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At least 451 records · Page 25Linked to original sources
PubMed2006

3-month and 12-month mortality after first liver transplant in adults in Europe: predictive models for outcome.

BACKGROUND: Mortality after liver transplantation depends on heterogeneous recipient and donor factors. Our aim was to assess risk of death and to develop models to help predict mortality after liver transplantation. METHODS: We analysed data from 34,664 first adult liver transplants from the European Liver Transplant Registry to identify factors associated with mortality at 3-months (n=21,605 in training dataset) and 12-months (n=18,852 in training dataset) after transplantation. We used multivariable logistic regression models to generate mortality scores for each individual, and assessed model discrimination and calibration on an independent validation dataset (n=9489 for 3-month model and n=8313 for 12-month model). FINDINGS: 2540 of 21,605 (12%) individuals in the 3-month training sample had died by 3 months. Compared with those transplanted in 2000-03, those transplanted earlier had a higher risk of death. Increased mortality at 3-months post-transplantation was associated with acute liver failure (adjusted odds ratio 1.61), donor age older than 60 years (1.16), compatible (1.22) or incompatible (2.07) donor-recipient blood group, older recipient age (1.12 per 5 years), split or reduced graft (1.96), total ischaemia time of longer than 13 h (1.38), and low United Network for Organ Sharing score (score 1: 2.43; score 2: 1.67). However, cirrhosis with hepatocellular carcinoma, alcohol cirrhosis, hepatitis C or primary biliary cirrhosis, donor age 40 years or younger, or less, hepatitis B, and larger size of transplant centre (> or = 70 transplants per year) were associated with improved early outcomes. The 3-month mortality score discriminated well between those who did and did not die in the validation sample (C statistic=0.688). We noted similar findings for 12-month mortality, although deaths were generally underestimated at this timepoint. INTERPRETATION: The 3-month and 12-month mortality models can be effectively used to assess outcomes both within and between centres. Furthermore, the models provide a means of assessing the risk of post-transplantation mortality, giving clinicians important data on which to base strategic decisions about transplant policy in particular individuals or groups.

Adult↗
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↗
PubMed2006

Persistent organic pollutants (POPs) in the conventional activated sludge treatment process: model predictions against experimental values.

The FATE and treatibility estimator (FATE) model, developed by the United States Environmental Protection Agency was used for the prediction of the FATE of 26 persistent organic pollutants (POPs), i.e. 7 PCBs and 19 organochlorine compounds (OCs), during the conventional activated sludge treatment process applied in the waste water treatment plant (WWTP) of Thessaloniki, Greece. The removal rates predicted by the model for the primary and the secondary treatment stages were found to differ substantially from those experimentally measured. When the overall treatment was considered, the differences between measured and model predicted removals were within acceptable limits of confidence. Possible reasons that might cause deviations from experimental values were suggested to be the wastewater content in dissolved organic carbon (DOC), and/or the low concentrations of POPs in untreated wastewater.

Adsorption↗
PubMed2001

Family history and the risk of coronary heart disease: comparing predictive models.

Family history is commonly used when evaluating coronary heart disease (CHD) risk yet it is usually treated as a simple binary variable according to the occurrence or non-occurrence of disease. This definition however fails to consider the potential components of a family history which may in fact exert different degrees of influence on the overall risk profile. The purpose of this paper is to compare different predictive models for CHD which incorporate family history as either a binary variable or different types of family risk indices in terms of their predictive ability. Models for estimating CHD risk were constructed based on usual risk factors and different family history variables. This construction was accomplished using logistic regression and RECursive Partition and AMalgamation (RECPAM) trees. Our analyses demonstrate the importance of using more sophisticated definitions of family history variables compared to a simple binary approach since this leads to a significant improvement in the predictive ability of CHD risk models.

Adult↗
PubMed2005

External validation of the biodegradability prediction model CATABOL using data sets of existing and new chemicals under the Japanese Chemical Substances Control Law.

External validation of the biodegradability prediction model CATABOL was conducted using test data of 338 existing chemicals and 1123 new chemicals under the Japanese Chemical Substances Control Law. CATABOL predicts that 1089 chemicals will have a BOD < 60% while 925 (85%) actually have an observed BOD<60%. The percentage of chemicals with an observed BOD value <60% tends to increase as the predicted BOD values decrease. In contrast, 340 chemicals were predicted to have a BOD > or = 60% and 234 (69%) actually had an observed BOD > or = 60%. The prediction of poor biodegradability was more accurate than the predictions of high biodegradability. The features of chemical structures affecting CATABOL predictability were also investigated.

Biodegradation, Environmental↗
PubMed2002

Predictive model to identify positive tuberculosis skin test results during contact investigations.

