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At least 469 records · Page 26Linked to original sources

PubMed2001

Risk of falls for hospitalized patients: a predictive model based on routinely available data.

The incidence rate of falls is often used as an indicator of nursing care outcome. Comparing outcome between different settings should, however, make allowance for case mix. To measure the incidence of falls, describe their circumstances and develop a prediction model based on routinely collected data to allow comparison between hospital settings with different case mix. A dynamic population of patients hospitalized over a year in which a case was defined as any accidental fall systematically reported on an ad hoc form. A Swiss university hospital of 800 beds; 634 falls were reported for 26,643 hospitalizations over 236,307 hospitalization days. First fall rates were analyzed using a Poisson regression model with routinely computerized discharge data as independent variables. The incidence rate of first falls was 2.2 per 1000 patient-days. For subsequent falls the rates of incidence increased with the number of falls. Five independent variables played a significant role: age, gender, morbidity predisposition, surgical procedure and length of stay. Two of the interactions between these variables were significant and remained in the model (length of stay with age, morbidity with age). The model offers good medical plausibility and satisfactory predictive performance. The proposed model can be used by national health agencies to compute expected first fall rates accounting for case mix. Hospitals can use these rates for evaluation. Recommendations for measuring, monitoring and assessing fall rates are also given.

Accidental Falls↗

PubMed2005

Widespread access to predictive models in the motor system: a short review.

Recent behavioural and computational studies suggest that access to internal predictive models of arm and object dynamics is widespread in the sensorimotor system. Several systems, including those responsible for oculomotor and skeletomotor control, perceptual processing, postural control and mental imagery, are able to access predictions of the motion of the arm. A capacity to make and use predictions of object dynamics is similarly widespread. Here, we review recent studies looking at the predictive capacity of the central nervous system which reveal pervasive access to forward models of the environment.

Brain↗

PubMed1996

A compartmental model predicts that dietary potassium affects lithium dynamics in rats.

Lithium is the treatment of choice for manic depression, but therapy often results in nephrogenic diabetes insipidus and lithium intoxication. To investigate the effects of dietary potassium on potential side effects of lithium therapy, a mathematical model was built using the modeling program SAAM (Simulation, Analysis, And Modeling). Experimental data modeled were from adult male Sprague-Dawley rats fed diets with or without lithium and one of three levels of potassium for 17 d. A five-compartment model of lithium dynamics was built that was consistent with data from rats fed a lithium-containing diet adequate in potassium. This model was then compared with data from rats fed the other two lithium-containing diets. The model predicts that both the fractional transfer coefficient and rate of transport of lithium to the serum compartment from the kidney compartment are lower in rats fed the potassium-adequate diet than in those fed the potassium-deficient diet, and even lower in those fed the potassium-supplemented diet. In addition, fractional transfer coefficients into the serum compartment from the sampled and simulated tissue compartments changed differently with time depending on the amount of dietary potassium. The model also predicts that there would be less accumulation of lithium in the kidney, sampled tissue and simulated tissue compartments with supplemental dietary potassium. The model suggests that potassium supplementation, after a 7-d delay, protects against nephrogenic diabetes insipidus and the potentially toxic accumulation of lithium by decreasing the reabsorption of lithium from the kidneys and increasing lithium efflux from the tissues.

Absorption↗

PubMed1996

[Description and validation of a predictive model of the geographic distribution of chronic home-bound patients].

OBJECTIVE: To find a predictive model for the geographical distribution of the homebound chronically ill in function of variables corresponding to censal areas. DESIGN: A study of an ecological type. SETTING: Raval Nord Health District, Barcelona. PATIENTS AND OTHER PARTICIPANTS: The study units are the 30 censal areas of Raval Nord. In each area researchers counted the number of homebound chronically ill recorded during the first three months of a home care programme (May 1994). MEASUREMENTS AND MAIN RESULTS: A multiple regression analysis was performed, with selection of the variables by the stepwise method of the SPSS-PC+ statistical package, taking as dependent variable "y" the number of homebound chronically ill per 1,000 inhabitants. The prognostic variables were taken from the 1991 municipal roll, and evaluated along with the distance in metres from each censal area to the primary care centre (PCC). The distribution of the homebound chronically ill in the different censal areas of Raval Nord was connected to 3 variables which, in order of analysis, were: distance to the PCC, the relationship with senile dependency and the index of family socio-economic capacity. CONCLUSIONS: Distance, senile dependence and economic resources are factors which condition the number and distribution of the homebound chronically ill.

