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Results for “Predictive modeling”

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At least 127 records · Page 7Linked to original sources

PubMed1996

Final height prediction models for pubertal boys.

Accurate adult height prediction is of clinical importance in assessing the need for pharmacological intervention and in the evaluation of the outcome of therapy. The methods currently in use are subject to a wide range of error, one source of which is the use of bone age (BA) measurements. We have developed a computer model for predicting adult height in pubertal boys without using BA determinations. The model is based on the existing Infancy-Childhood-Puberty model and calculates the onset of the pubertal growth spurt. Predicted adult height was assessed using this new model and four others in a group of normal boys and in a group of short normal boys receiving growth hormone. Calculated final heights by all the methods were not significantly different. Incorporation of paternal height into the prediction equations increased the accuracy of the prediction. It was concluded that our new model is as accurate as existing methods of predicting final height that involve assessing BA.

Adolescent↗

PubMed2025

Genetic mapping and predictive modeling of paralog synthetic lethality.

Paralogs are abundant in the human genome and thought to be a primary source of synthetic lethality, yet the vast paralogome remains largely uncharacterized. A digenic screen of 36,648 paralogous pairs in the human genome revealed that synthetic lethalities were infrequent and varied in penetrance in different tumor backgrounds. We hypothesized that the variable penetrance of synthetic lethalities resulted from complex polygenic interactions with different cellular contexts. A machine learning classifier of a subset of paralog pairs tested across 49 cancer models revealed that endogenous perturbations in related pathways predicted paralog synthetic lethality. Further, predictive modeling of paralog synthetic lethality showed that the strength of synthetic lethal interactions was largely due to the overlap and essentiality of the protein-protein interaction networks shared by the paralog pairs. Collectively, this study tested 36,648 digenic paralog interactions and delineated the key feature classes that underlie the heterogeneity of paralog synthetic lethalities.

Humans↗

PubMed1992

A comparative investigation of hepatic clearance models: predictions of metabolite formation and elimination.

Liver clearance models serve to improve our understanding of the relationships between the physiological determinants and hepatic clearance and predict changes in the disposition of substrates when homeostasis of the organ is perturbed. Their ability to describe metabolism was presently extended to the sequential formation and elimination of primary (M1), secondary (M2), and tertiary (M3) metabolites during a single passage of drug (P) across the liver, under steady state and first-order conditions. The well-stirred model is distinct from other models in that metabolite formation and elimination is independent of enzymic distributions, the number of steps involved in metabolite formation, and the intrinsic clearances of the precursors. This model predicts that the extraction ratio of a formed primary metabolite derived from drug (E[M1, P]) is identical to that for the preformed primary metabolite (E[M1]), and that the extraction ratios of a secondary metabolite derived from drug (E[M2, P]) and primary metabolite (E[M2, M1]) or preformed secondary metabolite (E[M2]) are identical. For the more physiologically acceptable, parallel-tube and dispersion models, metabolite sequential elimination is highly influenced by the intrinsic clearances of the precursors and the enzymic distributions that mediate removal of precursor species and the metabolites. Furthermore, the extent of sequential metabolism recedes as the number of steps involved for metabolite formation increases. These models predict that E[M1, P] less than E[M1], and E[M2, P] less than E[M2, M1] less than E[M2], with the magnitude of the changes being less for the dispersion model than for the parallel-tube model. Competing pathways that divert substrate from entering the sequential pathway were found to exert only minimal influence on the sequential pathway.

Liver↗

PubMed1994

A prediction model of performance in level II fieldwork in physical disabilities.

