Body temperature is usually employed in clinical practice by strict binary thresholding, aiming to classify patients as having fever or not. In the last years, other approaches based on the continuous analysis of body temperature time series have emerged. These are not only based on absolute thresholds but also on patterns and temporal dynamics of these time series, thus providing promising tools for early diagnosis. The present study applies three time series entropy calculation methods (Slope Entropy, Approximate Entropy, and Sample Entropy) to body temperature records of patients with bacterial infections and other causes of fever in search of possible differences that could be exploited for automatic classification. In the comparative analysis, Slope Entropy proved to be a stable and robust method that could bring higher sensitivity to the realm of entropy tools applied in this context of clinical thermometry. This method was able to find statistically significant differences between the two classes analyzed in all experiments, with sensitivity and specificity above 70% in most cases.

data was collected from 7 highly sensitized patients who underwent transplant from March 2003 to September 2008 but did not receive PMAMP.Demographics and outcome data were collected.Results: Demographics and outcomes are described in Table 2. To date, all 5 patients who underwent PMAMP are alive with CLAD-free survival and no episodes of rejection with a median transplant follow-up time of 427 days.Of note, all patients in both groups experienced at least one infectious complication following transplant.Conclusion: Our PMAMP shows promise for mediating CLAD-free survival, rejection, and extending post-transplant survival in highly sensitized candidates undergoing lung transplant; however, more data and follow-up is needed to ensure the safety and efficacy of this protocol.

BACKGROUND:The endoscopically implanted duodenal-jejunal bypass liner (DJBL) is an attractive alternative to bariatric surgery for obese diabetic patients. This article aims to study dynamical aspects of the glycaemic profile that may influence DJBL effects.METHODS:Thirty patients underwent DJBL implantation and were followed for 10 months. Continuous glucose monitoring (CGM) was performed before implantation and at month 10. Dynamical variables from CGM were measured: coefficient of variation of glycaemia, mean amplitude of glycaemic excursions (MAGE), detrended fluctuation analysis (DFA), % of time with glycaemia under 6.1 mmol/L (TU6.1), area over 7.8 mmol/L (AO7.8) and time in range. We analysed the correlation between changes in both anthropometric (body mass index, BMI and waist circumference) and metabolic (fasting blood glucose, FBG and HbA1c) variables and dynamical CGM-derived metrics and searched for variables in the basal CGM that could predict successful outcomes.RESULTS:There was a poor correlation between anthropometric and metabolic outcomes. There was a strong correlation between anthropometric changes and changes in glycaemic tonic control (∆BMI-∆TU6.1: rho = - 0.67, P < .01) and between metabolic outcomes and glycaemic phasic control (∆FBG-∆AO7.8: r = .60, P < .01). Basal AO7.8 was a powerful predictor of successful metabolic outcome (0.85 in patients with AO7.8 above the median vs 0.31 in patients with AO7.8 below the median: Chi-squared = 5.67, P = .02).CONCLUSIONS:In our population, anthropometric outcomes of DJBL correlate with improvement in tonic control of glycaemia, while metabolic outcomes correlate preferentially with improvement in phasic control. Assessment of basal phasic control may help in candidate profiling for DJBL implantation.

Despite its widely demonstrated usefulness, there is still room for improvement in the basic Permutation Entropy (PE) algorithm, as several subsequent studies have proposed in the recent years. For example, some improved PE variants try to address possible PE weaknesses, such as its only focus on ordinal information, and not on amplitude, or the possible detrimental impact of equal values in subsequences due to motif ambiguity. Other evolved PE methods try to reduce the influence of input parameters. A good representative of this last point is the Bubble Entropy (BE) method. BE is based on sorting relations instead of ordinal patterns, and its promising capabilities have not been extensively assessed yet. The objective of the present study was to comparatively assess the classification performance of this new method, and study and exploit the possible synergies between PE and BE. The claimed superior performance of BE over PE was first evaluated by conducting a series of time series classification tests over a varied and diverse experimental set. The results of this assessment apparently suggested that there is a complementary relationship between PE and BE, instead of a superior/inferior relationship. A second set of experiments using PE and BE simultaneously as the input features of a clustering algorithm, demonstrated that with a proper algorithm configuration, classification accuracy and robustness can benefit from both measures.

