Evaluation of a Continuous Blood Glucose Monitor: A Novel and Non-Invasive Wearable Using Bioimpedance Technology.

BACKGROUND: Frequent blood glucose level (BGL) monitoring is essential for effective diabetes management. Poor compliance is common due to the painful finger pricking or subcutaneous lancet implantation required from existing technologies. There are currently no commercially available non-invasive devices that can effectively measure BGL. In this real-world study, a prototype non-invasive continuous glucose monitoring system (NI-CGM) developed as a wearable ring was used to collect bioimpedance data. The aim was to develop a mathematical model that could use these bioimpedance data to estimate BGL in real time. METHODS: The prototype NI-CGM was worn by 14 adult participants with type 2 diabetes for 14 days in an observational clinical study. Bioimpedance data were collected alongside paired BGL measurements taken with a Food and Drug Administration (FDA)-approved self-monitoring blood glucose (SMBG) meter and an FDA-approved CGM. The SMBG meter data were used to improve CGM accuracy, and CGM data to develop the mathematical model. RESULTS: A gradient boosted model was developed using a randomized 80-20 training-test split of data. The estimated BGL from the model had a Mean Absolute Relative Difference (MARD) of 17.9%, with the Parkes error grid (PEG) analysis showing 99% of values in clinically acceptable zones A and B. CONCLUSIONS: This study demonstrated the reliability of the prototype NI-CGM at collecting bioimpedance data in a real-world scenario. These data were used to train a model that could successfully estimate BGL with a promising MARD and clinically relevant PEG result. These results will enable continued development of the prototype NI-CGM as a wearable ring.

Novel Methods of Measuring Adherence Patterns Reveal Adherence Phenotypes with Distinct Asthma Outcomes.

Rationale: Poor adherence with asthma controller medication contributes to worse symptom control and increased exacerbation risk. Adherence is often expressed as the mean proportion of prescribed doses taken over a period of 6-12 months. New metrics may capture individual day-to-day variability patterns linked with distinct clinical outcomes. Objectives: To test the hypotheses that novel time- and dose-based adherence variability metrics offer independent value to mean adherence in identifying distinct adherence patterns that are associated with symptom control (Asthma Control Test [ACT] score) and exacerbation risk, using electronically recorded medication data from a 6-month cluster randomized trial examining the effect of inhaler reminders on adherence. Methods: Adherence metrics were calculated from the first 2 months (Months 0-2) of the study period. In addition to mean adherence (percentage prescribed puffs/day taken), we examined novel metrics, including time adherence area under the curve (T-AUC), reflecting cumulative gaps in adherence over time; entropy, reflecting disorder in the ways in which a patient changed their medication dose adherence from day to day; and standard deviation of the percentage prescribed puffs/day taken. Dominant metrics identified from factor analysis were included in hierarchical clustering analysis. We compared the resultant clusters in terms of outcomes over Months 2-6 and exacerbation risk over the entire study period. Results: Two factors explained >65% of the total variance in adherence, primarily driven by T-AUC and entropy. Two main patient clusters based on their adherence metrics were identified: cluster 1 (high time adherence, n = 75) had better T-AUC (i.e., fewer gaps between medication-taking days) than cluster 2 (low time adherence, n = 23). Though both clusters had similar symptom control at 2 months, cluster 1 showed less subsequent decline in ACT over Months 2-6 (median [interquartile range] change in ACT score: 1 [-1 to 4] vs. -2 [-3.75 to 0.75]; P = 0.012), and had better symptom control at 6 months (ACT score: 20 [17-23] vs. 17 [15-20]; P = 0.034). There were no significant differences between the clusters in terms of proportion of exacerbators or time to exacerbation. Conclusions: Novel metrics showed that low time adherence was associated with greater risk of decline in asthma symptom control. Adherence patterns may exhibit "memory" relevant to future clinical status, warranting validation in a larger dataset.

Dynamics of inhaled corticosteroid use are associated with asthma attacks.

