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PubMed2005

Equally valid models gave divergent predictions for mortality in acute myocardial infarction patients in a comparison of logistic [corrected] regression models.

OBJECTIVE: Models that predict mortality after acute myocardial infarction (AMI) contain different predictors and are based on different populations. We studied the agreement and validity of predictions for individual patients. STUDY DESIGN AND SETTING: We compared predictions from five predictive logistic regression models for short-term mortality after AMI. Three models were developed previously, and two models were developed in the GUSTO-I data, where all five models were applied (n =40,830, 7.0% 30-day mortality). Agreement was studied with weighted kappa statistics of categorized predictions. Validity was assessed by comparing observed frequencies with predictions (indicating calibration) and by the area under the receiver operating characteristic curve (AUC), indicating discriminative ability. RESULTS: The predictions from the five models varied considerably for individual patients, with low agreement between most (kappa <0.6). Risk predictions from the three previously developed models were on average too high, which could be corrected by re-calibration of the model intercept. The AUC ranged from 0.76-0.78 and increased to 0.78-0.79 with re-estimated regression coefficients that were optimal for the GUSTO-I patients. The two more detailed GUSTO-I based models performed better (AUC approximately 0.82). CONCLUSION: Models with different predictors may have a similar validity while the agreement between predictions for individual patients is poor. The main concerns in the applicability of predictive models for AMI should relate to the selected predictors and average calibration.

Aged↗

PubMed2005

Modeling dermal granulation tissue with the linear fibroblast-populated collagen matrix: a comparison with the round matrix model.

BACKGROUND: Wound contraction typically is not symmetrical; for example, a square-shaped wound will not yield a square scar. Interestingly, the round fibroblast-populated collagen matrix has been used as a model of wound contraction, even though contraction in this model is mostly symmetrical. OBJECTIVE: We wanted to compare the round versus linear fibroblast-populated collagen matrix to see which would be a better model of dermal granulation tissue. METHODS: Gross and microscopic morphology, contraction kinetics, cytoskeletal architecture, and apoptotic and proliferative indices were compared between the round versus the linear fibroblast-populated collagen matrix. A rat excisional wound model was used as an in vivo standard of healing. RESULTS: The rate of contraction was similar between the two models, although the mode of contraction was grossly asymmetric in the linear while remaining symmetric in the round model. Cellular survival and proliferation were both dependent on matrix attachment in both models; this was analogous to the attachment-dependence of granulation tissue. In the attached (restrained) condition, the level of cellular organization was higher in the linear than in the round matrix; the tissue architecture of the linear matrix, moreover, mimicked that of the excisional wound model. CONCLUSION: The round versus linear fibroblast-populated collagen matrix displayed a similar proliferative and survival response to matrix attachment. The latter model, however, demonstrated tissue organization with attachment and asymmetrical contraction after detachment analogous to that of the in vivo wound model. The linear fibroblast-populated collagen matrix appears to be the better model of dermal granulation tissue.

Apoptosis↗

PubMed2000

Thresholds for step initiation induced by support-surface translation: a dynamic center-of-mass model provides much better prediction than a static model.

The need to initiate a step in order to recover balance could, in theory, be predicted by a static model based solely on displacement of the center of mass (COM) with respect to the base of support (BOS), or by a dynamic model based on the interaction between COM displacement and velocity. The purpose of this study was to determine whether the dynamic model provides better prediction than the static model regarding the need to step in response to moving-platform perturbation. The COM phase plane trajectories were determined for 10 healthy young adults for trials where the supporting platform was translated at three different acceleration levels in anterior and posterior directions. These trajectories were compared with the thresholds for step initiation predicted by the static and dynamic COM models. A single-link-plus-foot biomechanical model was employed to mathematically simulate termination of the COM movement, without stepping, using the measured platform acceleration as the input. An optimization routine was used to determine the stability boundaries in COM state space so as to establish the dynamic thresholds where a compensatory step must be initiated in order to recover balance. In the static model, the threshold for step initiation was reached if the COM was displaced beyond the BOS limits. The dynamic model showed substantially better accuracy than the static model in predicting the need to step in order to recover balance: 71% of all stepping responses predicted correctly by the dynamic model versus only 11% by the static model. These results support the proposition that the central nervous system must react to and control dynamic effects, i.e. COM velocity, as well as COM displacement in order to maintain stability with respect to the existing BOS without stepping.