Adolescent↗

PubMed2005

An examination of four models predicting fatigue in multiple sclerosis.

Fatigue is a common symptom of multiple sclerosis (MS) that is purported to cause significant distress and have detrimental effects on daily functioning, social and occupational obligations, and overall well-being. The prevalence of fatigue in MS is high, with 53-87% of patients reporting significant problems with fatigue across different studies reported in the literature. The cause of fatigue in MS is still poorly understood. Some researchers have suggested that fatigue is a direct consequence of the MS disease process, but several studies have failed to find a relationship between disease severity and MS fatigue. A number of investigations have reported that depression and sleep are significantly related to fatigue in MS, as well as to one another. The purpose of the present investigation was to examine the relationships among disease severity, depression, and sleep disturbance in MS, and their possible role in predicting fatigue. Four models were proposed to explore these relationships. The best fitting model showed that all three were significant independent contributors to fatigue in MS, accounting for 43% of the variance, with sleep disturbance reigning as the largest contributor. Furthermore, although disease severity predicted fatigue in our sample, both depression and sleep disturbance emerged as stronger predictors. These findings suggest that, beyond core physical/neurological MS symptomatology, there are other factors that contribute to fatigue in MS, namely, depression and sleep disturbance.

Adult↗

PubMed2002

A predictive model for survival in metastatic cancer patients attending an outpatient palliative radiotherapy clinic.

PURPOSE: To develop a predictive model for survival from the time of presentation in an outpatient palliative radiotherapy clinic. METHODS AND MATERIALS: Sixteen factors were analyzed prospectively in 395 patients seen in a dedicated palliative radiotherapy clinic in a large tertiary cancer center using Cox's proportional hazards regression model. RESULTS: Six prognostic factors had a statistically significant impact on survival, as follows: primary cancer site, site of metastases, Karnofsky performance score (KPS), and fatigue, appetite, and shortness of breath scores from the modified Edmonton Symptom Assessment Scale. Risk group stratification was performed (1) by assigning weights to the prognostic factors based on their levels of significance, and (2) by the number of risk factors present. The weighting method provided a Survival Prediction Score (SPS), ranging from 0 to 32. The survival probability at 3, 6, and 12 months was 83%, 70%, and 51%, respectively, for patients with SPS or=20 (n = 133) (p < 0.0001). Corresponding survival probabilities based on number of risk factors were as follows: 85%, 72%, and 52% ( or=5 factors)(n = 180)(p < 0.0001). CONCLUSION: Clinical prognostic factors can be used to predict prognosis among patients attending a palliative radiotherapy clinic. If validated in an independent series of patients, the model can be used to guide clinical decisions, plan supportive services, and allocate resource use.

Adult↗

PubMed1995

The ion parametric resonance model predicts magnetic field parameters that affect nerve cells.

An ion parametric resonance (IPR) model recently developed by Blanchard and Blackman predicts distinct magnetic field interactions with biological systems based on a selective relation among four factors: the flux density of the static magnetic field, the frequency and flux density (Bac) of the parallel ac magnetic field, and the charge-to-mass ratio of ions of biological relevance. To test this model, PC-12 cells stimulated by nerve growth factor to produce neurites were exposed for 23 h in a 5% CO2 incubator using a multiple-coil exposure system to produce 45 Hz ac and dc (366 mG parallel to ac; less than 2 mG perpendicular to ac) magnetic fields. Our earlier work showed a cycle of inhibition/no inhibition of neurite outgrowth consistent with the IPR model predictions for Bac exposures between 0 and 468 mG rms. The work described here tests neurite outgrowth over a broader range of Bac (233-1416 mG rms). The experimental results remain consistent with earlier results, and with IPR model predictions of a second cycle of inhibition, return to control values, followed by a third cycle of inhibition of neurite outgrowth. These responses support the fundamental relationships predicted by the IPR model. The results have broad significance for biology.

Animals↗

PubMed2005

Use of diffusive gradients in thin films to measure cadmium speciation in solutions with synthetic and natural ligands: comparison with model predictions.

