Discriminating Bacterial Infection from Other Causes of Fever Using Body Temperature Entropy Analysis.

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.

Influence of glucometric 'dynamical' variables on duodenal-jejunal bypass liner (DJBL) anthropometric and metabolic outcomes.

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.

New insights from continuous glucose monitoring into the route to diabetes.

AIM: 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. MATERIAL AND METHODS: Observational study of 209 hypertensive patients, aged 18 to 85 years who wore at entry a CGMS. Two CGMS metrics, percent of time under the 100 mg/dL glycaemic threshold (TU100) (impaired fasting glucose surrogate phenotype) and area above the 140 mg/dL glycemic threshold (AO140) (impaired glucose tolerance surrogate phenotype) were measured. The median follow-up was 32 months (6-72 mo), and there were 17 new cases of T2DM. RESULTS: In a multivariate Cox proportional hazard survival analysis including the conventional prediabetes-defining criteria and the 2 CGMS-derived variables, only TU100 and HbA1c were significant and independent variables in predicting T2DM development. An increase in 0.1 in TU100 resulted in a 0.69 (95% CI, 0.54-0.88; P < .01) odds ratio of developing T2DM. With cut-off points of 0.5 for TU100 and 5.7% for HbA1c , the test "TU < 0.5 and HbA1c  > 5.7%" had a sensitivity of 0.81 (SD, 0.10), a specificity of 0.83 (SD, 0.03), and a likelihood ratio of 4.82 (SD, 1.03) for T2DM development. CONCLUSIONS: Continuous glucose monitoring system allows for a better T2DM risk-development categorization than fasting glucose and HbA1c in a high-risk population. Continuous glucose monitoring system-derived phenotyping reveals clinical differences, not disclosed by conventional fasting plasma glucose/HbA1c categorization. These differences may correlate with distinct pathophysiological mechanisms.

Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics.

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.

Individualised perioperative open-lung approach versus standard protective ventilation in abdominal surgery (iPROVE): a randomised controlled trial.

BACKGROUND: The effects of individualised perioperative lung-protective ventilation (based on the open-lung approach [OLA]) on postoperative complications is unknown. We aimed to investigate the effects of intraoperative and postoperative ventilatory management in patients scheduled for abdominal surgery, compared with standard protective ventilation. METHODS: We did this prospective, multicentre, randomised controlled trial in 21 teaching hospitals in Spain. We enrolled patients who were aged 18 years or older, were scheduled to have abdominal surgery with an expected time of longer than 2 h, had intermediate-to-high-risk of developing postoperative pulmonary complications, and who had a body-mass index less than 35 kg/m2. Patients were randomly assigned (1:1:1:1) online to receive one of four lung-protective ventilation strategies using low tidal volume plus positive end-expiratory pressure (PEEP): open-lung approach (OLA)-iCPAP (individualised intraoperative ventilation [individualised PEEP after a lung recruitment manoeuvre] plus individualised postoperative continuous positive airway pressure [CPAP]), OLA-CPAP (intraoperative individualised ventilation plus postoperative CPAP), STD-CPAP (standard intraoperative ventilation plus postoperative CPAP), or STD-O2 (standard intraoperative ventilation plus standard postoperative oxygen therapy). Patients were masked to treatment allocation. Investigators were not masked in the operating and postoperative rooms; after 24 h, data were given to a second investigator who was masked to allocations. The primary outcome was a composite of pulmonary and systemic complications during the first 7 postoperative days. We did the primary analysis using the modified intention-to-treat population. This trial is registered with ClinicalTrials.gov, number NCT02158923. FINDINGS: Between Jan 2, 2015, and May 18, 2016, we enrolled 1012 eligible patients. Data were available for 967 patients, whom we included in the final analysis. Risk of pulmonary and systemic complications did not differ for patients in OLA-iCPAP (110 [46%] of 241, relative risk 0·89 [95% CI 0·74-1·07; p=0·25]), OLA-CPAP (111 [47%] of 238, 0·91 [0·76-1·09; p=0·35]), or STD-CPAP groups (118 [48%] of 244, 0·95 [0·80-1·14; p=0·65]) when compared with patients in the STD-O2 group (125 [51%] of 244). Intraoperatively, PEEP was increased in 69 (14%) of patients in the standard perioperative ventilation groups because of hypoxaemia, and no patients from either of the OLA groups required rescue manoeuvres. INTERPRETATION: In patients who have major abdominal surgery, the different perioperative open lung approaches tested in this study did not reduce the risk of postoperative complications when compared with standard lung-protective mechanical ventilation. FUNDING: Instituto de Salud Carlos III of the Spanish Ministry of Economy and Competitiveness, and Grants Programme of the European Society of Anaesthesiology.

Glucose series complexity at the threshold of diabetes.

BACKGROUND: One of the earliest signs of dysfunction in a complex system is the simplification of its output. A well-accepted method to measure this phenomenon is detrended fluctuation analysis (DFA). Herein, we evaluated the usefulness of DFA at the threshold of type 2 diabetes mellitus (T2DM). METHODS: We report on the clinical and glucometric characteristics of a sample of 103 patients at increased risk of developing T2DM. All patients had HbA1c levels 5%-6.4% and met at least one of the following criteria: body mass index (BMI) > 30 kg/m2, essential hypertension or a first-degree relative with T2DM. For each patient, a 24-h glucose time series was obtained, and the clinical and glucometric variables were compared. RESULTS: There was a significant correlation between the number of National Cholesterol Education Program--Adult Treatment Panel (ATP III) metabolic syndrome (MS)-defining criteria and DFA (ρ = 0.231, P = 0.019), and DFA differed significantly between patients meeting or not the ATP III definition of MS (1.443 vs. 1.399, respectively; P = 0.018). The DFA was not correlated with HbA1c. Depending on how it was calculated, the area under the log(Fn)∼log(n) curve correlated with HbA1c levels or the number of MS criteria. Conventional variability metrics (mean amplitude of glycemic excursions) did not differ between patients complying or not with the definition of MS. CONCLUSIONS: Complexity analysis is capable of detecting differences in variables related to the risk of developing T2DM and could be a useful tool to study the initial phases of glucoregulatory dysfunction leading to T2DM.

Glucose series complexity in hypertensive patients.

Nonlinear methods have been applied to the analysis of biological signals. Complexity analysis of glucose time series may be a useful tool for the study of the initial phases of glucoregulatory dysfunction. This observational, cross-sectional study was performed in patients with essential hypertension. Glucose complexity was measured with detrended fluctuation analysis (DFA), and glucose variability was measured by the mean amplitudes of glycemic excursion (MAGE). We included 91 patients with a mean age of 59 ± 10 years. We found significant correlations for the number of metabolic syndrome (MS)-defining criteria with DFA (r = 0.233, P = .026) and MAGE (r = 0.396, P < .0001). DFA differed significantly between patients who complied with MS and those who did not (1.44 vs. 1.39, P = .018). The MAGE (f = 5.3, P = .006), diastolic blood pressures (f = 4.1, P = .018), and homeostasis model assessment indices (f = 4.2, P = .018) differed between the DFA tertiles. Multivariate analysis revealed that the only independent determinants of the DFA values were MAGE (ß coefficient = 0.002, 95% confidence interval: 0.001-0.004, P = .001) and abdominal circumference (ß coefficient = 0.002, 95% confidence interval: 0.000015-0.004, P = .048). In our population, DFA was associated with MS and a number of MS criteria. Complexity analysis seemed to be capable of detecting differences in variables that are arguably related to the risk of the development of type 2 diabetes.