Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics

[EN] This paper analyses the performance of SampEn and one of its derivatives, Fuzzy Entropy (FuzzyEn), in the context of artifacted blood glucose time series classification. This is a difficult and practically unexplored framework, where the availability of more sensitive and reliable measures could be of great clinical impact. Although the advent of new blood glucose monitoring technologies may reduce the incidence of the problems stated above, incorrect device or sensor manipulation, patient adherence, sensor detachment, time constraints, adoption barriers or affordability can still result in relatively short and artifacted records, as the ones analyzed in this paper or in other similar works. This study is aimed at characterizing the changes induced by such artifacts, enabling the arrangement of countermeasures in advance when possible. Despite the presence of these disturbances, results demonstrate that SampEn and FuzzyEn are sufficiently robust to achieve a significant classification performance, using records obtained from patients with duodenal-jejunal exclusion. The classification results, in terms of area under the ROC of up to 0.9, with several tests yielding AUC values also greater than 0.8, and in terms of a leave-one-out average classification accuracy of 80%, confirm the potential of these measures in this context despite the presence of artifacts, with SampEn having slightly better performance than FuzzyEn.The Czech partners were supported by DROIKEM000023001 and RVOVFN64165. No funding was received to support this research work by the Spanish partners.Cuesta Frau, D.; Novák, D.; Burda, V.; Molina Picó, A.; Vargas-Rojo, B.; Mraz, M.; Kavalkova, P.... (2018). Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics. Entropy. 20(11):1-18. https://doi.org/10.3390/e20110871S118201

Glycaemia dynamics in gestational diabetes mellitus

Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of hyperglycaemia. Thus, it is determinant to keep glycaemia within physiological ranges avoiding short but repetitive periods of hyper or hypoglycaemia. The variation of glycaemia over time is defined as 'glycaemia dynamics'. The latter concept regards with a variety of mechanisms and environmental conditions leading to blood glucose handling. In this review we summarized the different metrics for glycaemia dynamics derived from quantitative, plane distribution, amplitude, score values, variability estimation, and time series analysis. The potential application of the derived metrics from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in the potential alterations of pregnancy outcome in GDM are discussed

Continuous Glucose Monitoring and Tight Glycaemic Control in Critically Ill Patients