Adult↗

PubMed2003

Heavy gas dispersion: integral models and shallow layer models.

Integral models for heavy gas dispersion approximate a dispersing cloud in terms of a small number of variables; each of these is ultimately a function of an independent variable which is usually time (instantaneous releases) or downwind distance (continuous releases). This type of model is used almost exclusively in risk assessment [HSE's risk assessment tool, RISKAT, in: Major Hazards: Onshore and Offshore, October 1992, pp. 607-638; Ann. Rev. Fluid Mech. 21 (1989) 317], but many distinct integral models exist. The code comparison exercise of Mercer et al. [CEA/AEA exchange agreement on external event. Comparison of heavy gas dispersion models for instantaneous releases: final report, Technical Report IR/L/HA/91/6, Health and Safety Laboratory, Sheffield, June 1991; J. Hazard. Mater. 36 (1994) 193] presented the results from a number of integral models in a common format; Mercer found that the range of predictions for some scenarios exceeded three orders of magnitude. Here, the TWODEE shallow layer model [J. Hazard. Mater. 66 (3) (1999) 211; J. Hazard. Mater. 66 (3) (1999) 227; J. Hazard. Mater. 66 (3) (1999) 239] is added to Mercer's code comparison exercise. The physical assumptions used in shallow layer models differ profoundly from those used in integral models and the implications of these differences for risk assessment are discussed. TWODEE was used to simulate four representative cases considered by Mercer. In terms of cloud averaged concentration (CAC) vs. centroid position, the present model gave predictions that were consistent with the integral models used by Mercer. As the model neglects horizontal diffusion for passive clouds, overprediction at large downwind distances was expected, but not generally observed.

Air Pollutants↗

PubMed2003

On specifying the null model for incremental fit indices in structural equation modeling.

In structural equation modeling, incremental fit indices are based on the comparison of the fit of a substantive model to that of a null model. The standard null model yields unconstrained estimates of the variance (and mean, if included) of each manifest variable. For many models, however, the standard null model is an improper comparison model. In these cases, incremental fit index values reported automatically by structural modeling software have no interpretation and should be disregarded. The authors explain how to formulate an acceptable, modified null model, predict changes in fit index values accompanying its use, provide examples illustrating effects on fit index values when using such a model, and discuss implications for theory and practice of structural equation modeling.

Data Interpretation, Statistical↗

PubMed2006

Model selection in magnetic resonance imaging measurements of vascular permeability: Gadomer in a 9L model of rat cerebral tumor.

Vasculature in and around the cerebral tumor exhibits a wide range of permeabilities, from normal capillaries with essentially no blood-brain barrier (BBB) leakage to a tumor vasculature that freely passes even such large molecules as albumin. In measuring BBB permeability by magnetic resonance imaging (MRI), various contrast agents, sampling intervals, and contrast distribution models can be selected, each with its effect on the measurement's outcome. Using Gadomer, a large paramagnetic contrast agent, and MRI measures of T(1) over a 25-min period, BBB permeability was estimated in 15 Fischer rats with day-16 9L cerebral gliomas. Three vascular models were developed: (1) impermeable (normal BBB); (2) moderate influx (leakage without efflux); and (3) fast leakage with bidirectional exchange. For data analysis, these form nested models. Model 1 estimates only vascular plasma volume, v(D), Model 2 (the Patlak graphical approach) v(D) and the influx transfer constant K(i). Model 3 estimates v(D), K(i), and the reverse transfer constant, k(b), through which the extravascular distribution space, v(e), is calculated. For this contrast agent and experimental duration, Model 3 proved the best model, yielding the following central tumor means (+/-s.d.; n = 15): v(D) = 0.07 +/- 0.03 for K(i) = 0.0105 +/- 0.005 min(-1) and v(e) = 0.10 +/- 0.04. Model 2 K(i) estimates were approximately 30% of Model 3, but highly correlated (r = 0.80, P < 0.0003). Sizable inhomogeneity in v(D), K(i), and k(b) appeared within each tumor. We conclude that employing nested models enables accurate assessment of transfer constants among areas where BBB permeability, contrast agent distribution volumes, and signal-to-noise vary.

