The Entropy Universe.

About 160 years ago, the concept of entropy was introduced in thermodynamics by Rudolf Clausius. Since then, it has been continually extended, interpreted, and applied by researchers in many scientific fields, such as general physics, information theory, chaos theory, data mining, and mathematical linguistics. This paper presents The Entropy Universe, which aims to review the many variants of entropies applied to time-series. The purpose is to answer research questions such as: How did each entropy emerge? What is the mathematical definition of each variant of entropy? How are entropies related to each other? What are the most applied scientific fields for each entropy? We describe in-depth the relationship between the most applied entropies in time-series for different scientific fields, establishing bases for researchers to properly choose the variant of entropy most suitable for their data. The number of citations over the past sixteen years of each paper proposing a new entropy was also accessed. The Shannon/differential, the Tsallis, the sample, the permutation, and the approximate entropies were the most cited ones. Based on the ten research areas with the most significant number of records obtained in the Web of Science and Scopus, the areas in which the entropies are more applied are computer science, physics, mathematics, and engineering. The universe of entropies is growing each day, either due to the introducing new variants either due to novel applications. Knowing each entropy's strengths and of limitations is essential to ensure the proper improvement of this research field.

Comparison of Invasive and Noninvasive Blood Pressure Measurements for Assessing Signal Complexity and Surgical Risk in Cardiac Surgical Patients.

BACKGROUND: Continuous arterial blood pressure (ABP) is typically recorded by placement of an intraarterial catheter. Recently, noninvasive ABP monitors have been shown to be comparable in accuracy to invasive measurements. In a previous study, we showed that the fluctuations in beat-to-beat ABP measurements were not random variations but had a complex dynamical structure, and that ABP dynamical complexity was inversely associated with surgical risk estimated using the Society of Thoracic Surgeons (STS) index. Dynamical complexity is a mathematical construct that reflects the capacity of a physiological system to adapt to stimuli. The objectives of present study were to: (1) determine whether noninvasive beat-to-beat ABP measurements also exhibit a complex temporal structure; (2) compare the complexity of noninvasive versus invasive ABP time series; and (3) quantify the relationship between the complexity of noninvasive ABP time series and the STS risk scores. METHODS: Fifteen adult patients undergoing coronary artery bypass graft, valve, or combined coronary artery bypass graft/valve surgery were enrolled in this observational study. Preoperative ABP waveforms were simultaneously recorded for ≥15 minutes using a radial artery catheter (invasive) and a continuous noninvasive arterial pressure monitor. Beat-to-beat systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and mean arterial pressure (MAP) time series were extracted from the continuous waveforms. Complexity was assessed using the multiscale entropy method. The Wilcoxon signed-rank test was used to compare the mean ranks of indices derived from invasive versus noninvasive ABP time series. Spearman correlation coefficients were used to quantify the relationship between invasive and noninvasive indices. Linear regression analysis was used to quantify the association between each of the complexity indices and the STS risk scores. RESULTS: Beat-to-beat fluctuations in noninvasive ABP measurements were not random but complex; however, their degree of complexity was lower than that of fluctuations in invasively obtained ABP signals (SBP: 7.05 vs 8.66, P < .001; DBP: 7.40 vs 8.41, P < .001; PP: 6.83 vs 8.82, P < .001; and MAP: 7.17 vs 8.68, P < .005). Invasive and noninvasive indices for MSEΣ·slope showed good correlation (rs) (0.53 for SBP, 0.79 for DBP, 0.42 for PP, 0.60 for MAP). The complexity of noninvasive ABP time series (-0.70 [-1.28 to -0.11]; P = .023 for DBP), like that of invasive time series (-0.94 [-1.52 to -0.35]; P = .004 for DBP), was inversely associated with estimated surgical risk in patients undergoing cardiovascular operations. CONCLUSIONS: Our results support the use of noninvasive ABP monitoring in computations of complexity-based indices that correlate with estimated surgical risk.

Preoperative Blood Pressure Complexity Indices as a Marker for Frailty in Patients Undergoing Cardiac Surgery.