Inhaled corticosteroids (ICS) suppress eosinophilic airway inflammation in asthma, but patients may not adhere to prescribed use. Mean adherence-averaging total doses taken over prescribed-fails to capture many aspects of adherence. Patients with difficult-to-treat asthma underwent electronic monitoring of ICS, with data collected over 50 days. These were used to calculate entropy (H) a measure of irregular inhaler use over this period, defined in terms of transitional probabilities between different levels of adherence, further partitioned into increasing (Hinc) or decreasing (Hdec) adherence. Mean adherence, time between actuations (Gapmax), and cumulative time- and dose-based variability (area-under-the-curve) were measured. Associations between adherence metrics and 6-month asthma status and attacks were assessed. Only H and Hdec were associated with poor baseline status and 6-month outcomes: H and Hdec correlated negatively with baseline quality of life (H:Spearman rS = - 0·330, p = 0·019, Hdec:rS = - 0·385, p = 0·006) and symptom control (H:rS = - 0·288, p = 0·041, Hdec: rS = - 0·351, p = 0·012). H was associated with subsequent asthma attacks requiring hospitalisation (Wilcoxon Z-statistic = - 2.34, p = 0·019), and Hdec with subsequent asthma attacks of other severities. Significant associations were maintained in multivariable analyses, except when adjusted for blood eosinophils. Entropy analysis may provide insight into adherence behavior, and guide assessment and improvement of adherence in uncontrolled asthma.

Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3-24 Months.

Undernutrition in infants and young children is a major problem leading to millions of deaths every year. The objective of this study was to provide a new model for body composition assessment using near-infrared reflectance (NIR) to help correctly identify low body fat in infants and young children. Eligibility included infants and young children from 3-24 months of age. Fat mass values were collected from dual-energy x-ray absorptiometry (DXA), deuterium dilution (DD) and skin fold thickness (SFT) measurements, which were then compared to NIR predicted values. Anthropometric measures were also obtained. We developed a model using NIR to predict fat mass and validated it against a multi compartment model. One hundred and sixty-four infants and young children were included. The evaluation of the NIR model against the multi compartment reference method achieved an r value of 0.885, 0.904, and 0.818 for age groups 3-24 months (all subjects), 0-6 months, and 7-24 months, respectively. Compared with conventional methods such as SFT, body mass index and anthropometry, performance was best with NIR. NIR offers an affordable and portable way to measure fat mass in South African infants for growth monitoring in low-middle income settings.

Day-to-day variability of forced oscillatory mechanics for early detection of acute exacerbations in COPD.

BACKGROUND: Telemonitoring trials for early detection of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) have provided mixed results. Day-to-day variations in lung function measured by the forced oscillation technique (FOT) may yield greater insight. We evaluated the clinical utility of home telemonitoring of variability in FOT measures in terms of 1) the relationship with symptoms and quality of life (QoL); and 2) the timing of variability of FOT measures and symptom changes prior to AECOPD. METHODS: Daily FOT parameters at 5 Hz (resistance (R) and reactance (X); Resmon Pro Diary, Restech Srl, Milan, Italy), daily symptoms (COPD Assessment Test (CAT)) and 4-weekly QoL data (St George's Respiratory Questionnaire (SGRQ)) were recorded over 8-9 months from chronic obstructive pulmonary disease (COPD) patients. Variability of R and X was calculated as the standard deviation (sd) over 7-day running windows and we also examined the effect of varying window size. The relationship of FOT versus CAT and SGRQ was assessed using linear mixed modelling, daily changes in FOT variability and CAT prior to AECOPD using one-way repeated measures ANOVA. RESULTS: Fifteen participants with a mean±sd age of 69±10 years and a % predicted forced expiratory volume in 1 s (FEV1) of 39±10% had a median (interquartile range (IQR)) adherence of 95.4% (79.0-98.8%). Variability of the inspiratory component of X (indicated by the standard deviation of inspiratory reactance (SDXinsp)) related to CAT and weakly to SGRQ (fixed effect estimates 1.57, 95% CI 0.65-2.49 (p=0.001) and 4.41, 95% CI -0.06 to 8.89 (p=0.05), respectively). SDXinsp changed significantly on the same day as CAT (1 day before AECOPD, both p=0.02) and earlier when using shorter running windows (3 days before AECOPD, p=0.01; accuracy=0.72 for 5-day windows). CONCLUSIONS: SDXinsp from FOT telemonitoring reflects COPD symptoms and may be a sensitive biomarker for early detection of AECOPD.

Time-based pulmonary features from electrical impedance tomography demonstrate ventilation heterogeneity in chronic obstructive pulmonary disease.

