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PubMed1994

Evaluation of a predictive model for the shelf life of cod (Gadus morhua) fillets stored in two different atmospheres at varying temperatures.

The shelf life of fish and food in general is difficult to predict especially if stored at varying temperatures. Shelf life models were constructed (Einarsson, 1992) for cod fillets stored at constant temperatures. The aim of this study was to evaluate if these models could be used to predict spoilage and bacterial growth in cod fillets stored in air and modified atmosphere at constant and varying temperatures. Fresh fillets were packed and stored at constant or varying temperatures between -2 degrees C and 5 degrees C. Samples were taken at regular intervals for bacteriological and sensory evaluation. The results showed that fish stored at +0.6 degrees C in air had a shelf life (assessed by sensory analysis) of 11 days which is close to what could be expected and predicted. The increase in bacterial number observed was generally less than predicted. For fish fillets, stored in air at +5 degrees C for 3 days, then at +0.6 degrees C for 3 days and finally at -2 degrees C, the shelf life was found to be 7 days which was in good agreement with the predicted shelf life. The shelf life of fillets stored at same the temperatures in modified atmosphere was found to be 9 days but by prediction 11 to 12 days. The models for predicting changes in sensory score were more accurate than those predicting changes in bacterial numbers.

Air↗

PubMed1996

Immediate results of percutaneous mitral commissurotomy. A predictive model on a series of 1514 patients.

BACKGROUND: The wide use of percutaneous mitral commissurotomy (PMC) underlines the need to identify the predictive factors of the results. Using a large series allowed us to develop a multivariate model that can be applied to improve patient selection. METHODS AND RESULTS: Between 1986 and 1995. PMC was undertaken in 1514 patients. Mean age was 45 +/- 15 years. Echocardiography showed that 245 patients (16%) had pliable valves and mild chordal thickening (group 1), 886 (59%) had extensive subvalvular disease (group 2), and 383 (25%) had calcified valves (group 3). PMC failed in 22 patients; it was performed with a single balloon in 30 patients, a double balloon in 586, and the Inoue balloon in 876. Good immediate results were defined as a valve area > or = 1.5 cm2 with mitral regurgitation Sellers' grade < or = 2 and were obtained in 1348 patients (89%). A logistic model developed from the first 1088 cases identified the following predictors of immediate results: age (P = .004), echocardiographic group (P < .0001), valve area (P < .0001), and effective balloon dilating area (EBDA) (P = .03). Two interactions were significant: age at previous commissurotomy (P = .013) and EBDA by initial mitral regurgitation (P = .034). The type of balloon was of borderline significance (P = .09). The model was validated on an independent sample comprising the subsequent 426 procedures. For a threshold of probability of good results of .75, sensitivity was 92%, specificity 25%, and predictive accuracy 87%. CONCLUSIONS: Prediction of the immediate results of PMC is multifactorial. The predictive model developed and validated can be contributive in decision making for individual patients.

Adolescent↗

PubMed1978

A predictive model for visual recovery following retinal detachment surgery.

By multiple regression analysis we have identified 26 out of 200 observations which significantly affect the visual acuity following retinal detachment surgery. In addition, we have developed a highly significant mathematical model, which is able to predict in rather broad ranges of visual acuity to approximately 67% accuracy. There is still a large percentage of patients for whom we cannot account for the variability in final vision, a problem requiring future investigation. Potentially important factors which were not analyzed in this study include duration of macular detachment, afferent pupil defect, drainage of subretinal fluid, extent of the scleral-buckling procedure, and postoperative follow-up longer than six months. While most of the variables are fixed and cannot be alterd, such as age, senile cataract, and refractive error, improved knowledge of influential factors may allow us to manipulate some of them and provide mechanisms for improving results in recovery or maintenance of macular function after retinal detachment surgery.

Adult↗

PubMed2002

Influence of the structural diversity of data sets on the statistical quality of three-dimensional quantitative structure-activity relationship (3D-QSAR) models: predicting the estrogenic activity of xenoestrogens.