OBJECTIVE: Frailty, a state of decreased physiological reserve, increases the risk of adverse outcomes. There is no standard tool for frailty during perioperative period. Autonomic dysfunction, an underlying process in frailty, could result in hemodynamic fluctuations. Complexity, the physiological adaptability of a system can quantify these fluctuations. The authors hypothesized that complexity could be a marker for frailty and explored their relationship in cardiac surgical patients. DESIGN: Prospective, observational study. SETTING: Single-center teaching hospital. PARTICIPANTS: Three hundred and sixty-four adult patients undergoing cardiac surgery. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: Preoperative beat-to-beat systolic arterial pressure (SAP) and mean arterial pressure (MAP) time series were obtained. Complexity indices were calculated using multiscale entropy (MSE) analysis. Frailty was assessed from: age >70 years, body mass index <18.5, hematocrit <35%, albumin <3.4 g/dL, and creatinine >2.0 mg/dL. The association between complexity indices and frailty was explored by logistic regression and predictive ability by C-statistics. In total, 190 (52%) patients had frailty. The complexity index (MSEΣ) median (quartile 1, quartile 3) of SAP and MAP time series decreased significantly in frail patients (SAP: 8.32 [7.27, 9.24] v 9.13 [8.00, 9.72], p < 0.001 and MAP: 8.56 [7.56; 9.27] v 9.18 [8.26; 9.83], p < 0.001). MSE Σ demonstrated a fair predictive ability of frailty (C-statistic: SAP 0.62 and MAP 0.64). CONCLUSION: Preoperative BP complexity indices correlate and predict frailty. Impaired autonomic control is the underlying mechanism to explain this finding. A simple automated measure of preoperative BP complexity in the surgeon's office has the potential to reliably assess frailty.

Changes in nonlinear dynamic complexity measures of blood pressure during anesthesia for cardiac surgeries using cardio pulmonary bypass.

Nonlinear complexity measures computed from beat-to-beat arterial BP dynamics have shown associations with standard cardiac surgical risk indices. They reflect the physiological adaptability of a system and has been proposed as dynamical biomarkers of overall health status. We sought to determine the impact of anesthetic induction and cardiopulmonary bypass (CPB) upon the complexity measures computed from perioperative BP time series. In this prospective, observational study, 300 adult patients undergoing cardiac surgery were included. Perioperative period was divided as: (1) Preoperative (PreOp); (2) ORIS-induction to sternotomy; (3) ORSB- sternotomy to CPB; (4) ORposB-post CPB and within 30 min before leaving OR and (5) postoperative phase (PostOp)-initial 30 min in the cardiac surgical intensive care unit. BP waveforms for systolic (SAP), diastolic (DAP), mean arterial pressure (MAP) and pulse pressure (PP) were recorded, and their corresponding complexity index (MSE∑) was calculated. Significant decrease in MSE∑ from Preop to PostOp phases was observed for all BP time series. Maximum fall was seen during post anesthetic induction (ORIS) phase. Mild recovery during the subsequent phases was observed but they never reached the baseline values. In an exploratory analysis, preoperative MSE∑ showed a significant correlation with postoperative length of ICU stay. Blood pressure complexity varies at different time points and is not fixed for a given individual. Preoperative BP Complexity decreased significantly following anesthetic induction and did not recover to baseline until 30 min after surgery. Prevention of this significant fall may offer restoration of MSE∑ throughout surgery. Furthermore, preoperative BP complexity needs to be explored as a predictor of major postoperative adverse events by itself or in addition with the current risk indices.

Complexity of Cardiotocographic Signals as A Predictor of Labor.

Prediction of labor is of extreme importance in obstetric care to allow for preventive measures, assuring that both baby and mother have the best possible care. In this work, the authors studied how important nonlinear parameters (entropy and compression) can be as labor predictors. Linear features retrieved from the SisPorto system for cardiotocogram analysis and nonlinear measures were used to predict labor in a dataset of 1072 antepartum tracings, at between 30 and 35 weeks of gestation. Two groups were defined: Group A-fetuses whose traces date was less than one or two weeks before labor, and Group B-fetuses whose traces date was at least one or two weeks before labor. Results suggest that, compared with linear features such as decelerations and variability indices, compression improves labor prediction both within one (C-Statistics of 0.728) and two weeks (C-Statistics of 0.704). Moreover, the correlation between compression and long-term variability was significantly different in groups A and B, denoting that compression and heart rate variability look at different information associated with whether the fetus is closer to or further from labor onset. Nonlinear measures, compression in particular, may be useful in improving labor prediction as a complement to other fetal heart rate features.

Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review.

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

Complexity of preoperative blood pressure dynamics: possible utility in cardiac surgical risk assessment.

Complexity measures are intended to assess the cardiovascular system's capacity to respond to stressors. We sought to determine if decreased BP complexity is associated with increased estimated risk as obtained from two standard instruments: the Society of Thoracic Surgeons' (STS) Risk of Mortality and Morbidity Index and the European System for Cardiac Operative Risk Evaluation Score (EuroSCORE II). In this observational cohort study, preoperative systolic, diastolic, mean (MAP) and pulse pressure (PP) time series were derived in 147 patients undergoing cardiac surgery. The complexity of the fluctuations of these four variables was quantified using multiscale entropy (MSE) analysis. In addition, the traditional time series measures, mean and standard deviation (SD) were also computed. The relationships between time series measures and the risk indices (after logarithmic transformation) were then assessed using nonparametric (Spearman correlation, rs) and linear regression methods. A one standard deviation change in the complexity of systolic, diastolic and MAP time series was negatively associated (p < 0.05) with the STS and EuroSCORE indices in both unadjusted (21-34%) and models adjusted for age, gender and SD of the BP time series (15-31%). The mean and SD of BP time series were not significantly associated with the risk index except for a positive association with the SD of the diastolic BP. Lower preoperative BP complexity was associated with a higher estimated risk of adverse cardiovascular outcomes and may provide a novel approach to assessing cardiovascular risk. Future studies are needed to determine whether dynamical risk indices can improve current risk prediction tools.

Multiscale Poincaré plots for visualizing the structure of heartbeat time series.

BACKGROUND: Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincaré (MSP) plots. METHODS: Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincaré plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. RESULTS: We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. CONCLUSIONS: This generalized multiscale approach to Poincaré plots may be useful in visualizing other types of time series.

Dynamical glucometry: use of multiscale entropy analysis in diabetes.

Diabetes mellitus (DM) is one of the world's most prevalent medical conditions. Contemporary management focuses on lowering mean blood glucose values toward a normal range, but largely ignores the dynamics of glucose fluctuations. We probed analyte time series obtained from continuous glucose monitor (CGM) sensors. We show that the fluctuations in CGM values sampled every 5 min are not uncorrelated noise. Next, using multiscale entropy analysis, we quantified the complexity of the temporal structure of the CGM time series from a group of elderly subjects with type 2 DM and age-matched controls. We further probed the structure of these CGM time series using detrended fluctuation analysis. Our findings indicate that the dynamics of glucose fluctuations from control subjects are more complex than those of subjects with type 2 DM over time scales ranging from about 5 min to 5 h. These findings support consideration of a new framework, dynamical glucometry, to guide mechanistic research and to help assess and compare therapeutic interventions, which should enhance complexity of glucose fluctuations and not just lower mean and variance of blood glucose levels.

AF-React study: Prevalence of thrombotic events in patients with atrial fibrillation receiving NOACs - real-world data analysis from northern Portugal primary healthcare.

Introduction: Anticoagulation is recommended for stroke prevention in patients with atrial fibrillation (AF). The guidelines suggest non-vitamin K antagonist anticoagulants (NOACs) as the primary therapy for anticoagulation in AF. Several patient-related factors increase the risk of thrombotic events: elderly individuals, a previous history of stroke, and chronic kidney disease. This study aims to determine the association between NOACs and other patient variables in AF and the occurrence of thrombotic events. Methods: The database included all adults with the code K78 (ICPC-2 code for AF) who received clinical care in Northern Portugal's Primary Health Care between January 2016 and December 2018 and were dispensed the same NOAC at the pharmacy. Results: The results indicate that 10.2% of AF patients on NOAC anticoagulation experienced a stroke. Furthermore, patients treated with apixaban and dabigatran had higher odds of experiencing a stroke compared to those treated with rivaroxaban. Among patients with the same age, gender, and CHA2DS2Vasc Score, apixaban was significantly associated with a higher likelihood of thrombotic events than rivaroxaban. Discussion: These results have not been previously reported in studies with real-world data; therefore, a more detailed analysis should be conducted to enhance the validity of these findings.

