Is EEG Entropy a Useful Measure for Alzheimer's Disease?

BACKGROUND: The number of individuals diagnosed with Alzheimer's disease (AD) has increased, and it is estimated to continue rising in the coming years. The diagnosis of this disease is challenging due to variations in onset and course, its diverse clinical manifestations, and the indications for measuring deposit biomarkers. Hence, there is a need to develop more precise and less invasive diagnostic tools. Multiple studies have considered using electroencephalography (EEG) entropy measures as an indicator of the onset and course of AD. Entropy is deemed suitable as a potential indicator based on the discovery that variations in its complexity can be associated with specific pathologies such as AD. METHODOLOGY: Following PRISMA guidelines, a literature search was conducted in 4 scientific databases, and 40 articles were analyzed after discarding and filtering. RESULTS: There is a diversity in entropy measures; however, Sample Entropy (SampEn) and Multiscale Entropy (MSE) are the most widely used (21/40). In general, it is found that when comparing patients with controls, patients exhibit lower entropy (20/40) in various areas. Findings of correlation with the level of cognitive decline are less consistent, and with neuropsychiatric symptoms (2/40) or treatment response less explored (2/40), although most studies show lower entropy with greater severity. Machine learning-based studies show good discrimination capacity. CONCLUSIONS: There is significant difficulty in comparing multiple studies due to their heterogeneity; however, changes in Multiscale Entropy (MSE) scales or a decrease in entropy levels are considered useful for determining the presence of AD and measuring its severity.

Development of a web application to evaluate spirometric curve and clinical variables to support COPD diagnosis in primary care. / Desarrollo de una aplicación web para evaluar los datos de la espirometría y las variables clínicas para apoyar el diagnóstico de EPOC en atención primaria

INTRODUCTION: Choric obstructive pulmonary disease (COPD) is the third mortality cause in the world, and the development of useful diagnostic tools is necessary to improve timely diagnostic rates in primary care settings. OBJECTIVE: To develop a web application displaying spirometric and clinical information - including respiratory symptoms and risk factors- to facilitate a COPD diagnosis. MATERIALS AND METHODS: In this cross-sectional study, an expert consensus was carried out with three specialists using the Delphi method to choose the relevant variables for COPD diagnosis. We developed a Python-based web application to diagnose COPD, displaying the clinical variables deemed relevant by the experts along the spirometric curve. RESULTS: Twenty-six clinical variables were included in the web application for the diagnosis of COPD. A fourth expert used the web application to classify a cohort of 695 patients who had undergone spirometry in a third-level centre and had answered at least one of five questionnaires for COPD screening. Out of the 695 subjects, 34% had COPD, according to the expert that diagnosed them using the web application. Only 42% of the patients in the COPD group had received a previous COPD diagnosis and 19% of the patients in the no COPD group had been misdiagnosed with the disease. CONCLUSION: We developed a web application that displays demographic and clinical information, as well as spirometric data, to facilitate the process of diagnosing COPD in primary care settings.

Introducción. La enfermedad pulmonar obstructiva crónica (EPOC) es la tercera causa de mortalidad en el mundo y es necesario el desarrollo de herramientas diagnósticas útiles para mejorar las tasas de diagnóstico oportuno en los entornos de atención primaria. Objetivo. Desarrollar una aplicación web que muestre la información clínica y de la espirometría ­incluyendo síntomas respiratorios y factores de riesgo­ para facilitar el diagnóstico de la EPOC. Materiales y métodos. En este estudio transversal se realizó un consenso de expertos con tres especialistas usando el método Delphi para elegir las variables relevantes para el diagnóstico de EPOC. Se desarrolló una aplicación web basada en Python que muestra la información clínica relevante según los expertos, junto con la curva y los datos de la espirometría para el diagnóstico de la EPOC. Resultados. Se incluyeron 26 variables clínicas para el diagnóstico de la EPOC. Un cuarto experto utilizó la aplicación web para clasificar una cohorte de 695 pacientes a los que se les había realizado una espirometría en un centro de tercer nivel y que habían contestado al menos uno de los cinco cuestionarios para la detección de la EPOC. De los 695 sujetos, el 34 % tenían EPOC según el experto que les diagnosticó usando la aplicación web. Sólo el 42 % de los pacientes del grupo con EPOC había recibido un diagnóstico previo de la enfermedad y el 19 % de los pacientes del grupo sin EPOC había sido diagnosticado erróneamente con la enfermedad. Conclusión. Se desarrolló una aplicación web que muestra información demográfica y clínica, así como datos espirométricos, para facilitar el proceso de diagnóstico de la EPOC en entornos de atención primaria.

