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Results for “Predictive modeling”

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PubMed2002

An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score.

PURPOSE: Currently outcome prediction in renal cell carcinoma is largely based on pathological stage and tumor grade. We developed an outcome prediction model for patients treated with radical nephrectomy for clear cell renal cell carcinoma, which was based on all available clinical and pathological features significantly associated with death from renal cell carcinoma. MATERIALS AND METHODS: We identified 1,801 adult patients with unilateral clear cell renal cell carcinoma treated with radical nephrectomy between 1970 and 1998. Clinical features examined included age, sex, smoking history, and signs and symptoms at presentation. Pathological features examined included 1997 TNM stage, tumor size, nuclear grade, histological tumor necrosis, sarcomatoid component, cystic architecture, multifocality and surgical margin status. Cancer specific survival was estimated using the Kaplan-Meier method. Cox proportional hazards regression models were used to test associations between features studied and outcome. The selection of features included in the multivariate model was validated using bootstrap methodology. RESULTS: Mean followup was 9.7 years (range 0.1 to 31). Estimated cancer specific survival rates at 1, 3, 5, 7 and 10 years were 86.6%, 74.0%, 68.7%, 63.8% and 60.0%, respectively. Several features were multivariately associated with death from clear cell renal cell carcinoma, including 1997 TNM stage (p <0.001), tumor size 5 cm. or greater (p <0.001), nuclear grade (p <0.001) and histological tumor necrosis (p <0.001). CONCLUSIONS: In patients with clear cell renal cell carcinoma 1997 TNM stage, tumor size, nuclear grade and histological tumor necrosis were significantly associated with cancer specific survival. We present a scoring system based on these features that can be used to predict outcome.

Adult↗

PubMed2006

Oil well produced water discharges to the North Sea. Part I: comparison of deployed mussels (Mytilus edulis), semi-permeable membrane devices, and the DREAM model predictions to estimate the dispersion of polycyclic aromatic hydrocarbons.

The oil companies operating in the Norwegian sector of the North Sea have conducted field studies since the mid-1990s to monitor produced water discharges to the ocean. These studies have been used to refine monitoring methods, and to develop and validate a dispersion and impact assessment model. This paper summarizes monitoring data from surveys conducted in two major oil and gas production areas, and compares the results to concentrations of polycyclic aromatic hydrocarbons (PAH) in surface waters predicted by the dose-related risk and effect assessment model (DREAM). Blue mussels and semi-permeable membrane devices (SPMDs) were deployed in the Ekofisk and Tampen Regions and analyzed for more than 50 PAH. PAH concentrations in ambient seawater were estimated based on the mussels and SPMD concentrations, and compared to model predictions. Surface water total PAH concentrations ranged from 25 to 350 ng/L within 1 km of the platform discharges and reached background levels of 4-8 ng/L within 5-10 km of the discharge; a 100,000-fold dilution of the PAH in the discharge water. The PAH concentrations in surface water, predicted by three methods, compared well for the Ekofisk Region. The model predicted higher concentrations than the field-based methods for parts of the Tampen Region; particularly the most tidally influenced areas. Tidally-mediated fluctuations in PAH concentrations in surface water must be considered because they affect the estimation of PAH concentrations from mussel and SPMD residue data, and the predictions by the DREAM model. Predictions using mussels, SPMDs, and modeling support and complement each other; all are valuable tools for estimating the fate and impact of chemical contaminants in produced water that are discharged to the ocean.

Animals↗

PubMed2005

Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases.

Numerous human diseases-malaria, dengue, yellow fever and leishmaniasis, to name a few-are transmitted by insect vectors with brief life cycles and biting activity that varies in both space and time. Although the general geographic distributions of these epidemiologically important species are known, the spatiotemporal variation in their emergence and activity remains poorly understood. We used ecological niche modeling via a genetic algorithm to produce time-specific predictive models of monthly distributions of Aedes aegypti in Mexico in 1995. Significant predictions of monthly mosquito activity and distributions indicate that predicting spatiotemporal dynamics of disease vector species is feasible; significant coincidence with human cases of dengue indicate that these dynamics probably translate directly into transmission of dengue virus to humans. This approach provides new potential for optimizing use of resources for disease prevention and remediation via automated forecasting of disease transmission risk.

