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PubMed2004

Value and role of intensive care unit outcome prediction models in end-of-life decision making.

In the United States, intensive care unit (ICU) admission at the end of life is commonplace. What is the value and role of ICU mortality prediction models for informing the utility of ICU care?In this article, we review the history, statistical underpinnings,and current deployment of these models in clinical care. We conclude that the use of outcome prediction models to ration care that is unlikely to provide an expected benefit is hampered by imperfect performance, the lack of real-time availability, failure to consider functional outcomes beyond survival, and physician resistance to the use of probabilistic information when death is guaranteed by the decision it informs. Among these barriers, the most important technical deficiency is the lack of automated information systems to provide outcome predictions to decision makers, and the most important research and policy agenda is to understand and address our national ambivalence toward rationing care based on any criterion.

Advance Care Planning↗

PubMed2001

An efficient formulation of Krylov's prediction model for train induced vibrations based on the dynamic reciprocity theorem.

In Krylov's analytical prediction model, the free field vibration response during the passage of a train is written as the superposition of the effect of all sleeper forces, using Lamb's approximate solution for the Green's function of a halfspace. When this formulation is extended with the Green's functions of a layered soil, considerable computational effort is required if these Green's functions are needed in a wide range of source-receiver distances and frequencies. It is demonstrated in this paper how the free field response can alternatively be computed, using the dynamic reciprocity theorem, applied to moving loads. The formulation is based on the response of the soil due to the moving load distribution for a single axle load. The equations are written in the wave-number-frequency domain, accounting for the invariance of the geometry in the direction of the track. The approach allows for a very efficient calculation of the free field vibration response, distinguishing the quasistatic contribution from the effect of the sleeper passage frequency and its higher harmonics. The methodology is validated by means of in situ vibration measurements during the passage of a Thalys high-speed train on the track between Brussels and Paris. It is shown that the model has good predictive capabilities in the near field at low and high frequencies, but underestimates the response in the midfrequency band.

Journal Article↗

PubMed2005

Predictive modelling of growth and measurement of enzymatic synthesis and activity by a cocktail of selected Enterobacteriaceae and Aeromonas hydrophila.

The possibility was examined of developing a predictive model that would predict food spoilage by combining microbial growth (increase in cellular number) with extracellular enzymatic activity of a cocktail of five strains of Enterobacteriaceae: Escherichia coli, Enterobacter agglomerans, Klebsiella oxytoca, Klebsiella pneumoniae and Proteus vulgaris and one Aeromonas hydrophila strain. Estimations of growth and enzyme activity were made within a three-dimensional matrix of conditions: temperature 2-20 degrees C, pH value 4.0-7.5 and water activity (a(w)) 0.95-0.995. A mathematical model was constructed which predicted growth based on increases in cell number. However, although notable effects of extracellular lipases and proteases were detected, it was not possible to model enzymatic activity and prepare a combined model because the data did not follow the characteristic profile that would allow curve-fitting. Nevertheless, the model for microbial growth and information relating to enzyme activity will be made freely available in a database on the internet.

Aeromonas hydrophila↗

PubMed1999

How generalizable are coronary risk prediction models? Comparison of Framingham and two national cohorts.

BACKGROUND: Previous models used to predict individual risk of death from coronary heart disease (CHD) were developed from data of 3 decades ago from the Framingham Heart Study. CHD mortality rates have declined markedly since that period as a result of improvement in both risk factor status and medical interventions. Generalization of the results from this one study to the population at large remains a matter of concern. We compared predictive functions derived from the major risk factors for CHD from Framingham and 2 more recent national cohorts, the First and Second National Health and Nutrition Examination Survey (NHANES I and NHANES II). METHODS AND RESULTS: The participants included 1846 men and 2323 women 35 to 69 years of age and free of CHD at the fourth examination (1954 to 1958) from the Framingham Study; 2753 men and 3858 women from the NHANES I (1971 to 1975); and 2655 men and 3050 women from NHANES II (1976 to 1980). The 3 cohorts were monitored for 24, 20, and 15 years, respectively. Significant heterogeneity existed among studies in the magnitude of the Cox coefficients for the individual factors (ie, age, systolic blood pressure, serum total cholesterol, and smoking status), especially among men. When risk factors were considered collectively, however, functions derived from and applied to different cohorts had a similar ability to rank individual risk. The areas under the receiver operating characteristic curves were 0. 71 to 0.76 in men and 0.76 to 0.81 in women when different risk functions were applied to their own population or to a second population. The cumulative CHD survival observed in women in the 2 national cohorts was close to what was predicted from the Framingham equation. However, Framingham overestimated the cumulative CHD mortality rates in men in NHANES I and NHANES II. CONCLUSIONS: The Framingham risk model for the prediction of CHD mortality rates provides a reasonable rank ordering of risk for individuals in the US white population for the period 1975 to 1990. However, prediction of absolute risk is less accurate.

