Efectividad de la administración de urea para el tratamiento de la hiponatremia en la insuficiencia cardiaca / Effectiveness of urea administration for the treatment of hyponatremia in heart failure

Objetivos Valorar la eficacia y la seguridad de la urea en pacientes con hiponatremia e insuficiencia cardiaca (IC). Métodos y resultados Se trata de un estudio observacional retrospectivo analítico de pacientes con IC e hiponatremia (Na+ <135mmol/l). Se incluyeron 49 pacientes tratados con urea y 47 pacientes que no recibieron urea, todos ellos bajo tratamiento estándar (según práctica clínica habitual) de la IC, con seguimiento en el hospital Álvaro Cunqueiro de Vigo entre enero de 2013 y mayo de 2022. En el estudio se evaluó la normalización de los niveles de sodio (Na >135mmol/l). La natremia al inicio del tratamiento con urea oral era de 127±5,22mmol/l, a las 24horas el sodio era de 128±2,47 (p<0,009) y la media el día de la normalización fue de 135,19±4,23mmol/l (p<0,005). Los días de media para conseguir la normalización del sodio fueron 5,03±2,37. La uremia al inicio del tratamiento con urea era de 73±46,93mg/dl y la media el día de la normalización del Na+ fue de 116,05±63,64mg/dl (p<0,002). La dosis media de urea oral fue 22,5g/día. No se observaron efectos adversos relevantes, ni cambios en cuanto a las cifras de creatinina. Conclusiones El tratamiento con urea oral añadido al tratamiento estándar, durante cortos periodos de tiempo, es seguro y eficaz para corregir la natremia en pacientes con IC hipervolémica con hiponatremia.

Objectives To assess the efficacy and safety of urea in patients with hyponatremia and heart failure (HF). Methods and results This is a retrospective observational analytical study of patients with HF and hyponatremia (Na+ <135mmol/L). Forty-nine patients treated with urea and 47 patients who did not receive urea, all under standard treatment (according to usual clinical practice) for HF, were included and followed up at Álvaro Cunqueiro Hospital in Vigo (Spain) between January 2013 and May 2022. The study evaluated the normalization of sodium levels (Na >135mmol/L). The initial natremia at the start of oral urea treatment was 127±5.22 mmol/L, at 24h the sodium level was 128±2.47 (P<.009), and the mean on the day of normalization was 135.19±4.23mmol/L (P<.005). The average number of days to achieve sodium normalization was 5.03±2.37 days. The initial uremia at the start of urea treatment was 73±46.93mg/dL, and the mean on the day of Na+ normalization was 116.05±63.64mg/dL (P<.002). The average oral urea dose was 22.5g/day. No relevant adverse effects were observed, nor were there significant changes in creatinine levels. Conclusions Oral urea treatment, when added to standard treatment for short periods of time, is safe and effective in correcting natremia in patients with hypervolemic HF with hyponatremia. (AU)

Efectividad de la administración de urea para el tratamiento de la hiponatremia en la insuficiencia cardiaca / Effectiveness of urea administration for the treatment of hyponatremia in heart failure

Objetivos Valorar la eficacia y la seguridad de la urea en pacientes con hiponatremia e insuficiencia cardiaca (IC). Métodos y resultados Se trata de un estudio observacional retrospectivo analítico de pacientes con IC e hiponatremia (Na+ <135mmol/l). Se incluyeron 49 pacientes tratados con urea y 47 pacientes que no recibieron urea, todos ellos bajo tratamiento estándar (según práctica clínica habitual) de la IC, con seguimiento en el hospital Álvaro Cunqueiro de Vigo entre enero de 2013 y mayo de 2022. En el estudio se evaluó la normalización de los niveles de sodio (Na >135mmol/l). La natremia al inicio del tratamiento con urea oral era de 127±5,22mmol/l, a las 24horas el sodio era de 128±2,47 (p<0,009) y la media el día de la normalización fue de 135,19±4,23mmol/l (p<0,005). Los días de media para conseguir la normalización del sodio fueron 5,03±2,37. La uremia al inicio del tratamiento con urea era de 73±46,93mg/dl y la media el día de la normalización del Na+ fue de 116,05±63,64mg/dl (p<0,002). La dosis media de urea oral fue 22,5g/día. No se observaron efectos adversos relevantes, ni cambios en cuanto a las cifras de creatinina. Conclusiones El tratamiento con urea oral añadido al tratamiento estándar, durante cortos periodos de tiempo, es seguro y eficaz para corregir la natremia en pacientes con IC hipervolémica con hiponatremia.

