938 resultados para Predictive model


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Rationale: Aging adults represent the fastest growing population segment in many countries. Physiological and metabolic changes in the aging process may alter how aging adults respond to exposures compared to younger workers. Current preventive workplace exposure measures may therefore not be sufficiently protective for the aging workforce. In a controlled human toxicokinetic study (exposure chamber; 12m3), the volunteers (n=11) were men and women over the age of 58 years and exposed to a commonly used, low neurotoxic glycol ether; PGME (CAS no. 107-98- 2) (50 ppm, 6 hours). Oxidative metabolism (Michaelis-Menten) is the major pathway and conjugation the minor in humans. Metabolites, conjugated and free PGME are eliminated through the kidneys, and the elimination kinetics is dose-dependent (0 order). Scope: (1) compare the toxicokinetic profile of PGME obtained in the aging volunteers (58- 62 years) to young volunteers (20-25 years) from a previous study; (2) Test the predictive power of an existing PGME toxicokinetic compartment model for aging persons against urinary PGME concentrations found in volunteers from our experimental study. Experimental procedure: Urine samples were collected before, every 2-hour during exposures for six hours, and ad-lib for additional 20 hours. Urinary analysis of free and total PGME was performed using capillary GC/FID. The toxicokinetic model (Berkley Madonna software) was ageadjusted. Results. Urinary free and total PGME concentration rose rapidly, and did not reach an apparent plateau level during exposure. Less conjugation was observed in the older group. The predictive model developed for the young group predicted well total PGME in the aging group but not free PGME. The age adjusted toxicokinetic model's Vmax1 had to be changed for the aging group, implying slower enzymatic pathway. Conclusion: The toxicokinetic model did not predict well if only the physiological parameters were adjusted for aging adults (existing model); a substance specific metabolic rate parameter was also needed.

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Introduction.- Knowledge of predictors of an unfavourable outcome, e.g. non-return to work after an injury enables to identify patients at risk and to target interventions for modifiable predictors. It has been recently shown that INTERMED; a tool to measure biopsychosocial complexity in four domains (biologic, psychologic, social and care, with a score between 0-60 points) can be useful in this context. The aim of this study was to set up a predictive model for non-return to work using INTERMED in patients in vocational rehabilitation after orthopaedic injury.Patients and methods.- In this longitudinal prospective study, the cohort consisted of 2156 consecutively included inpatients with orthopaedic trauma attending a rehabilitation hospital after a work, traffic or sport related injury. Two years after discharge, a questionnaire regarding return to work was sent (1502 returned their questionnaires). In addition to INTERMED, 18 predictors known at baseline of the rehabilitation were selected based on previous research. A multivariable logistic regression was performed.Results.- In the multivariate model, not-returning to work at 2 years was significantly predicted by the INTERMED: odds-ratio (OR) 1.08 (95% confidence interval, CI [1.06; 1.11]) for a one point increase in scale; by qualified work-status before the injury OR = 0.74, CI (0.54; 0.99), by using French as preferred language OR = 0.60, CI (0.45; 0.80), by upper-extremity injury OR = 1.37, CI (1.03; 1.81), by higher education (> 9 years) OR = 0.74, CI (0.55; 1.00), and by a 10 year increase in age OR = 1.15, CI (1.02; 1.29). The area under the receiver-operator-characteristics curve (ROC)-curve was 0.733 for the full model (INTERMED plus 18 variables).Discussion.- These results confirm that the total score of the INTERMED is a significant predictor for return to work. The full model with 18 predictors combined with the total score of INTERMED has good predictive value. However, the number of variables (19) to measure is high for the use as screening tool in a clinic.