Pulmonary electrical impedance tomography (EIT) is a functional imaging technique that allows real-time monitoring of ventilation distribution. Ventilation heterogeneity (VH) is a characteristic feature of chronic obstructive pulmonary disease (COPD) and has previously been quantified using features derived from tidal variations in the amplitude of the EIT signal. However, VH may be better described by time-based metrics, the measurement of which is made possible by the high temporal resolution of EIT. We aimed 1) to quantify VH using novel time-based EIT metrics and 2) to determine the physiological relevance of these metrics by exploring their relationships with complex lung mechanics measured by the forced oscillation technique (FOT). We performed FOT, spirometry, and tidal-breathing EIT measurements in 11 healthy controls and 9 volunteers with COPD. Through offline signal processing, we derived 3 features from the impedance-time (Z-t) curve for each image pixel: 1) tE, mean expiratory time; 2) PHASE, mean time difference between pixel and global Z-t curves; and 3) AMP, mean amplitude of Z-t curve tidal variation. Distribution was quantified by the coefficient of variation (CV) and the heterogeneity index (HI). Both CV and HI of the tE and PHASE features were significantly increased in COPD compared with controls, and both related to spirometry and FOT resistance and reactance measurements. In contrast, distribution of the AMP feature showed no relationships with lung mechanics. These novel time-based EIT metrics of VH reflect complex lung mechanics in COPD and have the potential to allow real-time visualization of pulmonary physiology in spontaneously breathing subjects.NEW & NOTEWORTHY Pulmonary electrical impedance tomography (EIT) is a real-time imaging technique capable of monitoring ventilation with exquisite temporal resolution. We report novel, time-based EIT measurements that not only demonstrate ventilation heterogeneity in chronic obstructive pulmonary disease (COPD), but also reflect oscillatory lung mechanics. These EIT measurements are noninvasive, radiation-free, easy to obtain, and provide real-time visualization of the complex pathophysiology of COPD.

Cardiovascular impact of intravenous caffeine in preterm infants.

AIM: To evaluate the acute effect of intravenous caffeine on heart rate and blood pressure variability in preterm infants. METHODS: We extracted and compared linear and nonlinear features of heart rate and blood pressure variability at two time points: prior to and in the two hours following a loading dose of 10 mg/kg caffeine base. RESULTS: We studied 31 preterm infants with arterial blood pressure data and 25 with electrocardiogram data, and compared extracted features prior to and following caffeine administration. We observed a reduction in both scaling exponents (α1 , α2 ) of mean arterial pressure from detrended fluctuation analysis and an increase in the ratio of short- (SD1) and long-term (SD2) variability from Poincare analysis (SD1/SD2). Heart rate variability analyses showed a reduction in α1 (mean (SD) of 0.92 (0.21) to 0.86 (0.21), p < 0.01), consistent with increased vagal tone. Following caffeine, beat-to-beat pulse pressure variability (SD) also increased (2.1 (0.64) to 2.5 (0.65) mmHg, p < 0.01). CONCLUSION: This study highlights potential elevation in autonomic nervous system responsiveness following caffeine administration reflected in both heart rate and blood pressure systems. The observed increase in pulse pressure variability may have implications for caffeine administration to infants with potentially impaired cerebral autoregulation.

The effect of caffeine loading on cerebral autoregulation in preterm infants.

AIM: To evaluate cerebral autoregulation changes in preterm infants receiving a loading dose of caffeine base. METHODS: In a cohort of 30 preterm infants, we extracted measures of cerebral autoregulation using time and frequency domain techniques to determine the correlation between mean arterial pressure (MAP) and tissue oxygenation index (TOI) signals. These measures included the cerebral oximetry index (COx), cross-correlation and coherence measures, and were extracted prior to caffeine loading and in the 2 hours following administration of 10 mg/kg caffeine base. RESULTS: We observed acute reductions in time domain correlation measures, including the cerebral oximetry index (linear mixed model coefficient -0.093, standard error 0.04; p = 0.028) and the detrended cross-correlation coefficient (ρ5 coefficient -0.13, standard error 0.055; p = 0.025). These reductions suggested an acute improvement in cerebral autoregulation. Features from detrended cross-correlation analysis also showed greater discriminative value than other methods in identifying changes prior to and following caffeine administration. CONCLUSION: We observed a reduced correlation between MAP and TOI from near-infrared spectroscopy following caffeine administration. These findings suggest an acute enhanced capacity for cerebral autoregulation following a loading dose of caffeine in preterm infants, contributing to our understanding of the physiological impact of caffeine therapy.