Fever is a common symptom of many diseases. Fever temporal patterns can be different depending on the specific pathology. Differentiation of diseases based on multiple mathematical features and visual observations has been recently studied in the scientific literature. However, the classification of diseases using a single mathematical feature has not been tried yet. The aim of the present study is to assess the feasibility of classifying diseases based on fever patterns using a single mathematical feature, specifically an entropy measure, Sample Entropy. This was an observational study. Analysis was carried out using 103 patients, 24 hour continuous tympanic temperature data. Sample Entropy feature was extracted from temperature data of patients. Grouping of diseases (infectious, tuberculosis, non-tuberculosis, and dengue fever) was made based on physicians diagnosis and laboratory findings. The quantitative results confirm the feasibility of the approach proposed, with an overall classification accuracy close to 70%, and the capability of finding significant differences for all the classes studied.

Diabetes is a disease of great and rising prevalence, with the obesity epidemic being a significant contributing risk factor. Duodenal–jejunal bypass liner (DJBL) is a reversible implant that mimics the effects of more aggressive surgical procedures, such as gastric bypass, to induce weight loss. We hypothesized that DJBL also influences the glucose dynamics in type II diabetes, based on the induced changes already demonstrated in other physiological characteristics and parameters. In order to assess the validity of this assumption, we conducted a quantitative analysis based on several nonlinear algorithms (Lempel–Ziv Complexity, Sample Entropy, Permutation Entropy, and modified Permutation Entropy), well suited to the characterization of biomedical time series. We applied them to glucose records drawn from two extreme cases available of DJBL implantation: before and after 10 months. The results confirmed the hypothesis and an accuracy of 86.4% was achieved with modified Permutation Entropy. Other metrics also yielded significant classification accuracy results, all above 70%, provided a suitable parameter configuration was chosen. With the Leave–One–Out method, the results were very similar, between 72% and 82% classification accuracy. There was also a decrease in entropy of glycaemia records during the time interval studied. These findings provide a solid foundation to assess how glucose metabolism may be influenced by DJBL implantation and opens a new line of research in this field.

Many measures to quantify the nonlinear dynamics of a time series are based on estimating the probability of certain features from their relative frequencies. Once a normalised histogram of events is computed, a single result is usually derived. This process can be broadly viewed as a nonlinear I R n mapping into I R , where n is the number of bins in the histogram. However, this mapping might entail a loss of information that could be critical for time series classification purposes. In this respect, the present study assessed such impact using permutation entropy (PE) and a diverse set of time series. We first devised a method of generating synthetic sequences of ordinal patterns using hidden Markov models. This way, it was possible to control the histogram distribution and quantify its influence on classification results. Next, real body temperature records are also used to illustrate the same phenomenon. The experiments results confirmed the improved classification accuracy achieved using raw histogram data instead of the PE final values. Thus, this study can provide a very valuable guidance for the improvement of the discriminating capability not only of PE, but of many similar histogram-based measures.