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There is growing interest in understanding the role of the non-injured contra-lateral hemisphere in stroke recovery. In the experimental field, histological evidence has been reported that structural changes occur in the contra-lateral connectivity and circuits during stroke recovery. In humans, some recent imaging studies indicated that contra-lateral sub-cortical pathways and functional and structural cortical networks are remodeling, after stroke. Structural changes in the contra-lateral networks, however, have never been correlated to clinical recovery in patients. To determine the importance of the contra-lateral structural changes in post-stroke recovery, we selected a population of patients with motor deficits after stroke affecting the motor cortex and/or sub-cortical motor white matter. We explored i) the presence of Generalized Fractional Anisotropy (GFA) changes indicating structural alterations in the motor network of patientsâeuro? contra-lateral hemisphere as well as their longitudinal evolution ii) the correlation of GFA changes with patientsâeuro? clinical scores, stroke size and demographics data iii) and a predictive model.

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Background: Modelling epidemiological knowledge in validated clinical scores is a practical mean of integrating EBM to usual care. Existing scores about cardiovascular disease have been largely developed in emergency settings, but few in primary care. Such a toll is needed for general practitioners (GP) to evaluate the probability of ischemic heart disease (IHD) in patients with non-traumatic chest pain. Objective: To develop a predictive model to use as a clinical score for detecting IHD in patients with non-traumatic chest-pain in primary care. Methods: A post-hoc secondary analysis on data from an observational study including 672 patients with chest pain of which 85 had IHD diagnosed by their GP during the year following their inclusion. Best subset method was used to select 8 predictive variables from univariate analysis and fitted in a multivariate logistic regression model to define the score. Reliability of the model was assessed using split-group method. Results: Significant predictors were: age (0-3 points), gender (1 point), having at least one cardiovascular risks factor (hypertension, dyslipidemia, diabetes, smoking, family history of CVD; 3 points), personal history of cardiovascular disease (1 point), duration of chest pain from 1 to 60 minutes (2 points), substernal chest pain (1 point), pain increasing with exertion (1 point) and absence of tenderness at palpation (1 point). Area under the ROC curve for the score was of 0.95 (IC95% 0.93; 0.97). Patients were categorised in three groups, low risk of IHD (score under 6; n = 360), moderate risk of IHD (score from 6 to 8; n = 187) and high risk of IHD (score from 9-13; n = 125). Prevalence of IHD in each group was respectively of 0%, 6.7%, 58.5%. Reliability of the model seems satisfactory as the model developed from the derivation set predicted perfectly (p = 0.948) the number of patients in each group in the validation set. Conclusion: This clinical score based only on history and physical exams can be an important tool in the practice of the general physician for the prediction of ischemic heart disease in patients complaining of chest pain. The score below 6 points (in more than half of our population) can avoid demanding complementary exams for selected patients (ECG, laboratory tests) because of the very low risk of IHD. Score above 6 points needs investigation to detect or rule out IHD. Further external validation is required in ambulatory settings.

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The efficacy and safety of anti-infective treatments are associated with the drug blood concentration profile, which is directly correlated with a dosing adjustment to the individual patient's condition. Dosing adjustments to the renal function recommended in reference books are often imprecise and infrequently applied in clinical practice. The recent generalisation of the KDOQI (Kidney Disease Outcome Quality Initiative) staging of chronically impaired renal function represents an opportunity to review and refine the dosing recommendations in patients with renal insufficiency. The literature has been reviewed and compared to a predictive model of the fraction of drug cleared by the kidney based on the Dettli's principle. Revised drug dosing recommendations integrating these predictive parameters are proposed.

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OBJECTIVES: In this study, we investigated the structural plasticity of the contralesional motor network in ischemic stroke patients using diffusion magnetic resonance imaging (MRI) and explored a model that combines a MRI-based metric of contralesional network integrity and clinical data to predict functional outcome at 6 months after stroke. METHODS: MRI and clinical examinations were performed in 12 patients in the acute phase, at 1 and 6 months after stroke. Twelve age- and gender-matched controls underwent 2 MRIs 1 month apart. Structural remodeling after stroke was assessed using diffusion MRI with an automated measurement of generalized fractional anisotropy (GFA), which was calculated along connections between contralesional cortical motor areas. The predictive model of poststroke functional outcome was computed using a linear regression of acute GFA measures and the clinical assessment. RESULTS: GFA changes in the contralesional motor tracts were found in all patients and differed significantly from controls (0.001 ≤ p < 0.05). GFA changes in intrahemispheric and interhemispheric motor tracts correlated with age (p ≤ 0.01); those in intrahemispheric motor tracts correlated strongly with clinical scores and stroke sizes (p ≤ 0.001). GFA measured in the acute phase together with a routine motor score and age were a strong predictor of motor outcome at 6 months (r(2) = 0.96, p = 0.0002). CONCLUSION: These findings represent a proof of principle that contralesional diffusion MRI measures may provide reliable information for personalized rehabilitation planning after ischemic motor stroke. Neurology® 2012;79:39-46.

