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Results for “predictive modelling”

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PubMed2006

Informed prognosis [corrected] after abdominal aortic aneurysm repair using predictive modeling techniques [corrected].

OBJECTIVE: To identify the best method for the prediction of postoperative mortality in individual abdominal aortic aneurysm surgery (AAA) patients by comparing statistical modelling with artificial neural networks' (ANN) and clinicians' estimates. METHODS: An observational multicenter study was conducted of prospectively collected postoperative Acute Physiology and Chronic Health Evaluation II data for a 9-year period from 24 intensive care units (ICU) in the Thames region of the United Kingdom. The study cohort consisted of 1205 elective and 546 emergency AAA patients. Four independent physiologic variables-age, acute physiology score, emergency operation, and chronic health evaluation-were used to develop multiple regression and ANN models to predict in-hospital mortality. The models were developed on 75% of the patient population and their validity tested on the remaining 25%. The results from these two models were compared with the observed outcome and clinicians' estimates by using measures of calibration, discrimination, and subgroup analysis. RESULTS: Observed in-hospital mortality for elective surgery was 9.3% (95% confidence interval [CI], 7.7% to 11.1%) and for emergency surgery, 46.7% (95% CI, 42.5 to 51.0%). The ANN and the statistical models were both more accurate than the clinicians' predictions. Only the statistical model was internally valid, however, when applied to the validation set of observations, as evidenced by calibration (Hosmer-Lemeshow C statistic, 14.97; P = .060), discrimination properties (area under receiver operating characteristic curve, 0.869; 95% CI, 0.824 to 0.913), and subgroup analysis. CONCLUSIONS: The prediction of in-hospital mortality in AAA patients by multiple regression is more accurate than clinicians' estimates or ANN modelling. Clinicians can use this statistical model as an objective adjunct to generate informed prognosis.

Aortic Aneurysm, Abdominal↗

PubMed2004

Generalized abdominal visceral fat prediction models for black and white adults aged 17-65 y: the HERITAGE Family Study.

OBJECTIVE: To determine if the relationship between abdominal visceral fat (AVF) and measures of adiposity are different between Black and White subjects and to develop valid field prediction models that accurately identify those individuals with AVF levels associated with high risk for chronic disease. DESIGN: Cross-sectional measurements obtained from 91 Black men, 137 Black women, 227 White men, and 237 White women subjects, ages 17-65 y, who were participants in the HERITAGE Family Study, both at baseline and following 20 weeks of endurance training. MEASUREMENTS: AVF, abdominal subcutaneous fat (ASF), abdominal total fat (ATF), and sagittal diameter (SagD) were measured by computed tomography (CT). Body density was determined by hydrostatic weighing and was used to estimate relative body fat. Arm, waist (WC), and hip circumferences and skinfold thickness measures were taken, and BMI was calculated from weight (kg) and height (m(2)). Since CT abdominal fat variables were skewed, a natural log transformation (Ln) was used to produce a normal distribution. The General Linear Model (GLM) procedure was used to test the relationship between AVF and two different groups of variables-CT and anthropometric. RESULTS: The AVF of White men and women was significantly higher than that of Black men and women, independent of BMI, WHR, WC, and age, and was greater for men than for women. The CT model showed that the combination of SagD, Ln (ASF), age, and race accounted for 84 and 75% of the variance in AVF in men and women, respectively. The anthropometric model provided two valid generalized field AVF prediction equations. The Field-I equation, which included BMI, WHR, age and race, had an r(2) of 0.78 and 0.73 for men and women, respectively. The Field-II equation, which included BMI (women only), WC, age, and race, had an r(2) of 0.78 and 0.72 for men and women, respectively. The field model equations became less accurate as the estimated AVF increased. CONCLUSIONS: (1) At the same age and level of adiposity, Black men and women have less AVF than White men and women. These differences are greater in men than in women. (2) The field regression equations can be generalized to the diverse group of adults studied, both in an untrained and trained state. However, their accuracy decreases with increasing levels of AVF.

Abdomen↗

PubMed1997

A predictive model of student satisfaction with the medical school learning environment.