Animals↗

PubMed2000

The Roper, Logan and Tierney (1996) model: perceptions and operationalization of the model in psychiatric nursing within a health board in Ireland.

This study aimed to determine whether the Roper, Logan & Tierney model was an appropriate model for planning nursing care for clients who are mentally ill. Data were collected from two sources in one Health Board region in Ireland. A care plan audit was used to evaluate the extent to which the Roper, Logan & Tierney model was used to assess, plan and evaluate nursing care in nursing documentation. Qualitative interviews with nurses explored their experiences of using the model and their perceptions of the model's usefulness and appropriateness for planning care. Both data sets were complementary, the qualitative data often providing contextual information which helped put the findings into perspective. It was found that there was little evidence that the Roper, Logan and Tierney model guided care planning and that goals and nursing interventions were frequently not explicitly documented. Interviews with nurses indicated that they lacked educational preparation for using the model and found the model constraining and physically orientated. The appropriateness of the Roper, Logan and Tierney model for planning care for clients who are mentally ill is questioned. It is suggested that nurses need to be adequately prepared if they are to use a model appropriately. Consideration should be given when selecting a model as to its 'fit' with the needs of the client group and the ward team philosophy.

Attitude of Health Personnel↗

PubMed2002

Validity of prognostic models: when is a model clinically useful?

Prognostic models combine patient characteristics to predict medical outcomes. Unfortunately, such models do not always perform as well for other patients as those from whose data the models were derived. Therefore, validity of prognostic models needs to be assessed in new patients. Predicted probabilities can be calculated with the model and compared with the actually observed outcomes. We may distinguish several aspects of validity: (1) agreement between predicted probabilities and observed probabilities (calibration), (2) ability of the model to distinguish subjects with different outcomes (discrimination), and (3) ability of the model to improve the decision-making process (clinical usefulness). We discuss those aspects and show some measures by using models for testicular and prostate cancer. We conclude that good calibration and discriminative ability are not sufficient for a model to be clinically useful. Application of a prognostic model is sensible, if the model is able to provide useful additional information for clinical decision making.

Decision Support Techniques↗

PubMed2000

A General and Dynamic Species Abundance Model, Embracing the Lognormal and the Gamma Models.

One aspect of community ecology that has been given particular attention is the pattern of species abundances in a community. The species may have a wide range of abundances; some are very common and others rare. When species abundance models are fitted to observations, the lognormal model and one of the gamma models (e.g., the log-series model) are usually applied. The model that gives the best fit according to some goodness-of-fit test is then chosen. By applying a diffusion approximation for each species' dynamics with density regulation of the θ-logistic type, we here present a general species abundance model that embraces the two most widely applied species abundance models, the lognormal and the gamma. Our general model will, therefore, provide a better fit than the two special cases, except when it corresponds to one of them. In contrast to the classical models, ours is also dynamic, making it possible to evaluate the fluctuations in species abundance over time through both biotic and abiotic factors. The model is fitted to several species abundance data sets and our results compared to previous attempts to fit a model, usually either the lognormal or the log-series.

demographic variance↗

PubMed1999

A physiological model for tert-amyl methyl ether and tert-amyl alcohol: hypothesis testing of model structures.