Appropriateness of prescribing profiles and intake adherence to non-vitamin K antagonist oral anticoagulants in patients with atrial fibrillation: analysis of a retrospective longitudinal study using real-world data from Northern Portugal (AF-React Study).

OBJECTIVES: This study aimed to assess the appropriateness of prescribing profiles and intake adherence to non-vitamin K antagonist oral anticoagulants (NOACs) in patients with atrial fibrillation (AF). DESIGN: Retrospective longitudinal study. SETTING: The study was conducted in the Regional Health Administration of Northern Portugal. PARTICIPANTS: The authors selected a database of 21 854 patients with prescriptions for NOACs between January 2016 and December 2018 and were classified with AF until December 2018. OUTCOME MEASURES: The appropriate dosage of NOAC for patients with AF divided into three categories: contraindicated, inconsistent and consistent, based on the 2020 European Society of Cardiology guidelines for AF. RESULTS: Dabigatran had a lower percentage of guideline-consistent doses (n=1657, 50.1%) than other drugs such as rivaroxaban (n=4737, 81.6%), apixaban (n=3830, 78.7%) and edoxaban (n=436, 82.1%). Most patients with an inconsistent dose were prescribed a lower dose than recommended based on their glomerular filtration rate (GFR). Among patients younger than 75 years with GFR >60 mL/min, 59.8% (n=10 028) had an adequate GFR range, while 27.8% (n=7166) of GFR measurements from patients older than 75 years old and 29.4% (n=913) of GFR measurements from patients younger than 75 years with GFR <60 mL/min were within an adequate time range. Adherence to NOACs varied across different drugs, with 59.1% (n=540) adhering to edoxaban, 56.3% (n=5443) to rivaroxaban, 55.3% (n=3143) to dabigatran and 53.3% (n=4211) to apixaban. CONCLUSIONS: Dabigatran had the lowest percentage of guideline-consistent doses. Patients younger than 75 years with GFR >60 mL/min had the highest percentage with an adequate GFR range, while other groups who require closer GFR monitoring had lower percentages within an adequate GFR range. Adherence to NOACs differed among different drugs, with greater adherence to treatment with edoxaban and less adherence to apixaban.

Diagnostic Test Accuracy of Urinary DNA Methylation-based Biomarkers for the Detection of Primary and Recurrent Bladder Cancer: A Systematic Review and Meta-analysis.

BACKGROUND AND OBJECTIVE: Diagnosis of primary and relapsed bladder carcinomas is accomplished by urethrocystoscopy, an invasive procedure, combined with urinary cytology, with limited sensitivity, resulting in a substantial burden. Thus, noninvasive biomarkers have been investigated, among which DNA methylation has shown promise. This systematic review and meta-analysis sought to assess the diagnostic accuracy of DNA methylation biomarkers reported in the literature for bladder cancer detection, pinpointing the most informative one. METHODS: The search for this systematic review and meta-analysis was conducted on PubMed, Scopus, and Cochrane Library for relevant studies published until December 31, 2022. A meta-analysis was performed using a random-effect model, to compute the pooled sensitivity and specificity of the markers. PROSPERO's registration ID for the study is CRD42023397703. KEY FINDINGS AND LIMITATIONS: Out of the 2297 studies retrieved, 68 were included in the final analysis, despite considerable heterogeneity. These involved 12 696 participants, of whom 5557 were diagnosed with bladder cancer. Using diagnostic odds ratio (DOR) as a comparative measure, the five most promising markers (pooled sensitivity, specificity, and DOR) were SALL3 (61%, 97%, and 55.67, respectively), PENK (77%, 93%, and 47.90, respectively), ZNF154 (87%, 90%, and 45.07, respectively), VIM (82%, 90%, and 44.81, respectively), and POU4F2 (81%, 89%, and 34.89, respectively). Urinary cytology identified bladder cancer with 55% sensitivity, 92% specificity, and 14.37 DOR. CONCLUSIONS AND CLINICAL IMPLICATIONS: DNA methylation biomarkers disclose high accuracy for bladder cancer detection in urine. Nonetheless, validation studies in different clinical settings are scarce, hampering clinical use. The identified biomarkers should be prioritized in future validation studies. PATIENT SUMMARY: In this meta-analysis, we include previously published studies that used urine samples of bladder cancer patients' from all around the globe. We were able to compare the diagnostic accuracy of noninvasive markers across different populations. We were able to conclude on the most promising DNA methylation markers to detect bladder cancer using urine.