Curve-Modelling and Machine Learning for a Better COPD Diagnosis.

Background: Development of new tools in artificial intelligence has an outstanding performance in the recognition of multidimensional patterns, which is why they have proven to be useful in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD). Methods: This was an observational analytical single-centre study in patients with spirometry performed in outpatient medical care. The segment that goes from the peak expiratory flow to the forced vital capacity was modelled with quadratic polynomials, the coefficients obtained were used to train and test neural networks in the task of classifying patients with COPD. Results: A total of 695 patient records were included in the analysis. The COPD group was significantly older than the No COPD group. The pre-bronchodilator (Pre BD) and post-bronchodilator (Post BD) spirometric curves were modelled with a quadratic polynomial, and the coefficients obtained were used to feed three neural networks (Pre BD, Post BD and all coefficients). The best neural network was the one that used the post-bronchodilator coefficients, which has an input layer of 3 neurons and three hidden layers with sigmoid activation function and two neurons in the output layer with softmax activation function. This system had an accuracy of 92.9% accuracy, a sensitivity of 88.2% and a specificity of 94.3% when assessed using expert judgment as the reference test. It also showed better performance than the current gold standard, especially in specificity and negative predictive value. Conclusion: Artificial Neural Networks fed with coefficients obtained from quadratic and cubic polynomials have interesting potential of emulating the clinical diagnostic process and can become an important aid in primary care to help diagnose COPD in an early stage.

Signal processing and machine learning algorithm to classify anaesthesia depth.

BACKGROUND: Poor assessment of anaesthetic depth (AD) has led to overdosing or underdosing of the anaesthetic agent, which requires continuous monitoring to avoid complications. The evaluation of the central nervous system activity and autonomic nervous system could provide additional information on the monitoring of AD during surgical procedures. METHODS: Observational analytical single-centre study, information on biological signals was collected during a surgical procedure under general anaesthesia for signal preprocessing, processing and postprocessing to feed a pattern classifier and determine AD status of patients. The development of the electroencephalography index was carried out through data processing and algorithm development using MATLAB V.8.1. RESULTS: A total of 25 men and 35 women were included, with a total time of procedure average of 109.62 min. The results show a high Pearson correlation between the Complexity Brainwave Index and the indices of the entropy module. A greater dispersion is observed in the state entropy and response entropy indices, a partial overlap can also be seen in the boxes associated with deep anaesthesia and general anaesthesia in these indices. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. A high Pearson correlation might be explained by the coinciding values corresponding to the awake and general anaesthesia states. CONCLUSION: Biological signal filtering and a machine learning algorithm may be used to classify AD during a surgical procedure. Further studies will be needed to confirm these results and improve the decision-making of anaesthesiologists in general anaesthesia.

The Challenges of Spirometric Diagnosis of COPD.

Chronic obstructive pulmonary disease (COPD) is one of the top causes of morbidity and mortality worldwide. Although for many years its accurate diagnosis has been a focus of intense research, it is still challenging. Due to its simplicity, portability, and low cost, spirometry has been established as the main tool to detect this condition, but its flawed performance makes it an imperfect COPD diagnosis gold standard. This review aims to provide an up-to-date literature overview of recent studies regarding COPD diagnosis; we seek to identify their limitations and establish perspectives for spirometric diagnosis of COPD in the XXI century by combining deep clinical knowledge of the disease with advanced computer analysis techniques.

