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Evaluation of a multivariate model predicting noncompliance with medication regimens among renal transplant patients.

BACKGROUND: Because noncompliance with medication regimens is a major cause of renal allograft failure, we evaluated the stability over time of two logistic regression models (sets of variables) that predict noncompliance with immunosuppressive regimens. METHODS: Models were based on questionnaire data from 1402 patients (all over 18, receiving cyclosporine or a cyclosporine-like replacement drug, and with a functioning renal graft). The same questionnaire was completed by a subset of 548 (39.1%) patients approximately 18 months later. The goodness of fit of each model to the new data set was tested. RESULTS: The noncompliance logistic regression model including patient beliefs as well as patient and transplant characteristics was an excellent fit to the second data set. A noncompliance model composed of only patient and transplant characteristics fit the new data set less well. CONCLUSIONS: Clinicians and educators need to take explicit account of renal transplant patients' attitudes when evaluating risks of noncompliance and when developing interventions and educational programs to minimize noncompliance.

Adult↗

Biomechanical model predicts directional tuning of spindles in finger muscles facilitates precision pinch and power grasp.

Humans have a sense of static limb position derived primarily from the output of secondary muscle spindle endings. The features of finger pose these proprioceptors signal best were predicted by singular value decomposition of a kinematic model of the human long finger and the six muscles that actuate it. The analysis indicated that muscle spindles signal the location of the fingertip with less error than they signal angles of individual finger joints. The fingertip displacements for which proprioceptors have greatest sensitivity were also predicted. These fingertip displacements seem to correspond to the fine positioning of an object pinched between the fingertip and distal phalanx of the thumb. The analysis also predicted the directions in which subjects can displace the fingertip most rapidly. The directions seem to correspond to rapid closure of precision pinch or power grasp.

Biomechanical Phenomena↗

Modeling prediction of membrane bioreactor process with the concept of soluble microbial product.

The existence of soluble microbial products (SMP) produced by microbial cultures involved in biological wastewater treatment process has been widely investigated. This paper aims to establish an available mathematical model by incorporating the SMP concept into the Activated Sludge Model (ASM) No. 3. Prediction of sewage treatment performance in membrane bioreactor process under intermittent aerobic condition by model simulation was presented, and the results provided a more comprehensive image for this process. It was found that SMP could not be ignored and it contributed about 15% of total COD in the reactor under HRT = 12 hr and SRT = 10 days condition. The model also provided reasonable simulation results for nitrogen, biomass concentration, and other treatment behaviors. Furthermore, the treatment performance can be predicted under various operating conditions by this proposed model.

Bioreactors↗

Finite element models predict cancellous apparent modulus when tissue modulus is scaled from specimen CT-attenuation.

High-resolution architecture-based finite element models are commonly used for characterizing the mechanical behavior of cancellous bone. The vast majority of studies use homogeneous material properties to model trabecular tissue. The objectives of this study were to demonstrate that inhomogeneous finite element models that account for microcomputed tomography-measured tissue modulus variability more accurately predict the apparent stiffness of cancellous bone than homogeneous models, and to examine the sensitivity of an inhomogeneous model to the degree of tissue property variability. We tested five different material cases in finite element models of ten cancellous cubes in simulated uniaxial compression. Three of these cases were inhomogeneous and two were homogeneous. Four of these cases were unique to each specimen, and the remaining case had the same tissue modulus for all specimens. Results from all simulations were compared with measured elastic moduli from previous experiments. Tissue modulus variability for the most accurate of the three inhomogeneous models was then artificially increased to simulate the effects of non-linear CT-attenuation-modulus relationships. Uniqueness of individual models was more critical for model accuracy than level of inhomogeneity. Both homogeneous and inhomogeneous models that were unique to each specimen had at least 8% greater explanatory power for apparent modulus than models that applied the same material properties to all specimens. The explanatory power for apparent modulus of models with a tissue modulus coefficient of variation (COV) range of 21-31% was 13% greater than homogeneous models (COV=0). The results of this study indicate that inhomogenous finite element models that have tissue moduli unique to each specimen more accurately predict the elastic behavior of cancellous cubic specimens than models that have common tissue moduli between all specimens.

Absorptiometry, Photon↗

Design of immune-based interventions in autoimmunity and viral infections--the need for predictive models that integrate time, dose and classes of immune responses.