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Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (<0.11Hz) of region-averaged time series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p<0.005) and specificity of 86% (p<0.01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (ρ=0.61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These findings suggest that predictive models of resting state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers.

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Mushroom picking has become a widespread autumn recreational activity in the Central Pyrenees and other regions of Spain. Predictive models that relate mushroom production or fungal species richness with forest stand and site characteristics are not available. This study used mushroom production data from 24 Scots pine plots over 3 years to develop a predictive model that could facilitate forest management decisions when comparing silvicultural options in terms of mushroom production. Mixed modelling was used to model the dependence of mushroom production on stand and site factors. The results showed that productions were greatest when stand basal area was approximately 20 m2 ha-1. Increasing elevation and northern aspect increased total mushroom production as well as the production of edible and marketed mushrooms. Increasing slope decreased productions. Marketed Lactarius spp., the most important group collected in the region, showed similar relationships. The annual variation in mushroom production correlated with autumn rainfall. Mushroom species richness was highest when the total production was highest.

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Intravenous thrombolysis (IVT) as treatment in acute ischaemic strokes may be insufficient to achieve recanalisation in certain patients. Predicting probability of non-recanalisation after IVT may have the potential to influence patient selection to more aggressive management strategies. We aimed at deriving and internally validating a predictive score for post-thrombolytic non-recanalisation, using clinical and radiological variables. In thrombolysis registries from four Swiss academic stroke centres (Lausanne, Bern, Basel and Geneva), patients were selected with large arterial occlusion on acute imaging and with repeated arterial assessment at 24 hours. Based on a logistic regression analysis, an integer-based score for each covariate of the fitted multivariate model was generated. Performance of integer-based predictive model was assessed by bootstrapping available data and cross validation (delete-d method). In 599 thrombolysed strokes, five variables were identified as independent predictors of absence of recanalisation: Acute glucose > 7 mmol/l (A), significant extracranial vessel STenosis (ST), decreased Range of visual fields (R), large Arterial occlusion (A) and decreased Level of consciousness (L). All variables were weighted 1, except for (L) which obtained 2 points based on β-coefficients on the logistic scale. ASTRAL-R scores 0, 3 and 6 corresponded to non-recanalisation probabilities of 18, 44 and 74 % respectively. Predictive ability showed AUC of 0.66 (95 %CI, 0.61-0.70) when using bootstrap and 0.66 (0.63-0.68) when using delete-d cross validation. In conclusion, the 5-item ASTRAL-R score moderately predicts non-recanalisation at 24 hours in thrombolysed ischaemic strokes. If its performance can be confirmed by external validation and its clinical usefulness can be proven, the score may influence patient selection for more aggressive revascularisation strategies in routine clinical practice.

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The enzymatic modification of proteins has been widely studied with the aim of add value to low commercial value fish. The objective of this work was to evaluate and optimize the parameters involved in the production process of an enzymatic protein hydrolyzed with high protein content. The results showed that for Alcalase the most significant parameters were temperature, pH and substrate concentration and for Flavourzyme were pH, substrate concentration and enzyme concentration. It was obtained for Alcalase a predictive model for the recovered nitrogen and for Flavourzyme a predictive model for the hydrolysis degree.

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The present study was carried out to establish the optimal conditions for performing ochratoxin A (OTA) and citrinin (CIT) extraction using the QuEChERS method in rice. Employing the factorial experimental design, variables that significantly influenced the extraction stages were determined. The following variables were analyzed: addition of water, acidification of acetonitrile with glacial acetic acid, as well as amounts of magnesium sulfate, sodium acetate, sodium citrate and diatomaceous earth. The best combining procedure resulted in a predictive model using more water and less diatomaceous earth. Recoveries of CIT and OTA were 78-105%.