CONTEXT: Budgetary constraints in tuberculosis (TB) control programs require streamlining contact investigations without sacrificing disease control. OBJECTIVE: To develop more efficient methods of TB contact investigation by creating a model of TB transmission using variables that best predict a positive tuberculin skin test among contacts of an active TB case. DESIGN, SETTING, AND SUBJECTS: After standardizing the interview and documentation process, data were collected on 292 consecutive TB cases and their 2941 contacts identified by the Alabama Department of Public Health between January and October 1998. Generalized estimating equations were used to create a model for predicting positive skin test results in contacts of active TB cases. The model was then validated using data from a prospective cohort of 366 new TB cases and their 3162 contacts identified between October 1998 and April 2000. MAIN OUTCOME MEASURE: Tuberculin skin test result. RESULTS: Using generalized estimating equations to build a predictive model, 7 variables were found to significantly predict a positive tuberculin skin test result among contacts of an active TB case. Further testing showed this model to have a sensitivity, specificity, and positive predictive value of approximately 89%, 36%, and 26%, respectively. The false-negative rate was less than 10%, and about 40% of the contact workload could be eliminated using this model. CONCLUSIONS: Certain characteristics can be used to predict contacts most likely to have a positive tuberculin skin test result. Use of such models can significantly reduce the number of contacts that public health officials need to investigate while still maintaining excellent disease control.

Adolescent↗
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↗
PubMed2002

Robustness of MetaNet graph models: predicting control of urea production in humans.

Urea production in human liver was described by a MetaNet graph, a flowchart-like representation of metabolic pathways that includes parameters for the kinetic constants of the constituent enzymes. Formal operations on the graph facilitate the identification of ligand-binding equilibria that participate in feedback regulation in the network of biochemical reactions. The state of the biochemical network is specified by the concentrations of the intermediates. At any particular time, the influence of an identified locus of regulation is proportional to the respective fractional saturation of the corresponding binding site. Enzymes that make or consume the feedback chemicals share in the control of the strength of the feedback signal in proportion to their fractional saturation. This model predicts control of urea production by the processes that deliver amino groups to the urea cycle enzymes more than by the cycle enzymes themselves. Mitochondrial membrane transport processes are important for transmission of information through the network, but irreversible enzymes and processes far from equilibrium control the strength of the feedback signal. Systematic variation of the parameter values by amounts comparable to the expected variability of their measured values indicated a high probability of invariance in the identities of the predicted control points. The properties of the model are consistent with those of error-tolerant scale-free networks. These results demonstrate the robustness of a MetaNet model's predictions with respect to uncertainties in the values of its parameters.

Computer Simulation↗
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↗
PubMed2004

Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms.

Hereditary predisposition and causative environmental exposures have long been recognized in human malignancies. In most instances, cancer cases occur sporadically, suggesting that environmental influences are critical in determining cancer risk. To test the influence of genetic polymorphisms on breast cancer risk, we have measured 98 single nucleotide polymorphisms (SNPs) distributed over 45 genes of potential relevance to breast cancer etiology in 174 patients and have compared these with matched normal controls. Using machine learning techniques such as support vector machines (SVMs), decision trees, and naïve Bayes, we identified a subset of three SNPs as key discriminators between breast cancer and controls. The SVMs performed maximally among predictive models, achieving 69% predictive power in distinguishing between the two groups, compared with a 50% baseline predictive power obtained from the data after repeated random permutation of class labels (individuals with cancer or controls). However, the simpler naïve Bayes model as well as the decision tree model performed quite similarly to the SVM. The three SNP sites most useful in this model were (a) the +4536T/C site of the aldosterone synthase gene CYP11B2 at amino acid residue 386 Val/Ala (T/C) (rs4541); (b) the +4328C/G site of the aryl hydrocarbon hydroxylase CYP1B1 at amino acid residue 293 Leu/Val (C/G) (rs5292); and (c) the +4449C/T site of the transcription factor BCL6 at amino acid 387 Asp/Asp (rs1056932). No single SNP site on its own could achieve more than 60% in predictive accuracy. We have shown that multiple SNP sites from different genes over distant parts of the genome are better at identifying breast cancer patients than any one SNP alone. As high-throughput technology for SNPs improves and as more SNPs are identified, it is likely that much higher predictive accuracy will be achieved and a useful clinical tool developed.

Algorithms↗
PubMed2002

Model predictive controller for biodegradable polyhydroxyalkanoate production in fed-batch culture.

The aim of this study is to develop a model predictive controller (MPC) accompanied with a metabolic reaction model controller for controlling ethanol and n-pentanol concentrations and the mole fraction of monomer units in the production of poly(beta-hydroxybutyrate-co-beta-hydroxyvalerate), P(HB-co-HV), a biodegradable copolyester. The controller consists of two parts: one is for alcohol concentration control and the other one is for mole fraction control, and is based on the concept of metabolic flux distribution control. For control of alcohol concentration, conventional proportional and integral (PI) controller and feedforward/feedback controller did not function sufficiently because the large sampling interval of the biosensor led to a severe overshoot of concentration. A single-input and single-output (SISO) MPC is constructed for control of ethanol concentration in the growth phase, whereas a multi-input and multi-output (MIMO) MPC is constructed for control of both alcohol concentrations in the production phase. Specific ethanol consumption rate was estimated by the MPC using the past time series data of ethanol concentration. By means of simulations and experiments, the weighting parameters of the noise filters in the MPC were well adjusted. Ethanol and n-pentanol concentrations were well controlled by the MPC, compared with PI controller and feedforward/feedback controller. As a result, P(HB-co-HV) production was maximized with a given value of mole fraction of 3HV units at the end of cultivation.