The outcome of both autoimmune reactions and antiviral responses depends on a complex network of multiple components of the immune system. For example, most immune reactions can be viewed as a balance of aggressive and regulatory processes. Thus, a component of the immune system that has beneficial effects in one situation might have detrimental effects elsewhere: organ-specific immunity and autoimmunity are both governed by this paradigm. Additionally, the precise timing and magnitude of an immune response can frequently be more critical than its composition for determining efficacy as well as damage. These issues make the design of immune-based interventions very difficult, because it is frequently impossible to predict the outcome. For example, certain cytokines can either cure or worsen autoimmune processes depending on their dose and timing in relation to the ongoing disease process. Consequently, there is a strong need for models that can predict the outcome of immune-based interventions taking these considerations into account.

Animals↗

A 3-D computational model predicts that cell deformation affects selectin-mediated leukocyte rolling.

Leukocyte recruitment to sites of inflammation is initiated by their tethering and rolling on the activated endothelium under flow. Even though the fast kinetics and high tensile strength of selectin-ligand bonds are primarily responsible for leukocyte rolling, experimental evidence suggests that cellular properties such as cell deformability and microvillus elasticity actively modulate leukocyte rolling behavior. Previous theoretical models either assumed cells as rigid spheres or were limited to two-dimensional representations of deformable cells with deterministic receptor-ligand kinetics, thereby failing to accurately predict leukocyte rolling. We therefore developed a three-dimensional computational model based on the immersed boundary method to predict receptor-mediated rolling of deformable cells in shear flow coupled to a Monte Carlo method simulating the stochastic receptor-ligand interactions. Our model predicts for the first time that the rolling of more compliant cells is relatively smoother and slower compared to cells with stiffer membranes, due to increased cell-substrate contact area. At the molecular level, we show that the average number of bonds per cell as well as per single microvillus decreases with increasing membrane stiffness. Moreover, the average bond lifetime decreases with increasing shear rate and with increasing membrane stiffness, due to higher hydrodynamic force experienced by the cell. Taken together, our model captures the effect of cellular properties on the coupling between hydrodynamic and receptor-ligand bond forces, and successfully explains the stable leukocyte rolling at a wide range of shear rates over that of rigid microspheres.

Biophysics↗

Predictive models for hERG potassium channel blockers.

We report here a general method for the prediction of hERG potassium channel blockers using computational models generated from correlation analyses of a large dataset and pharmacophore-based GRIND descriptors. These 3D-QSAR models are compared favorably with other traditional and chemometric based HQSAR methods.

Anti-Arrhythmia Agents↗

Development of a predictive model for role strain in registered nurses returning to school.

This study identified the individual characteristics that are expected to predict and explain role strain in registered nurses (RNs) returning to school. A model was developed and tested to predict and explain role strain. The characteristics expected to predict role strain were personality, stage of career development, and marital status. Personality was assessed by the Comrey Personality Scales, stage of career development was assessed by the Career Concerns Inventory, and the criterion variable role strain was assessed by the Lengacher Role Strain Inventory. A convenience sample of 123 RN students were asked to participate in the study and 86 RNs volunteered. The data were analyzed using multiple stepwise regression analysis. Results of the stepwise regression analysis identified 13 variables as significant predictors of role strain. A double cross-validation regression procedure was completed to validate the results of the multiple regression analysis. In subsample A of the cross-validation analysis, six variables were found to be significant predictors of role strain, while in subsample B, 12 variables were significant predictors.

Adult↗

New approaches to toxicity: a seven-gas predictive model and toxicant suppressants.