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Objectif: Évaluer l'efficacité du dépistage de l’hypertension gestationnelle par les caractéristiques démographiques maternelles, les biomarqueurs sériques et le Doppler de l'artère utérine au premier et au deuxième trimestre de grossesse. Élaborer des modèles prédictifs de l’hypertension gestationnelle fondées sur ces paramètres. Methods: Il s'agit d'une étude prospective de cohorte incluant 598 femmes nullipares. Le Doppler utérin a été étudié par échographie transabdominale entre 11 +0 à 13 +6 semaines (1er trimestre) et entre 17 +0 à 21 +6 semaines (2e trimestre). Tous les échantillons de sérum pour la mesure de plusieurs biomarqueurs placentaires ont été recueillis au 1er trimestre. Les caractéristiques démographiques maternelles ont été enregistrées en même temps. Des courbes ROC et les valeurs prédictives ont été utilisés pour analyser la puissance prédictive des paramètres ci-dessus. Différentes combinaisons et leurs modèles de régression logistique ont été également analysés. Résultats: Parmi 598 femmes, on a observé 20 pré-éclampsies (3,3%), 7 pré-éclampsies précoces (1,2%), 52 cas d’hypertension gestationnelle (8,7%) , 10 cas d’hypertension gestationnelle avant 37 semaines (1,7%). L’index de pulsatilité des artères utérines au 2e trimestre est le meilleur prédicteur. En analyse de régression logistique multivariée, la meilleure valeur prédictive au 1er et au 2e trimestre a été obtenue pour la prévision de la pré-éclampsie précoce. Le dépistage combiné a montré des résultats nettement meilleurs comparés avec les paramètres maternels ou Doppler seuls. Conclusion: Comme seul marqueur, le Doppler utérin du deuxième trimestre a la meilleure prédictive pour l'hypertension, la naissance prématurée et la restriction de croissance. La combinaison des caractéristiques démographiques maternelles, des biomarqueurs sériques maternels et du Doppler utérin améliore l'efficacité du dépistage, en particulier pour la pré-éclampsie nécessitant un accouchement prématuré.

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Les études mettent en évidence des problèmes de santé psychologique chez les professionnels de la santé. Par contre, les facteurs permettant d’expliquer ces difficultés sont peu connus (p. ex., Cohen & Patten, 2005). Le but de cette thèse est d’étudier les déterminants de la santé psychologique des professionnels de la santé en se basant sur une théorie validée empiriquement. À cette fin, la théorie de l’autodétermination (Deci & Ryan, 1985, 2000) est utilisée comme cadre conceptuel et le soutien à l’autonomie (Black & Deci, 2000; Grolnick & Ryan, 1989) est proposé comme déterminant principal de la santé psychologique. Le premier article consiste en une recension des conséquences et corrélats associés au soutien à l’autonomie dans divers domaines de vie. Le deuxième article a pour objectif de tester un modèle prédictif de la santé psychologique auprès de médecins résidents. Le modèle propose que la concordance des tâches (Sheldon & Elliot, 1999) et la conscience de soi (Goldman & Kernis, 2002) sont deux sources distinctes d’autonomie qui prédisent de façon indépendante la santé psychologique. De plus, le soutien à l’autonomie de la part des superviseurs est suggéré comme étant un déterminant important de la concordance des tâches et de la conscience de soi. Au total, 333 médecins résidents de la province de Québec (Canada) ont rempli un questionnaire comportant différentes mesures. Des analyses par équations structurelles révèlent une excellente adéquation du modèle. Le troisième article examine l’influence du soutien à l’autonomie des collègues sur la satisfaction au travail ainsi que sur la santé psychologique des professionnels de la santé. Au total, 597 jeunes professionnels de la santé ont rempli un questionnaire incluant diverses mesures. Les résultats confirment que le soutien à l’autonomie perçu de la part des collègues prédit la santé psychologique et la satisfaction au travail. De plus, des régressions hiérarchiques démontrent que le soutien à l’autonomie des collègues contribue à la prédiction de la satisfaction au travail, du bien-être subjectif et des idéations suicidaires au-delà de ce qui est prédit par le soutien à l’autonomie des superviseurs. Les implications théoriques et pratiques de ces recherches sont discutées.