The performance of the technique of diffusive gradients in thin films (DGT) was characterized in well-defined systems containing cadmium with chloride and nitrate ions, simple organic ligands (nitrilotriacetic acid and diglycolic acid), and Suwannee river fulvic acid for the pH range 5-8. Cd was fully labile in all Cd, Cl-, and NO3- solutions tested (I= 0.1 and 0.01 M), even atvery low Cd concentrations (10 nM), consistent with there being no binding of Cd to the diffusive gel. Diffusion coefficients of Cd-nitritotriacetic acid (NTA) and Cd-diglycolic acid (DGA) species were measured and found to be ca. 25-30% lower than the equivalent coefficient for free metal ions. These values were used to calculate concentrations of labile Cd from DGT measurements in solutions of Cd with NTA or DGA. Cd-NTA and Cd-DGA species were found to be fully DGT-labile. DGT devices that used a diffusive gel with a reduced pore size, which retarded the passage of fulvic acid species through the gel, were used to estimate the proportion of Cd complexed by fulvic acid. These results were compared with predictions of the solution speciation from models with default parameter values. ECOSAT, incorporating the NICA-Donnan model, correctly predicted the magnitude of the binding and its pH dependence, while predictions from WHAM V (with humic ion binding model V) and WHAM 6 (with humic ion binding model VI) were less satisfactory at predicting the pH dependence. Reasonable fits to the data could be obtained from WHAM 6 when the effective binding constant log K(MA) was changed from 1.6 to 1.5, the value of deltaLK1 from 2.8 to 1.0 to minimize the dependence on pH, and the value of deltaLK2 from 1.48 to 1.0 to decrease the strength of the strong bidentate and tridentate binding sites.

Benzopyrans↗

PubMed2006

Rediscovering the species in community-wide predictive modeling.

Broadening the scope of conservation efforts to protect entire communities provides several advantages over the current species-specific focus, yet ecologists have been hampered by the fact that predictive modeling of multiple species is not directly amenable to traditional statistical approaches. Perhaps the greatest hurdle in community-wide modeling is that communities are composed of both co-occurring groups of species and species arranged independently along environmental gradients. Therefore, commonly used "short-cut" methods such as the modeling of so-called "assemblage types" are problematic. Our study demonstrates the utility of a multiresponse artificial neural network (MANN) to model entire community membership in an integrative yet species-specific manner. We compare MANN to two traditional approaches used to predict community composition: (1) a species-by-species approach using logistic regression analysis (LOG) and (2) a "classification-then-modeling" approach in which sites are classified into assemblage "types" (here we used two-way indicator species analysis and multiple discriminant analysis [MDA]). For freshwater fish assemblages of the North Island, New Zealand, we found that the MANN outperformed all other methods for predicting community composition based on multiscaled descriptors of the environment. The simple-matching coefficient comparing predicted and actual species composition was, on average, greatest for the MANN (91%), followed by MDA (85%), and LOG (83%). Mean Jaccard's similarity (emphasizing model performance for predicting species' presence) for the MANN (66%) exceeded both LOG (47%) and MDA (46%). The MANN also correctly predicted community composition (i.e., a significant proportion of the species membership based on a randomization procedure) for 82% of the study sites compared to 54% (MDA) and 49% (LOG), resulting in the MANN correctly predicting community composition in a total of 311 sites and an additional 117 sites (n = 379), on average, compared to LOG and MDA. The MANN also provided valuable explanatory power by simultaneously quantifying the nature of the relationships between the environment and both individual species and the entire community (composition and richness), which is not readily available from traditional approaches. We discuss how the MANN approach provides a powerful quantitative tool for conservation planning and highlight its potential for biomonitoring programs that currently depend on modeling discrete assemblage types to assess aquatic ecosystem health.

Animals↗

PubMed1995

Use of predictive modeling to evaluate the manipulation of milking frequency, temperature, and oxygen tension on growth of Escherichia coli in an artificial intramammary environment.

A method was developed to evaluate frequent milking as a means of controlling intramammary infection. An artificial intramammary environment was used to determine growth responses of Escherichia coli (P4) to natural changes in the mammary gland resulting from bacterial invasion. Physical conditions manipulated in this model were growth medium, temperature, and oxygen tension. Mathematical modeling was then incorporated to generate predictions concerning growth dynamics of the organism when milking frequency was changed. To test accuracy of the model, initial predictions were derived from bacterial growth data in which E coli was incubated in tryptose soy broth for 12 hours at 37 C and PO2 equal to 23.3 mm of Hg. These predictions matched closely with experimental data in which 12-, 4-, and 2-hour milking intervals were simulated in the artificial intramammary environment. The mathematical model was then used to characterize growth rate data from in vitro experiments in ultra-high temperature-treated milk and in vivo experimental infection data generated with E coli (P4). Predictions generated from this model suggested that increasing milking frequency to 4 or 6 times daily controls growth of E coli for a prolonged period and that 12 times daily milking may lead to elimination of the bacterium.