Federal legislation has resulted in the two-tiered in vitro and in vivo screening of some 80 000 structurally diverse chemicals for possible endocrine disrupting effects. To maximize efficiency and minimize expense, prioritization of these chemicals with respect to their estrogenic disrupting potential prior to this time-consuming and labor-intensive screening process is essential. Computer-based quantitative structure-activity relationship (QSAR) models, such as those obtained using comparative molecular field analysis (CoMFA), have been demonstrated as useful for risk assessment in this application. In general, however, CoMFA models to predict estrogenicity have been developed from data sets with limited structural diversity. In this study, we constructed CoMFA models based on biological data for a structurally diverse set of compounds spanning eight chemical families. We also compared two standard alignment schemes employed in CoMFA, namely, atom-fit and flexible field-fit, with respect to the predictive capabilities of their respective models for structurally diverse data sets. The present analysis indicates that flexible field-fit alignment fares better than atom-fit alignment as the structural diversity of the data set increases. Values of log(RP), where RP = relative potency, predicted by the final flexible field-fit CoMFA models are in good agreement with the corresponding experimental values. These models should be effective for predicting the endocrine disrupting potential of existing chemicals as well as prospective and newly prepared chemicals before they enter the environment.

Animals↗

PubMed1987

Performance of dust respirators with facial seal leaks: II. Predictive model.

A performance model for half-mask and single-use respirators is presented. It represents a possible alternative to field measurements of respirator performance. Experimental data on filter and leak performance given in Part I were used to develop a model that allows one to predict 1) the overall respirator penetration as a function of particle size for any work rate and 2) overall total mass penetration for any work rate and exposure aerosol-size distribution for a known respirator filter and facial seal leak condition. A simplified method based on general regression equations is presented that allows one to estimate these quantities based on QNFT (quantitative fit testing) measurements and a knowledge of the exposure aerosol-size distribution. Example calculations are given for a situation in which QNFT gives a fit factor of 50 for a half-mask with dust, fume and mist filter cartridges, but predicted protection factors for various use conditions range from 20 to 81 depending on exposure particle-size distribution and work rate of the wearer.

Aerosols↗

PubMed2000

Validation and analysis of modeled predictions of growth of Bacillus cereus spores in boiled rice.

The growth of psychrotrophic Bacillus cereus 404 from spores in boiled rice was examined experimentally at 15, 20, and 30 degrees C. Using the Gompertz function, observed growth was modeled, and these kinetic values were compared with kinetic values for the growth of mesophilic vegetative cells as predicted by the U.S. Department of Agriculture's Pathogen Modeling Program, version 5.1. An analysis of variance indicated no statistically significant difference between observed and predicted values. A graphical comparison of kinetic values demonstrated that modeled predictions were "fail safe" for generation time and exponential growth rate at all temperatures. The model also was fail safe for lag-phase duration at 20 and 30 degrees C but not at 15 degrees C. Bias factors of 0.55, 0.82, and 1.82 for generation time, lag-phase duration, and exponential growth rate, respectively, indicated that the model generally was fail safe and hence provided a margin of safety in its growth predictions. Accuracy factors of 1.82, 1.60, and 1.82 for generation time, lag-phase duration, and exponential growth rate, respectively, quantitatively demonstrated the degree of difference between predicted and observed values. Although the Pathogen Modeling Program produced reasonably accurate predictions of the growth of psychrotrophic B. cereus from spores in boiled rice, the margin of safety provided by the model may be more conservative than desired for some applications. It is recommended that if microbial growth modeling is to be applied to any food safety or processing situation, it is best to validate the model before use. Once experimental data are gathered, graphical and quantitative methods of analysis can be useful tools for evaluating specific trends in model prediction and identifying important deviations between predicted and observed data.

Bacillus cereus↗

PubMed2002

Interaction and intervention modeling: predicting and extrapolating the impact of multiple interventions.