OBJECTIVES: A prediction model of performance in physical disabilities fieldwork was generated with grades received in the occupational therapy curriculum and in prerequisite courses. METHOD: Grades included those from functional anatomy, neuroanatomy, physical disabilities lecture, physical disabilities clinic, and prerequisite anatomy and physiology courses. Sampling was done collectively over graduated occupational therapy classes from 1987 to 1992 at the University of Puget Sound. A multiple regression analysis was performed and prediction equations were generated for each subscale of the Fieldwork Evaluation for the Occupational Therapist. Equations for combinations of the subscale categories were also produced. RESULTS: Adjusted R2 values were found to be less than 10% in all equations. CONCLUSION: This poor ability of grades to predict fieldwork performance suggests that future investigation be focused on variables other than grades. Such variables might include student motivation, rapport between the student and fieldwork supervisor, and hospital experience in physical disabilities.

Educational Measurement↗

PubMed1991

Mathematical analysis of perifusion data: models predicting elution concentration.

System models are constructed and analyzed for combined convective flow and for dispersion in distorting concentrations of a chemical signal as it passes through a packed column. We derive general analytical solutions for these models. The results have applications to analyses such as in biological experiments involving hormonal stimulation of perifused cells, elution chromatography, adsorption columns, and studies of groundwater flow. The models reveal that the column distorts an incoming signal (such as a change in solute concentration in the flowing liquid) at the inlet. This distortion is greatest at low values of the Peclet number of the flow and is small at larger values. We explore the effects of the approximations inherent in the mathematical models of the system. Specification of the boundary conditions of the problem are shown to be particularly important. With the use of incorrect models, it is possible to obtain accurate interpolations to data obtained from perfusion experiments. However, the parameters derived (in particular the dispersion constant and the peak concentration of a solute concentration pulse) may be considerably in error. This may lead to errors when these parameter estimates are used to predict results in other experimental situations.

Animals↗

PubMed2025

A prediction model for metachronous colorectal cancer: development and validation.

BACKGROUND: Being able to estimate the risk of metachronous disease in a patient with colorectal cancer (CRC) could enable risk-appropriate surveillance. The aim of this study was to develop a risk-prediction model to estimate individual 10-year risk of metachronous disease following a CRC diagnosis. METHODS: A population-based cohort of patients with CRC was recruited soon after diagnosis between 1997 and 2012 from the United States, Canada, and Australia. Cox regression with the least absolute shrinkage and selection operator penalization was used to identify factors that predicted the risk of a new primary CRC diagnosed at least 1 year after the initial CRC diagnosis. Potential predictors included demography, anthropometry, lifestyle factors, comorbidities, personal and family cancer history, medication use, age at diagnosis, and pathological features of the first CRC. Internal validation through bootstrapping was used to evaluate the discrimination and calibration. RESULTS: We included 6085 CRC cases; 138 (2.3%) of these cases were diagnosed with metachronous disease over a median of 12 years (IQR = 5-17 years). Metachronous CRC risk was predicted by body mass index; smoking status; level of physical activity; family history of cancer and synchronous CRC; stage, grade, histological type, and DNA mismatch repair status; and age at diagnosis of the first CRC. The model was valid with a C statistic of 0.65 (95% CI = 0.63 to 0.68) and a calibration slope of 0.873 (SD = 0.087). CONCLUSIONS: Metachronous CRC can be predicted with reasonable accuracy using a prediction model that consists of clinical variables collected as part of routine practice.

Humans↗

PubMed1994

Investigation of computer lung model-predicted DLCO during single-breath maneuvers.

A computer model based on the theoretical gas absorption equations of Martonen and Wilson (J. Math. Biol. 14: 203-220, 1982) was developed to predict changes in single-breath diffusing capacity of the lung for carbon monoxide (DLCO) with alterations in anatomic dead space, alveolar volume, and inspiratory time. The computer model predictions were compared with results obtained from normal subjects performing DLCO maneuvers under correspondingly altered conditions to assess the validity of the Martonen-Wilson equations. In normal subjects, a theoretical "ideal" single-breath DLCO was derived from measured DLCO and adjusted to instantaneous inspiratory and expiratory times with this three-phase computer model. Clinical single-breath DLCO measurements with increased external dead space and inspiratory and expiratory times in normal subjects correlated well with our mathematical model predictions (all r > 0.80, P < 0.001) but did not correlate closely with the predicted effects of alveolar volume on DLCO. This study demonstrates that uniform gas absorption equations of Martonen and Wilson closely predict changes in observed DLCO with systematic variation of inspiratory and expiratory time in normal subjects at full lung volume and appear to be a reasonable estimation of normal physiological events during single-breath carbon monoxide absorption.