Aedes↗

PubMedDate not supplied

Support vector machines for predictive modeling in heterogeneous catalysis: a comprehensive introduction and overfitting investigation based on two real applications.

This works provides an introduction to support vector machines (SVMs) for predictive modeling in heterogeneous catalysis, describing step by step the methodology with a highlighting of the points which make such technique an attractive approach. We first investigate linear SVMs, working in detail through a simple example based on experimental data derived from a study aiming at optimizing olefin epoxidation catalysts applying high-throughput experimentation. This case study has been chosen to underline SVM features in a visual manner because of the few catalytic variables investigated. It is shown how SVMs transform original data into another representation space of higher dimensionality. The concepts of Vapnik-Chervonenkis dimension and structural risk minimization are introduced. The SVM methodology is evaluated with a second catalytic application, that is, light paraffin isomerization. Finally, we discuss why SVMs is a strategic method, as compared to other machine learning techniques, such as neural networks or induction trees, and why emphasis is put on the problem of overfitting.

Alkenes↗

PubMed2004

A model predicting suicidal ideation and hopelessness in depressed older adults: the impact of emotion inhibition and affect intensity.

The purpose of this study was to begin a preliminary examination of constructs theorized to be related to suicidal behavior by testing a model of the influence of both temperament and emotion regulation on suicidal ideation and hopelessness in a sample of depressed older adults. The model was evaluated using structural equation modeling procedures in a sample of depressed, older adults. Findings supported a temporally predictive model in which negative affect intensity and reactivity lead to emotion inhibition, operationalized as ambivalence over emotional expression and thought suppression, which in turn lead to increased presence of suicidal predictors, operationalized as hopelessness and suicidal ideation. These results suggest that suicide prevention efforts in older adults may be improved by targeting emotion inhibition in treatment, especially among affectively intense and reactive older adults.

Affect↗

PubMed1997

[Surgically treated esophageal cancers: predictive model of survival].

OBJECTIVES: The aim of this study was to identify prognostic factors in patients with esophageal cancer after curative resection, and to establish a predictive model of their long-term prognosis. PATIENTS--METHODS: Eighty-nine patients operated on for neoplasia of the esophagus, who underwent a curative resection, an who did not die within one month or during the hospital stay, were included in this study. Twenty-one variables were studied by univariate analysis. The variables linked with survival were include in a Cox model. Regression coefficient of independent prognostic factors allowed to compute a score. RESULTS: Life table analysis of the entire population, showed 2 and 5 year survival rates of 48% and 28%, respectively. In univariate analysis, 5 out of 21 factors were statistically linked with survival. In multivariate analysis (Cox model), 4 independent factors were linked with survival: age (P = 0.02), the American Society of Anesthesiologist classification (P = 0.01), parietal invasion (P = 0.03), and lymph node invasion (P = 0.009). The score established with these 4 factors allowed to distinguish 3 sub-groups, discriminated by their long term prognosis. Life table analysis of the 3 sub-groups were at 2 and 5 years 83%, 55%, 20% and 60%, 32%, 0%, respectively. CONCLUSION: This model may be useful for the assessment of prognosis in patients with esophageal cancer after curative surgical treatment.

Adenocarcinoma↗

PubMedDate not supplied

[Effects of environmental factors on the number of emergency admissions to the Hospital Complex Juan Canalejo in La Coruña: creation of a prediction model].