Information-based measure of disagreement for more than two observers: a useful tool to compare the degree of observer disagreement.

BACKGROUND: Assessment of disagreement among multiple measurements for the same subject by different observers remains an important problem in medicine. Several measures have been applied to assess observer agreement. However, problems arise when comparing the degree of observer agreement among different methods, populations or circumstances. METHODS: The recently introduced information-based measure of disagreement (IBMD) is a useful tool for comparing the degree of observer disagreement. Since the proposed IBMD assesses disagreement between two observers only, we generalized this measure to include more than two observers. RESULTS: Two examples (one with real data and the other with hypothetical data) were employed to illustrate the utility of the proposed measure in comparing the degree of disagreement. CONCLUSION: The IBMD allows comparison of the disagreement in non-negative ratio scales across different populations and the generalization presents a solution to evaluate data with different number of observers for different cases, an important issue in real situations.A website for online calculation of IBMD and respective 95% confidence interval was additionally developed. The website is widely available to mathematicians, epidemiologists and physicians to facilitate easy application of this statistical strategy to their own data.

Equity matrix for kidney transplant allocation.

There is a general agreement that the distribution of kidneys for transplantation should balance utility criteria with justice. Moreover, a kidney allocation system must be based on transparent policies and seen as an ongoing process. This study aims to present an allocation system grounded on an equity matrix that balances the criteria of utility and justice. Synthetic data for a waiting list with 2000 transplant candidates and a pool of 280 donors were generated. A color priority system, the Eurotransplant (ET) kidney allocation system, and the proposed Equity Matrix (EQM) allocation system were compared after 1000 iterations of kidney allocations. Distributions of variables like the age difference, Human Leukocyte Antigens (HLA) mismatches (mmHLA), recipients' time on dialysis, cPRA, and a transplant score obtained by different allocation models were compared graphically and with Cohen's d effect size. For the analyzed variables, when we compare only the selected recipients from ET with the selected recipients from the EQM neutral model, we can conclude that the former model selects more hypersensitized recipients, a higher number of 65+ years' old recipients with 65+ years' old donors and higher number of recipients with 0 mmHLA. While recipients from EQM neutral are slightly older, have a lower age difference with their donors, have a lower number of mmHLA, are less likely to have 6 mmHLA with their donors, and have more time on dialysis. The proposed EQM model attempts to provide a simple, transparent, and equitable response to a complex question with results that outperform established practices.

Dysfunction of norepinephrine and its metabolites in Alzheimer's dementia - A review with meta-analysis.

Some studies point locus coeruleus cell loss, the central nervous system main source of norepinephrine, to be one of the earliest neuropathological events of Alzheimer's disease (AD). However, there are conflicting reports regarding the level of norepinephrine and its metabolites (3-Methoxy-4-hydroxyphenylglycol (MHPG), 3,5-dihydroxyphenylglycine (DHPG) and 3,4 -dihydroxyphenylglycolaldehyde (DOPEGAL)) in AD patients. Uncover these alterations may be a key factor for understanding cognitive deficits and AD pathology. We review the literature that compare norepinephrine and its metabolites between AD patients and non-demented controls. A meta-analysis did not reveal significant statistical differences, but there was a trend towards a lower level of norepinephrine of AD, with almost no difference in MHPG in the cerebrospinal fluid. Regarding MHPG in plasma, DHPG and DOPEGAL we only performed a qualitative analyse due to the small or absent number of studies. These findings point to a decrease in norepinephrine, what is in line with locus coeluleus cell loss in AD. The absence of statistical difference and an equal level of MHGP could indicate a compensatory mechanism.

Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses-Porto retrospective intrapartum study.

Introduction: Perinatal asphyxia is one of the most frequent causes of neonatal mortality, affecting approximately four million newborns worldwide each year and causing the death of one million individuals. One of the main reasons for these high incidences is the lack of consensual methods of early diagnosis for this pathology. Estimating risk-appropriate health care for mother and baby is essential for increasing the quality of the health care system. Thus, it is necessary to investigate models that improve the prediction of perinatal asphyxia. Access to the cardiotocographic signals (CTGs) in conjunction with various clinical parameters can be crucial for the development of a successful model. Objectives: This exploratory work aims to develop predictive models of perinatal asphyxia based on clinical parameters and fetal heart rate (fHR) indices. Methods: Single gestations data from a retrospective unicentric study from Centro Hospitalar e Universitário do Porto de São João (CHUSJ) between 2010 and 2018 was probed. The CTGs were acquired and analyzed by Omniview-SisPorto, estimating several fHR features. The clinical variables were obtained from the electronic clinical records stored by ObsCare. Entropy and compression characterized the complexity of the fHR time series. These variables' contribution to the prediction of asphyxia perinatal was probed by binary logistic regression (BLR) and Naive-Bayes (NB) models. Results: The data consisted of 517 cases, with 15 pathological cases. The asphyxia prediction models showed promising results, with an area under the receiver operator characteristic curve (AUC) >70%. In NB approaches, the best models combined clinical and SisPorto features. The best model was the univariate BLR with the variable compression ratio scale 2 (CR2) and an AUC of 94.93% [94.55; 95.31%]. Conclusion: Both BLR and Bayesian models have advantages and disadvantages. The model with the best performance predicting perinatal asphyxia was the univariate BLR with the CR2 variable, demonstrating the importance of non-linear indices in perinatal asphyxia detection. Future studies should explore decision support systems to detect sepsis, including clinical and CTGs features (linear and non-linear).

Characterization of an obese population: a retrospective longitudinal study from real-world data in northern Portugal.

BACKGROUND: Obesity is a serious and largely preventable global health problem. Obesity-related electronic health records can be a useful resource to identify and address obesity. The analysis of real-world data from T82-coded (International Classification of Primary Care coding, for obesity) primary care individuals can be an excellent national source of data on obesity's prevalence, characteristics, and impact on the National Health Service. METHODS: Retrospective longitudinal study, based on a database of electronic medical records, from the Regional Health Administration of northern Portugal. The study objectives were to determine the prevalence of obesity and to characterize an adult obese population in northern Portugal from a bio-demographic point of view along with profiles of comorbidities and the use of health resources. This study used a database of 266,872 patients in December 2019 and screened for diagnostic code T82 from the International Classification of Primary Care. RESULTS: The prevalence of obesity was 10.2% and the highest prevalence of obesity was in the 65-74 age group (16.1%). The most prevalent morbidities in patients with obesity as coded through ICPC-2 were K86 (uncomplicated hypertension), T90 (non-insulin-dependent diabetes), and K87 (complicated hypertension). Descriptive information showed that T82 subjects used more consultations, medications, and diagnostic tests than non-T82 subjects. CONCLUSIONS: Routine recording of weight and height deserves special attention to allow obesity recognition at an early stage and move on to the appropriate intervention. Future work is necessary to automate the codification of obesity for subjects under 18 years of age, to raise awareness and anticipate the prevention of problems associated with obesity. Practical strategies need to be implemented, such as the creation of a specific program consultation with truly targeted approaches to obesity.

Kidney allocation rules simulator.