Objectives To assess the efficacy and safety of urea in patients with hyponatremia and heart failure (HF). Methods and results This is a retrospective observational analytical study of patients with HF and hyponatremia (Na+ <135mmol/L). Forty-nine patients treated with urea and 47 patients who did not receive urea, all under standard treatment (according to usual clinical practice) for HF, were included and followed up at Álvaro Cunqueiro Hospital in Vigo (Spain) between January 2013 and May 2022. The study evaluated the normalization of sodium levels (Na >135mmol/L). The initial natremia at the start of oral urea treatment was 127±5.22 mmol/L, at 24h the sodium level was 128±2.47 (P<.009), and the mean on the day of normalization was 135.19±4.23mmol/L (P<.005). The average number of days to achieve sodium normalization was 5.03±2.37 days. The initial uremia at the start of urea treatment was 73±46.93mg/dL, and the mean on the day of Na+ normalization was 116.05±63.64mg/dL (P<.002). The average oral urea dose was 22.5g/day. No relevant adverse effects were observed, nor were there significant changes in creatinine levels. Conclusions Oral urea treatment, when added to standard treatment for short periods of time, is safe and effective in correcting natremia in patients with hypervolemic HF with hyponatremia. (AU)

[Effectiveness of urea administration for the treatment of hyponatremia in heart failure]. / Efectividad de la administración de urea para el tratamiento de la hiponatremia en la insuficiencia cardiaca.

OBJECTIVES: To assess the efficacy and safety of urea in patients with hyponatremia and heart failure (HF). METHODS AND RESULTS: This is a retrospective observational analytical study of patients with HF and hyponatremia (Na+ <135mmol/L). Forty-nine patients treated with urea and 47 patients who did not receive urea, all under standard treatment (according to usual clinical practice) for HF, were included and followed up at Álvaro Cunqueiro Hospital in Vigo (Spain) between January 2013 and May 2022. The study evaluated the normalization of sodium levels (Na >135mmol/L). The initial natremia at the start of oral urea treatment was 127±5.22 mmol/L, at 24h the sodium level was 128±2.47 (P<.009), and the mean on the day of normalization was 135.19±4.23mmol/L (P<.005). The average number of days to achieve sodium normalization was 5.03±2.37 days. The initial uremia at the start of urea treatment was 73±46.93mg/dL, and the mean on the day of Na+ normalization was 116.05±63.64mg/dL (P<.002). The average oral urea dose was 22.5g/day. No relevant adverse effects were observed, nor were there significant changes in creatinine levels. CONCLUSIONS: Oral urea treatment, when added to standard treatment for short periods of time, is safe and effective in correcting natremia in patients with hypervolemic HF with hyponatremia.

Design of an experimental protocol to examine medication non-adherence among young drivers diagnosed with ADHD: A driving simulator study.

The diagnosis of ADHD among teens and young adults has been associated with a higher likelihood of motor vehicle crashes. Some studies suggest a beneficial effect of ADHD medication but the exact efficacy is still being debated. Further, medication adherence, which is low in this age group, can further reduce effectiveness. Our long-term objective is to reduce unsafe driving among drivers with ADHD by detecting medication non-adherence through driver behavior modeling and monitoring. As a first step, we developed the described lab study protocol to obtain reliable driver behavior data that will then be used to design and train behavior models built through machine learning. This experimental study protocol was developed to systematically compare driving behaviors under two medication conditions (before and after intake of medication) among young adults with ADHD and a control group of non-ADHD. A driving simulator was used to examine driving behaviors and interactions with traffic. The primary outcome was speed management for two comparisons (ADHD vs. non-ADHD and before vs. after medication), and secondary objectives involved understanding differences among the participants utilizing self-reported surveys about ADHD symptoms, drivers' knowledge, and perception about safety. The study protocol was designed to maximize participant safety and efficiency of data collection, as multiple measures were collected over two 2-h study visits. The sampled ADHD drivers were demographically and psychosocially similar but clinically different from the non-ADHD group. Overall, this protocol was effective in participant recruitment and retention, allowed staggered data collection, and can be incorporated in a subsequent clinical trial that examines the efficacy of a machine-learning based driver monitoring intervention.