PURPOSE: To examine differences in attitudes toward the medical school learning environment among student subgroups based on gender and race-ethnicity, to identify the most influential predictors of student satisfaction with the learning environment, and to create a model of student satisfaction with the learning environment. METHOD: Three years of survey data (1992-93 to 1994-95) from first-year students at the University of Michigan Medical School were combined. The total sample consisted of 430 respondents, broken into two sets of subgroups: women (n = 171) and men (n = 259), and whites (n = 239) and underrepresented minorities (n = 74). Asian students were removed from analyses when comparisons were made by race-ethnicity, but were included in the analyses for all students and those comparing men and women. Student's t-tests were used to identify differences between gender and racial-ethnic groups in mean responses to seven survey items, and effect sizes were used to characterize the magnitudes and practical significances of the differences. Forward stepwise regression was conducted to determine the best predictive models for each student subgroup and for the total sample; the subgroup models were compared with each other as well as with the total-sample model. RESULTS: Cross-validation of the gender and race-ethnicity models showed that the men's satisfaction and the women's satisfaction were predicted equally well using either subgroup's model, and that the white students' satisfaction and the underrepresented-minority students' satisfaction were predicted equally well using either subgroup's model. Furthermore, the total-sample model, employing a subset of five predictors, was similar in its predictive power to the subgroup models. CONCLUSION: The study's findings suggest that curriculum structure (timely feedback and the promotion of critical thinking) and students' perceptions of the priority faculty place on students' education are prominent predictors of student satisfaction (across all subgroups) with the learning environment. In contrast, students' perceptions of the learning environment as a comfortable place for all gender and racial-ethnic groups, although less prominent predictors of satisfaction, will discriminate among the subgroups.

Consumer Behavior↗

PubMed2004

A prediction model for superimposed preeclampsia in women with chronic hypertension during pregnancy.

OBJECTIVE: Women with chronic hypertension are at increased risk for superimposed preeclampsia. We developed a prediction algorithm for superimposed preeclampsia using clinical and laboratory information that were measured early in pregnancy. STUDY DESIGN: A secondary analysis of data that were collected from 110 women with chronic hypertension who were enrolled in a trial of calcium supplementation was performed. Blood pressure, the renin-angiotensin system, and calcium metabolism were assessed at 12, 20, 28, and 36 weeks of gestation and 6 weeks after delivery. Multivariable logistic regression was used to develop the predictive model. RESULTS: Thirty-seven women had superimposed preeclampsia. The final model included systolic blood pressure, serum uric acid, and plasma renin activity, which were all measured at 20 weeks of gestation. Women with high systolic blood pressure (>140 mm Hg), elevated uric acid (>3.6 mg/dL), and low plasma renin activity (<4 ng/mL/hr) had an 86% probability of having superimposed preeclampsia. Women with 2 risk factors had a 62% probability of superimposed preeclampsia, and women with only 1 risk factor had a 30% to 40% probability of superimposed preeclampsia. CONCLUSION: We developed a prediction algorithm that can be validated in future studies for superimposed preeclampsia for women with chronic hypertension.

Adult↗

PubMed2004

Disinfection by-products (DBPs) in drinking water and predictive models for their occurrence: a review.

Disinfection for drinking water reduces the risk of pathogenic infection but may pose chemical threat to human health due to disinfection residues and their by-products (DBPs) when the organic and inorganic precursors are present in water. More than 250 DBPs have been identified, but the behavioural profile of only approximately 20 DBPs are adequately known. In the last 2 decades, many modelling attempts have been made to predict the occurrence of DBPs in drinking water. Models have been developed based on data generated in laboratory-scaled and field-scaled investigations. The objective of this paper is to review DBPs predictive models, identify their advantages and limitations, and examine their potential applications as decision-making tools for water treatment analysis, epidemiological studies and regulatory concerns. The paper concludes with a discussion about the future research needs in this area.

Acetates↗

PubMed2006

A reflected energy prediction model for long-range hydroacoustic reflection in the oceans.

Acoustic energy from underwater earthquakes and explosions can propagate over long distances with very little attenuation in the deep ocean. When this sound encounters a seamount, island, or continental margin, it can scatter and again propagate over long distances. Hydrophones in the deep sound channel can detect these reflections tens of minutes or hours after arrivals from the direct source-to-receiver path. This paper presents the Reflected Energy Prediction (REP) model, a model for predicting these reflected arrivals. For a given source and receiver, the REP model uses a detailed knowledge of the underwater environment and components of the Hydroacoustic Coverage Assessment Model, HydroCAM, to predict the impulse response of the ocean. When this impulse response is convolved with a source function, a waveform envelope prediction is made that can be compared with recorded data. In this paper we present the model and a few applications of the model using data recorded from earthquakes and explosions in the Atlantic and Indian Oceans. These examples illustrate the use of the model and initial steps toward model calibration.

Journal Article↗

PubMed2004

Predictive model for the length of hospital stay of children with hematologic malignancies, neutropenia, and presumed infection.