Biodegradation, Environmental↗
PubMedDate not supplied

Cross-validation of a model predicting discharge home after stroke rehabilitation. Validating stroke discharge predictors.

A new sample of 116 stroke patients was collected in order to validate a logistic regression model, predicting the chances of severely affected stroke patients being discharged home to independent living. The model was found to be accurate in the new sample, especially for those patients who had a high estimated probability of being discharged home. When the dividing line for the predicted probability for discharge home was set at a value of >/=0.5, the positive and negative predictive values were 74 and 73%, respectively. Further modelling resulted in a new extended model including the variables postural stability on admission, cohabiting, age and perceptual impairment on admission that formed the basis for an index predicting discharge home. This index was then validated in the sample of 93 patients that the first developed model was derived from and showed positive and negative predictive values of 85 and 77%, respectively.

Activities of Daily Living↗
PubMed2003

A predictive model to determine the effects of temperature, sodium pyrophosphate, and sodium chloride on thermal inactivation of starved Listeria monocytogenes in pork slurry.

The effects and interactions of 27 combinations of heating temperature (57.5 to 62.5 degrees C), sodium pyrophosphate (SPP) level (0 to 0.5%, wt/vol), and salt (NaCl) level (0 to 6%, wt/vol) on the thermal inactivation of starved Listeria monocytogenes ATCC 19116 in pork slurry were investigated. A split-split plot experimental design was used to compare all 27 combinations. L. monocytogenes survivors were enumerated on tryptic soy agar supplemented with 0.6% yeast extract. The natural logarithm (loge) of the means of decimal reduction times (D-values) were modeled as a function of temperature, SPP level, and NaCl level. Increasing concentrations of SPP or NaCl protected starved L. monocytogenes from the destructive effect of heat. For example, D-values for the pathogen at 57.5 degrees C in pork slurry with 0, 3, and 6% NaCl were 2.79, 7.75, and 14.59 min, respectively. All three variables interacted to affect the thermal inactivation of L. monocytogenes. A mathematical model describing the combined effect of temperature, SPP level, and NaCl level on the thermal inactivation of starved L. monocytogenes was developed. There was strong correlation (R2 = 0.97) between loge D-values predicted by the model and those observed experimentally. The model can predict D-values for any combination of variables that falls within the range of those tested. This predictive model can be used to assist food processors in designing thermal processes that include an adequate margin of safety for the control of L. monocytogenes in processed meats.

Animals↗
PubMed2005

Application of predictive models to estimate Listeria monocytogenes growth on frankfurters treated with organic acid salts.

Organic acid salts including sodium lactate, sodium diacetate, potassium benzoate, potassium sorbate, and their combinations were assessed as potential inhibitors of Listeria monocytogenes growth on frankfurters. Predictive models for L. monocytogenes growth on frankfurters treated with these salts were compared to select a proper L. monocytogenes growth curve model under these conditions. Sigmoidal equations, including logistic and Gompertz equations, are widely used to describe bacterial growth. In this study, the reparameterized Gompertz model provided a better fit to the L. monocytogenes growth data compared with the other models that were included in this study. Rather than a fixed value for the maximum number of organisms, the reparameterized Gompertz model allows this quantity to be estimated from the data to determine the effect, if any, of the treatments on maximum population density. This information is expected to improve practical methodology for hazard characterization of microbial pathogens on ready-to-eat meat products.

Animals↗
PubMed2006

A predictive model for copper partitioning to suspended particulate matter in river waters.

A chemical equilibrium-based predictive model expressing Cu partitioning as a function of aqueous and solid phase characteristics was developed. The model takes into account only the most important factors that govern Cu partitioning, and therefore results in a relatively simple formulation. It assumes particulate organic carbon (POC) and dissolved organic carbon (DOC) binding sites play the most important role in solid and aqueous phases. The model formulation assumed one-surface site and two dissolved organic matter (DOM) sites, and included the "solids effect". Proton effects were considered for both the particle surface sites and the DOM. The model was calibrated with data for samples collected from the Susquehanna River, and validated with White Clay Creek and Delaware River samples. Copper partitioning in natural water systems with different pH, and concentrations of alkalinity, DOC, POC, total suspended solids (TSS), and total copper was predicted reasonably well.

Binding Sites↗
PubMed2005

Model predictive control of discrete-time hybrid systems with discrete inputs.

This paper proposes and discusses a model predictive control approach to hybrid systems with discrete inputs only. The algorithm, which takes into account a model of a hybrid system, described as a mixed logical dynamical system, is based on a performance-driven reachability analysis. The algorithm abstracts the behavior of the hybrid system by building a "tree of evolution." The nodes of the tree represent the reachable states of a process, and the branches connect two nodes if a transition exists between the corresponding states. A cost-function value is associated with each node, and based on this value the exploration of the tree is driven. As soon as the exploration of the tree is finished, the corresponding input is applied to the system and the procedure is repeated.

Journal Article↗