Complexity analysis of glucose time series with Detrended Fluctuation Analysis (DFA) has been proved to be useful for the prediction of type 2 diabetes mellitus (T2DM) development. We propose a modified DFA algorithm, review some of its characteristics and compare it with other metrics derived from continuous glucose monitorization in this setting. Several issues of the DFA algorithm were evaluated: (1) Time windowing: the best predictive value was obtained including all time-windows from 15 minutes to 24 hours. (2) Influence of circadian rhythms: for 48-hour glucometries, DFA alpha scaling exponent was calculated on 24-hour sliding segments (1-hour gap, 23-hour overlap), with a median coefficient of variation of 3.2%, which suggests that analysing time series of at least 24-hour length avoids the influence of circadian rhythms. (3) Influence of pretreatment of the time series through integration: DFA without integration was more sensitive to the introduction of white noise and it showed significant predictive power to forecast the development of T2DM, while the pretreated time series did not. (4) Robustness of an interpolation algorithm for missing values: The modified DFA algorithm evaluates the percentage of missing values in a time series. Establishing a 2% error threshold, we estimated the number and length of missing segments that could be admitted to consider a time series as suitable for DFA analysis. For comparison with other metrics, a Principal Component Analysis was performed and the results neatly tease out four different components. The first vector carries information concerned with variability, the second represents mainly DFA alpha exponent, while the third and fourth vectors carry essentially information related to the two “pre-diabetic behaviours” (impaired fasting glucose and impaired glucose tolerance). The scaling exponent obtained with the modified DFA algorithm proposed has significant predictive power for the development of T2DM in a high-risk population compared with other variability metrics or with the standard DFA algorithm.

Background and objectives: The adoption in clinical practice of electronic portable blood or interstitial glucose monitors has enabled the collection, storage, and sharing of massive amounts of glucose level readings. This availability of data opened the door to the application of a multitude of mathematical methods to extract clinical information not discernible with conventional visual inspection. The objective of this study is to assess the capability of Permutation Entropy (PE) to find differences between glucose records of healthy and potentially diabetic subjects. Methods: PE is a mathematical method based on the relative frequency analysis of ordinal patterns in time series that has gained a lot of attention in the last years due to its simplicity, robustness, and performance. We study in this paper the applicability of this method to glucose records of subjects at risk of diabetes in order to assess the predictability value of this metric in this context. Results: PE, along with some of its derivatives, was able to find significant differences between diabetic and non-diabetic patients from records acquired up to 3 years before the diagnosis. The quantitative results for PE were 3.5878 +/- 0.3916 for the nondiabetic class, and 3.1564 +/- 0.4166 for the diabetic class. With a classification accuracy higher than 70%, and by means of a Cox regression model, PE demonstrated that it is a very promising candidate as a risk stratification tool for continuous glucose monitoring. Conclusion: PE can be considered as a prospective tool for the early diagnosis of the glucoregulatory system. (c) 2018 Elsevier B.V. All rights reserved.

Objective: Nonalcoholic steatohepatitis (NASH) is strongly associated with overweight or obesity, the Metabolic Syndrome (MS) and type 2 diabetes mellitus (DM2). Our objective was to analyse their relationship with essential hypertension, a condition frequently linked to these pathologies. Design and method: Prospective, cross-sectional study conducted in a Hypertension Unit. We defined NASH as the presence of ultrasound hepatic steatosis with the increase in AST > 1.5 times high-reference laboratory values, in the absence of other causes of hepatopathy and with an alcohol intake less of 30 g/day (males) and 15 g/day (females). Results: We included a total of 2251 patients (51.3% males), with an average age of 56 years and a BMI of 30. 57% had MS and 11.5% DM2. 91 patients (4%) presented NASH criteria (4.9% males and 3.1% females, p = 0.032). Patients with NASH had higher abdominal circumference (106 vs. 100 cm, p < 0.0001), uric acid (6.4 vs. 5.8 mg/dl, p = 0.002), triglycerides (162 vs. 128 mg/dl, p = 0.005), basal glycaemia (116 vs. 107 mg/ dl, p = 0.026), HbA1c (6.5% vs. 6.1%, p = 0.015), basal insulin (18.4 vs. 13 mUI/ml, p = 0.025), DBP (83 vs. 80 mmHg, p = 0.036), ferritin (365 vs. 157 mg/dl, p < 0.0001), prevalence of MS (78% vs. 56%, p < 0.0001) and DM2 (20% vs.11%, p = 0.017). NASH also correlated with the number of MS factors (r = 0.045, p = 0.035). In the multivariate analysis, the variables independently associated with NASH were the abdominal circumference (Exp.(B) = 1034, 95%CI: 1001–1.068, p = 0.042), uric acid (Exp.(B) = 1.263, 95%CI: 1.005–1.588, p = 0.045), ferritin (Exp.(B) = 1.003, 95%CI: 1.002–1.005, p < 0.0001), the presence of MS (Exp.(B) = 4.358, 95%CI: 1.001–19.529, p = 0.005) and DM2 (Exp.(B) = 2.399, 95%CI: 1.040–5.537, p = 0.04). Triglycerides, basal glycaemia, HbA1c, basal insulin and DBP resulted excluded in the final model (model R2: 0.49). Conclusions: In our patients NASH was independently associated with the presence of DM2 and MS and with several of the defining or related components of MS. Thus NASH can represent the hepatic correlation of MS in essential hypertension.

