1 week of age with severe hypoglycemia (blood glucose concentration <22 mg/dL or <36 mg/dL despite 2 doses of dextrose gel) or recurrent hypoglycemia (≥3 episodes of a blood glucose concentration <47 mg/dL within 48 hours). Interventions: Newborns were randomized 1:1 to receive diazoxide suspension (loading dose, 5 mg/kg; maintenance, 1.5 mg/kg every 12 hours) or placebo, titrated per protocol. Main Outcome and Measures: The primary outcome was time to resolution of hypoglycemia, defined as enteral bolus feeding without intravenous fluids and normoglycemia (blood glucose concentration of 47-98 mg/dL) for at least 24 hours, compared between groups using adjusted Cox proportional hazards regression. Hazard ratios adjusted for stratification variables and gestation length are reported. Prespecified secondary outcomes, including number of blood glucose tests and episodes of hypoglycemia, duration of hypoglycemia, and time to enteral bolus feeding and weaning from intravenous fluids, were compared by generalized linear models. Newborns were followed up for at least 2 weeks. Results: Of 154 newborns screened, 75 were randomized and 74 with evaluable data were included in the analysis (mean [SD] gestational age for the full cohort, 37.6 [1.6] weeks), 36 in the diazoxide group and 38 in the placebo group. Baseline characteristics were similar: in the diazoxide group, mean (SD) gestational age was 37.9 (1.6) weeks and 26 (72%) were male; in the placebo group, mean (SD) gestational age was 37.4 (1.5) weeks and 27 (71%) were male. There was no significant difference in time to resolution of hypoglycemia (adjusted hazard ratio [AHR], 1.39; 95% CI, 0.84-2.23), possibly due to increased episodes of elevated blood glucose concentration and longer time to normoglycemia in the diazoxide group. Resolution of hypoglycemia, when redefined post hoc as enteral bolus feeding without intravenous fluids for at least 24 hours with no further hypoglycemia, was reached by more newborns in the diazoxide group (AHR, 2.60; 95% CI, 1.53-4.46). Newborns in the diazoxide group had fewer blood glucose tests (adjusted count ratio [ACR], 0.63; 95% CI, 0.56-0.71) and episodes of hypoglycemia (ACR, 0.32; 95% CI, 0.17-0.63), reduced duration of hypoglycemia (adjusted ratio of geometric means [ARGM], 0.18; 95% CI, 0.06-0.53), and reduced time to enteral bolus feeding (ARGM, 0.74; 95% CI, 0.58-0.95) and weaning from intravenous fluids (ARGM, 0.72; 95% CI, 0.60-0.87). Only 2 newborns (6%) treated with diazoxide had hypoglycemia after the loading dose compared with 20 (53%) with placebo. Conclusions and Relevance: In this randomized clinical trial, early treatment of severe or recurrent neonatal hypoglycemia with low-dose oral diazoxide did not reduce time to resolution of hypoglycemia but reduced time to enteral bolus feeding and weaning from intravenous fluids, duration of hypoglycemia, and frequency of blood glucose testing compared with placebo. Trial Registration: ANZCTR.org.au Identifier: ACTRN12620000129987.

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.

Model-based Patient Matching for in-parallel Multiplexing Mechanical Ventilation Support.

Surges of COVID-19 infections could lead to insufficient supply of mechanical ventilators, and rationing of needed care. Multiplexing mechanical ventilators (co-MV) to serve multiple patients is a potential temporary solution. However, if patients are ventilated in parallel ventilation, there is currently no means to match ventilation requirements or patients, with no guidelines to date for co-MV. This research uses patient-specific clinically validated respiratory mechanics models to propose a method for patient matching and mechanical ventilator settings for two-patient co-MV under pressure control mode. The proposed method can simulate and estimate the resultant tidal volume of different combinations of co-ventilated patients. With both patients fulfilling the specified constraint under similar ventilation settings, the actual mechanical ventilator settings for co-MV are determined. This method allows clinicians to analyze in silico co-MV before clinical implementation.

Does Facemask Impact Diagnostic During Pulmonary Auscultation?

Facemasks have been widely used in hospitals, especially since the emergence of the coronavirus 2019 (COVID-19) pandemic, often severely affecting respiratory functions. Masks protect patients from contagious airborne transmission, and are thus more specifically important for chronic respiratory disease (CRD) patients. However, masks also increase air resistance and thus work of breathing, which may impact pulmonary auscultation and diagnostic acuity, the primary respiratory examination. This study is the first to assess the impact of facemasks on clinical auscultation diagnostic. Lung sounds from 29 patients were digitally recorded using an electronic stethoscope. For each patient, one recording was taken wearing a surgical mask and one without. Recorded signals were segmented in breath cycles using an autocorrelation algorithm. In total, 87 breath cycles were identified from sounds with mask, and 82 without mask. Time-frequency analysis of the signals was used to extract comparison features such as peak frequency, median frequency, band power, or spectral integration. All the features extracted in frequency content, its evolution, or power did not significantly differ between respiratory cycles with or without mask. This early stage study thus suggests minor impact on clinical diagnostic outcomes in pulmonary auscultation. However, further analysis is necessary such as on adventitious sounds characteristics differences with or without mask, to determine if facemask could lead to no discernible diagnostic outcome in clinical practice.

A Nonlinear Hysteretic Model for Automated Prediction of Lung Mechanics during Mechanical Ventilation.

Mechanical ventilation (MV) is core intensive care unit (ICU) therapy during the Covid-19 pandemic. Optimising MV care to a specific patient with respiratory failure is difficult due to inter- and intra- patient variability in lung mechanics and condition. The ability to accurately predict patient-specific lung response to a change in MV settings would enable semi-automated care and significantly improve the efficiency of MV monitoring and care. It has particular emphasis when considering MV care required to treat Covid-19 patients, who require longer MV care, where patient-specific care can reduce the time on MV required. This study develops a nonlinear smooth hysteresis loop model (HLM) able to capture the essential lung dynamics in a patient-specific fashion from measured ventilator data, particularly for changes of compliance and infection points of the pressure-volume loop. The automated (no human input) hysteresis loop analysis (HLA) method is applied to identify HLM model parameters, enabling automated digital cloning to create a virtual patient model to accurately predict lung response at a specified positive end expiratory pressure (PEEP) level, as well as in response to the changes of PEEP. The performance of this automated digital cloning approach is assessed using clinical data from 4 patients and 8 recruitment maneuver (RM) arms. Validation results show the HLM-based hysteresis loops identified using HLA match clinical pressure-volume loops very well with root-mean-square (RMS) errors less than 2% for all 8 data sets over 4 patients, validating the accuracy of the developed HLM in capturing the essential lung physiology and respiratory behaviours at different patient conditions. More importantly, the patient-specific digital clones at lower PEEP levels accurately predict lung response at higher PEEP levels with predicted peak inspiratory pressure (PIP) errors less than 2% in average. In addition, the resulted additional lung volume Vfrc obtained with PEEP changes are predicted with average absolute difference of 0.025L. The overall results validate the versatility and potential of the developed HLM for delineating changes of nonlinear lung dynamics, and its capability to create a predictive virtual patient with use of HLA for future treatment personalization and optimisation in MV therapy.