Soluble interleukin-6 receptor in young adults and its relationship with body composition and autonomic nervous system.

BACKGROUND: The immune system generates inflammatory responses through cytokines like Interleukin 6 (IL-6) and the Tumor Necrosis Factor alpha (TNF α); these cytokines mediate cellular responses aided by the presence of soluble receptors such as: Soluble Interleukin 6 Receptor (sIL6R) and Soluble Tumor Necrosis Factor Receptors Type 1 and 2 (sTNFR1, sTNFR2); the literature is limited about the relationship between this cytokines and the role of its soluble receptors. OBJECTIVES: This study is to determine a possible relationship between specific inflammatory markers and their soluble receptors with the autonomic nervous system's activity and body composition. METHODS: 27 subjects (13 men of 19.3 ± 1.6 years old and 14 women of 19.1 ± 1.7 years old) were evaluated. Body composition, autonomic nervous system activity and plasma concentration of inflammatory markers IL-6, TNF α, sIL6R, sTNFR1 and sTNFR2 were measured using bio-impedance, heart rate variability and ELISA respectively. RESULTS: A positive association between body-fat percentage and the sIL6R (0.47, p = .013) as well as inverse relationship between muscular mass and the sIL6R (-0.45, p = .019) were found. The sIL6R was also positively correlated with sympathetic activity markers: Relation LF/HF (0.52, p = .006), cardiac sympathetic index (0.45, p = .008), and cardiac vagal index (-0.44, p = .022). CONCLUSION: This study suggested that the IL-6 trans-signaling involving both the soluble receptor, sIL6R, and gp130 membrane co-receptor could produce inflammatory responses that generate an impact on the autonomic nervous system, possibly due to its direct action on the hypothalamus, the solitary tract nucleus, or the heart.

Lactato como marcador temprano de hipoxia cerebral en pacientes neurológicos bajo anestesia general: una revisión sistemática / Lactate as an early marker of cerebral hypoxia in neurological patients under general anesthesia: a systematic review

Resumen: Las complicaciones neurológicas perioperatorias secundarias a hipoxia durante procedimientos de sedación y anestesia general son frecuentes en cirugía cardiovascular y en pacientes con comorbilidades. Sin embargo, hasta el momento no existe un consenso para el diagnóstico de estas posibles complicaciones. En pacientes con trauma encefálico severo y/o hemorragia subaracnoidea el lactato cerebral no fue útil para predicción de hipoxia cerebral; pese a ello, la relación de lactato/piruvato podría ser una herramienta para diagnóstico intraoperatorio de hipoxia cerebral aguda. Los estudios sugieren que éste debe asociarse a otros marcadores y/o a monitoreo multimodal. Es necesario realizar estudios que evalúen su valor predictivo para hipoxia cerebral.

Abstract: Perioperative neurological complications secondary to hypoxia during sedation and general anesthesia procedures are frequent in cardiovascular surgery, and in patients with comorbidities. However, so far there is no consensus for the diagnosis of these possible complications. In patients with head trauma severe and/or subarachnoid hemorrhage cerebral lactate was not useful for predicting cerebral hypoxia, however the lactate/pyruvate ratio could be a tool for intraoperative diagnosis of acute cerebral hypoxia. Studies suggest that it must be associated with other markers or multimodal monitoring. Further studies are needed to evaluate lactate predictive value for the diagnosis of cerebral hypoxia.