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.

Objective: Both insulin resistance (IR) and metabolic syndrome (MS) has been related with the presence or the development of chronic renal disease (CRD). Our aim was to analyse this association in a hypertensive population. Design and method: Prospective, observational study conducted in a Hypertension Unit. We defined MS by ATP-III criteria, IR as the presence of a HOMA-index > 75% percentile (4.4) and the presence of CRD as an eGFR (EPI-creatinine equation) < 60 ml/min /1.73m2 and /or the presence of albuminuria (<30 mg/gr. creatinine, average of two consecutive days samples). Results: We include 773 patients (50.8% males) with a mean age of 54 years. 54.7% had MS. 64% of the patients with MS presented IR and just 8.5% had IR without MS. 21% had CRD: 11.9% with albuminuria, 9.1% with eGFR < 60 ml/min./1.73 m2 and 2.5% with both criteria. In univariate analisys the presence of CRD was associated with MS (28% vs. 18%, p = 0.05) and with IR (29% vs. 22%, p = 0.05). In multivariate analisys (logistic regression), including in a first model the MS and IR and adjusted by sex and age, the MS but no IR was associated with the presence of CRD (OR = 1.63, p = 0.014). In a second model, including as variables all the defining MS-criteria and adjusted as well by sex and age, the presence of CRD was independently associated with the abdominal circumference (OR = 1.020, p = 0.023), HDL-cholesterol (OR = 0.980, p = 0.016) and SBP (OR = 1.012, p = 0.039). In this last model just the HOMA-index was independently associated with a eGFR < 60 ml/min/1.73 m2 (OR = 1.064, p = 0.042) whereas the presence de albuminuria was associated with the abdominal circumference (OR = 1.022, p = 0.028), HDL-cholesterol (OR = 0.976, p = 0.022) and SBP (OR = 1.020, p = 0.002). Conclusions: In our hypertensive patients the simultaneous presence of MS an IR was frequent, resulting scanty the presence of IR without MS. An decreased eGFR was independently associated with HOMA-index and the presence of albuminuria with several components of MS. So, HOMA-index measurement can be an useful tool in the evaluation of renal prognosis of these patients.

Type 2 diabetes mellitus (T2DM) is preceded by a period of impaired glucoregulation. We investigated if continuous glucose monitoring system (CGMS) (1) could improve our capacity to predict the development of T2DM in subjects at risk. (2) Find out if impaired fasting glucose/impaired glucose tolerance differentiation through CGMS would also elucidate differences in clinical phenotypes.

Two main weaknesses have been identified for permutation entropy (PE): the neglect of subsequence pattern differences in terms of amplitude and the possible ambiguities introduced by equal values in the subsequences. A number of variations or customizations to the original PE method to address these issues have been proposed in the scientific literature recently. Specifically for ties, methods have tried to remove the ambiguity by assigning different weighted or computed orders to equal values. Although these methods are able to circumvent such ambiguity, they can substantially increase the algorithm costs, and a general characterization of their practical effectiveness is still lacking. This paper analyses the performance of PE using several biomedical datasets (electroencephalogram, heartbeat interval, body temperature, and glucose records) in order to quantify the influence of ties on its signal class segmentation capability. This capability is assessed in terms of statistical significance of the PE differences between classes and classification sensitivity and specificity. Being obvious that ties modify the PE results, we hypothesize that equal values are intrinsic to the acquisition process, and therefore, they impact all the classes more or less equally. The experimental results confirm ties are often not the limiting factor for PE, even they can be beneficial as a sort of stochastic resonance, and it can be far more effective to focus on the embedding dimension instead.