Rationale, design and preliminary results of the GALIPEMIAS study (prevalence and lipid control of familial dyslipidemia in Galicia, northwest Spain).

AIMS: There is little information on the familial nature of dyslipidemias in the Spanish population. This knowledge could have potential diagnostic and treatment implications. The objective of the GALIPEMIAS study was to determine the prevalence of familial dyslipidemia in Galicia, as well as determine the degree of lipid control in the participants. Prevalence of atherosclerotic cardiovascular disease (ASCVD) was also estimated. This paper presents the design, methodology and selected preliminary results. METHODOLOGY: A cross-sectional study was performed in the population aged ≥18 years using cluster sampling and then random sampling. A sample of 1000 subjects was calculated and divided into three sequential phases with a specific methodology for each one. Phase I: selection of subjects from the general population and collection of informed consent documents; Phase II: collection of data from the digital clinical history to select subjects with dyslipidemia according to study criteria; Phase III: personal interview, blood analysis, family tree, and definitive diagnosis of dyslipidemia. Prevalence of different diseases and active medication was analysed. Corrected prevalence (to the reference population) of different risk factors and ASCVD was estimated. RESULTS: Phase I participation was 89.5%. We extracted complete information from 93% of the participants (Phase II). According to the study's own criteria, 56.5% (n = 527) of the participants had some form of dyslipidemia and almost 33.7% of them had familial dyslipidemia with autosomal dominant inherit pattern. The corrected prevalence of ASCVD was 5.1% (95% CI 3.1-7.2). CONCLUSIONS: Dyslipidemia was the most prevalent cardiovascular risk factor in our population with an autosomal dominant inheritance pattern in one out of every three dyslipidemia cases. Approximately, 5.1% of the sample population aged ≥18 has suffered an episode of ACVD.

Behavioral Modeling Based on Probabilistic Finite Automata: An Empirical Study.

Imagine an agent that performs tasks according to different strategies. The goal of Behavioral Recognition (BR) is to identify which of the available strategies is the one being used by the agent, by simply observing the agent's actions and the environmental conditions during a certain period of time. The goal of Behavioral Cloning (BC) is more ambitious. In this last case, the learner must be able to build a model of the behavior of the agent. In both settings, the only assumption is that the learner has access to a training set that contains instances of observed behavioral traces for each available strategy. This paper studies a machine learning approach based on Probabilistic Finite Automata (PFAs), capable of achieving both the recognition and cloning tasks. We evaluate the performance of PFAs in the context of a simulated learning environment (in this case, a virtual Roomba vacuum cleaner robot), and compare it with a collection of other machine learning approaches.

Building high-performing human-like tactical agents through observation and experience.

This paper describes a two-phase approach for automating the agent-building process when the agent is to perform tactical tasks. The research is inspired by how humans learn-first by observation of a teacher's performance and then by practicing the performance themselves. The objectives of this approach are to produce a high-performing agent that 1) approaches or exceeds the proficiency of a human and 2) does so in a human-like manner. We accomplish these objectives by combining observational learning with experiential learning. These processes are executed sequentially, with the former creating a competent but somewhat limited human-like model from scratch, and the latter improving its performance without significantly eroding its human-like qualities. The process is described in detail, and test results confirming our hypothesis are described.

Discovery of high-level behavior from observation of human performance in a strategic game.