Variability of anesthetic depth in total intravenous anesthesia vs balanced anesthesia using entropy indices: a randomized, crossover, controlled clinical trial / Variabilidad de la profundidad anestésica en anestesia total intravenosa vs. anestesia general balanceada usando índices de entropía. Un ensayo clínico aleatorizado, cruzado y controlado

Abstract Introduction: Total intravenous anesthesia (TIVA) and balanced anesthesia (BA) are the most commonly used anesthetic techniques. The differences are the variability of the depth of anesthesia between these techniques that might predict which one is safer for patients and presents a lower risk of intraoperative awakening. Objective: To determine whether a difference exists in the variability of depth of anesthesia obtained by response entropy (RE). Methods: A crossover clinical trial was conducted on 20 healthy patients receiving upper or lower limb ambulatory orthopedic surgery. Patients were randomly assigned to (a) target-controlled infusion of propofol using the Schnider model at a target concentration of 2.5 µg/mL for 15 minutes and a 10-minute washout, followed by sevoflurane administration at 0.8 minimal alveolar concentration (MAC) for the reminder of the surgery, or (b) the reverse sequence. Differences in the variability of the depth of anesthesia using RE were evaluated using paired t-test. Results: The treatment effect showed no significant difference in the average values of RE, during TIVA = 97.23 vs BA 97.04 (P = 0.39). Carry Over (-4.98 vs 4.08) and Period (100.3 vs 94.68) effects were not significantly different. Conclusion: The present study suggests that both anesthetic techniques are equivalent in terms of the stability of the depth of anesthesia. It is important to keep testing the determinants of the efficacy of different populations because the individual behaviors of patients might ultimately tip the scale.

Resumen Introducción: La anestesia total intravenosa (TIVA, por sus siglas en inglés) y la anestesia balanceada (AB) son las técnicas anestésicas más comúnmente utilizadas. La diferencia está en la variabilidad de la profundidad de la anestesia entre estas dos técnicas, lo cual pudiera predecir cuál es más segura para los pacientes y representar un menor riesgo de despertar intraoperatorio. Objetivo: Determinar si existe alguna diferencia en la variabilidad de la profundidad de la anestesia obtenida según los índices de entropía de respuesta (ER). Métodos: Se llevó a cabo un estudio clínico cruzado en 20 pacientes sanos que se sometieron a cirugía ortopédica ambulatoria de miembros superiores o inferiores. Los pacientes se asignaron aleatoriamente así: a) infusión controlada por objetivo (TCI, por sus siglas en inglés) de propofol, utilizando el modelo Schnider a una concentración objetivo de 2,5 µg/mL durante 15 min y un período de lavado de 10 minutos, seguido de la administración de sevoflurano a 0,8 de concentración alveolar mínima (CAM) durante el tiempo restante de la cirugía; o b) la secuencia inversa. Las diferencias en la variabilidad de la profundidad de la anestesia utilizando entropía de respuesta se evaluaron utilizando la prueba t pareada. Resultados: El efecto del tratamiento no mostró ninguna diferencia significativa en los valores promedio de entropía de respuesta (ER) durante TIVA = 97,23 vs. AB 97,04 (P = 0,39). Los efectos de arrastre (-4,98 vs. 4,08) y período (100,3 vs. 94,68) no fueron significativamente diferentes. Conclusiones: El presente estudio sugiere que ambas técnicas anestésicas son equivalentes en términos de estabilidad de la profundidad de la anestesia. Es importante continuar probando los factores determinantes de eficacia en las distintas poblaciones, ya que el comportamiento individual de cada paciente pudiera finalmente inclinar la balanza.

[University students'’ cardiovascular risk factors and their relationship with body composition]. / Factores de riesgo cardiovascular y su relación con la composición corporal en estudiantes universitarios.