Adult↗

PubMed1989

Individual outcome prediction models for intensive care units.

Prognostic criteria based on static analysis of group statistics do not help much in decisions to withhold or withdraw therapy from intensive care unit (ICU) patients too ill to benefit, since they do not provide adequate information on the features that distinguish non-survivors from survivors. A predictive model which uses dynamic analysis of severity scores based on physiological variables is presented here along with the results of tests of the model in 831 ICU patients. 109 patients were correctly predicted to die by the model. Of 722 whose prediction was outcome unknown, 181 died. Thus, the odds for prediction of death among non-survivors were 0.376. Since there were no false predictions of death, the estimated chance of a false prediction was 0.0055.

Bayes Theorem↗

PubMed1995

Results of B-vitamin supplementation study used in a prediction model to define a reference range for plasma homocysteine.

Because high plasma concentrations of homocysteine constitute an enhanced risk for premature coronary heart disease, it is necessary to establish a reference range for normal concentrations of plasma homocysteine. The frequency distribution of plasma homocysteine concentrations tails to the right, and the nonparametric approach is unsatisfactory for defining a normal plasma homocysteine reference range. By using subjects' responses to appropriate vitamin supplementation, we developed a mathematical prediction model to calculate the plasma homocysteine concentration that could be expected for each individual treated with a vitamin supplement. With this model, we can predict that plasma homocysteine concentrations will approach a normal frequency distribution with a 95% reference range (mean +/- 2 SD) of 4.9-11.7 mumol/L, provided the vitamin status of the study population is improved.

Adolescent↗

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↗

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↗

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↗

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↗

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↗

PubMed1995

Predicting common bile duct lithiasis: determination and prospective validation of a model predicting low risk.

BACKGROUND: The aim of this two-part prospective study was: (1) to identify simple, noninvasive, preoperative factors associated with low or very low risk of common bile duct lithiasis (CBDL); and (2) to test the validity of the statistical model obtained during Part One by the postcholecystectomy follow-up of patients classified into a low-risk group. PATIENTS AND METHODS: In Part One of the study, preoperative clinical, biologic, and ultrasonographic data, and intraoperative cholangiographic findings were collected from 503 consecutive patients undergoing cholecystectomy for symptomatic lithiasis from 1985 to 1989. Using the data obtained in Part One, a linear logistic model was used prospectively in Part Two to determine the prediction of absence of CBDL in 279 consecutive patients. No jaundice, normal transaminase levels, common bile duct (CBD) diameter < 8 mm, and no intrahepatic duct enlargement defined the low-risk group of CBDL. RESULTS: In Part One, CBDL was present in 84 (17%) of all patients. Five parameters were used to classify 73% of all patients as low risk of CBDL and 27% as high risk. In the low-risk groups, CBDL was present in 1% of 116 cases with acute gallbladder complications, and 5% of 250 cases with no acute gallbladder complications. In Part Two, 171 (61%) patients were classified in the low-risk group (Group 1), and CBD stones were not sought by any additional preoperative investigations or intraoperative cholangiography (IOC). One hundred eight patients (39%) were considered at risk of CBDL (Group 2). Mean follow-up was 20.6 months (median 19); 2 patients (1%) in the low-risk group presented a symptomatic retained stone. CONCLUSIONS: This study validated this simple model for predicting risk of CBDL and avoiding invasive preoperative investigations--as well as IOC--in more than 60% of symptomatic cholelithiases. In addition, this model seemed useful for defining patients in whom further exploration for CBDL was justified, since 42 (39%) of the 108 Group 2 patients were proved to have CBDL.

Analysis of Variance↗