Adolescent↗

PubMed2004

Experimental evaluation of lesion prediction modelling in the presence of cavitation bubbles: intended for high-intensity focused ultrasound prostate treatment.

The accuracy of high-intensity focused ultrasound (HIFU) lesion prediction modelling was evaluated for a truncated spherical transducer designed for prostate cancer treatment The modelling adapted the bio heat transfer equation (BHTE) to take into account the activity of cavitation bubbles generated during HIFU exposure. This modelling was used to predict the lesions produced by three different transducer geometries: fixed-focus, concentric-ring and 1.5D phased-array. Lesions were predicted for different ultrasound exposure conditions close to those used in prostate cancer treatment. Twenty-one in vitro and nine in vitro experiments were performed on pig liver to validate the accuracy of the predictions. A good match was found between the predicted and experimental lesion shapes. Lesion dimensions (maximum depth and length, area at the centre of the lesion or central surface area) were measured on experimental and predicted lesions. The central surface area was predicted by the model with a range of error of 0.15-6.5% for in vitro tests and 0.97-9% in vivo. For comparison, BHTE without bubbles had a range of error of 0.4-55.5% (in vitro) and 9-25.5% (in vivo). The model should be accurate enough to predict HIFU lesions under ultrasound exposure conditions used in prostate cancer treatment.

Acoustics↗

PubMed2006

A prediction model of MF radiation in environmental assessment.

OBJECTIVE: To predict the impact of MF radiation on human health. METHODS: The vertical distribution of field intensity was estimated by analogism on the basis of measured values from simulation measurement. RESULTS: A kind of analogism on the basis of geometric proportion decay pattern is put forward in the essay. It showed that with increasing of height the field intensity increased according to geometric proportion law. CONCLUSION: This geometric proportion prediction model can be used to estimate the impact of MF radiation on inhabited environment, and can act as a reference pattern in predicting the environmental impact level of MF radiation.

Electromagnetic Phenomena↗

PubMedDate not supplied

An outcome in need of clarity: building a predictive model of subjective quality of life for persons with severe mental illness living in the community.

PURPOSE: The study purpose was to construct a predictive model of subjective quality of life for persons with severe mental illness living in the community with particular attention to participation in occupations. METHOD: Persons with severe mental illness (N=154) rated their subjective quality of life. Several measures for each of the following categories of variables were completed: demographics, clinical, social participation, and self-measured well-being. Regression analysis was used to determine the significant predictors for each category and then to build the predictive model from these significant variables. RESULTS: Symptom distress accounted for the most variance (33%) in subjective quality of life, followed by psychological integration (3%) and physical integration (2%). CONCLUSIONS: The study suggests that occupational therapists should attend to subjective experience of symptoms to influence quality of life. Therapists are also in a good position to address their clients' sense of belonging to their communities and to enable community participation.

Adolescent↗

PubMed2004

Predictive modelling of the microbial lag phase: a review.