Two new research approaches in combustion toxicology are: 1. the prediction of smoke toxicity from mathematical equations, which are empirically derived from, experiments on the toxicological interactions of complex fire gas mixtures and 2. the use of toxicant suppressants in materials or products to prevent the formation of toxic combustion products. The predictive approach consists of burning materials using a bench-scale method that simulates realistic fire conditions, measuring the concentrations of the primary fire gases--CO, CO2, low O2, HCN, HCl, HBr, and NO2--and predicting the toxicity of the smoke using either the 6- or 7-gas N-Gas Model. These models are based on the results of toxicological studies of these primary gases as individual gases and as complex mixtures. The predicted toxic potency is checked with a small number of animal (Fischer 344 male rats) tests to assure that an unanticipated toxic gas is not generated or an unexpected synergistic or antagonistic effect has not occurred. The results indicate if the smoke from a material or product is extremely toxic (based on mass consumed at the predicted toxic level) or unusually toxic (based on the gases deemed responsible). The predictions based on bench-scale laboratory tests have been validated with full-scale room burns of a limited number of materials of widely differing characteristics chosen to challenge the system. The advantages of this new approach are 1. the number of test animals is minimized by predicting the toxic potency from the chemical analysis of the smoke, 2. smoke may be produced under conditions that simulate the fire scenario of concern, 3. fewer tests are needed, thereby reducing the overall cost of the testing and 4, information is obtained on both the toxic potency of the smoke and the responsible gases. The N-Gas Models have been developed into the N-Gas Method (described in this paper) and these results have been used in computations of fire hazard. The 6-Gas Model is now part of the international standard ISO 13344 approved by 16 member countries of the International Standards Organization (ISO) and is also included in the U.S. national standard ASTM E1678 approved by the American Society for Testing and Materials (ASTM). In addition, the 6-Gas Model is used in the American National Standard--NFPA 269--approved by the National Fire Protection Association (Quincy, MA). The second new research approach, toxicant suppressants, examines the potential of chemical compounds, which when added to a material, to inhibit or reduce the concentration of a specific toxic gas normally generated during the material's thermal decomposition. The effectiveness of this approach was demonstrated at the National Institute of Standards and Technology (NIST) when HCN generation was reduced by 90% and the resultant toxicity of the combustion products was lowered by 50% when a flexible polyurethane foam (FPU) was treated with 0.1% (by weight) cuprous oxide (Cu2O). Copper and cupric oxide (CuO) also reduced the HCN generation but were not as efficient as Cu2O. Although melamine-treated FPU foams are being promoted as more fire safe than standard foams, a melamine-treated foam generated 10 times more HCN than a foam without melamine. The addition of Cu2O to this melamine foam also reduced the HCN generation by 90%.

Animal Testing Alternatives↗

Predictive models for difficult laryngoscopy and intubation. A clinical, radiologic and three-dimensional computer imaging study.

PURPOSE: To identify the variables most useful in predicting difficult laryngoscopy and intubation from various clinical, skeletal (lateral x-rays) and soft tissue (three-dimensional computed tomography imaging) measurements. METHODS: Twenty-four adult patients in whom an unanticipated difficult tracheal intubation was identified according to established criteria were evaluated. Further, a control group of 32 patients in whom tracheal intubation was easily accomplished was studied. We applied multivariate discriminant analysis to clinical and radiological data of all patients to select those variables most useful in predicting difficult laryngoscopy and intubation. The receiver operating characteristic (ROC) curve was used to describe the discrimination abilities and to explore the trade-offs between sensitivity and specificity of the model. RESULTS: With the clinical data alone, discriminant analysis identified four risk factors that correlated with the prediction of difficult laryngoscopy and intubation: thyrosternal distance, thyromental distance, neck circumference and Mallampati classification. With both clinical and radiological data, discriminant analysis identified five risk factors: thyrosternal distance, thyromental distance, Mallampati classification, depth of spine C2 and angle A (the most antero-inferior point of the upper central incisor tooth). The positive predictive value of this combined (clinical and radiological) model was greater than that of the clinical model alone (95.8% vs 87.5%, respectively). The areas under the ROC curves, that measure the probability of the correct prediction of the clinical and the combined models, were found to be 0.933 and 0.973, respectively. CONCLUSIONS: These models can be used for predicting difficult laryngoscopy and intubation in clinical practice.

Adult↗

A rabbit Langendorff heart proarrhythmia model: predictive value for clinical identification of Torsades de Pointes.

BACKGROUND AND PURPOSE: The rabbit isolated Langendorff heart model (SCREENIT) was used to investigate the proarrhythmic potential of a range of marketed drugs or drugs intended for market. These data were used to validate the SCREENIT model against clinical outcomes. EXPERIMENTAL APPROACH: Fifty-five drugs, 3 replicates and 2 controls were tested in a blinded manner. Proarrhythmia variables included a 10% change in MAPD(60), triangulation, instability and reverse frequency-dependence of the MAP. Early after-depolarisations, ventricular tachycardia, TdP and ventricular fibrillation were noted. Data are reported at nominal concentrations relative to EFTPC(max). Proarrhythmic scores were assigned to each drug and each drug category. KEY RESULTS: Category 1 and 2 drugs have the highest number of proarrhythmia variables and overt proarrhythmia while Category 5 drugs have the lowest, at every margin. At 30-fold the EFTPC(max), the mean proarrhythmic scores are: Category 1, 101+/-24; Category 2, 101+/-14; Category 3, 72+/-20; Category 4, 59+/-16 and Category 5, 22+/-9 points. Only drugs in Category 5 have mean proarrhythmic scores, below 30-fold, that remain within the Safety Zone. CONCLUSIONS AND IMPLICATIONS: A 30-fold margin between effects and EFTPC(max) is sufficiently stringent to provide confidence to proceed with a new chemical entity, without incurring the risk of eliminating potentially beneficial drugs. The model is particularly useful where compounds have small margins between the hERG IC(50) and predicted EFTPC(max). These data suggest this is a robust and reliable assay that can add value to an integrated QT/TdP risk assessment.

