Validation and refinement of survival models for liver retransplantation

Orthotopic liver retransplantation (re-OLT) is highly controversial. The objectives of this study were to determine the validity of a recently developed United Network for Organ Sharing (UNOS) multivariate model using an independent cohort of patients undergoing re-OLT outside the United States, to determine whether incorporation of other variables that were incomplete in the UNOS registry would provide additional prognostic information, to develop new models combining data sets from both cohorts, and to evaluate the validity of the model for end-stage liver disease (MELD) in patients undergoing re-OLT. Two hundred eighty-one adult patients undergoing re-OLT (between 1986 and 1999) at 6 foreign transplant centers comprised the validation cohort. We found good agreement between actual survival and predicted survival in the validation cohort; 1-year patient survival rates in the low-, intermediate-, and high-risk groups (as assigned by the original UNOS model) were 72%, 68%, and 36%, respectively (P < .0001). In the patients for whom the international normalized ratio (INR) of prothrombin time was available, MELD correlated with outcome following re-OLT; the median MELD scores for patients surviving at least 90 days compared with those dying within 90 days were 20.75 versus 25.9, respectively (P = .004). Utilizing both patient cohorts (n = 979), a new model, based on recipient age, total serum bilirubin, creatinine, and interval to re-OLT, was constructed (whole model χ(2) = 105, P < .0001). Using the c-statistic with 30-day, 90-day, 1-year, and 3-year mortality as the end points, the area under the receiver operating characteristic (ROC) curves for 4 different models were compared. In conclusion, prospective validation and use of these models as adjuncts to clinical decision making in the management of patients being considered for re-OLT are warranted.

Analysis of Melanoma Onset: Assessing Familial Aggregation by Using Estimating Equations and Fitting Variance Components via Bayesian Random Effects Models

We investigate whether relative contributions of genetic and shared environmental factors are associated with an increased risk in melanoma. Data from the Queensland Familial Melanoma Project comprising 15,907 subjects arising from 1912 families were analyzed to estimate the additive genetic, common and unique environmental contributions to variation in the age at onset of melanoma. Two complementary approaches for analyzing correlated time-to-onset family data were considered: the generalized estimating equations (GEE) method in which one can estimate relationship-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modeled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov Chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the free ware package BUGS. In addition, we also used a Bayesian model to investigate the relative contribution of genetic and environmental effects on the expression of naevi and freckles, which are known risk factors for melanoma.

A clinical and echocardiographic score for assigning risk of major events after dobutamine echocardiograms

OBJECTIVES We sought to develop and validate a risk score combining both clinical and dobutamine echocardiographic (DbE) features in 4,890 patients who underwent DbE at three expert laboratories and were followed for death or myocardial infarction for up to five years. BACKGROUND In contrast to exercise scores, no score exists to combine clinical, stress, and echocardiographic findings with DbE. METHODS Dobutamine echocardiography was performed for evaluation of known or suspected coronary artery disease in 3,156 patients at two sites in the U.S. After exclusion of patients with incomplete follow-up, 1,456 DbEs were randomly selected to develop a multivariate model for prediction of events. After simplification of each model for clinical use, the models were internally validated in the remaining DbE patients in the same series and externally validated in 1,733 patients in an independent series. RESULTS The following score was derived from regression models in the modeling group (160 events): DbE risk = (age (.) 0.02) + (heart failure + rate-pressure product <15,000) (.) 0.4 + (ischemia + scar) (.) 0.6. The presence of each variable was scored as 1 and its absence scored as 0, except for age (continuous variable). Using cutoff values of 1.2 and 2.6, patients were classified into groups with five-year event-free survivals >95%, 75% to 95%, and <75%. Application of the score in the internal validation group (265 events) gave equivalent results, as did its application in the external validation group (494 events, C index = 0.72). CONCLUSIONS A risk score based on clinical and echocardiographic data may be used to quantify the risk of events in patients undergoing DbE. (C) 2004 by the American College of Cardiology Foundation.

Genetic effects on alcohol dependence risk: re-evaluating the importance of psychiatric and other heritable risk factors

Background. Genetic influences have been shown to play a major role in determining the risk of alcohol dependence (AD) in both women and men; however, little attention has been directed to identifying the major sources of genetic variation in AD risk. Method. Diagnostic telephone interview data from young adult Australian twin pairs born between 1964 and 1971 were analyzed. Cox regression models were fitted to interview data from a total of 2708 complete twin pairs (690 MZ female, 485 MZ male, 500 DZ female, 384 DZ male, and 649 DZ female/male pairs). Structural equation models were fitted to determine the extent of residual genetic and environmental influences on AD risk while controlling for effects of sociodemographic and psychiatric predictors on risk. Results. Risk of AD was increased in males, in Roman Catholics, in those reporting a history of major depression, social anxiety problems, and conduct disorder, or (in females only) a history of suicide attempt and childhood sexual abuse; but was decreased in those reporting Baptist, Methodist, or Orthodox religion, in those who reported weekly church attendance, and in university-educated males. After allowing for the effects of sociodemographic and psychiatric predictors, 47 % (95 % CI 28-55) of the residual variance in alcoholism risk was attributable to additive genetic effects, 0 % (95 % CI 0-14) to shared environmental factors, and 53 % (95 % CI 45-63) to non-shared environmental influences. Conclusions. Controlling for other risk factors, substantial residual heritability of AD was observed, suggesting that psychiatric and other risk factors play a minor role in the inheritance of AD.