Animals↗

PubMed2004

PREDICT modeling and in-silico screening for G-protein coupled receptors.

G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in-silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9-fold to 44-fold better than random screening. Namely, the PREDICT models can be used to identify active small-molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non-GPCR targets.

Algorithms↗

PubMed1992

Structure and parameterization of pharmacokinetic models: their impact on model predictions.

There has been an increasing interest in physiologically based pharmacokinetic (PBPK) models in the area of risk assessment. The use of these models raises two important issues: (1) How good are PBPK models for predicting experimental kinetic data? (2) How is the variability in the model output affected by the number of parameters and the structure of the model? To examine these issues, we compared a five-compartment PBPK model, a three-compartment PBPK model, and nonphysiological compartmental models of benzene pharmacokinetics. Monte Carlo simulations were used to take into account the variability of the parameters. The models were fitted to three sets of experimental data and a hypothetical experiment was simulated with each model to provide a uniform basis for comparison. Two main results are presented: (1) the difference is larger between the predictions of the same model fitted to different data sets than between the predictions of different models fitted to the dame data; and (2) the type of data used to fit the model has a larger effect on the variability of the predictions than the type of model and the number of parameters.

Animals↗

PubMed1998

Octanol-water partition: searching for predictive models.

The log n-octanol/water partition coefficient (log Po/w) still represents one of the most informative physicochemical parameters available to medicinal chemists. In the present work, principles, methodologies, and parameters are briefly reviewed for a variety of models developed to predict this parameter based on molecular structure. To include the developments of recent years, a total of more than 40 different approaches are mentioned with relevant bibliography within four major categories: group contribution methods, atomic contribution methods, molecular methods, and other physicochemical methods. To underscore once more the utility of this partition coefficient, a comprehensive and reevaluated correlation between log Po/w and in vivo permeability data of rat brain capillaries is included. Most deviants that fell below the trendline are those that have been recently found to be substrates for P-glycoprotein, a multidrug transporter that actively removes them from the brain. Accurate predictions of log Po/w may necessitate many parameters, but there is mounting evidence that molecular size and hydrogen bonding ability can account for a major part of the variance. Our recently developed, molecular size-based approach is reviewed, and it is argued that introduction of three-dimensionality allows the elimination of many empirically derived fragment constants without a significant deterioration of the predictive accuracy. A comparison of predictive power for six different methods on 145 molecules of interest for medicinal chemists is also included.

Animals↗

PubMed2006

Metal accumulation in the earthworm Lumbricus rubellus. Model predictions compared to field data.

The mechanistic bioaccumulation model OMEGA (Optimal Modeling for Ecotoxicological Applications) is used to estimate accumulation of zinc (Zn), copper (Cu), cadmium (Cd) and lead (Pb) in the earthworm Lumbricus rubellus. Our validation to field accumulation data shows that the model accurately predicts internal cadmium concentrations. In addition, our results show that internal metal concentrations in the earthworm are less than linearly (slope<1) related to the total concentration in soil, while risk assessment procedures often assume the biota-soil accumulation factor (BSAF) to be constant. Although predicted internal concentrations of all metals are generally within a factor 5 compared to field data, incorporation of regulation in the model is necessary to improve predictability of the essential metals such as zinc and copper.

Animals↗

PubMed2005

Joint action of polycyclic aromatic hydrocarbons: predictive modeling of sublethal toxicity.