The oxygenate tert-amyl methyl ether (TAME) is a gasoline fuel additive used to reduce carbon monoxide in automobile emissions. To evaluate the relative health risk of TAME as a gasoline additive, information is needed on its pharmacokinetics and toxicity. The objective of this study was to use a physiologically-based pharmacokinetic (PBPK) model to describe the disposition of TAME and its major metabolite, tert-amyl alcohol (TAA), in male Fischer-344 rats. The model compartments for TAME and TAA were flow-limited. The TAME physiological model had 6 compartments: lung, liver, rapidly perfused tissues, slowly perfused tissues, fat, and kidney. The TAA model had 3 compartments: lung, liver, and total-body water. The 2 models were linked through metabolism of TAME to TAA in the liver. Model simulations were compared with data on blood concentrations of TAME and TAA taken from male Fischer-344 rats during and after a 6-hour inhalation exposure to 2500, 500, or 100 ppm TAME. The PBPK model predicted TAME pharmacokinetics when 2 saturable pathways for TAME oxidation were included. The TAA model, which included pathways for oxidation and glucuronide conjugation of TAA, underpredicted the experimental data collected at later times postexposure. To account for biological processes occurring during this time, three hypotheses were developed: nonspecific binding of TAA, diffusion-limited transport of TAA, and enterohepatic circulation of TAA glucuronide. These hypotheses were tested using three different model structures. Visual inspection and statistical evaluation involving maximum likelihood techniques indicated that the model incorporating nonspecific binding of TAA provided the best fit to the data. A correct model structure, based upon experimental data, statistical analyses, and biological interpretation, will allow a more accurate extrapolation to humans and, consequently, a greater understanding of human risk from exposure to TAME.

Administration, Inhalation↗

PubMed2004

Population pharmacokinetic modeling of oral cyclosporin using NONMEM: comparison of absorption pharmacokinetic models and design of a Bayesian estimator.

There have been very few population pharmacokinetic (PopPK) studies and Bayesian forecasting methods dealing with cyclosporin (CsA) so far, probably because of the difficulty of modeling the particular absorption profiles of CsA. The present study was conducted in stable renal transplant patients treated with Neoral and employed the NONMEM program. Its goals were (1) to develop a population pharmacokinetic model for CsA based on an Erlang frequency distribution (which describes asymmetric S-shaped absorption profiles) combined with a 2-compartment model; (2) to compare this model with models combining a time-lag parameter and either a zero-order or first-order rate constant and with a model based on a Weibull distribution; and (3) to develop a PK Bayesian estimator for full AUC estimation based on that "Erlang model." The PopPK model was developed in an index set of 70 patients, and then individual PK parameters and AUC were estimated in 10 other patients using Bayesian estimation. The "Erlang" model best described the data, with mean absorption time (MAT), apparent clearance (CL/F), and apparent volume of the central compartment (Vc/F) of 0.78 hours, 26.3 L/h, and 76 L, respectively (interindividual variability CV = 33, 30, and 48%). Bayesian estimation allowed accurate prediction of systemic exposure using only 3 samples collected at 0, 1, and 3 hours. Regression analysis found no significant difference between the predicted and observed concentrations (10 per patient), and AUC(0-12) were estimated with a nonsignificant bias (0.6 to 8.7%) and good precision (RMSE = 5.3%). In conclusion, the Erlang distribution best described CsA absorption profiles, and a Bayesian estimator developed using this model and a mixed-effect PK modeling program provided accurate estimates of CsA systemic exposure using only 3 blood samples.

Area Under Curve↗

PubMed2001

Towards NeuroML: model description methods for collaborative modelling in neuroscience.

Biological nervous systems and the mechanisms underlying their operation exhibit astonishing complexity. Computational models of these systems have been correspondingly complex. As these models become ever more sophisticated, they become increasingly difficult to define, comprehend, manage and communicate. Consequently, for scientific understanding of biological nervous systems to progress, it is crucial for modellers to have software tools that support discussion, development and exchange of computational models. We describe methodologies that focus on these tasks, improving the ability of neuroscientists to engage in the modelling process. We report our findings on the requirements for these tools and discuss the use of declarative forms of model description--equivalent to object-oriented classes and database schema--which we call templates. We introduce NeuroML, a mark-up language for the neurosciences which is defined syntactically using templates, and its specific component intended as a common format for communication between modelling-related tools. Finally, we propose a template hierarchy for this modelling component of NeuroML, sufficient for describing models ranging in structural levels from neuron cell membranes to neural networks. These templates support both a framework for user-level interaction with models, and a high-performance framework for efficient simulation of the models.