Precautionary principles: a jurisdiction-free framework for decision-making under risk

Fundamental principles of precaution are legal maxims that ask for preventive actions, perhaps as contingent interim measures while relevant information about causality and harm remains unavailable, to minimize the societal impact of potentially severe or irreversible outcomes. Such principles do not explain how to make choices or how to identify what is protective when incomplete and inconsistent scientific evidence of causation characterizes the potential hazards. Rather, they entrust lower jurisdictions, such as agencies or authorities, to make current decisions while recognizing that future information can contradict the scientific basis that supported the initial decision. After reviewing and synthesizing national and international legal aspects of precautionary principles, this paper addresses the key question: How can society manage potentially severe, irreversible or serious environmental outcomes when variability, uncertainty, and limited causal knowledge characterize their decision-making? A decision-analytic solution is outlined that focuses on risky decisions and accounts for prior states of information and scientific beliefs that can be updated as subsequent information becomes available. As a practical and established approach to causal reasoning and decision-making under risk, inherent to precautionary decision-making, these (Bayesian) methods help decision-makers and stakeholders because they formally account for probabilistic outcomes, new information, and are consistent and replicable. Rational choice of an action from among various alternatives-defined as a choice that makes preferred consequences more likely-requires accounting for costs, benefits and the change in risks associated with each candidate action. Decisions under any form of the precautionary principle reviewed must account for the contingent nature of scientific information, creating a link to the decision-analytic principle of expected value of information (VOI), to show the relevance of new information, relative to the initial ( and smaller) set of data on which the decision was based. We exemplify this seemingly simple situation using risk management of BSE. As an integral aspect of causal analysis under risk, the methods developed in this paper permit the addition of non-linear, hormetic dose-response models to the current set of regulatory defaults such as the linear, non-threshold models. This increase in the number of defaults is an important improvement because most of the variants of the precautionary principle require cost-benefit balancing. Specifically, increasing the set of causal defaults accounts for beneficial effects at very low doses. We also show and conclude that quantitative risk assessment dominates qualitative risk assessment, supporting the extension of the set of default causal models.

Multidisciplinary strategies for the management of heart failure patients at high risk for admission - A systematic review of randomized trials

OBJECTIVES The aim of this study was to determine whether multidisciplinary strategies improve outcomes for heart failure (HF) patients. BACKGROUND Because the prognosis of HF remains poor despite pharmacotherapy, there is increasing interest in alternative models of care delivery for these patients. METHODS Randomized trials of multidisciplinary management programs in HF were identified by searching electronic databases and bibliographies and via contact with experts. RESULTS Twenty-nine trials (5,039 patients) were identified but were not pooled, because of considerable heterogeneity. A priori, we divided the interventions into homogeneous groups that were suitable for pooling. Strategies that incorporated follow-up by a specialized multidisciplinary team (either in a clinic or a non-clinic setting) reduced mortality (risk ratio [RR] 0.75, 95% confidence interval [CI] 0.59 to 0.96), HF hospitalizations (RR 0.74, 95% CI 0.63 to 0.87), and all-cause hospitalizations (RR 0.81, 95% CI 0.71 to 0.92). Programs that focused on enhancing patient self-care activities reduced HF hospitalizations (RR 0.66, 95% CI 0.52 to 0.83) and all-cause hospitalizations (RR 0.73, 95% CI 0.57 to 0.93) but had no effect on mortality (RR 1.14, 95% CI 0.67 to 1.94). Strategies that employed telephone contact and advised patients to attend their primary care physician in the event of deterioration reduced HF hospitalizations (RR 0.75, 95% CI 0.57 to 0.99) but not mortality (RR 0.91, 95% CI 0.67 to 1.29) or all-cause hospitalizations (RR 0.98, 95% CI 0.80 to 1.20). In 15 of 18 trials that evaluated cost, multidisciplinary strategies were cost-saving. CONCLUSIONS Multidisciplinary strategies for the management of patients with HF reduce HF hospitalizations. Those programs that involve specialized follow-up by a multidisciplinary team also reduce mortality and all-cause hospitalizations. (C) 2004 by the American College of Cardiology Foundation.