OBJECTIVE: Assessing the prevalence of major risk factors for cardiovascular disease in a sample population of university students and their relationship with body composition. METHODS: A prevalence study was carried out on a random sample of 193 16 to 26 year-old university students (94 females and 99 males). Total cholesterol, high density lipoproteins (HDL), low density lipoproteins (LDL), triglycerides (TG) and glucose, resting blood pressure, waist circumference, height, weight and body fat and muscle mass percentages were measured. The participants answered a survey to assess their nutritional habits, lifestyle and stress. RESULTS: The major cardiovascular risk factors for males were ≥100 mg/dl (60.6%) LDL-C, >20% (50.0%) body fat percentage (BF%) and <40 mg/dl (39.4%) HDL-C; female risk factors were >33% (63.0%) BF%, ≥100 mg/dl (39.4%) LDL-C and <50 mg/dl (91.5%) HDL-C. Both male (12.1%) and female students (21.3 %) had criteria for metabolic syndrome and males (19.2%) and females (27.7%) had a high atherogenic index of plasma (AIP). Significant correlations with body composition were found. CONCLUSION: Cardiovascular risk factors had high prevalence in this sample of undergraduate students due to alterations in their blood, lipid profile and anthropometric changes, suggesting that modifications must be made regarding their lifestyles and body composition.

Factores de riesgo cardiovascular y su relación con la composición corporal en estudiantes universitarios / University students’ cardiovascular risk factors and their relationship with body composition

Objetivo Evaluar la prevalencia de factores de riesgo cardiovascular en una población de jóvenes universitarios y su relación con la composición corporal. Materiales y Métodos Se realizó un estudio de prevalencia, con muestreo aleatorio en 193 jóvenes (94 mujeres y 99 hombres) entre 16 y 26 años. Se midió: colesterol total, lipoproteínas de baja y alta densidad (LDL-C y HDL-C), triglicéridos (TG) y glucosa, presión arterial en reposo, perímetro abdominal, talla, peso, porcentaje de grasa y masa muscular, se aplicó una encuesta para evaluar hábitos nutricionales, de vida y estrés. Resultados Los factores de riesgo cardiovascular más relevantes encontrados en hombres fueron: valores de LDL-C ≥100 mg/dl (60,6 %), porcentaje de grasa >20% (50,0 %) y valores de HDL-C <40 mg/dl (39,4 %); en mujeres: porcentaje de grasa >33% (63,0 %), LDL-C ≥100 mg/dl (39,4 %) y HDL-C <50 mg/dl (91,5 %). 12,1 % de los hombres y 21,3 % de las mujeres reunieron criterios de síndrome metabólico, 19,2 % de los hombres y 27,7 % de las mujeres tienen riesgo aterogénico alto. Se encontraron correlaciones significativas con la composición corporal. Conclusión Existe una alta prevalencia de factores de riesgo cardiovascular en jóvenes universitarios, dados por alteraciones del perfil lipídico y cambios antropométricos relacionados, esto sugiere la necesidad de modificaciones en sus hábitos de vida y la composición corporal.(AU)

Objective Assessing the prevalence of major risk factors for cardiovascular disease in a sample population of university students and their relationship with body composition. Methods A prevalence study was carried out on a random sample of 193 16 to 26 year-old university students (94 females and 99 males). Total cholesterol, high density lipoproteins (HDL), low density lipoproteins (LDL), triglycerides (TG) and glucose, resting blood pressure, waist circumference, height, weight and body fat and muscle mass percentages were measured. The participants answered a survey to assess their nutritional habits, lifestyle and stress. Results The major cardiovascular risk factors for males were ≥100 mg/dl (60.6%) LDL-C, >20% (50.0%) body fat percentage (BF%) and <40 mg/dl (39.4%) HDL-C; female risk factors were >33% (63.0%) BF%, ≥100 mg/dl (39.4%) LDL-C and <50 mg/dl (91.5%) HDL-C. Both male (12.1%) and female students (21.3 %) had criteria for metabolic syndrome and males (19.2%) and females (27.7%) had a high atherogenic index of plasma (AIP). Significant correlations with body composition were found. Conclusion Cardiovascular risk factors had high prevalence in this sample of undergraduate students due to alterations in their blood, lipid profile and anthropometric changes, suggesting that modifications must be made regarding their lifestyles and body composition.(AU)