Animals↗

Predictive modelling of Escherichia coli O157:H7: inclusion of carbon dioxide as a fourth factor in a pre-existing model.

Two models for Escherichia coli O157:H7 are compared, one with growth-controlling factors pH (4.5-7.0), temperature (10-30 degrees C) and NaCl concentration (0.5-6.5% w/v) and the other with the same factors and ranges, but with the addition of carbon dioxide (CO2: 10-80% v/v). Validation of the four-factor model, to include food packed in modified atmospheres containing CO2, was not possible due to lack of published data. However, where CO2 concentration was entered as 0%, only minor differences occurred between the predictions from the two models for the same conditions of pH, NaCl and temperature; consequently reliable, safe predictions using the four-factor model, with CO2 concentration recorded as 0%, can be made for foods packed in air. At temperatures from 10 to 30 degrees C, it was found that lower (10 and 20%) concentrations of CO2 had little effect on lag times and growth rates, and higher concentrations still permitted growth of E. coli O157:H7 under a wide range of conditions of NaCl concentration, pH value and temperature, suggesting that the organism is relatively CO2-tolerant.

Carbon Dioxide↗

Comparison between pore model predictions and sheep lung fluid and protein transport.

The multiple pore model of T. R. Harris and R. J. Roselli (1981, J. Appl. Physiol: Respir. Environ. Exercise Physiol. 50, 1-14), was used to simulate lung lymph flow and protein transport at various levels of microvascular pressure. Response of the three-pore structure determined in that study was found to be in excellent agreement with the experimental sheep lung lymph measurements of R. E. Parker, R. J. Roselli, T. R. Harris, and K. L. Brigham (1981, Circ. Res. 49, 1164-1172). Optimal one- and two-pore model structures were also determined and their responses compared with the experimental data. The two-pore model behavior was found to be very similar to that of the three-pore model but a homoporous model which reproduced the experimental findings could not be found. All simulations required interstitial fluid pressure to increase as microvascular pressure was elevated. True filtration-independent conditions could only be simulated when lung vascular pressures were raised to physiologically unrealistic values.

Animals↗

Development and validation of an echocardiographic model for predicting progression of discrete subaortic stenosis in children.

The clinical course of discrete subaortic stenosis (DSS) varies considerably between patients. This study was performed to identify echocardiographic characteristics of DSS that distinguish progressive from nonprogressive disease. The study included 100 patients from 2 institutions and was performed in 2 stages. In phase I, a prediction model was developed based on multivariate analysis of morphometric and Doppler variables obtained from the initial echocardiogram in 52 children with DSS from Texas Children's Hospital. In phase II, the performance characteristics of the prediction model were tested in 48 patients with DSS followed at Children's Hospital in Boston. Patients were divided into 3 outcome groups: nonprogressive, progressive, and intermediate progression. In phase I, multivariate analysis identified 3 independent predictors of progressive disease: indexed aortic valve to subaortic membrane distance, anterior mitral leaflet involvement, and initial Doppler gradient. The logistic regression equation--Probability = [1 + e-(-322+0.334X1+4.06X2-0.708X3)](-1), where X = initial gradient in mm Hg; X2 = absence (0) or presence (1) of mitral leaflet involvement; and X3 = indexed distance between aortic valve and subaortic membrane in mm/body surface area0.5 were used to predict progression. When the prediction model was applied to phase II study patients, none of the patients with nonprogressive DSS had a prediction value > 0.29 and none of the patients with progressive DSS had a prediction value < 0.58. Thus, a prediction value > 0.55 yielded a 100% sensitivity and 100% specificity for distinguishing progressive from nonprogressive DSS. Patients with intermediate progression were indistinguishable from progressive DSS but were clearly separable from nonprogressing patients. We conclude that progressive subaortic obstruction in children with DSS can be predicted from morphologic, morphometric, and Doppler echocardiographic analysis of left ventricular outflow.

Aortic Valve Stenosis↗

Assessment of milk transfer coefficients for use in prediction models of radioactivity transport.

The transfer coefficient (Fm) which is widely used to predict the fraction of ingested radionuclides found in milk is an important parameter for modelling. The accuracy of estimates can be improved by considering the following factors that influence the Fm; (1) the physical-chemical form of the radionuclide in the feed of cows, (2) the hay to concentrate ratio of cows diets and (3) correcting for a steady state situation for feed intake and milk concentrations. Factors such as stable element intake, soil intake, milk production rate, metabolic rate and inhalation do not appear to have significant effects upon the transfer coefficient.