This paper summarises recent trends in predictive modelling of microbial lag phenomena. The lag phase is approached from both a qualitative and a quantitative point of view. First, a definition of lag and an analysis of the prevailing measuring techniques for the determination of lag time is presented. Furthermore, based on experimental results presented in literature, factors influencing the lag phase are discussed. Major modelling approaches concerning lag phase estimation are critically assessed. In predictive microbiology, a two-step modelling approach is used. Primary models describe the evolution of microbial numbers with time and can be subdivided into deterministic and stochastic models. Primary deterministic models, e.g., Baranyi and Roberts [Int. J. Food Microbiol. 23 (1994) 277], Hills and Wright [J. Theor. Biol. 168 (1994) 31] and McKellar [Int. J. Food Microbiol. 36 (1997) 179], describe the evolution of microorganisms, using one single (deterministic) set of model parameters. In stochastic models, e.g., Buchanan et al. [Food Microbiol. 14 (1997) 313], Baranyi [J. Theor. Biol. 192 (1998) 403] and McKellar [J. Appl. Microbiol. 90 (2001) 407], the model parameters are distributed or random variables. Secondary models describe the relation between primary model parameters and influencing factors (e.g., environmental conditions). This survey mainly focuses on the influence of temperature and culture history on the lag phase during growth of bacteria.

Bacteria↗

PubMed2001

Outcome prediction model for very elderly critically ill patients.

CONTEXT: Very elderly critically ill patients have three possible hospital outcomes: discharge to home, discharge to a skilled nursing or rehabilitation facility, or death. The factors associated with these outcomes are unknown. OBJECTIVE: To develop a three-outcome prediction model for very elderly critically ill patients. DESIGN: Retrospective chart abstraction with ordered logistic regression analysis. SETTING: Academic medical center. PATIENTS: Four hundred and fifty-five patients 85 yrs or older admitted to intensive care units (ICU) during 1996 and 1997. MEASUREMENTS AND MAIN RESULTS: A fitted ordinal logistic regression predictive model was developed using data from 243 patients hospitalized in 1996, and validated on data from 212 patients hospitalized in 1997. Model variables include age, gender, baseline support level, type of ICU, heart rate at ICU admission, use of mechanical ventilation, vasopressors or a pulmonary artery catheter during the ICU stay, and the development of respiratory, neurologic or hematologic failure or sepsis while in the ICU. When tested on the 1997 data, the model was well calibrated and had a high discriminant index. CONCLUSIONS: This mathematical model can be used to predict the risks of these three hospital outcomes for this population of patients. These predictions can provide a context when discussing goals and expectations with patients, families, and other healthcare providers and to aid in hospital discharge planning.

Academic Medical Centers↗

PubMed1993

A predictive model for the behavior of radionuclides in lake systems.

This paper describes a predictive model for the behavior of 137Cs in lacustrine systems. The model was tested by comparing its predictions to contamination data collected in various lakes in Europe and North America. The migration of 137Cs from catchment basin and from bottom sediments to lake water was discussed in detail; these two factors influence the time behavior of contamination in lake water. The contributions to the levels of radionuclide concentrations in water, due to the above factors, generally increase in the long run. The uncertainty of the model, used as a generic tool for prediction of the levels of contamination in lake water, was evaluated. Data sets of water contamination analyzed in the present work suggest that the model uncertainty, at a 68% confidence level, is a factor 1.9.

Cesium Radioisotopes↗

PubMed2003

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Adolescent↗

PubMed2004

Deposition of coarse particles in cystic fibrosis: model predictions versus experimental results.

In patients with cystic fibrosis (CF), the lung regions most affected by infection are presumed to be poorly ventilated and, hence, difficult to treat with inhaled therapeutic aerosols. Current dosimetric models do not adequately describe regional particle deposition in CF. We have developed a multiple-path particle deposition model and compared model predictions with the observed pattern of coarse particle (5 microm, mass median aerodynamic diameter) deposition in ten CF patients and eight healthy volunteers. Our model divides the lung into quadrants, separated at lobar bronchi, representing apical and basal lung regions. The volume and ventilation of quadrants were experimentally determined from a xenon equilibrium and multi-breath washout, respectively. Regional ventilation in the healthy lung was assumed to be determined largely by regional compliance. In CF patients, the deviations in regional ventilation from that observed in the healthy subjects were assumed to be due to regional resistance. A "custom" lung morphology was calculated for each subject based on their lung volume (functional residual capacity plus one-half tidal volume) and ventilation to each quadrant. Input parameters for particle deposition calculations were "custom" lung morphology, breathing pattern, and inhaled particle size. Relative to healthy subjects, the CF patients had reduced ventilation to the apices and increased ventilation to the bases of the lung. In healthy subjects, the general pattern of particle deposition followed ventilation. However, in the CF patients, the model predicted increased particle deposition in the large airways of the apices (an obstructed and poorly ventilated region) and to a lesser extent in the basal lung (relatively healthier and better-ventilated region), whereas particle deposition in the parenchyma was only increased in the basal lung and was decreased or absent in the apical lung. Our modeling strategy improves estimates of regional aerosol deposition and may be useful for predicting breathing conditions and particle size for optimal drug delivery in a given CF patient.