An anthropometric approach to characterising neonatal morbidity and body composition, using air displacement plethysmography as a criterion method.

BACKGROUND: With the greatest burden of infant undernutrition and morbidity in low and middle income countries (LMICs), there is a need for suitable approaches to monitor infants in a simple, low-cost and effective manner. Anthropometry continues to play a major role in characterising growth and nutritional status. METHODS: We developed a range of models to aid in identifying neonates at risk of malnutrition. We first adopted a logistic regression approach to screen for a composite neonatal morbidity, low and high body fat (BF%) infants. We then developed linear regression models for the estimation of neonatal fat mass as an assessment of body composition and nutritional status. RESULTS: We fitted logistic regression models combining up to four anthropometric variables to predict composite morbidity and low and high BF% neonates. The greatest area under receiver-operator characteristic curves (AUC with 95% confidence intervals (CI)) for identifying composite morbidity was 0.740 (0.63, 0.85), resulting from the combination of birthweight, length, chest and mid-thigh circumferences. The AUCs (95% CI) for identifying low and high BF% were 0.827 (0.78, 0.88) and 0.834 (0.79, 0.88), respectively. For identifying composite morbidity, BF% as measured via air displacement plethysmography showed strong predictive ability (AUC 0.786 (0.70, 0.88)), while birthweight percentiles had a lower AUC (0.695 (0.57, 0.82)). Birthweight percentiles could also identify low and high BF% neonates with AUCs of 0.792 (0.74, 0.85) and 0.834 (0.79, 0.88). We applied a sex-specific approach to anthropometric estimation of neonatal fat mass, demonstrating the influence of the testing sample size on the final model performance. CONCLUSIONS: These models display potential for further development and evaluation in LMICs to detect infants in need of further nutritional management, especially where traditional methods of risk management such as birthweight for gestational age percentiles may be variable or non-existent, or unable to detect appropriately grown, low fat newborns.

Vibroarthrography for early detection of knee osteoarthritis using normalized frequency features.

Vibroarthrography is a radiation-free and inexpensive method of assessing the condition of knee cartilage damage during extension-flexion movements. Acoustic sensors were placed on the patella and medial tibial plateau (two accelerometers) as well as on the lateral tibial plateau (a piezoelectric disk) to measure the structure-borne noise in 59 asymptomatic knees and 40 knees with osteoarthritis. After semi-automatic segmentation of the acoustic signals, frequency features were generated for the extension as well as the flexion phase. We propose simple and robust features based on relative high-frequency components. The normalized nature of these frequency features makes them insusceptible to influences on the signal gain, such as attenuation by fat tissue and variance in acoustic coupling. We analyzed their ability to serve as classification features for detection of knee osteoarthritis, including the effect of normalization and the effect of combining frequency features of all three sensors. The features permitted a distinction between asymptomatic and non-healthy knees. Using machine learning with a linear support vector machine, a classification specificity of approximately 0.8 at a sensitivity of 0.75 could be achieved. This classification performance is comparable to existing diagnostic tests and hence qualifies vibroarthrography as an additional diagnostic tool. Graphical Abstract Acoustic frequency features were used to detect knee osteoarthritis at 80% specificity and 75% sensitivity.

How do different brands of size 1 laryngeal mask airway compare with face mask ventilation in a dedicated laryngeal mask airway teaching manikin?