BACKGROUND: This study is aimed at establishing the possible associations between the number of admissions through the emergency room at the "Juan Canalejol" Hospital in Corunna in 1994-1994 due to organic, circulatory and respiratory reasons and the weather variables introduced as being exogenous for the purpose of preparing a prediction model. METHODS: The Box-Jenkins methodology is used for obtaining univariate ARIMA models of the time-based series taken into consideration. Cross-Correlation Functions (CCF's) are established among the series of residuals which afford the possibility of establishing weights and lags among the variables for a subsequent modeling by means of multivariate ARIMA models which include environmental variables. RESULTS: The emergency admissions for organic reasons significantly increase 0-2 days following a rise in temperature. The admissions due to respiratory ailments are associated with drops in temperature with 10-14 lags, whilst the admissions for circulatory reasons increase significantly due to long-lasting spells of hot weather (10 lags). For people over age 65, significant increases in emergency admissions for circulatory reasons are also recorded with cold snaps. The multivariate ARIMA models that take into account the effect of environmental variables provided the best adjustment for all of the admissions variables. CONCLUSIONS: The number of emergency room admissions at the "Juan Canalejo" Medical Center Complex in Corunna due to organic, respiratory and circulatory causes shows a seasonal behavior pattern. The admissions for respiratory reasons are associated with a drop in temperature, whilst the admissions for circulatory reasons are affected fundamentally by hot weather, although also by cold weather as regards people over age 65. The multivariate ARIMA models including climate-related variables provide a system for predicting admissions in terms of said variables that can be useful from the standpoint of hospital management.

Age Factors↗

PubMed1996

A predictive model to identify Clostridium difficile toxin in hospitalized patients with diarrhea.

OBJECTIVES: Although Clostridium difficile is a common pathogen in hospitalized patients with diarrhea, no study has attempted to develop a predictive model to estimate the likelihood of C. difficile positivity. METHODS: We conducted a cross-sectional study at a single hospital of 271 patients with diarrhea for whom stool was tested for C. difficile toxin. The sample was randomly divided into a subset to derive the model (n = 180) and another to validate it (n =91), and independent predictors of toxin positivity were identified using logistical regression analysis. RESULTS: C. difficile toxin was present in 81 patients and absent in 190. In the derivation set, a positive toxin was independently predicted (p < 0.0005) by readmission within 2 wk of prior hospitalization, by a white blood cell count > or = 10,000/mm3, and by presence of fecal leukocytes. In the validation set, C. difficile toxin was present in 24, 29, and 77% of patients with 0, 1, and > or = 2 risk factors, respectively. CONCLUSION: If validated prospectively and/or other centers, the model could identify patients who should be considered for empirical management while awaiting results of toxin assays.

Aged↗

PubMed1976

A simplified in vitro classification for prognosis in adult acute leukemia: the application of in vitro results in remission-predictive models.

Previous classification in vitro of adult acute leukemia incorporating morphology has been complex and difficult to understand. We have devised a simplified classification based solely on leuekemic proliferation in vitro. Seventy-six patients with adult acute leukemia previously untreated were included in this study and received identical chemotherapy. Three groups were recognized. The complete remission rate was 76% in the 21 patients with no leukemic growth in vitro (Group 1), 75% in 36 patients with leukemic cell growth but aggregates of 20 cells or less (Group 2), and only 21% in the 15 patients with aggregates of greater than 20 (Group 3). There was a highly significant difference in complete remission rates between Group 3 and the other two groups (p less than 0.001). Linear logistic regression analysis demonstrated the independence of the growth in vitro from other prognostic variables. A predictive model utilizing the in vitro result more accurately predicted for remission, both retrospectively and prospectively, than a model constructed with presently known prognostic parameters. The cause of death in failures suggested that this system detects resistance to the chemotherapy.

Acute Disease↗

PubMed2003

Evaluation of the HSE COSHH Essentials exposure predictive model on the basis of BAuA field studies and existing substances exposure data.

