778 resultados para predictive algorithm


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AIM: Hyperglycaemia is now a recognized predictive factor of morbidity and mortality after coronary artery bypass grafting (CABG). For this reason, we aimed to evaluate the postoperative management of glucose control in patients undergoing cardiovascular surgery, and to assess the impact of glucose levels on in-hospital mortality and morbidity. METHODS: This was a retrospective study investigating the association between postoperative blood glucose and outcomes, including death, post-surgical complications, and length of stay in the intensive care unit (ICU) and in hospital. RESULTS: A total of 642 consecutive patients were enrolled into the study after cardiovascular surgery (CABG, carotid endarterectomy and bypass in the lower limbs). Patients' mean age was 68+/-10 years, and 74% were male. In-hospital mortality was 5% in diabetic patients vs 2% in non-diabetic patients (OR: 1.66, P=0.076). Having blood glucose levels in the upper quartile range (> or =8.8 mmol/L) on postoperative day 1 was independently associated with death (OR: 10.16, P=0.0002), infectious complications (OR: 1.76, P=0.04) and prolonged ICU stay (OR: 3.10, P<0.0001). Patients presenting with three or more hypoglycaemic episodes (<4.1 mmol/L) had increased rates of mortality (OR: 9.08, P<0.0001) and complications (OR: 8.57, P<0.0001). CONCLUSION: Glucose levels greater than 8.8 mmol/L on postoperative day 1 and having three or more hypoglycaemic episodes in the postoperative period were predictive of mortality and morbidity among patients undergoing cardiovascular surgery. This suggests that a multidisciplinary approach may be able to achieve better postoperative blood glucose control.

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The impact of curative radiotherapy depends mainly on the total dose delivered homogenously in the targeted volume. Nevertheless, the dose delivered to the surrounding healthy tissues may reduce the therapeutic ratio of many radiation treatments. In a same population treated in one center with the same technique, it appears that individual radiosensitivity clearly exists, namely in terms of late side effects that are in principle non-reversible. This review details the different radiobiological approaches that have been developed to better understand the mechanisms of radiation-induced late effects. We also present the possibilities of clinical use of predictive assays in the close future.

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Literature from 1928 through 2004 was compiled from different document sources published in Mexico or elsewhere. From these 907 publications, we found 19 different topics of Chagas disease study in Mexico. The publications were arranged by decade and also by state. This information was used to construct maps describing the distribution of Chagas disease according to different criteria: the disease, vectors, reservoirs, and strains. One of the major problems confronting study of this zoonotic disease is the great biodiversity of the vector species; there are 30 different species, with at least 10 playing a major role in human infection. The high variability of climates and biogeographic regions further complicate study and understanding of the dynamics of this disease in each region of the country. We used a desktop Genetic Algorithm for Rule-Set Prediction procedure to provide ecological models of organism niches, offering improved flexibility for choosing predictive environmental and ecological data. This approach may help to identify regions at risk of disease, plan vector-control programs, and explore parasitic reservoir association. With this collected information, we have constructed a data base: CHAGMEX, available online in html format.

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To assess the effectiveness of a multidisciplinary evaluation and referral process in a prospective cohort of general hospital patients with alcohol dependence. Alcohol-dependent patients were identified in the wards of the general hospital and its primary care center. They were evaluated and then referred to treatment by a multidisciplinary team; those patients who accepted to participate in this cohort study were consecutively included and followed for 6 months. Not included patients were lost for follow-up, whereas all included patients were assessed at time of inclusion, 2 and 6 months later by a research psychologist in order to collect standardized baseline patients' characteristics, process salient features and patients outcomes (defined as treatment adherence and abstinence). Multidisciplinary evaluation and therapeutic referral was feasible and effective, with a success rate of 43%for treatment adherence and 28%for abstinence at 6 months. Among patients' characteristics, predictors of success were an age over 45, not living alone, being employed and being motivated to treatment (RAATE-A score &lt; 18), whereas successful process characteristics included detoxification of the patient at time of referral and a full multidisciplinary referral meeting. This multidisciplinary model of evaluation and referral of alcohol dependent patients of a general hospital had a satisfactory level of effectiveness. Predictors of success and failure allow to identify subsets of patients for whom new strategies of motivation and treatment referral should be designed.