The greatest challenge of any kidney transplant program lies in finding enough organ donors (in number and quality) for all waitlisted transplant candidates. Unfortunately, we must resign ourselves to a manifestly insufficient supply of organs for the current demand. Furthermore we must be able to predict kidney transplant success at organ allocation if we want to minimize the number of patients who return to an already overcrowded waiting list for transplantation. Therefore, the definition of deceased donors' kidney allocation rules on transplantation must be supported by simulations that allow foreseeing, as much as possible, the consequences of these rules. Here we present the Kidney Allocation Rules Simulator (KARS) application that enables testing different kidney transplant allocation' systems with different donors and transplant candidates' datasets. In this application, it is possible to simulate allocation rules implemented in Portugal, in the United Kingdom, in countries within Eurtotransplant, and a previously suggested color priority system. As inputs, this application needs three data files: a file with transplant candidates' information, a file with candidates' anti-HLA antibodies, and a file with donors' information. As output, we will have a file with donor-recipient pairs selected according to the kidney allocation system simulated. When seeking waste reduction while ensuring a fair distribution of organs from deceased donors, the definition of rules selecting donor-recipient pairs in renal transplantation must be based on evidence supported by data. On the continuously changing process for a better distribution of an increasingly scarce resource must, we must be able to predict transplant outcomes when defining the best allocation rules.

Evolution of Heart Rate Complexity Indices in the Early Detection of Neonatal Sepsis.

Despite advances in prenatal health care, neonatal sepsis remains a major cause of neonatal mortality. Early diagnosis and adequate treatment are essential to reduce morbidity and mortality related to this disease. In this paper, we propose a new method to detect neonatal sepsis based on heart rate (HR) complexity measures (entropy and compression indices) that takes into consideration neonatal gestational age. First, the percentile curves were computed for all the complexity indices using data from 118 control neonates. Eight indices were computed: the sample entropy (SampEn) and three indices to quantify the multiscale entropy (MSE) curve - the sum, the slope, and the product of the previous two - and the compression ratio (CR), using the bzip2 compressor, as well as the same three indices but related to the multiscale compression (MSC) curve. Then, the corresponding percentile was estimated for 23 sepsis neonates. Results show a significant decrease in the entropy indices SampEn and MSEsum and in the MSCslope a day before the detection of sepsis by the clinicians. The indices CR and MSCsum increased before the antibiotic take. These results imply that sepsis causes a random, uncorrelated pattern on the HR signal. Future studies should include a bigger data set to calculate a compound index comprising information of other physiological signals. Clinical Relevance - Prompt and accurate diagnosis of neona-tal sepsis is essential for the successful clinical management of neonates and significantly reduce morbidity and mortality. Complexity measures applied to the HR time series appear to detect sepsis in neonates starting one day before the clinical detection.

When Problems Only Get Bigger: The Impact of Adverse Childhood Experience on Adult Health.

Introduction: Previous studies have shown that adverse childhood experiences negatively impact child development, with consequences throughout the lifespan. Some of these consequences include the exacerbation or onset of several pathologies and risk behaviors. Materials and Methods: A convenience sample of 398 individuals aged 20 years or older from the Porto metropolitan area, with quotas, was collected. The evaluation was conducted using an anonymous questionnaire that included sociodemographic questions about exposure to adverse childhood experiences, a list of current health conditions, questions about risk behaviors, the AUDIT-C test, the Fagerström test and the Childhood Trauma Questionnaire-brief form. Variables were quantified to measure adverse childhood experiences, pathologies, and risk behaviors in adult individuals for comparison purposes. Results: Individuals with different forms of adverse childhood experiences present higher rates of smoking dependence, self-harm behaviors, victimization of/aggression toward intimate partners, early onset of sexual life, sexually transmitted infections, multiple sexual partners, abortions, anxiety, depression, diabetes, arthritis, high cholesterol, hypertension, and stroke. Different associations are analyzed and presented. Discussion and Conclusions: The results show that individuals with adverse childhood experiences have higher total scores for more risk behaviors and health conditions than individuals without traumatic backgrounds. These results are relevant for health purposes and indicate the need for further research to promote preventive and protective measures.