This paper presents an in-house BAuA study on the evaluation of the COSHH Essentials exposure predictive model. External validation is based on measurement data obtained in BAuA field studies performed in various industries, e.g. printing industry and textile industry. In addition, measurement data and information on industrial hygiene provided by the chemical industry within the framework of the Existing Substances Risk Assessment programme are used. Although the evaluated exposure data cover a wide variety of activities and workplace scenarios, there is still a considerable lack of appropriate exposure data, especially for the more stringent control strategies. It was found that the level of agreement between the measurements for solid substances (powders, dusts) and the predicted ranges is reasonably good. The situation is in part different for liquids. In workplaces where organic solvents are used in litre quantities, exposure levels are within the predicted ranges or are often lower. For small-scale uses of liquids (millilitre scale), e.g. in carpenters' workshops, there were indications that the exposure levels can exceed the predicted ranges. However, it must be noted that the database is rather small.

Hazardous Substances↗

PubMed2004

A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry.

CONTEXT: Accurate estimation of risk for untoward outcomes after patients have been hospitalized for an acute coronary syndrome (ACS) may help clinicians guide the type and intensity of therapy. OBJECTIVE: To develop a simple decision tool for bedside risk estimation of 6-month mortality in patients surviving admission for an ACS. DESIGN, SETTING, AND PATIENTS: A multinational registry, involving 94 hospitals in 14 countries, that used data from the Global Registry of Acute Coronary Events (GRACE) to develop and validate a multivariable stepwise regression model for death during 6 months postdischarge. From 17,142 patients presenting with an ACS from April 1, 1999, to March 31, 2002, and discharged alive, 15,007 (87.5%) had complete 6-month follow-up and represented the development cohort for a model that was subsequently tested on a validation cohort of 7638 patients admitted from April 1, 2002, to December 31, 2003. MAIN OUTCOME MEASURE: All-cause mortality during 6 months postdischarge after admission for an ACS. RESULTS: The 6-month mortality rates were similar in the development (n = 717; 4.8%) and validation cohorts (n = 331; 4.7%). The risk-prediction tool for all forms of ACS identified 9 variables predictive of 6-month mortality: older age, history of myocardial infarction, history of heart failure, increased pulse rate at presentation, lower systolic blood pressure at presentation, elevated initial serum creatinine level, elevated initial serum cardiac biomarker levels, ST-segment depression on presenting electrocardiogram, and not having a percutaneous coronary intervention performed in hospital. The c statistics for the development and validation cohorts were 0.81 and 0.75, respectively. CONCLUSIONS: The GRACE 6-month postdischarge prediction model is a simple, robust tool for predicting mortality in patients with ACS. Clinicians may find it simple to use and applicable to clinical practice.

Aged↗

PubMed2006

Development and validation of a model predicting graft survival after liver transplantation.

This study aimed to develop and validate a comprehensive model that predicts survival after liver transplantation based on pretransplant donor and recipient characteristics. Complete data were available from the United Network for Organ Sharing for 20,301 persons who underwent liver transplantation in the United States between 1994 and 2003. Proportional-hazards regression was used to identify the donor and recipient characteristics that best predicted survival and incorporate these characteristics in a multivariate model. A data-splitting approach was used to compare survival predicted by the model to the observed survival in samples not used in the derivation of the model. A model was derived using 4 donor characteristics (age, cold ischemia time, gender, and race/ethnicity) and 9 recipient characteristics (age, body max index, model for end-stage liver disease score, United Network for Organ Sharing priority status, gender, race/ethnicity, diabetes mellitus, cause of liver disease, and serum albumin) that adequately predicted survival after liver transplantation in patients without hepatitis C virus, and a slightly different model was used for patients with hepatitis C virus. The models illustrate that variations in both pretransplant donor and recipient characteristics have a large effect on posttransplant survival. In conclusion, the models presented here can be used to derive scores that are proportional to the excess risk of graft loss after liver transplantation for potential donors, recipients, or donor/recipient combinations. The models may be used to inform liver transplant candidates and their doctors what posttransplant survival would be expected when a given donor is offered and may be particularly helpful for marginal or high-risk donors.

Adult↗

PubMed2003

Fibril reinforced poroelastic model predicts specifically mechanical behavior of normal, proteoglycan depleted and collagen degraded articular cartilage.