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Our purpose in this article is to define a network structure which is based on two egos instead of the egocentered (one ego) or the complete network (n egos). We describe the characteristics and properties for this kind of network which we call “nosduocentered network”, comparing it with complete and egocentered networks. The key point for this kind of network is that relations exist between the two main egos and all alters, but relations among others are not observed. After that, we use new social network measures adapted to the nosduocentered network, some of which are based on measures for complete networks such as degree, betweenness, closeness centrality or density, while some others are tailormade for nosduocentered networks. We specify three regression models to predict research performance of PhD students based on these social network measures for different networks such as advice, collaboration, emotional support and trust. Data used are from Slovenian PhD students and their s

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The multiscale finite volume (MsFV) method has been developed to efficiently solve large heterogeneous problems (elliptic or parabolic); it is usually employed for pressure equations and delivers conservative flux fields to be used in transport problems. The method essentially relies on the hypothesis that the (fine-scale) problem can be reasonably described by a set of local solutions coupled by a conservative global (coarse-scale) problem. In most cases, the boundary conditions assigned for the local problems are satisfactory and the approximate conservative fluxes provided by the method are accurate. In numerically challenging cases, however, a more accurate localization is required to obtain a good approximation of the fine-scale solution. In this paper we develop a procedure to iteratively improve the boundary conditions of the local problems. The algorithm relies on the data structure of the MsFV method and employs a Krylov-subspace projection method to obtain an unconditionally stable scheme and accelerate convergence. Two variants are considered: in the first, only the MsFV operator is used; in the second, the MsFV operator is combined in a two-step method with an operator derived from the problem solved to construct the conservative flux field. The resulting iterative MsFV algorithms allow arbitrary reduction of the solution error without compromising the construction of a conservative flux field, which is guaranteed at any iteration. Since it converges to the exact solution, the method can be regarded as a linear solver. In this context, the schemes proposed here can be viewed as preconditioned versions of the Generalized Minimal Residual method (GMRES), with a very peculiar characteristic that the residual on the coarse grid is zero at any iteration (thus conservative fluxes can be obtained).

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This study analyzes the fluorimetric determination of alanyl- (Ala), glutamyl- (Glu), leucyl-cystinyl- (Cys) and aspartyl-aminopeptidase (AspAp) urinary enzymatic activities as early and predictive biomarkers of renal dysfunction in cisplatin-treated rats. Male Wistar rats (n = 8 each group) received a single subcutaneous injection of either saline or cisplatin 3.5 or 7 mg/kg, and urine samples were taken at 0, 1, 2, 3 and 14 days after treatment. In urine samples we determined Ala, Glu, Cys and AspAp activities, proteinuria, N-acetyl-β-D-glucosaminidase (NAG), albumin, and neutrophil gelatinase-associated lipocalin (NGAL). Plasma creatinine, creatinine clearance and renal morphological variables were measured at the end of the experiment. CysAp, NAG and albumin were increased 48 hours after treatment in the cisplatin 3.5 mg/kg treated group. At 24 hours, all urinary aminopeptidase activities and albuminuria were significantly increased in the cisplatin 7 mg/kg treated group. Aminopeptidase urinary activities correlated (p<0.011; r(2)>0.259) with plasma creatinine, creatinine clearance and/or kidney weight/body weight ratio at the end of the experiment and they could be considered as predictive biomarkers of renal injury severity. ROC-AUC analysis was made to study their sensitivity and specificity to distinguish between treated and untreated rats at day 1. All aminopeptidase activities showed an AUC>0.633. We conclude that Ala, Cys, Glu and AspAp enzymatic activities are early and predictive urinary biomarkers of the renal dysfunction induced by cisplatin. These determinations can be very useful in the prognostic and diagnostic of renal dysfunction in preclinical research and clinical practice.

