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At least 199 records · Page 11Linked to original sources
PubMed2006

Estrogens, enzyme variants, and breast cancer: a risk model.

Oxidative metabolites of estrogens have been implicated in the development of breast cancer, yet relatively little is known about the metabolism of estrogens in the normal breast. We developed a mathematical model of mammary estrogen metabolism based on the conversion of 17beta-estradiol (E(2)) by the enzymes cytochrome P450 (CYP) 1A1 and CYP1B1, catechol-O-methyltransferase (COMT), and glutathione S-transferase P1 into eight metabolites [i.e., two catechol estrogens, 2-hydroxyestradiol (2-OHE(2)) and 4-hydroxyestradiol (4-OHE(2)); three methoxyestrogens, 2-methoxyestradiol, 2-hydroxy-3-methoxyestradiol, and 4-methoxyestradiol; and three glutathione (SG)-estrogen conjugates, 2-OHE(2)-1-SG, 2-OHE(2)-4-SG, and 4-OHE(2)-2-SG]. When used with experimentally determined rate constants with purified enzymes, the model provides for a kinetic analysis of the entire metabolic pathway. The predicted concentration of each metabolite during a 30-minute reaction agreed well with the experimentally derived results. The model also enables simulation for the transient quinones, E(2)-2,3-quinone (E(2)-2,3-Q) and E(2)-3,4-quinone (E(2)-3,4-Q), which are not amenable to direct quantitation. Using experimentally derived rate constants for genetic variants of CYP1A1, CYP1B1, and COMT, we used the model to simulate the kinetic effect of enzyme polymorphisms on the pathway and identified those haplotypes generating the largest amounts of catechols and quinones. Application of the model to a breast cancer case-control population identified a subset of women with an increased risk of breast cancer based on their enzyme haplotypes and consequent E(2)-3,4-Q production. This in silico model integrates both kinetic and genomic data to yield a comprehensive view of estrogen metabolomics in the breast. The model offers the opportunity to combine metabolic, genetic, and lifetime exposure data in assessing estrogens as a breast cancer risk factor.

Area Under Curve↗
PubMed2005

Issues related to time averaging of exposure in modeling risks associated with intermittent exposures to lead.

Typical exposures to lead often involve a mix of long-term exposures to relatively constant exposure levels (e.g., residential yard soil and indoor dust) and highly intermittent exposures at other locations (e.g., seasonal recreational visits to a park). These types of exposures can be expected to result in blood lead concentrations that vary on a temporal scale with the intermittent exposure pattern. Prediction of short-term (or seasonal) blood lead concentrations arising from highly variable intermittent exposures requires a model that can reliably simulate lead exposures and biokinetics on a temporal scale that matches that of the exposure events of interest. If exposure model averaging times (EMATs) of the model exceed the shortest exposure duration that characterizes the intermittent exposure, uncertainties will be introduced into risk estimates because the exposure concentration used as input to the model must be time averaged to account for the intermittent nature of the exposure. We have used simulation as a means of determining the potential magnitude of these uncertainties. Simulations using models having various EMATs have allowed exploration of the strengths and weaknesses of various approaches to time averaging of exposures and impact on risk estimates associated with intermittent exposures to lead in soil. The International Commission of Radiological Protection (ICRP) model of lead pharmacokinetics in humans simulates lead intakes that can vary in intensity over time spans as small as one day, allowing for the simulation of intermittent exposures to lead as a series of discrete daily exposure events. The ICRP model was used to compare the outcomes (blood lead concentration) of various time-averaging adjustments for approximating the time-averaged intake of lead associated with various intermittent exposure patterns. Results of these analyses suggest that standard approaches to time averaging (e.g., U.S. EPA) that estimate the long-term daily exposure concentration can, in some cases, result in substantial underprediction of short-term variations in blood lead concentrations when used in models that operate with EMATs exceeding the shortest exposure duration that characterizes the intermittent exposure. Alternative time-averaging approaches recommended for use in lead risk assessment more reliably predict short-term periodic (e.g., seasonal) elevations in blood lead concentration that might result from intermittent exposures. In general, risk estimates will be improved by simulation on shorter time scales that more closely approximate the actual temporal dynamics of the exposure.