Few studies have assessed the predictive factors for the length of hospital stay for children with malignancies admitted with a diagnosis of presumed infection. We performed an observational prospective study of children 13 years old or younger with hematologic malignancies and neutropenia admitted for presumed infection to set up a predictive model for the length of stay using variables available at admission. Episodes in which children were on induction chemotherapy were excluded from the study. In a multivariate linear regression model, age 6 years or older, an ill appearance, relapse of the hematologic malignancy, and the presence of a central venous catheter were the factors associated with longer lengths of stay for these children.

Age Factors↗

PubMed2006

Stochastic modeling predictions for the clearance of insoluble particles from the tracheobronchial tree of the human lung.

Bronchial clearance of deposited particles was simulated using a stochastic model of the tracheobronchial tree. The clearance model introduced in this study considers (1) a continuous decrease of the mucus thickness from the trachea to the terminal bronchioles according to a linear or an exponential function, (2) the possibility of mucus discontinuities, which are mainly found in intermediate and distal airways of the tracheobronchial compartment, (3) mucus production in proximal airways, (4) a slow bronchial clearance phase due to the capture of a defined particle fraction f (s) in the periciliary sol phase, and (5) an eventual delay of the mucociliary transport at carinal ridges of airway bifurcations. Based on the concept of mucus volume conservation in single bifurcations, a reduction of the thickness of the mucus blanket from proximal to distal airways causes a significant increase of the mucus velocities in small ciliated airways compared to other stochastic modeling predictions assuming a constant thickness of the mucus layer throughout the conducting airways. This effect is further enhanced by the consideration of mucus discontinuities. In contrast, the ability of bronchial airways to produce a certain volume of mucus has a decreasing effect on the mucus velocities. In all generated clearance velocity models, mucociliary clearance is completely terminated within 24 h after exposure, consistent with the experimental evidence. Implementation of a slow bronchial clearance phase predicts a long-term retention fraction, which is fully cleared from the lung after several weeks. For 1-microm MMAD particles, 24-h retention varies between 0.42 and 0.52, in line with the suggestions of the ICRP. Mucus delay at carinal ridges only affects short-term clearance by increasing the retained particle fraction at a given time, while long-term retention is not influenced.

Bronchi↗

PubMed2006

Risk prediction models for familial breast cancer.

A positive family history of breast cancer, reflecting genetic susceptibility, is one of the strongest risk factors for the disease. A number of breast cancer susceptibility genes have been identified to date, with the most important being BRCA1 and BRCA2. Risk prediction models can be used to identify individuals likely to carry BRCA1 and BRCA2 mutations and individuals at high risk of developing the disease. This information can then be used to target genetic testing, screening and interventions more effectively. In this article, the authors review the risk models that have been developed for familial breast cancer and discuss their applicability, strengths and weaknesses, and present examples of classifying women into risk categories according to the predictions by the various models. The review concludes with a discussion of the ways in which risk models could be improved in the immediate- and long-term future.

BRCA2 Protein↗

PubMed2005

Predictive model for Clostridium perfringens growth in roast beef during cooling and inhibition of spore germination and outgrowth by organic acid salts.

Spores of foodborne pathogens can survive traditional thermal processing schedules used in the manufacturing of processed meat products. Heat-activated spores can germinate and grow to hazardous levels when these products are improperly chilled. Germination and outgrowth of Clostridium perfringens spores in roast beef during chilling was studied following simulated cooling schedules normally used in the processed-meat industry. Inhibitory effects of organic acid salts on germination and outgrowth of C. perfringens spores during chilling and the survival of vegetative cells and spores under abusive refrigerated storage was also evaluated. Beef top rounds were formulated to contain a marinade (finished product concentrations: 1% salt, 0.2% potassium tetrapyrophosphate, and 0.2% starch) and then ground and mixed with antimicrobials (sodium lactate and sodium lactate plus 2.5% sodium diacetate and buffered sodium citrate and buffered sodium citrate plus 1.3% sodium diacetate). The ground product was inoculated with a three-strain cocktail of C. perfringens spores (NCTC 8238, NCTC 8239, and ATCC 10388), mixed, vacuum packaged, heat shocked for 20 min at 75 degrees C, and chilled exponentially from 54.5 to 7.2 degrees C in 9, 12, 15, 18, or 21 h. C. perfringens populations (total and spore) were enumerated after heat shock, during chilling, and during storage for up to 60 days at 10 degrees C using tryptose-sulfite-cycloserine agar. C. perfringens spores were able to germinate and grow in roast beef (control, without any antimicrobials) from an initial population of ca. 3.1 log CFU/g by 2.00, 3.44, 4.04, 4.86, and 5.72 log CFU/g after 9, 12, 15, 18, and 21 h of exponential chilling. A predictive model was developed to describe sigmoidal C. perfringens growth curves during cooling of roast beef from 54.5 to 7.2 degrees C within 9, 12, 15, 18, and 21 h. Addition of antimicrobials prevented germination and outgrowth of C. perfringens regardless of the chill times. C. perfringens spores could be recovered from samples containing organic acid salts that were stored up to 60 days at 10 degrees C. Extension of chilling time to > or =9 h resulted in >1 log CFU/g growth of C. perfringens under anaerobic conditions in roast beef. Organic acid salts inhibited outgrowth of C. perfringens spores during chilling of roast beef when extended chill rates were followed. Although C. perfringens spore germination is inhibited by the antimicrobials, this inhibition may represent a hazard when such products are incorporated into new products, such as soups and chili, that do not contain these antimicrobials, thus allowing spore germination and outgrowth under conditions of temperature abuse.