Degradation of collagen network and proteoglycan (PG) macromolecules are signs of articular cartilage degeneration. These changes impair cartilage mechanical function. Effects of collagen degradation and PG depletion on the time-dependent mechanical behavior of cartilage are different. In this study, numerical analyses, which take the compression-tension nonlinearity of the tissue into account, were carried out using a fibril reinforced poroelastic finite element model. The study aimed at improving our understanding of the stress-relaxation behavior of normal and degenerated cartilage in unconfined compression. PG and collagen degradations were simulated by decreasing the Young's modulus of the drained porous (nonfibrillar) matrix and the fibril network, respectively. Numerical analyses were compared to results from experimental tests with chondroitinase ABC (PG depletion) or collagenase (collagen degradation) digested samples. Fibril reinforced poroelastic model predicted the experimental behavior of cartilage after chondroitinase ABC digestion by a major decrease of the drained porous matrix modulus (-64+/-28%) and a minor decrease of the fibril network modulus (-11+/-9%). After collagenase digestion, in contrast, the numerical analyses predicted the experimental behavior of cartilage by a major decrease of the fibril network modulus (-69+/-5%) and a decrease of the drained porous matrix modulus (-44+/-18%). The reduction of the drained porous matrix modulus after collagenase digestion was consistent with the microscopically observed secondary PG loss from the tissue. The present results indicate that the fibril reinforced poroelastic model is able to predict specifically characteristic alterations in the stress-relaxation behavior of cartilage after enzymatic modifications of the tissue. We conclude that the compression-tension nonlinearity of the tissue is needed to capture realistically the mechanical behavior of normal and degenerated articular cartilage.

Animals↗

PubMed2006

Simple epidemiological model predicts the relationships between prevalence and abundance in ixodid ticks.

We tested whether the prevalence of ticks can be predicted reliably from a simple epidemiological model that takes into account only mean abundance and its variance. We used data on the abundance and distribution of larvae and nymphs of 2 ixodid ticks parasitic on small mammals (Apodemus agrarius, Apodemus flavicollis, Apodemus uralensis, Clethrionomys glareolus and Microtus arvalis) in central Europe. Ixodes trianguliceps is active all year round, occurs in the study area in the mountain and sub-mountain habitats only and inhabits mainly host burrows and nests, whereas Ixodes ricinus occurs mainly during the warmer seasons, occupies a large variety of habitats and quests for hosts outside their shelters. In I. ricinus, the models with k values calculated from Taylor's power law overestimated prevalences. However, if moment estimates of k corrected for host number were used instead, expected prevalences of both larvae and nymphs I. ricinus in either host did not differ significantly from observed prevalences. In contrast, prevalences of larvae and nymphs of I. trianguliceps predicted by models using parameters of Taylor's power law did not differ significantly from observed prevalences, whereas the models with moment estimates of k corrected for host number in some cases under-estimated relatively lower larval prevalences and over-estimated relatively higher larval prevalences, but predicted nymphal prevalences well.

Animals↗

PubMed2006

Reliability of the ICRP'S dose coefficients for members of the public: IV. basis of the human alimentary tract model and uncertainties in model predictions.

The biokinetic and dosimetric model of the gastrointestinal (GI) tract applied in current documents of the International Commission on Radiological Protection (ICRP) was developed in the mid-1960s. The model was based on features of a reference adult male and was first used by the ICRP in Publication 30, Limits for Intakes of Radionuclides by Workers (Part 1, 1979). In the late 1990s an ICRP task group was appointed to develop a biokinetic and dosimetric model of the alimentary tract that reflects updated information and addresses current needs in radiation protection. The new age-specific and gender-specific model, called the Human Alimentary Tract Model (HATM), has been completed and will replace the GI model of Publication 30 in upcoming ICRP documents. This paper discusses the basis for the structure and parameter values of the HATM, summarises the uncertainties associated with selected features and types of predictions of the HATM and examines the sensitivity of dose estimates to these uncertainties for selected radionuclides. Emphasis is on generic biokinetic features of the HATM, particularly transit times through the lumen of the alimentary tract, but key dosimetric features of the model are outlined, and the sensitivity of tissue dose estimates to uncertainties in dosimetric as well as biokinetic features of the HATM are examined for selected radionuclides.