Animals↗

PubMed2001

Kinetic modeling of [(18)F]FDG in skeletal muscle by PET: a four-compartment five-rate-constant model.

Various modeling strategies have been developed to convert regional [(18)F]fluorodeoxyglucose ([(18)F]FDG) concentration measured by positron emission tomography (PET) to a measurement of physiological parameters. However, all the proposed models have been developed and tested mostly for brain studies. The purpose of the present study is to select the most accurate model for describing [(18)F]FDG kinetics in human skeletal muscle. The database consists of basal and hyperinsulinemic-euglycemic studies performed in normal subjects. PET data were first analyzed by an input-output modeling technique (often called spectral analysis). These results provided guidelines for developing a compartmental model. A new model with four compartments and five rate constants (5K model) emerged as the best. By accounting for plasma and extracellular and intracellular kinetics, this model allows, for the first time, PET assessment of the individual steps of [(18)F]FDG kinetics in human skeletal muscle, from plasma to extracellular space to transmembrane transport into the cell to intracellular phosphorylation. Insulin is shown to affect transport and phosphorylation but not extracellular kinetics, with the transport step becoming the main site of control. The 5K model also allows definition of the domain of validity of the classic three-compartment three- or four-rate-constant models. These models are candidates for an investigative tool to quantitatively assess insulin control on individual metabolic steps in human muscle in normal and physiopathological states.

Adult↗

PubMed1985

Animal models for protecting ischemic myocardium: results of the NHLBI Cooperative Study. Comparison of unconscious and conscious dog models.

The Animal Models for Protecting Ischemic Myocardium Study was undertaken for the purpose of developing reproducible animal models that could be used to assess interventions designed to limit infarct size. This paper describes the results obtained in an unconscious dog model and in a conscious dog model, developed in three participating laboratories. The unconscious dog model, involving reperfusion after 3 hours of ischemia in open-chest dogs, was intended to determine whether therapy followed by early reperfusion would limit infarct size more than reperfusion alone. The conscious dog model used chronically instrumented dogs and permanent coronary occlusion to better mimic myocardial infarction in man. In both models, the proximal circumflex artery was occluded, and the primary experimental endpoint was infarct size, as measured by histological techniques 3 days after the initial occlusion. Infarct size was analyzed in relation to baseline variables including the anatomic area at risk, collateral blood flow to the subepicardial zone of ischemia and hemodynamic determinants of myocardial metabolic demand. Most of the variation in infarct size in control dogs could be related to variation in the area at risk, collateral blood flow, and rate pressure product. Using multivariate analysis and groups of 15 dogs, an intervention that limited infarct size by 10-13% of the area at risk would have been detected 50% of the time. Larger treatment effects would be detected more readily, and smaller effects often would be missed, unless group sizes were larger. Two drugs, verapamil and ibuprofen, were evaluated in both models, with experimental group sizes averaging 13 and 20 dogs, in the unconscious and conscious models, respectively. Three of 15 verapamil-treated dogs in the unconscious model study had much smaller infarcts than expected from baseline parameters. With these exceptions, neither drug limited infarct size in either model.

Anesthesia↗

PubMed2004

Diversity of model approaches for breast cancer screening: a review of model assumptions by the Cancer Intervention and Surveillance Network (CISNET) Breast Cancer Groups.

The National Cancer Institute-sponsored Cancer Intervention and Surveillance Network program on breast cancer is composed of seven research groups working largely independently to model the impact of screening and adjuvant therapy on breast cancer mortality trends in the US from 1975 to 2000. Each of the groups has chosen a different modeling methodology without purposeful attempt to be in contrast with each other. The seven groups have met biannually since November 2000 to discuss their methodology and results. This article investigates the differences in methodology. To facilitate this comparison, each of the groups submitted a description of their model into a uniformly structured web based 'model profiler'. Six of the seven models simulate a preclinical natural history that cannot be observed directly with parameters estimated from published evidence concerning screening and therapy effects. The remaining model regards published evidence on intervention effects as prior information and updates that with information from the US population in a Bayesian type analysis. In general, the differences between the models appear to be small, particularly among the models driven by natural history assumptions. However, we demonstrate that such apparently small differences can have a large impact on surveillance of population trends. We describe a systematic approach to evaluating differences in model assumptions and results, as well as differences in modeling culture underlying the differences in model structure and parameters.