Critically ill patients often exhibit abnormal glycaemia that can lead to severe complications and potentially death. In critically ill adults, hyperglycaemia is a common problem that has been associated with increased morbidity and mortality. In contrast, critically ill infants often suffer from hypoglycaemia, which may cause seizures and permanent brain injury. Further complicating the matter, both of these conditions are diagnosed by blood glucose (BG) measurements, often taken several hours apart, and, as a result, these conditions can remain poorly managed or go completely undetected. Emerging ‘continuous’ glucose monitoring (CGM) devices with 1-5 minute measurement intervals have the potential to resolve many issues associated with conventional intermittent BG monitoring. The objective of this research was to investigate and develop methods and models to optimise the clinical use of CGM devices in critically ill patients. For critically ill adults, an in-silico study was conducted to quantify the potential benefits of introducing CGM devices into the intensive care unit (ICU). Mathematical models of CGM error characteristics were implemented with existing, clinically validated, models of the insulin-glucose regulatory system, to simulate the behaviour of CGM devices in critically ill patients. An alarm algorithm was also incorporated to provide a warning at the onset of predicted hypoglycaemia, allowing a virtual dextrose intervention to be administered as a preventative measure. The results of the in-silico study showed a potential reduction in nurse workload of approximately 75% and a significant reduction in hypoglycaemia, while also providing insight into the optimal rescue dose size and resulting dynamics of glucose recovery. During 2012, ten patients were recruited into a pilot clinical trial of CGM devices in critical care with a primary goal of assessing the reliability of CGM devices in this environment, with a specific interest in the effects of CGM device type and sensor site on sensor glucose (SG) data. Results showed the mean absolute relative difference of SG data across the cohort was between 12-24% and CGM devices were capable of monitoring some patients with a high degree of accuracy. However, certain illnesses, drugs and therapies can potentially affect sensor performance, and one particular set of results suggested severe oedema may have affected sensor performance. A novel and first of its kind metric, the Trend Compass was developed and used to assesses trend accuracy of SG in a mathematically precise fashion without approximation, and, importantly, does so independent of glucose level or sensor bias, unlike any other such metrics. In this analysis, the trend accuracy between CGM devices was typically good. A recent hypothesis suggesting that glucose complexity is associated with mortality was also investigated using the clinical CGM data. The results showed that complexity results from detrended fluctuation analysis (DFA) were influenced far more by CGM device type than patient outcome. In addition, the location of CGM sensors had no significant effect on complexity results in this data set. Thus, while this emerging analytical method has shown positive results in the literature, this analysis indicates that those results may be misleading given the impact of technology outweighing that of physiology. This particular result helps to further delineate the range of potential applications and insight that CGM devices might offer in this clinical scenario. In critically ill infants, CGM devices were used to investigate hypoglycaemia during the first 48 hours after birth. More than 50 CGM data sets were obtained from several studies of CGM in infants at risk of hypoglycaemia at the Waikato hospital neonatal ICU (NICU). In light of concerns regarding CGM accuracy, particularly during the first few hours of monitoring and/or at low BG levels, an alternative, novel calibration scheme was developed to increase the reliability of SG data. The recalibration algorithm maximised the value of very accurate calibration BG measurements from a blood gas analyser (BGA), by forcing SG data to pass through these calibration BG measurements. Recalibration increased all metrics of hypoglycaemia (number, duration, severity and hypoglycaemic index) as the factory CGM calibration was found to be reporting higher values at low BG levels due to its least squares calibration approach based on the assumption of a less accurate calibration glucose meter. Thus, this research defined new calibration methods to directly optimise the use of CGM devices in this clinical environment, where accurate reference BG measurements are available. Furthermore, this work showed that metrics such as duration or area under curve were far more robust to error than the typically used counted-incidence metrics, indicating how clinical assessment may have to change when using these devices. The impact of errors in calibration measurements on metrics used to classify hypoglycaemia was also assessed. Across the cohort, measurement error, particularly measurement bias, had a larger effect on hypoglycaemia metrics than delays in entering calibration measurements. However, for patients with highly variable glycaemia, timing error can have a significantly larger impact on output SG data than measurement error. Unusual episodes of hypoglycaemia could be successfully identified using a stochastic model, based on kernel density estimation, providing another level of information to aid decision making when assessing hypoglycaemia. Using the developed algorithms/tools, with CGM data from 161 infants, the incidence of hypoglycaemia was assessed and compared to results determined using BG measurements alone. Results from BG measurements showed that ~17% of BG measurements identified hypoglycaemia and over 80% of episodes occurred in the first day after birth. However, with concurrent BG and SG data available, the SG data consistently identified hypoglycaemia at a higher rate suggesting the BG measurements were not capturing some episodes. Duration of hypoglycaemia in SG data varied from 0-10+%, but was typically in the range 4-6%. Hypoglycaemia occurred most frequently on the first day after birth and an optimal measurement protocol for at risk infants would likely involve CGM for the first week after birth with frequent intermittent BG measurements for the first day. Overall, CGM devices have the potential to increase the understanding of certain glycaemic abnormalities and aid in the diagnosis/treatment of other conditions in critically ill patients. This research has used a range of prospective and retrospective clinical studies to develop methods to further optimise the use of CGM devices within the critically ill clinical environment, as well as delineating where they are less useful or less robust. These latter results clearly define areas where clinical practice needs to adapt when using these devices, as well as areas where device makers could target technological improvements for best effect. Although further investigations are required before these devices are regularly implemented in day-to-day clinical practice, as an observational tool they are capable of providing useful information that is not currently available with conventional intermittent BG monitoring

Human Health Engineering Volume II

In this Special Issue on “Human Health Engineering Volume II”, we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions could focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing physiological variables, papers highlighting health-monitoring applications, and examples of control and process management applications for human health. In comparison to biomedical engineering, we envision that the field of human health engineering will also cover applications for healthy humans (e.g., sports, sleep, and stress), and thus not only contribute to the development of technology for curing patients or supporting chronically ill people, but also to more general disease prevention and optimization of human well-being

Obesity-induced chronic inflammation in C57Bl6J mice, a novel risk factor in the progression of renal AA amyloidosis?