Dose-Response Relationship, Radiation↗

PubMedDate not supplied

Cybernetic model predictive control of a continuous bioreactor with cell recycle.

The control of poly-beta-hydroxybutyrate (PHB) productivity in a continuous bioreactor with cell recycle is studied by simulation. A cybernetic model of PHB synthesis in Alcaligenes eutrophus is developed. Model parameters are identified using experimental data, and simulation results are presented. The model is interfaced to a multirate model predictive control (MPC) algorithm. PHB productivity and concentration are controlled by manipulating dilution rate and recycle ratio. Unmeasured time varying disturbances are imposed to study regulatory control performance, including unreachable setpoints. With proper controller tuning, the nonlinear MPC algorithm can track productivity and concentration setpoints despite a change in the sign of PHB productivity gain with respect to dilution rate. It is shown that the nonlinear MPC algorithm is able to track the maximum achievable productivity for unreachable setpoints under significant process/model mismatch. The impact of model uncertainty upon controller performance is explored. The multirate MPC algorithm is tested using three controllers employing models that vary in complexity of regulation. It is shown that controller performance deteriorates as a function of decreasing biological complexity.

Algorithms↗

PubMed1999

A predictive model that describes the effect of prolonged heating at 70 to 90 degrees C and subsequent incubation at refrigeration temperatures on growth from spores and toxigenesis by nonproteolytic Clostridium botulinum in the presence of lysozyme.

Refrigerated processed foods of extended durability such as cook-chill and sous-vide foods rely on a minimal heat treatment at 70 to 95 degrees C and then storage at a refrigeration temperature for safety and preservation. These foods are not sterile and are intended to have an extended shelf life, often up to 42 days. The principal microbiological hazard in foods of this type is growth of and toxin production by nonproteolytic Clostridium botulinum. Lysozyme has been shown to increase the measured heat resistance of nonproteolytic C. botulinum spores. However, the heat treatment guidelines for prevention of risk of botulism in these products have not taken into consideration the effect of lysozyme, which can be present in many foods. In order to assess the botulism hazard, the effect of heat treatments at 70, 75, 80, 85, and 90 degrees C combined with refrigerated storage for up to 90 days on growth from 10(6) spores of nonproteolytic C. botulinum (types B, E, and F) in an anaerobic meat medium containing 2,400 U of lysozyme per ml (50 microg per ml) was studied. Provided that the storage temperature was no higher than 8 degrees C, the following heat treatments each prevented growth and toxin production during 90 days; 70 degrees C for >/=2,545 min, 75 degrees C for >/=463 min, 80 degrees C for >/=230 min, 85 degrees C for >/=84 min, and 90 degrees C for >/=33.5 min. A factorial experimental design allowed development of a predictive model that described the incubation time required before the first sample showed growth, as a function of heating temperature (70 to 90 degrees C), period of heat treatment (up to 2,545 min), and incubation temperature (5 to 25 degrees C). Predictions from the model provided a valid description of the data used to generate the model and agreed with observations made previously.

Botulinum Toxins↗

PubMed2005

Model predictive control as a tool for improving the process operation of MSW combustion plants.

In this paper a feasibility study is presented on the application of the advanced control strategy called model predictive control (MPC) as a tool for obtaining improved process operation performance for municipal solid waste (MSW) combustion plants. The paper starts with a discussion of the operational objectives and control of such plants, from which a motivation follows for applying MPC to them. This is followed by a discussion on the basic idea behind this advanced control strategy. After that, an MPC-based combustion control system is proposed aimed at tackling a typical MSW combustion control problem and, using this proposed control system, an assessment is made of the improvement in performance that an MPC-based MSW combustion control system can provide in comparison to conventional MSW combustion control systems. This assessment is based on simulations using an experimentally obtained process and disturbance model of a real-life large-scale MSW combustion plant.

Computer Simulation↗