This paper explores the issues faced in creating a system that can learn tactical human behavior merely by observing a human perform the behavior in a simulation. More specifically, this paper describes a technique based on fuzzy ARTMAP (FAM) neural networks to discover the criteria that cause a transition between contexts during a strategic game simulation. The approach depends on existing context templates that can identify the high-level action of the human, given a description of the situation along with his action. The learning task then becomes the identification and representation of the context sequence executed by the human. In this paper, we present the FAM/Template-based Interpretation Learning Engine (FAMTILE). This system seeks to achieve this learning task by constructing rules that govern the context transitions made by the human. To evaluate FAMTILE, six test scenarios were developed to achieve three distinct evaluation goals: 1) to assess the learning capabilities of FAM; 2) to evaluate the ability of FAMTILE to correctly predict human and context selections, given an observation; and 3) more fundamentally, to create a model of the human's behavior that can perform the high-level task at a comparable level of proficiency.

Pipelining of Fuzzy ARTMAP without matchtracking: correctness, performance bound, and Beowulf evaluation.

Fuzzy ARTMAP neural networks have been proven to be good classifiers on a variety of classification problems. However, the time that Fuzzy ARTMAP takes to converge to a solution increases rapidly as the number of patterns used for training is increased. In this paper we examine the time Fuzzy ARTMAP takes to converge to a solution and we propose a coarse grain parallelization technique, based on a pipeline approach, to speed-up the training process. In particular, we have parallelized Fuzzy ARTMAP without the match-tracking mechanism. We provide a series of theorems and associated proofs that show the characteristics of Fuzzy ARTMAP's, without matchtracking, parallel implementation. Results run on a BEOWULF cluster with three large databases show linear speedup as a function of the number of processors used in the pipeline. The databases used for our experiments are the Forrest CoverType database from the UCI Machine Learning repository and two artificial databases, where the data generated were 16-dimensional Gaussian distributed data belonging to two distinct classes, with different amounts of overlap (5% and 15%).

Learning tactical human behavior through observation of human performance.

It is widely accepted that the difficulty and expense involved in acquiring the knowledge behind tactical behaviors has been one limiting factor in the development of simulated agents representing adversaries and teammates in military and game simulations. Several researchers have addressed this problem with varying degrees of success. The problem mostly lies in the fact that tactical knowledge is difficult to elicit and represent through interactive sessions between the model developer and the subject matter expert. This paper describes a novel approach that employs genetic programming in conjunction with context-based reasoning to evolve tactical agents based upon automatic observation of a human performing a mission on a simulator. In this paper, we describe the process used to carry out the learning. A prototype was built to demonstrate feasibility and it is described herein. The prototype was rigorously and extensively tested. The evolved agents exhibited good fidelity to the observed human performance, as well as the capacity to generalize from it.

Data-partitioning using the Hilbert space filling curves: effect on the speed of convergence of Fuzzy ARTMAP for large database problems.

The Fuzzy ARTMAP algorithm has been proven to be one of the premier neural network architectures for classification problems. One of the properties of Fuzzy ARTMAP, which can be both an asset and a liability, is its capacity to produce new nodes (templates) on demand to represent classification categories. This property allows Fuzzy ARTMAP to automatically adapt to the database without having to a priori specify its network size. On the other hand, it has the undesirable side effect that large databases might produce a large network size (node proliferation) that can dramatically slow down the training speed of the algorithm. To address the slow convergence speed of Fuzzy ARTMAP for large database problems, we propose the use of space-filling curves, specifically the Hilbert space-filling curves (HSFC). Hilbert space-filling curves allow us to divide the problem into smaller sub-problems, each focusing on a smaller than the original dataset. For learning each partition of data, a different Fuzzy ARTMAP network is used. Through this divide-and-conquer approach we are avoiding the node proliferation problem, and consequently we speedup Fuzzy ARTMAP's training. Results have been produced for a two-class, 16-dimensional Gaussian data, and on the Forest database, available at the UCI repository. Our results indicate that the Hilbert space-filling curve approach reduces the time that it takes to train Fuzzy ARTMAP without affecting the generalization performance attained by Fuzzy ARTMAP trained on the original large dataset. Given that the resulting smaller datasets that the HSFC approach produces can independently be learned by different Fuzzy ARTMAP networks, we have also implemented and tested a parallel implementation of this approach on a Beowulf cluster of workstations that further speeds up Fuzzy ARTMAP's convergence to a solution for large database problems.