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Contexte: Bien que plusieurs algorithmes pharmacogénétiques de prédiction de doses de warfarine aient été publiés, peu d’études ont comparé la validité de ces algorithmes en pratique clinique réelle. Objectif: Évaluer trois algorithmes pharmacogénomiques dans une population de patients qui initient un traitement à la warfarine et qui souffrent de fibrillation auriculaire ou de problèmes de valves cardiaques. Analyser la performance des algorithmes de Gage et al., de Michaud et al. ainsi que de l’IWPC quant à la prédiction de la dose de warfarine permettant d’atteindre l’INR thérapeutique. Méthodes: Un devis de cohorte rétrospectif fut utilisé afin d’évaluer la validité des algorithmes chez 605 patients ayant débuté une thérapie de warfarine à l’Institut de Cardiologie de Montréal. Le coefficient de corrélation de Pearson ainsi que l’erreur absolue moyenne ont été utilisés pour évaluer la précision des algorithmes. L’exactitude clinique des prédictions de doses fut évaluée en calculant le nombre de patients pour qui la dose prédite était sous-estimée, idéalement estimée ou surestimée. Enfin, la régression linéaire multiple a été utilisée pour évaluer la validité d’un modèle de prédiction de doses de warfarine obtenu en ajoutant de nouvelles covariables. Résultats : L’algorithme de Gage a obtenu la proportion de variation expliquée la plus élevée (R2 ajusté = 44 %) ainsi que la plus faible erreur absolue moyenne (MAE = 1.41 ± 0.06). De plus, la comparaison des proportions de patients ayant une dose prédite à moins de 20 % de la dose observée a confirmé que l’algorithme de Gage était également le plus performant. Conclusion : Le modèle publié par Gage en 2008 est l’algorithme pharmacogénétique le plus exact dans notre population pour prédire des doses thérapeutiques de warfarine.

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Un nombre significatif d’enfants autistes ont une macrocéphalie. Malgré plusieurs études du périmètre crânien en autisme, peu d’études ont été faites sur des adultes. Aussi, les références actuelles en périmètre crânien (PC) adulte datent d’environ 20 ans. Les objectifs de cette étude étaient de construire une échelle de référence du PC adulte, et de comparer les taux de macrocéphalie entre un groupe d’adultes autistes et un groupe d’adultes neurotypiques. Dans cette étude, 221 sujets masculins adultes étaient recrutés de différents milieux afin de déterminer le meilleur modèle prédictif du PC et de construire l’échelle de référence. La hauteur et le poids étaient mesurés pour chaque participant afin de déterminer leur influence sur les dimensions crâniennes. Pour la partie comparative, 30 autistes et 36 sujets neurotypiques, tous adultes, étaient recrutés à partir de la base de données du laboratoire de recherche. Pour l’échelle de référence, les résultats démontraient des corrélations positives entre le PC avec la hauteur et le poids. Après analyse, la corrélation conjointe de la hauteur et du poids sur le PC a été déterminée comme étant le modèle qui offre les résultats les plus significatifs dans la prédiction du PC. Pour la partie comparative, les taux de macrocéphalie atteignaient 10,00% chez les autistes contre 2,56% chez les neurotypiques selon la formule de régression linéaire obtenue du modèle. Cependant le test d’exactitude de Fisher n’a révélé aucune différence significative entre les 2 groupes. Mes résultats suggèrent qu’il est nécessaire de considérer la hauteur et le poids en construisant une référence du PC et que, même en utilisant la nouvelle référence, les taux de macrocéphalie demeurent plus élevés chez les autistes adultes que chez les adultes neurotypiques en dépit de l’absence de différences significatives.