Accidents↗

Nonclinical proarrhythmia models: predicting Torsades de Pointes.

Prolongation of the QT interval and the cardiac action potential have been linked to a potentially fatal but rare tachyarrhythmia known as Torsades de Pointes (TdP). Nonclinical assays, such as those investigating the effect on I(Kr) (the hERG channel current), prolongation of the action potential duration (APD) and the QT interval, in vivo, have been developed to predict the risk of QT interval prolongation and TdP in man. However, there seems to be a dissociation between the risk of QT interval prolongation and the torsadogenic risk. There is an increasing mass of evidence showing that an increase in the QT interval does not necessarily lead to TdP. Thus, it appears that while standard assays are very good, although perhaps not infallible, at predicting the risk of QT interval prolongation in man they do not predict the proarrhythmic risk. Recently there has been a plethora of publications suggesting that there are electrophysiological markers associated with drug-induced TdP other than hERG channel activity, APD and the QT interval, and these markers may be better predictors of TdP. In this review, three in vitro and, briefly, three in vivo models or methods are discussed. These proarrhythmia models use electrophysiological markers such as transmural dispersion of repolarization, action potential triangulation, instability, reverse use-dependence, and the incidence of early after-depolarizations to predict the risk of TdP. Most of the models presented have been published widely. The particular variable or set of variables used by each model to predict the torsadogenic propensity of a drug has been reported to correlate with clinical outcome. While each variable/model has been shown to discriminate between antiarrhythmic and nonarrhythmic drugs, these reports should be interpreted cautiously since none has been independently (externally) assessed. Each model is discussed along with its particular merits and shortcomings; none, as yet, having shown a predictive value that makes it clearly superior to the others. Proarrhythmia models, in particular in vitro models, challenge current perceptions of appropriate surrogates for TdP in man and question existing nonclinical strategies for assessing proarrhythmic risk. The rapid emergence of such models, compounded by the lack of a clear understanding of the key proarrhythmic mechanisms has resulted in a regulatory reluctance to embrace such models. The wider acceptance of proarrhythmia models is likely to occur when there is a clear understanding and agreement on the key proarrhythmia mechanisms. Regardless of regulatory acceptance, with further validation these models may still enhance pharmaceutical company decision-making to provide a rational basis for drug progression, particularly in areas of unmet medical need.

Animals↗

[Usefulness of logistic regression model to predict the endometrial carcinoma based on blood flow indices measured wit the of three-dimensional Doppler sonography].

AIM: Construction and prospective verification of predictive model permitting to rate individual probability of existence of endometrial carcinoma with use of three-dimensional Doppler Doppler sonography. MATERIAL AND METHODS: We analyzed the results of 3D sonography of 123 women (mean age 53.8 +/- 10.6; mean BMI--28.2 +/- 5.4). We estimated: endometrial thickness and volume, blood flow indices. All ultrasound measurements were verified by histology. In aim of finding the best combination of features essentially affect on endometrial cancer's risk and for estimate individual probability of endometrial cancer we use a logistic regression analysis. The obtained model was verified on 20 new cases. RESULTS: There were 24 women with endometrial cancer, 59 women with endometrial hyperplasia and 40 women without endometrial changes. We affirmed that only three variables had statistical significant influence on the constructed predictive model. Probability of endometrial carcinoma was: P(X) = 1/(1 + e-z), where "e" is mathematical constant and z = 0.12 x age + 0.16 x endometrial thickness + 0.47 x VI -11.3. The best sensitivity and specificity were 70.8% and 98.9%. The sensitivity and specificity in 20 new cases was adequately 75% and 91.6%. CONCLUSION: The usefulness of a predictive model built with the help of logistic regression analysis increase sonographic diagnostic precision. The usefulness of endometrial blood flow indices permit to estimate the individual risk of endometrial cancer in women examined with three-dimensional sonography.

Adult↗

Acute stress disorder in youth: a multivariate prediction model.

There is little empirical support for the diagnosis of acute stress disorder (ASD) in children and adolescents. Most reports treat ASD as "provisional posttraumatic stress disorder (PTSD)" (meaning that children evidence ASD on the way to a formal diagnosis of PTSD), while speculating on factors that might moderate or mediate the transformation of ASD into PTSD. This report briefly reviews the literature on ASD in the context of presenting a testable, multivariate model for understanding acute stress responses in youth.

Adolescent↗