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This paper proposes a parallel architecture for estimation of the motion of an underwater robot. It is well known that image processing requires a huge amount of computation, mainly at low-level processing where the algorithms are dealing with a great number of data. In a motion estimation algorithm, correspondences between two images have to be solved at the low level. In the underwater imaging, normalised correlation can be a solution in the presence of non-uniform illumination. Due to its regular processing scheme, parallel implementation of the correspondence problem can be an adequate approach to reduce the computation time. Taking into consideration the complexity of the normalised correlation criteria, a new approach using parallel organisation of every processor from the architecture is proposed

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In computer graphics, global illumination algorithms take into account not only the light that comes directly from the sources, but also the light interreflections. This kind of algorithms produce very realistic images, but at a high computational cost, especially when dealing with complex environments. Parallel computation has been successfully applied to such algorithms in order to make it possible to compute highly-realistic images in a reasonable time. We introduce here a speculation-based parallel solution for a global illumination algorithm in the context of radiosity, in which we have taken advantage of the hierarchical nature of such an algorithm

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Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

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The k-symplectic formulation of field theories is especially simple, since only tangent and cotangent bundles are needed in its description. Its defining elements show a close relationship with those in the symplectic formulation of mechanics. It will be shown that this relationship also stands in the presymplectic case. In a natural way,one can mimick the presymplectic constraint algorithm to obtain a constraint algorithmthat can be applied to k-presymplectic field theory, and more particularly to the Lagrangian and Hamiltonian formulations offield theories defined by a singular Lagrangian, as well as to the unified Lagrangian-Hamiltonian formalism (Skinner--Rusk formalism) for k-presymplectic field theory. Two examples of application of the algorithm are also analyzed.

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ABSTRACT: BACKGROUND: The intuitive early diagnostic guess could play an important role in reaching a final diagnosis. However, no study to date has attempted to quantify the importance of general practitioners' (GPs) ability to correctly appraise the origin of chest pain within the first minutes of an encounter. METHODS: The validation study was nested in a multicentre cohort study with a one year follow-up and included 626 successive patients who presented with chest pain and were attended by 58 GPs in Western Switzerland. The early diagnostic guess was assessed prior to a patient's history being taken by a GP and was then compared to a diagnosis of chest pain observed over the next year. RESULTS: Using summary measures clustered at the GP's level, the early diagnostic guess was confirmed by further investigation in 51.0% (CI 95%; 49.4% to 52.5%) of patients presenting with chest pain. The early diagnostic guess was more accurate in patients with a life threatening illness (65.4%; CI 95% 64.5% to 66.3%) and in patients who did not feel anxious (62.9%; CI 95% 62.5% to 63.3%). The predictive abilities of an early diagnostic guess were consistent among GPs. CONCLUSIONS: The GPs early diagnostic guess was correct in one out of two patients presenting with chest pain. The probability of a correct guess was higher in patients with a life-threatening illness and in patients not feeling anxious about their pain.

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Background and Objectives: Guidelines for bariatric surgery demand a psychological evaluation of applicants. The aim of this study was to evaluate if the presence of "psychological risk factors" predicts postoperative weight loss after gastric bypass. Methods: Medical records of obese women who underwent bariatric surgery between 2000 and 2004 were reviewed. Psychological assessment consisted of a one-hour semi-structured interview, summarized in a written report. Anthropometric assessment at baseline and 6,12,18 and 24 months after surgery included body weight, height and body mass index. Results: The mean BMI of included patients (N = 92) was 46.2 + 6,3 kg/m(2) (range 38.4-69.7). Based on the psychological assessment, 27% (N = 25) of the patients were classified as having "psychological risk factors" and 28% (N = 26) were diagnosed with a psychiatric diagnosis, most often major depression. Two years after gastric bypass, 16% of patients with "psychological risk factors" achieved an excellent result (%EWL > 75) versus 39% of those without (p < 0.05). About 1 out of 4 patients was in postoperative psychiatric treatment, but only half of them were identified as having "psychological risk factors" at baseline. Weight loss of patients initiating a psychiatric treatment only after surgery was less than of patients who continued psychiatric treatment already initiated before surgery (55.7 + 14.8 versus 66.5 + 14.2 %EWL). Conclusions: A single semi-structured psychological interview may identify patients who are at risk for diminished postoperative weight loss; however, psychological assessment did not identify those patients who were in need of a psychiatric postoperative treatment.