Background: Compelling evidence links obesity induced systemic inflammation to the development of chronic kidney disease (CKD). This systemic inflammation may result from exacerbated adipose inflammation. Besides the known detrimental effects of typical pro-inflammatory factors secreted by the adipose tissue (TNF-α, MCP-1 and IL-6) on the kidney, we hypothesize the enhanced obesity-induced secretion of serum amyloid A (SAA), an acute inflammatory protein, to play a key role in aggravating obesity-induced CKD. Methods: Groups of male C57Bl/6J mice (n = 99 in total) were fed a low (10% lard) or high (45% lard) fat diet for a maximum of 52 weeks. Mice were sacrificed after 24, 40 and 52 weeks. Whole blood samples, kidneys and adipose tissues were collected. The development of adipose and renal tissue inflammation was assessed on gene expression and protein level. Adipocytokine levels were measured in plasma samples. Results: A distinct inflammatory phenotype was observed in the adipose tissue of HFD mice prior to renal inflammation, which was associated with an early systemic elevation of TNF-α, leptin and SAA (1A-C). With aging, sclerotic lesions appeared in the kidney, the extent of which was severely aggravated by HFD feeding. Lesions exhibited typical amyloid characteristics (2A) and pathological severity positively correlated with bodyweight (2B). Interestingly, more SAA protein was detected in lesions of HFD mice. Conclusion: Our data suggest a causal link between obesity induced chronic inflammation and AA amyloidosis in C57Bl/6J mice. Though future studies are necessary to prove this causal link and to determine its relevance for the human situation, obesity may hence be considered a risk factor for the development and progression of renal AA amyloidosis in the course of CKD. (Figure Presented)

Nutritional intake, diet quality and exercise training: an exploration of adults following vegetarian-based dietary patterns

Vegetarian-based dietary patterns have been associated with protection against many chronic diseases. These benefits have been attributed to the high intakes of food components such as fruits, vegetables, nuts, seeds and legumes and absence of animal foods as part of a vegetarian diet, with some researchers suggesting there may be anti-inflammatory effects of following a vegetarian-based diet. Interestingly, there are growing numbers of individuals adopting a diet of this nature, with athletes being overrepresented in the consumption of vegetarian-based diets. Despite the increase in vegetarian-based diet popularity, there is a lack of research describing the motives, dietary behaviours, supplementation patterns, nutrient intakes and diet quality of individuals following a vegetarian-based diet, particularly for athletes. If differences in nutritional composition between athletes following omnivorous and vegetarian-based dietary patterns exist, there may also be various differences in exercise related physiological outcomes including inflammatory and immunological markers. The central hypothesis of this thesis is that due to differences in nutrient composition and diet quality between vegetarian and non-vegetarian-based dietary patterns, disparities will result in various inflammatory and immune biomarkers as well as other endurance exercise related physiology. In order to explore the dietary behaviors, supplementation patterns and motives in this population group, an online survey was implemented (Study 1a). Here, it was found that self-reported recreational and competitive athletes adopting a vegetarian-based diet were likely doing so with the aspiration of improving their exercise performance. A nutrient analysis in a subset of this study (Study 1b) suggested that a vegetarian-based dietary pattern could provide a high diet quality for recreational athletes with sufficient nutrients to support physical activity. However, intake of some nutrients in this self-reported vegetarian-based population were insufficient such as for vitamin B12 and long chain omega-3 polyunsaturated fatty acids. As this was an online, self-reported population of individuals, it was important to contrast dietary patterns between vegetarian and non-vegetarian dietary patterns and to explore if differences in inflammatory and immune markers as a function of dietary pattern existed between groups which also have relevance to exercise outcomes