Polycyclic aromatic hydrocarbons (PAHs) typically contaminate the environment as complex assemblages of different chemical compounds. Modeling approaches provide a means of estimating the toxicity of these PAH mixtures. In the present study, we tested the hypothesis that the joint effects of four PAHs: pyrene, phenanthrene, fluoranthene and naphthalene, on the growth rate of the crustacean Daphnia magna during sub-chronic exposure could be accurately predicted using a mathematical algorithm for concentration addition based upon the assumption that these PAHs impact growth by a common mode of action. Assessment of the individual toxicity of the four PAHs confirmed that these compounds elicited the common effect of retarding growth of daphnids at concentrations below those that were lethal to the organisms. Using the experimentally derived toxicity parameters for the individual chemicals, the toxicity of multiple mixtures of these four PAHs was modeled. These mixtures were based on concentrations reported in the environment and on equi-toxic concentrations. The effects of over 140 combinations of four mixture formulations on the growth rate of daphnids were experimentally determined and compared to model predictions. The concentration addition models tended to over predict the joint toxicity of these PAH mixtures and experimental data was better represented by an alternative model based upon the concept of independent joint action. Mixtures at environmentally relevant concentrations were predicted and experimentally demonstrated to have no effect on daphnid growth rates. Results indicate that PAHs elicit toxicity to daphnids by multiple mechanisms and demonstrate an appropriate modeling approach to assess the toxicity of these mixtures.

Algorithms↗

PubMed1994

A predictive model for fetal lung maturity employing gestational age and test results.

Most laboratory tests for fetal lung maturity (FLM) are optimized to exclude false-negative predictions of absence of respiratory distress syndrome (RDS), with a reciprocal low predictive value for maturity. The authors employed FLM Surfactant/Albumin Ratio (FLM S/A) test results to construct a predictive model for FLM that included the obstetric estimates of gestational age. The charts of 388 newborns were abstracted and reviewed. The clinical outcome was the gold standard of the multivariate logistic analysis. Both the obstetric estimates of gestational age and the test result were significant predictors of the clinical outcome (P values of < .0002 and .001, respectively). The prediction rule for RDS as a function of both of these variables allows for adjustment of the test cutoffs, so that there is a consistent probability of RDS at the cutoff FLM S/A result for different gestational ages. Fetal lung maturity probability reporting may facilitate clinical decision-making.

Albumins↗

PubMed1991

A predictive model for DNA recognition by the herpes simplex virus protein ICP4.

The herpes simplex virus (HSV) type 1 immediate early protein ICP4 is an essential regulatory enzyme that binds DNA directly in order to stimulate or repress gene expression. The degree of transaction is related to the locations and affinities of the ICP4 binding sites. A number of binding sites have been identified; some sites showed obvious homology to one another, and these were called consensus ICP4 binding sites. Other binding sites did not appear to be related, and these were termed non-consensus sites. We hypothesized, however, that a single model could describe all ICP4 binding sites, given the appropriate characterizations of sites. We performed statistical analyses on a set of ICP4 binding sites and found that the bases important for defining binding were located within a 13 base region. Missing contact analyses on several high-affinity binding sites revealed the same 13 base region as important for critical protein-DNA contacts. From these data we derived the consensus sequence RTCGTCNNYNYSG, where R is purine, Y is pyrimidine, S is C or G, and N is any base. In addition, we found that a better profile for ICP4 binding sites involves use of a matrix of base proportions from the binding site data; sites are analyzed by calculating the Matrix Mean score. We show that this Matrix Mean model could accurately predict the locations of novel ICP4 binding sites. Finally, we analyzed the entire HSV-1 genome for potential ICP4 binding sites and speculate about what these results suggest for the role of ICP4 in viral gene regulation.

Amino Acid Sequence↗

PubMed1993

Water properties of hydrogel contact lens materials: a possible predictive model for corneal desiccation staining.

A set of properties of the water contained within hydrogel contact lens materials was determined with the aim of developing a model which would predict the propensity of a hydrogel contact lens material to induce corneal desiccation staining. We postulated that materials containing a larger proportion of water with the properties of bulk water would tend to induce corneal desiccation more readily than materials with the same overall water content but containing a larger proportion of water that interacts strongly with the polymer. The water structure [as measured by differential scanning calorimetry (DSC)] and the permeabilities of water and glucose were determined for a series of commercial hydrogel lenses. Both glucose permeability and DSC measurements are sensitive indicators of water structure and able to distinguish between various materials. To illustrate the potential of our model, the results of a short-term clinical study are presented. Lower levels of staining were noted for a material with a lower glucose permeability and a larger amount of water melting below 0 degrees C than for a control lens, even though both materials were similar in water content and water permeability. Further clinical studies are needed to validate this model.

Biocompatible Materials↗