BACKGROUND: International neonatal resuscitation guidelines recommend the use of laryngeal mask airway (LMA) with newborn infants (≥34 weeks' gestation or >2 kg weight) when bag-mask ventilation (BMV) or tracheal intubation is unsuccessful. Previous publications do not allow broad LMA device comparison. OBJECTIVE: To compare delivered ventilation of seven brands of size 1 LMA devices with two brands of face mask using self-inflating bag (SIB). DESIGN: 40 experienced neonatal staff provided inflation cycles using SIB with positive end expiratory pressure (PEEP) (5 cmH2O) to a specialised newborn/infant training manikin randomised for each LMA and face mask. All subjects received prior education in LMA insertion and BMV. RESULTS: 12 415 recorded inflations for LMAs and face masks were analysed. Leak detected was lowest with i-gel brand, with a mean of 5.7% compared with face mask (triangular 42.7, round 35.7) and other LMAs (45.5-65.4) (p<0.001). Peak inspiratory pressure was higher with i-gel, with a mean of 28.9 cmH2O compared with face mask (triangular 22.8, round 25.8) and other LMAs (14.3-22.0) (p<0.001). PEEP was higher with i-gel, with a mean of 5.1 cmH2O compared with face mask (triangular 3.0, round 3.6) and other LMAs (0.6-2.6) (p<0.001). In contrast to other LMAs examined, i-gel had no insertion failures and all users found i-gel easy to use. CONCLUSION: This study has shown dramatic performance differences in delivered ventilation, mask leak and ease of use among seven different brands of LMA tested in a manikin model. This coupled with no partial or complete insertion failures and ease of use suggests i-gel LMA may have an expanded role with newborn resuscitation as a primary resuscitation device.

Prediction of intraventricular haemorrhage in preterm infants using time series analysis of blood pressure and respiratory signals.

Despite the decline in mortality rates of extremely preterm infants, intraventricular haemorrhage (IVH) remains common in survivors. The need for resuscitation and cardiorespiratory management, particularly within the first 24 hours of life, are important factors in the incidence and timing of IVH. Variability analyses of heart rate and blood pressure data has demonstrated potential approaches to predictive monitoring. In this study, we investigated the early identification of infants at a high risk of developing IVH, using time series analysis of blood pressure and respiratory data. We also explore approaches to improving model performance, such as the inclusion of multiple variables and signal pre-processing to enhance the results from detrended fluctuation analysis. Of the models we evaluated, the highest area under receiver-operator characteristic curve (5th, 95th percentile) achieved was 0.921 (0.82, 1.00) by mean diastolic blood pressure and the long-term scaling exponent of pulse interval (PI α2), exhibiting a sensitivity of >90% at a specificity of 75%. Following evaluation in a larger population, our approach may be useful in predictive monitoring to identify infants at high risk of developing IVH, offering caregivers more time to adjust intensive care treatment.

Length-free near infrared measurement of newborn malnutrition.

Under-nutrition in neonates can cause immediate mortality, impaired cognitive development and early onset adult disease. Body fat percentage measured using air-displacement-plethysmography has been found to better indicate under-nutrition than conventional birth weight percentiles. However, air-displacement-plethysmography equipment is expensive and non-portable, so is not suited for use in developing communities where the burden is often the greatest. We proposed a new body fat measurement technique using a length-free model with near-infrared spectroscopy measurements on a single site of the body - the thigh. To remove the need for length measurement, we developed a model with five discrete wavelengths and a sex parameter. The model was developed using air-displacement-plethysmography measurements in 52 neonates within 48 hours of birth. We identified instrumentation required in a low-cost LED-based screening device and incorporated a receptor device that can increase the amount of light collected. This near-infrared method may be suitable as a low cost screening tool for detecting body fat levels and monitoring nutritional interventions for malnutrition in neonates and young children in resource-constrained communities.

Reducing false arrhythmia alarms in the ICU using multimodal signals and robust QRS detection.

This study developed algorithms to decrease the arrhythmia false alarms in the ICU by processing multimodal signals of photoplethysmography (PPG), arterial blood pressure (ABP), and two ECG signals. The goal was to detect the five critical arrhythmias comprising asystole (ASY), extreme bradycardia (EBR), extreme tachycardia (ETC), ventricular tachycardia (VTA), and ventricular flutter or fibrillation (VFB). The different characteristics of the arrhythmias suggested the application of individual signal processing for each alarm and the combination of the algorithms to enhance false alarm detection. Thus, different features and signal processing techniques were used for each arrhythmia type. The ECG signals were first processed to reduce the signal interference. Then, a Hilbert-transform based QRS detector algorithm was utilized to identify the QRS complexes, which were then processed to determine the instantaneous heart rate. The pulsatile signals (PPG and ABP) were processed to discover the pulse onset of beats which were then employed to measure the heart rate. The signal quality index (SQI) of the signals was implemented to verify the integrity of the heart rate information. The overall score obtained by our algorithms in the 2015 Computing in Cardiology Challenge was a score of 74.03% for retrospective and 69.92% for real-time analysis.