PURPOSE: Methods called interaction and intervention modeling are presented. Interaction modeling examines the interactions between variables as the basis for predicting the impact of multiple variables on a target population and on populations with difference distributions of risk factors. Intervention modeling incorporates these interactions and aims to extrapolate the impact of multiple interventions to new populations. The aim is to develop methods that will be useful for modeling and comparing intervention strategies using existing data and standard statistical methods. METHODS: Traditional hypothesis testing methods used for randomized clinical trials and cohort studies and extrapolating the results to new populations are compared with interaction and intervention modeling methods. Interaction and intervention modeling utilizes the same data as the traditional approach but examines the impact of multiple simultaneous interactions and allows extrapolation of the results to populations with different prevalences and distributions of risk factors. An example using real data demonstrates the potential of interaction and intervention modeling to predict the impact of multiple interacting variables and to compare the impact of alternative interventions. RESULTS: The methods outlined take into account the impact of the magnitude of the relative risks, prevalence of risk factors, and interaction of risk variables when predicting the impact on a new population or extrapolating the results of one or more interventions on a new population. Traditional methods that do not take into account interactions are shown to produce different conclusions from the intervention modeling approach that incorporates interactions. The impact of the intervention modeling approach compared with the traditional approach will be quite variable depending on the prevalence of the risk factors and their extent of interaction. CONCLUSIONS: Studies designed to test a hypothesis treat most variables as potential confounding variables adjusting for their impact and their interactions as part of the analysis using traditional regression methods. Interaction and intervention modeling focuses on the interactions themselves and allows comparison of the effectiveness of alternative interventions.

Clinical Trials as Topic↗

PubMed2002

A predictive fatigue model--II: Predicting the effect of resting times on fatigue.

We have recently developed a force- and fatigue-model system that accurately predicted the effect of stimulation frequency on muscle fatigue. The data used to test the model were produced by stimulation trains with resting times of 500 ms. Because the resting times between stimulation trains affect muscle fatigue, this study tested the model's ability to predict the effect of resting times on fatigue. In addition, because this study included different subjects than those used to develop the model, the validity of the model could be tested. Data were collected from human quadriceps femoris muscles using fatigue protocols that included resting times of 500, 750, or 1000 ms. Our results showed that the model predicted fatigue as being a decreasing function of resting time, which was consistent with experimental data. Reliability tests between the experimental data and predictions showed interclass correlation coefficients of 0.97, 0.95, and 0.81 for the initial, final, and percentage decline in peak forces, respectively, suggesting strong agreement between the experimental data and the predictions by the model. The success of our current force- and fatigue-model system helps to validate the model and suggests its potential use in identifying the optimal activation pattern during clinical application of functional electrical stimulation.

Algorithms↗

PubMed1997

Predictive model selection for repeated measures random effects models using Bayes factors.

The random effects model fit to repeated measures data is an extremely common model and data structure in current biostatistical practice. Modern data analysis often involves the selection of models within broad classes of prespecified models, but for models beyond the generalized linear model, few model-selection tools have been actively studied. In a Bayesian analysis, Bayes factors are the natural tool to use to explore these classes of models. In this paper, we develop a predictive approach for specifying the priors of a repeated measures random effects model with emphasis on selecting the fixed effects. The advantage of the predictive approach is that a single predictive specification is used to specify priors for all models considered. The methodology is applied to a pediatric pain data analysis.

Bayes Theorem↗

PubMed1993

Model predictions for anthelmintic resistance amongst Haemonchus contortus populations in southern Brazil.