Administration, Inhalation↗

PubMed2006

Bias arising from missing data in predictive models.

OBJECTIVE: The purpose of this study is to determine the effect of three common approaches to handling missing data on the results of a predictive model. STUDY DESIGN AND SETTING: Monte Carlo simulation study using simulated data was used. A baseline logistic regression using complete data was performed to predict hospital admission, based on the white blood cell count (WBC) (dichotomized as normal or high), presence of fever, or procedures performed (PROC). A series of simulations was then performed in which WBC data were deleted for varying proportions (15-85%) of patients under various patterns of missingness. Three analytic approaches were used: analysis restricted to cases with complete data, missing data assumed to be normal (MAN), and use of imputed values. RESULTS: In the baseline analysis, all three predictors were all significantly associated with admission. Using either the MAN approach or imputation, the odds ratio (OR) for WBC was substantially over- or underestimated depending on the missingness pattern, and there was considerable bias toward the null in the OR estimates for fever. In the CC analyses, OR for WBC was consistently biased toward the null, OR for PROC was biased away from the null, and the OR for fever was biased toward or away from the null. Estimates for overall model discrimination were substantially biased using all analytic approaches. CONCLUSIONS: All three methods of handling large amounts of missing data can lead to biased estimates of the OR and of model performance in predictive models. Predictor variables that are measured inconsistently can affect the validity of such models.

Adolescent↗

PubMed2026

Genomic selection in timothy (Phleum pratense L.): a comprehensive evaluation of prediction models, multi-trait strategies, and forward validation across Norwegian environments.

This study presents a comprehensive evaluation of genomic selection (GS) in timothy (Phleum pratense L.), comparing nine prediction models across yield and quality traits at two Norwegian locations. Forward validation with independent full-sib (FS2) families revealed a substantial generalization gap, highlighting the need for realistic accuracy assessment in polyploid forage breeding. Timothy (Phleum pratense L.) is the most important forage grass in Northern Europe, yet genomic selection has not been systematically evaluated in this hexaploid species. We assessed 889 FS2-families originating from biparental crosses among 49 cultivars/populations. The FS2-families were genotyped with 30,698 SNP markers derived from genotyping-by-sequencing (GBS) and field tested for three harvest years at a highland and a lowland continental location in Southern Norway. Nine genomic prediction models were compared for six yield traits (dry matter yield per cut and total) and six quality traits (protein, digestibility, and fiber fractions) across three cuts/year. Within-training cross-validation accuracies were moderate to high (mean r = 0.62), with Random Forest and SVR consistently outperforming GBLUP. However, forward validation using 213 independent FS2-families revealed dramatically lower accuracies (mean r = 0.16), with only 16 of 30 trait-dataset combinations reaching statistical significance (p < 0.05). Genomic heritabilities (GREML), estimated across environments, ranged from near zero for the quality traits to 0.55 for the yield traits. Multi-trait models improved accuracy by 3-5% over single-trait approaches, while FS2 families-by-environment interaction models with Random Forest achieved the highest within-training accuracy (mean r = 0.71). Marker density analysis showed accuracy plateauing at approximately 15000 SNPs. Genetic correlations among the yield component traits were estimated by multi-trait REML; correlations among the quality traits could not be estimated reliably because their genomic heritabilities were low. A multi-trait selection index identified top-performing FS2-families for further crossing recommendations. These results provide a benchmark for GS implementation in hexaploid timothy and emphasize that cross-validation substantially overestimates prediction accuracy for truly independent material.