Animals↗

PubMed2000

The application of predictive models in the environmental risk assessment of ECONOR.

Environmental risk assessment of products requires information on the physico-chemical properties, persistence and ecotoxicity of the product, its constituents and possible metabolic and degradation products. Experimental investigations are usually required to generate this information and consequently risk assessment can be costly and time consuming. One possible approach to minimising the amount of experimental testing is to supplement experimental data with data predicted using models such as quantitative structure-activity relationships (QSARs). Using these models, information can be generated based primarily on the knowledge of the chemical structure of the substance(s) under investigation. In this study predictive models were used to assess the environmental risk of the veterinary medicine, ECONOR which contains the active ingredient valnemulin. Available experimental data on the properties, degradability and ecotoxicity of valnemulin was supplemented with predicted data. Where possible, experimental data was used to validate the predicted approaches and this indicated that the predictions were accurate. Information on usage, properties and degradability was input to fate models to predict environmental concentrations (PECs) of valnemulin in soil, pore water and groundwater. Comparison of PECs with experimental and predicted ecotoxicity data for valnemulin indicated that that even under 'worst case' scenarios the environmental risk posed by valnemulin was low.

Diterpenes↗

PubMed2002

The effect of thermodynamic data on computer model predictions of uranium speciation in natural water systems.

Computer models have found widespread application in order to help elucidate and predict changes in environmental systems. One such application is the prediction of trace metal speciation in aqueous systems. This is achieved by solving a set of non-linear equations involving equilibrium constants for all the components in the system, within mass and charge balance constraints. In this study a comparison of the predicted uranium speciation from two computer programs, WHAM and PHREEQCI, is used to illustrate the effect variations in thermodynamic data can have on the models produced. Using the original thermodynamic data provided with the models, WHAM predicted the UO2(2+) ion as the major species (84%) while PHREEQCI predicted UO2(HPO4)2(2-) as the major species (86%). Substituting uranium data from the Nuclear Energy Agency Thermochemical Database project (NEA-TDB) into both programs produced similar results from each program, with UO2F+ predicted to dominate (68%) in a groundwater sample. Natural water samples often contain humic substances. The possible interaction of such substances with uranium was also modelled. The WHAM program includes a discreet site electrostatic humic substance model, however in order to use the PHREEQCI program to model humic substance interactions, a 'model fulvic acid' dataset was added to the program. These models predicted 85 to 98% uranium-humic substance species at neutral pH. This indicates that humic substances do need to be taken into account when modelling uranium speciation in natural water samples.

Computer Simulation↗

PubMed1990

The development of a model for predicting infants at high risk of sudden infant death syndrome in Tasmania.

A statutory 'Notification of Birth' form, containing obstetric and perinatal information, has been routinely collected for Tasmanian deliveries since 1974. For the period 1980 to 1984, birth notification data was collected for over 99% of Tasmanian deliveries. This data was examined for the 130 cases of sudden infant death syndrome (SIDS) that occurred from 1980 to 1984 and for 610 controls. It was then used to construct an at-birth scoring system to predict infants at higher risk of SIDS in the postneonatal period. A predictive model of the relative risk of SIDS was developed by fitting a binomial/logistic generalised linear model to the binary 1980-1984 case control data with birth variables used as predictors. The final predictive model contained five variables (maternal age, infant sex, birth weight, month of birth and feeding practice) and had a sensitivity of 62% and specificity of 73%. The model was then tested on independent birth cohorts from 1985 and 1986 and found to have a sensitivity of 47% and specificity of 77%. The risk of SIDS in the group of infants classified as high risk was 7.9 per 1000 live births and in the group at low risk it was 2.5 per 1000 live births. In addition, the model predicted 74% of neonatal deaths occurring during these 2 years. This compares well with other predictive models developed elsewhere. The predictive model will be used to identify infants at high risk for SIDS in a prospective cohort study.