A computer model developed to study Ostertagia circumcincta resistance to anthelmintics in UK sheep flocks has been adapted for use with Haemonchus contortus under southern Brazilian conditions. The model simulates the effect of different anthelmintic control regimens on the year-to-year pattern of resistance in breeding ewes. The nematode control regimen most used by Brazilian sheep farmers was found to increase the frequency of genes which confer resistance from approximately 3% to 14% in an H. contortus population over a 20 year period. The effect of early versus late season anthelmintic treatment was investigated. This indicated that early season treatment would select for resistance rapidly, whereas late season treatments would not, owing to large numbers of untreated parasites accumulating at the beginning of the season. A model which can predict the development of anthelmintic resistance in parasites of ewes is a valuable tool in the understanding of the effect of different strategies on nematode control programmes and merits further consideration.

Animals↗

PubMed2006

A multifocal electroretinogram model predicting the development of diabetic retinopathy.

The prevalence of diabetes has been accelerating at an alarming rate in the last decade; some describe it as an epidemic. Diabetic eye complications are the leading cause of blindness in adults aged 25-74 in the United States. Early diagnosis and development of effective preventatives and treatments of diabetic retinopathy are essential to save sight. We describe efforts to establish functional indicators of retinal health and predictors of diabetic retinopathy. These indicators and predictors will be needed as markers of the efficacy of new therapies. Clinical trials aimed at either prevention or early treatments will rely heavily on the discovery of sensitive methods to identify patients and retinal locations at risk, as well as to evaluate treatment effects. We report on recent success in revealing local functional changes of the retina with the multifocal electroretinogram (mfERG). This objective measure allows the simultaneous recording of responses from over 100 small retinal patches across the central 45 degrees field. We describe the sensitivity of mfERG implicit time measurement for revealing functional alterations of the retina in diabetes, the local correspondence between functional (mfERG) and structural (vascular) abnormalities in eyes with early nonproliferative retinopathy, and longitudinal studies to formulate models to predict the retinal sites of future retinopathic signs. A multivariate model including mfERG implicit time delays and 'person' risk factors achieved 86% sensitivity and 84% specificity for prediction of new retinopathy development over one year at specific locations in eyes with some retinopathy at baseline. A preliminary test of the model yielded very positive results. This model appears to be the first to predict, quantitatively, the retinal locations of new nonproliferative diabetic retinopathy development over a one-year period. In a separate study, the predictive power of a model was assessed over one- and two-year follow-ups. This permitted successful prediction of new retinopathy development in eyes with and without retinopathy at baseline. Finally, we briefly describe our current research efforts to (a) locally predict future sight-threatening diabetic macular edema, (b) investigate local retinal function change in adolescent patients with diabetes, and (c) better understand the physiological bases of the mfERG delays. The ability to predict the retinal locations of future retinopathy based on mfERG implicit time provides clinicians a powerful tool to screen, follow-up, and even consider early prophylactic treatment of the retinal tissue in diabetic patients. It also aids identification of 'at risk' populations for clinical trials of candidate therapies, which may greatly reduce their cost by decreasing the size of the needed sample and the duration of the trial.

Diabetic Retinopathy↗

PubMed2001

Can dose-response models predict reliable normal tissue complication probabilities in radical radiotherapy of urinary bladder cancer? The impact of alternative radiation tolerance models and parameters.

PURPOSE: To analyze the consequences of selecting alternative normal tissue complication probability (NTCP) models and parameters for evaluation of radiotherapy of urinary bladder cancer. METHODS AND MATERIALS: Treatment plans of 24 bladder cancer patients referred to radical 4-field conformal radiotherapy were analyzed. Small intestinal and rectal NTCPs were determined using both the probit and relative seriality models with several sets of published radiation tolerance parameters. Various combinations of NTCP models and parameters were applied to find the prescription dose in individual patients as well as to estimate the benefit of the conformal radiotherapy setup. RESULTS: Different risk estimates were predicted from the two NTCP models, even when the same clinical radiation tolerance doses were fitted into the two models. The demonstrated variability translated into significant deviations (7-10 Gy) in the recommended prescription doses. Even if it was possible to discriminate between a 2-field plan and the 4-field conformal setup using a given complication model and set of tolerance parameters, the estimated benefit of the conformal treatment in terms of permitted dose escalation varied with as much as 10-12 Gy between the different NTCP models/parameters used. CONCLUSION: Different NTCP models and tolerance parameters might propose different answers to important clinical questions in radiotherapy treatment of bladder cancer, such as dose prescription and scoring of rival treatment plans. We therefore recommend that the variability caused by tolerance parameter uncertainty and model selection should be taken into account in dose-response modeling of radiotherapy treatment.