Norway↗

PubMed2005

Monte carlo simulation of base and nucleotide excision repair of clustered DNA damage sites. II. Comparisons of model predictions to measured data.

Clustered damage sites other than double-strand breaks (DSBs) have the potential to contribute to deleterious effects of ionizing radiation, such as cell killing and mutagenesis. In the companion article (Semenenko et al., Radiat. Res. 164, 180-193, 2005), a general Monte Carlo framework to simulate key steps in the base and nucleotide excision repair of DNA damage other than DSBs is proposed. In this article, model predictions are compared to measured data for selected low-and high-LET radiations. The Monte Carlo model reproduces experimental observations for the formation of enzymatic DSBs in Escherichia coli and cells of two Chinese hamster cell lines (V79 and xrs5). Comparisons of model predictions with experimental values for low-LET radiation suggest that an inhibition of DNA backbone incision at the sites of base damage by opposing strand breaks is active over longer distances between the damaged base and the strand break in hamster cells (8 bp) compared to E. coli (3 bp). Model estimates for the induction of point mutations in the human hypoxanthine guanine phosphoribosyl transferase (HPRT) gene by ionizing radiation are of the same order of magnitude as the measured mutation frequencies. Trends in the mutation frequency for low- and high-LET radiation are predicted correctly by the model. The agreement between selected experimental data sets and simulation results provides some confidence in postulated mechanisms for excision repair of DNA damage other than DSBs and suggests that the proposed Monte Carlo scheme is useful for predicting repair outcomes.

DNA Damage↗

PubMed2004

Analysis and validation of a predictive model for growth and death of Aeromonas hydrophila under modified atmospheres at refrigeration temperatures.

Specific growth and death rates of Aeromonas hydrophila were measured in laboratory media under various combinations of temperature, pH, and percent CO(2) and O(2) in the atmosphere. Predictive models were developed from the data and validated by means of observations obtained from (i) seafood experiments set up for this purpose and (ii) the ComBase database (http://www.combase.cc; http://wyndmoor.arserrc.gov/combase/). Two main reasons were identified for the differences between the predicted and observed growth in food: they were the variability of the growth rates in food and the bias of the model predictions when applied to food environments. A statistical method is presented to quantitatively analyze these differences. The method was also used to extend the interpolation region of the model. In this extension, the concept of generalized Z values (C. Pin, G. García de Fernando, J. A. Ordóñez, and J. Baranyi, Food Microbiol. 18:539-545, 2001) played an important role. The extension depended partly on the density of the model-generating observations and partly on the accuracy of extrapolated predictions close to the boundary of the interpolation region. The boundary of the growth region of the organism was also estimated by means of experimental results for growth and death rates.

Aeromonas hydrophila↗

PubMed2001

Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Several internal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortality after an acute myocardial infarction. Random samples with a size between n = 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance, with large variability. Cross-validation on 10% of the sample had low bias and low variability, but was not suitable for all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude that split-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model.

Aged↗

PubMed2004

Genetic markers applied in regression tree prediction models.

Classification and regression tree (CART) modelling was used to determine infectious hypodermal and haematopoietic necrosis virus (IHHNV) resistance and susceptibility in Penaeus stylirostris. In a previous study, eight random amplified polymorphic DNA (RAPD) markers and viral load values using real-time quantitative PCR were obtained and used as the training data set in order to create numerous regression tree models. Specifically, the genetic markers were used as categorical predictor variables and viral load values as the dependent response variable. To determine which model has the highest predictive accuracy for future samples, RAPD fingerprint data was generated from new Penaues stylirostris IHHNV resistant and susceptible individuals and used to test the regression models. The best performing tree was a four terminal node tree with three genetic markers as significant variables. Marker-assisted breeding practices may benefit from the creation of regression tree models that apply genetic markers as predictive factors. To our knowledge this is the first study to use RAPD markers as predictors within a CART prediction model to determine viral susceptibility.

Animals↗