Cohort Studies↗

PubMed2005

Identification of chronic hepatitis B patients without significant liver fibrosis by a simple noninvasive predictive model.

OBJECTIVE: Histological assessment of liver fibrosis is important in the management of chronic hepatitis B (CHB) infection but poorly accepted by patients because of its invasiveness. The aim of this study was to develop a noninvasive model to assess liver fibrosis in CHB patients using clinical and routine laboratory data. PATIENTS AND METHODS: This was a retrospective study on 235 treatment-naive viremic CHB patients. Univariate analysis of data from the training cohort (n = 150) followed by multivariate logistic regression were performed to identify independent predictors of significant fibrosis and generate predictive models. The models were validated with the remaining patients or validation cohort (n = 85) and by receiver operating characteristics (ROC) analysis. RESULTS: Body mass index (BMI), platelet count, serum albumin, and total bilirubin levels were identified as independent predictors of bridging fibrosis or cirrhosis (Ishak stage 3-6). ROC analysis was performed using the predictive probabilities derived from the regression models. The area under the ROC curve of the best model was 0.803 (95% CI: 0.729-0.878) for the training cohort, 0.765 (95% CI: 0.644-0.885) for the validation cohort, and 0.791 (95% CI: 0.728-0.854) for the entire cohort. Using the low cut-off probability of 0.15, significant fibrosis could be excluded in 83 patients of the total patient population (negative predictive value 0.92). CONCLUSIONS: Our noninvasive model comprising BMI and three routine laboratory tests was accurate in predicting absence of significant fibrosis. Application of this model could provide useful additional information on the stage of disease, guide future management decisions, and potentially decrease the need for liver biopsy in some CHB patients.

Adult↗

PubMed2004

Automated soil resources mapping based on decision tree and Bayesian predictive modeling.

This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.

Algorithms↗

PubMed2002

Psychological aggression in dating relationships: predictive models for males and females.

Variables related to the use of physical aggression in dating relationships and conflict management strategies were used to predict the use of psychological aggression in courtship. Individual factors (i.e., variables associated with threat susceptibility) and situational variables (i.e., relationship length and emotional commitment to the partner, conflict management strategies, and weekly alcohol intake) were proposed to be important in the prediction of male's and female's use of psychological aggression with their partners. Our findings suggest that these variables successfully predict the use of psychologically aggressive acts in courtship. Further, interactions with sex of participant suggest that different variables are important in the prediction of males' and females' use of such negative behaviors. These differences in the relationships between the predictors and criteria for males and females suggest not only divergent predictive models but also potential motivational differences in the employment of such tactics.

Adolescent↗

PubMed1998

Predictive model to describe the combined effect of pH and NaCl on apparent heat resistance of Bacillus stearothermophilus.

The combined effect of pH and NaCl on the apparent thermal resistance of Bacillus stearothermophilus ATCC 12980 spores was studied. Spores were heated at different temperatures (115-125 degrees C) in mushroom substrate, acidified using glucono-delta-lactone to different pH levels (from 5.75 to 6.7), which contained concentrations of NaCl that ranged from 0.5 to 3% (w/v). The recovery medium was acidified to the same pH level and contained the same NaCl concentration as the heating menstruum. A factorial experimental design allowed a predictive model to be developed, which described the combined effect of heating temperature, pH and NaCl on the thermal resistance of B. stearothermophilus spores. Predictions from the model provided a valid description of the data used to generate the model, and agreed with observations from the literature and from an independent experiment performed using asparagus and bean substrates.

Geobacillus stearothermophilus↗

PubMed2002

[Experimental study on prediction model for leachate quality variation in MSW landfills].

The changing rule of leachate quality in Xiaping Sanitary Landfill was studied using pilot-scale leaching test for waste pollutants. And the parameters for Pollutants Leaching Model were determined using the data from the test by exponential regression method. Both the results from the test and the monitoring data in site indicated that the leachate concentration reached its maximum with COD of 31,581 mg/L after 75 days. Meanwhile, the accuracy of this prediction model was testified by comparing with the monitoring data in site, that means the model and its parameters achieved by leaching test for pollutants load can be used to predict the leachate quality variation in MSW landfills. Thus it provides a scientific numerical model and leaching test method for leachate quality prediction and basis for designing and managing leachate treatment facilities.

Models, Theoretical↗