Aged↗

PubMed1997

Development and evaluation of a predictive model for the effect of temperature and water activity on the growth rate of Vibrio parahaemolyticus.

The growth rates of four strains of Vibrio parahaemolyticus were measured and compared in a model broth system. The results for the fastest growing strain, based on 77 combinations of temperature and water activity (aw) using NaCl as the humectant, were summarised in the form of a predictive mathematical model. The model, of the square-root type includes a novel term to describe the effects of super-optimal water activity, and can be used to predict generation times for the temperature range (8-45 degrees C) and water activity range (0.936-0.995) which permit growth of Vibrio parahaemolyticus. Predicted generation times from the model were compared to literature data, using bias and accuracy factors, for both laboratory media and foods. The model was shown to give realistic growth estimates, with a bias value of 1.01, and an accuracy factor of 1.38.

Models, Biological↗

PubMed2006

Development and validation of a terrestrial biotic ligand model predicting the effect of cobalt on root growth of barley (Hordeum vulgare).

A Biotic Ligand Model was developed predicting the effect of cobalt on root growth of barley (Hordeum vulgare) in nutrient solutions. The extent to which Ca(2+), Mg(2+), Na(+), K(+) ions and pH independently affect cobalt toxicity to barley was studied. With increasing activities of Mg(2+), and to a lesser extent also K(+), the 4-d EC50(Co2+) increased linearly, while Ca(2+), Na(+) and H(+) activities did not affect Co(2+) toxicity. Stability constants for the binding of Co(2+), Mg(2+) and K(+) to the biotic ligand were obtained: logK(CoBL)=5.14, logK(MgBL)=3.86 and logK(KBL)=2.50. Limited validation of the model with one standard artificial soil and one standard field soil showed that the 4-d EC50(Co2+) could only be predicted within a factor of four from the observed values, indicating further refinement of the BLM is needed.

Biological Availability↗

PubMed2005

Development of a biotic ligand model and a regression model predicting acute copper toxicity to the earthworm Aporrectodea caliginosa.

The purpose of this study was to develop a terrestrial biotic ligand model (BLM) for predicting acute copper toxicity to the earthworm Aporrectodea caliginosa. To overcome the basic problems hampering development of BLMs for terrestrial organisms, an artificial flow-through exposure system was developed consisting of an inert quartz sand matrix and a nutrient solution, of which the composition was univariately modified. A. caliginosa was exposed for 7 days under varying concentrations of copper and the major cations modifying toxicity: H+, Ca2+, Mg2+, and Na+. In addition copper speciation was modulated by means of EDTA or dissolved organic carbon (DOC). An increase in pH or pNa resulted in a linear decrease of 7-days median lethal concentrations. Increasing Ca2+ and Mg2+ activities had inconsistent effects. EDTA addition decreased toxicity when the total copper concentration in the pore water was kept the same. This is attributed to the strong complexation capacity of EDTA and shows that total copper is not the toxic species. DOC was more protective than could be explained by its metal complexing properties. The BLM developed incorporates the effects of H+ and Na+. This BLM was validated with the results of a set of bioassays with artificial pore water in quartz sand and by a set of bioassays in spiked field soils. Prediction error was within a factor of 2, but some predictions were not within the 95% confidence interval. Therefore a more widely applicable regression type model was developed that was able to explain >95% of the (lack of) toxicity observed. To our knowledge this is the first report of the successful development of a terrestrial BLM.

Animals↗

PubMed2005

Biotic ligand model prediction of copper toxicity to daphnids in a range of natural waters in Chile.