Breast Neoplasms↗

PubMed2006

Efficient modelling necessitates standards for model documentation and exchange.

In this paper, problems related to simulation model documentation and model exchange between users are discussed. Complex simulation models have gained popularity in the environmental field, but require extensive documentation to allow independent implementation. The existence of different simulation platforms puts high demands on the quality of the original documentation. Recent experiences from cross-platform implementations with the ASM2d and ADM1 models reveal that error-free model documentation is difficult to obtain, and as a consequence, considerable time is spent on searching for documentation and implementation errors of various sources. As such, the list of errors and coding pitfalls provided for ASM2d and ADM1 in this paper is vital information for any future implementation of both models. The time needed to obtain an error-free model implementation can be significantly reduced if a standard language for model documentation and exchange is adopted. The extensible markup language (XML) and languages based on this format may provide a remedy to the problem of platform independent model documentation and exchange. In this paper the possibility to apply this to environmental models is discussed, whereas the practical model implementation examples corroborate the necessity for a standardised approach.

Documentation↗

PubMed1997

Pulmonary retention and clearance of inhaled biopersistent aerosol particles: data-reducing interpolation models and models of physiologically based systems--a review of recent progress and remaining problems.

During the last 40 years, most models of long-term clearance and retention of biopersistent particles in the pulmonary region of the lung were phenomenologically oriented and accounted for only a small portion of the growing insight into lung dynamics by pulmologists, histologists, and biochemists. In this review, theoretical developments of modeling pulmonary dynamics for biopersistent particles during or after inhalation exposure are discussed. Several characteristic examples are given of the present state of the art. Most of the models presently in use are pragmatical compartmental models with a single compartment for the pulmonary region. They relate to observed data and facilitate an interpolation within the range covered by observation. Occasionally, these models are unjustifiably used for extrapolations in efforts to derive hypothetical risk assessments. Modeling efforts aiming at models of physiologically based pulmonary systems with a potential for extrapolations are not common and were published only during the last decade. Of this kind of approach, the review covers four examples. Promising progress has been made, but scarcity of supporting experimental data slows validation and extension. The two most recent model developments are based on a hypothesis by P.E. Morrow. According to Morrow, alveolar clearance is accomplished by mobile alveolar macrophages after phagocytosis of particles on the alveolar surface. The macrophage mobility, however, and thus the efficiency of the transport to the mucociliary escalator of the tracheobronchial tract will eventually decline towards total loss of mobility after the particle burden of the macrophages exceeds a critical value. The POCK model has been evaluated for a variety of chronic and subchronic rat exposure studies with noncytotoxic aerosols and gave good simulation results. The model by Tran et al. appears to be still in the developing stage of facilitating simulations for cytotoxic aerosols, but the combination of both model approaches seems to be a sound route of future efforts.

Administration, Inhalation↗

PubMed2005

[An attempt to integrate the dual route cascaded model and the triangle model for reading English words aloud].

In this article, I discuss the implementation of neural network models for reading English words aloud. Since 1989, there has been existing a debate about the models of reading English words aloud. One is the Dual Route Cascaded (DRC) model. The other is the Triangle model, whose original version was developed in 1989. Because there are arbitrary variables of both models, we did not decide which model gives better accounts for the numerous data given by psychological experiments and neuropsychological evidence. Therefore, in order to give a solution of this debate, an attempt to integrate both models was made. Introducing the Mixture of Experts Network, an elegant solution to overcome the arbitrariness of both models could be given. The Mixture of Expert Network can include both the models as a special case of this Network. From the Mixture of Expert Network's point of view, the difference between the Dual Route Cascaded model and the Triangle model would be able to describe as the quantitative difference of the dispersion parameters.

Humans↗