Many entropy-related methods for signal classification have been proposed and exploited successfully in the last several decades. However, it is sometimes difficult to find the optimal measure and the optimal parameter configuration for a specific purpose or context. Suboptimal settings may therefore produce subpar results and not even reach the desired level of significance. In order to increase the signal classification accuracy in these suboptimal situations, this paper proposes statistical models created with uncorrelated measures that exploit the possible synergies between them. The methods employed are permutation entropy (PE), approximate entropy (ApEn), and sample entropy (SampEn). Since PE is based on subpattern ordinal differences, whereas ApEn and SampEn are based on subpattern amplitude differences, we hypothesized that a combination of PE with another method would enhance the individual performance of any of them. The dataset was composed of body temperature records, for which we did not obtain a classification accuracy above 80% with a single measure, in this study or even in previous studies. The results confirmed that the classification accuracy rose up to 90% when combining PE and ApEn with a logistic model.

Body temperature monitoring provides health carers with key clinical information about the physiological status of patients. Temperature readings are taken periodically to detect febrile episodes and consequently implement the appropriate medical countermeasures. However, fever is often difficult to assess at early stages, or remains undetected until the next reading, probably a few hours later. The objective of this article is to develop a statistical model to forecast fever before a temperature threshold is exceeded to improve the therapeutic approach to the subjects involved. To this end, temperature series of 9 patients admitted to a general internal medicine ward were obtained with a continuous monitoring Holter device, collecting measurements of peripheral and core temperature once per minute. These series were used to develop different statistical models that could quantify the probability of having a fever spike in the following 60 minutes. A validation series was collected to assess the accuracy of the models. Finally, the results were compared with the analysis of some series by experienced clinicians. Two different models were developed: a logistic regression model and a linear discrimination analysis model. Both of them exhibited a fever peak forecasting accuracy greater than 84%. When compared with experts' assessment, both models identified 35 (97.2%) of 36 fever spikes. The models proposed are highly accurate in forecasting the appearance of fever spikes within a short period in patients with suspected or confirmed febrile-related illnesses.

Complexity analysis of glucose profile may provide valuable information about the gluco‐regulatory system. We hypothesized that a complexity metric (detrended fluctuation analysis, DFA) may have a prognostic value for the development of type 2 diabetes in patients at risk.

Objective: The diagnosis of type 2 diabetes mellitus (TDM2) is based either on the plasmatic glycaemia or on HbA1c criteria. Our purpose was to compare the prognostic value of both determinations to identify subjects at increased risk of developing TDM2. Design and method: Observational, longitudinal study of a cohort of patients with an increased risk of T2DM, based on the presence of one of the following criteria: essential hypertension, obesity (BMI > = 30 kg/m2) or a 1st degree relative with TDM2. Routine analysis, including HbA1c and fasting plasma glucose, were obtained at baseline and subsequently every six months. The diagnosis of TDM2 was established according to standard criteria. Results: 206 patients were included. Basal clinical characteristics are showed at table 1. During 17.5 months of mean follow-up 18 patients eventually developed T2DM (58,25 cases /1000 patients /year). In a Cox survival analysis, adjusted for the main clinical and analytical variables, only basal glycaemia and HbA1c resulted as independent predictors of T2DM development (table 2). Figure. No caption available. Conclusions: In our population with an increased risk of T2DM, including 92% of hypertensive patients, only basal glycaemia and HbA1c were independent predictors of the new onset of T2DM. HbA1c should be included in the routine evaluation of these patients.