The objective of this study was to assess the predictive capacity of the biotic ligand model (BLM) for acute copper toxicity to daphnids as applied to a number of freshwaters from Chile and to synthetic laboratory-prepared waters. Thirty-seven freshwater bodies were sampled, chemically characterized, and used to determine the copper concentration associated with the 50% of mortality (LC50) for Daphnia magna, Daphnia pulex, and Daphnia obtusa (native to Chile). The data were then used to run three versions of the acute copper BLM, and the predicted LC50s were compared to the observed ones. The same was done with synthetic assay media at various hardness and dissolved organic carbon (DOC) levels. The BLM versions differed in the affinity constants for some biotic ligand-ion pairs, stability constants for inorganic Cu complexes, and assumptions regarding Cu binding to DOC. All three versions showed a high degree of predictive performance, mostly within a twofold range of observed toxicity values. The D. obtusa data set was used to compare water quality criteria (WQC) derived from the observed toxicity values with those derived from either the BLM or the U.S. Environmental Protection Agency (U.S. EPA) procedure. For most low DOC waters, the three procedures generated similar WQCs. For the high-DOC waters, the EPA-derived criteria were significantly lower, that is, greatly overprotective. The results are also discussed in terms of the validation of the BLM for regulatory use.

Animals↗

PubMed1986

Prediction modeling of physiological responses and human performance in the heat.

Over the last two decades, our laboratory has been establishing the data base and developing a series of predictive equations for deep body temperature, heart rate and sweat loss responses of clothed soldiers performing physical work at various environmental extremes. Individual predictive equations for rectal temperature, heart rate and sweat loss as a function of the physical work intensity, environmental conditions and particular clothing ensemble have been published in the open literature. In addition, important modifying factors such as energy expenditure, state of heat acclimation and solar heat load have been evaluated and appropriate predictive equations developed. Currently, we have developed a comprehensive model which is programmed on a Hewlett-Packard 41 CV hand held calculator. The primary physiological inputs are deep body (rectal) temperature and sweat loss while the predicted outputs are the expected physical work--rest cycle, the maximum single physical work time if appropriate, and the associated water requirements. This paper presents the mathematical basis employed in the development of the various individual predictive equations of our heat stress model. In addition, our current heat stress prediction model as programmed on the HP 41 CV is discussed from the standpoint of propriety in meeting the Army's needs and therefore assisting in military mission accomplishment.

Acclimatization↗

PubMed1996

Multivariable predictive models for adverse outcome of invasive meningococcal disease in children.

For prediction of adverse outcome (AO, defined as death or limb amputation) of invasive meningococcal disease (IMD) in children, two multivariable models were derived and validated by reviewing the data in the medical records of patients with IMD, who ranged from birth to 19 years of age, at three pediatric referral hospitals between 1985 and 1990 (derivation set, n = 153, 19 AO) and between 1991 and 1994 (validation set, n = 92, 11 AO). Variables in the derivation set significantly associated with AO (p < 0.05) were entered into a logistic regression analysis. Because coagulation studies (prothrombin time, partial thromboplastin time, and serum fibrinogen concentration) were available for only 50% of patients, two analyses were performed, either excluding (model 1) or including (model 2) coagulation studies. These analyses identified an absolute neutrophil count less than 3000/mm3, poor perfusion, and a platelet count less than 150,000/mm3 (model 1), and a serum fibrinogen concentration less than 2.5 gm/L (250 mg/dl) and an absolute neutrophil count less than 3000/mm3 (model 2), as independent predictors of AO (p < 0.05). When the models were tested on the validation set, the presence of at least two of the three predictors in model 1 had a sensitivity of 82% and a specificity of 97% in predicting AO; the presence of both predictors in model 2 had a sensitivity of 89% and a specificity of 97%. These models can reliably identify patients with IMD at high risk of AO for whom consideration of novel therapies is justified.

Adolescent↗