Environmental Exposure↗
PubMed2003

Risk models for neutropenia in patients with breast cancer.

Breast cancer is the most common noncutaneous malignancy in women in industrialized countries. Chemotherapy prolongs survival in patients with early-stage breast cancer, and maintaining the chemotherapy dose intensity is crucial for increasing overall survival. Many patients are, however, treated with less than the standard dose intensity because of neutropenia and its complications. Prophylactic colony-stimulating factor (CSF) reduces the incidence and duration of neutropenia, facilitating the delivery of the planned chemotherapy doses. Targeting CSF to only at-risk patients is cost-effective, and predictive models are being investigated and developed to make it possible for clinicians to identify patients who are at highest risk for neutropenic complications. Both conditional risk factors (e.g., the depth of the first-cycle absolute neutrophil count nadir) and unconditional risk factors (e.g., patient age, treatment regimen, and pretreatment blood cell counts) are predictors of neutropenic complications in early-stage breast cancer. Colony-stimulating factor targeted toward high-risk patients starting in the first cycle of chemotherapy may make it possible for full doses of chemotherapy to be administered, thereby maximizing patient benefit. Recent studies of dose-dense chemotherapy regimens with CSF support in early-stage breast cancer have shown improvements in disease-free and overall survival, with less hematologic toxicity than with conventional therapy. These findings could lead to changes in how early-stage breast cancer is managed.

Age Factors↗
PubMed2002

The impact of smoking-related illness in the ED: an attributable risk model.

The objective of this study was to estimate the clinical and economic impact of smoking among adult emergency department (ED) patients. An attributable risk analysis of patients seen in 2 urban EDs in 1998 was performed. Data were obtained from hospital databases, national sources describing the prevalence of smoking in the state, and risk ratios for smoking-related illnesses. Of 78,617 patient visits, 12,573 (16.0%) had any smoking-related International Classification of Diseases, 9th Revision code. The smoking-attributable risk fraction (SARF) for all patients was 4.9% (95% confidence interval [CI] 4.7%-5.0%). Of 13,245 admissions, 6.8% (95% CI 6.4%-7.2%) were smoking attributable. Of $296,962,685 in hospital charges, 10.0% (95% CI 9.9%-10.1%) were smoking attributable. The SARFs for ED visits, admissions, and charges for men were higher than for women (all P <.0001). Smoking-attributable illness accounts for 4.9% of ED adult visits, 6.8% of ED adult admissions, and 10.0% of hospital charges. The use of ED-based smoking intervention remains to be determined.

Adult↗
PubMed1995

Clinical indicators of progression in psoriatic arthritis: multivariate relative risk model.

OBJECTIVE: To identify markers for severe disease in psoriatic arthritis (PsA). METHODS: Patients with PsA followed prospectively according to a standard protocol over 14 years were included. Clinical and laboratory assessments of both active inflammation and clinical damage were performed at 6-month intervals according to a standard protocol. The information was entered into a computer database. Progression in damage was defined as transitions between damage states based on the number of damaged joints. Both univariate and multivariate models were developed to identify predictors for progression of damage. RESULTS: The best model available, based on patient characteristics at the time of being first seen in the psoriatic arthritis clinic suggests that a high number of effusions and of past medications predicts progression in damage, whereas a low sedimentation rate "protects" from such progression. CONCLUSIONS: Evidence of significant inflammation at first visit predicts progression of damage in the future, suggesting that patients with PsA should be offered more aggressive treatment early in the course of their disease.

Adolescent↗
PubMed2006

Spatial epidemiology of human schistosomiasis in Africa: risk models, transmission dynamics and control.

This paper reviews recent studies on the spatial epidemiology of human schistosomiasis in Africa. The integrated use of geographical information systems, remote sensing and geostatistics has provided new insights into the ecology and epidemiology of schistosomiasis at a variety of spatial scales. Because large-scale patterns of transmission are influenced by climatic conditions, an increasing number of studies have used remotely sensed environmental data to predict spatial distributions, most recently using Bayesian methods of inference. Such data-driven approaches allow for a more rational implementation of intervention strategies across the continent. It is suggested that improved incorporation of transmission dynamics into spatial models and assessment of uncertainties inherent in data and modelling approaches represent important future research directions.

Africa↗
PubMed2006

Lung cancer attributable to indoor radon exposure in france: impact of the risk models and uncertainty analysis.

OBJECTIVE: The inhalation of radon, a well-established human carcinogen, is the principal-and omnipresent-source of radioactivity exposure for the general population of most countries. Scientists have thus sought to assess the lung cancer risk associated with indoor radon. Our aim here is to assess this risk in France, using all available epidemiologic results and performing an uncertainty analysis. METHODS: We examined the exposure-response relations derived from cohorts of miners and from joint analyses of residential case-control studies and considered the interaction between radon and tobacco. The exposure data come from measurement campaigns conducted since the beginning of the 1980s by the Institute for Radiation Protection and Nuclear Safety and the Directorate-General of Health in France. We quantified the uncertainties associated with risk coefficients and exposures and calculated their impact on risk estimates. RESULTS: The estimated number of lung cancer deaths attributable to indoor radon exposure ranges from 543 [90% uncertainty interval (UI) , 75-1,097] to 3,108 (90% UI, 2,996-3,221) , depending on the model considered. This calculation suggests that from 2.2% (90% UI, 0.3-4.4) to 12.4% (90% UI, 11.9-12.8) of these deaths in France may be attributable to indoor radon. DISCUSSION: In this original work we used different exposure-response relations from several epidemiologic studies and found that regardless of the relation chosen, the number of lung cancer deaths attributable to indoor radon appears relatively stable. Smokers can reduce their risk not only by reducing their indoor radon concentration but also by giving up smoking.

Adult↗
PubMedDate not supplied

Hypotension and endoscopic stigmata of recent haemorrhage in bleeding peptic ulcer: risk models for rebleeding and mortality.

Clinical and endoscopic data were collected prospectively in 1050 patients with bleeding peptic ulcer admitted between September 1985 and July 1989 to the care of one surgical team. Seventy-nine patients underwent therapeutic endoscopy soon after admission and in 129 patients either immediate or early elective surgery was performed. Eight hundred and forty-two patients, in whom therapeutic endoscopy was not performed at any stage, underwent initial conservative management and data from this latter group are now presented. Shock on admission was defined as systolic blood pressure (BP) less than or equal to 100 mmHg on presentation. There were 10 deaths of 147 shocked patients (6.8%) compared with only 25 deaths of 695 patients (3.6%) not in shock (P less than 0.08). Bleeding recurred in 30 patients (20.4%) shocked on presentation but in only 96 (13.8%) with a BP greater than 100 mmHg (P less than 0.05). Twenty-one of 358 patients (5.9%) with endoscopic stigmata of recent haemorrhage (ESRH) died, but only 14 of 484 patients (2.9%) without such stigmata (P less than 0.05) died. In shocked patients rebleeding was evident in 21 of 73 (28.8%) cases with ESRH but in only 9 of 74 (12.2%) patients in whom ESRH were absent (P less than 0.02). In the absence of fresh blood at endoscopy rebleeding occurred in 22 of 124 (17.8%) shocked patients and only 74 of 629 (11.8%) of those not shocked on presentation (P less than 0.07). When ulcer size was documented rebleeding rates for ulcers less than or equal to 1 cm, less than or equal to 2 cm and greater than 2 cm in size were 54 of 485 (11.1%), 30 of 142 (21.2%) and 12 of 44 (27.3%) respectively.(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed2004

Modelling risk when binary outcomes are subject to error.

We present methods for binomial regression when the outcome is determined using the results of a single diagnostic test with imperfect sensitivity and specificity. We present our model, illustrate it with the analysis of real data, and provide an example of WinBUGS program code for performing such an analysis. Conditional means priors are used in order to allow for inclusion of prior data and expert opinion in the estimation of odds ratios, probabilities, risk ratios, risk differences, and diagnostic test sensitivity and specificity. A simple method of obtaining Bayes factors for link selection is presented. Methods are illustrated and compared with Bayesian ordinary binary regression using data from a study of the effectiveness of a smoking cessation program among pregnant women. Regression coefficient estimates are shown to change noticeably when expert prior knowledge and imperfect sensitivity and specificity are incorporated into the model.

Adult↗