Exposing extinction risk analysis to pathogens: Is disease just another form of density dependence?

In the United States and several other countries., the development of population viability analyses (PVA) is a legal requirement of any species survival plan developed for threatened and endangered species. Despite the importance of pathogens in natural populations, little attention has been given to host-pathogen dynamics in PVA. To study the effect of infectious pathogens on extinction risk estimates generated from PVA, we review and synthesize the relevance of host-pathogen dynamics in analyses of extinction risk. We then develop a stochastic, density-dependent host-parasite model to investigate the effects of disease on the persistence of endangered populations. We show that this model converges on a Ricker model of density dependence under a suite of limiting assumptions, including. a high probability that epidemics will arrive and occur. Using this modeling framework, we then quantify: (1) dynamic differences between time series generated by disease and Ricker processes with the same parameters; (2) observed probabilities of quasi-extinction for populations exposed to disease or self-limitation; and (3) bias in probabilities of quasi-extinction estimated by density-independent PVAs when populations experience either form of density dependence. Our results suggest two generalities about the relationships among disease, PVA, and the management of endangered species. First, disease more strongly increases variability in host abundance and, thus, the probability of quasi-extinction, than does self-limitation. This result stems from the fact that the effects and the probability of occurrence of disease are both density dependent. Second, estimates of quasi-extinction are more often overly optimistic for populations experiencing disease than for those subject to self-limitation. Thus, although the results of density-independent PVAs may be relatively robust to some particular assumptions about density dependence, they are less robust when endangered populations are known to be susceptible to disease. If potential management actions involve manipulating pathogens, then it may be useful to. model disease explicitly.

Impact of gender on risk stratification by exercise and dobutamine stress echocardiography: long-term mortality in 4234 women and 6898 men

Aims Prior research is limited with regard to the diagnostic and prognostic accuracy of commonplace cardiac imaging modalities in women. The aim of this study was to examine 5-year mortality in 4234 women and 6898 men undergoing exercise or dobutamine stress echocardiography at three hospitals. Methods and results Univariable and multivariable Cox proportional hazards models were used to estimate time to cardiac death in this multi-centre, observational registry. Of the 11 132 patients, women had a greater frequency of cardiac risk factors (P < 0.0001). However, men more often had a history of coronary disease including a greater frequency of echocardiographic wall motion abnormalities (P < 0.0001). During 5 years of follow-up, 103 women and 226 men died from ischaernic heart disease (P < 0.0001). Echocardiographic estimates of left ventricular function (P < 0.0001) and the extent of ischaernic watt motion abnormalities (P < 0.0001) were highly predictive of cardiac death. Risk-adjusted 5-year survival was 99.4, 97.6, and 95% for exercising women with no, single, and multi-vessel ischaemia (P < 0.0001). For women undergoing dobutamine stress, 5-year survival was 95, 89, and 86.6% for those with 0, 1, and 2-3 vessel ischaemia (P < 0.0001). Exercising men had a 2.0-fold higher risk at every level of worsening ischaemia (P < 0.0001). Significantly worsening cardiac survival was noted for the 1568 men undergoing dobutamine stress echocardiography (P < 0.0001); no ischaemia was associated with 92% 5-year survival as compared with death rates of &GE; 16% for men with ischaemia on dobutamine stress echocardiography (P < 0.0001). Conclusion Echocardiographic measures of inducible wall motion abnormalities and global and regional left ventricutar function are highly predictive of long-term outcome for women and men alike.

Smoking and elevated blood pressure are the most important risk factors for subarachnoid hemorrhage in the Asia-Pacific region - An overview of 26 cohorts involving 306,620 participants

Background and Purpose - The cause of subarachnoid hemorrhage ( SAH) is poorly understood and there are few large cohort studies of risk factors for SAH. We investigated the risk of SAH mortality and morbidity associated with common cardiovascular risk factors in the Asia-Pacific region and examined whether the strengths of these associations were different in Asian and Australasian ( predominantly white) populations. Methods - Cohort studies were identified from Internet electronic databases, searches of proceedings of meetings, and personal communication. Hazard ratios (HRs) for systolic blood pressure (SBP), current smoking, total serum cholesterol, body mass index (BMI), and alcohol drinking were calculated from Cox models that were stratified by sex and cohort and adjusted for age at risk. Results - Individual participant data from 26 prospective cohort studies ( total number of participants 306 620) that reported incident cases of SAH ( fatal and/or nonfatal) were available for analysis. During the median follow-up period of 8.2 years, a total of 236 incident cases of SAH were observed. Current smoking (HR, 2.4; 95% CI, 1.8 to 3.4) and SBP > 140 mm Hg ( HR, 2.0; 95% CI, 1.5 to 2.7) were significant and independent risk factors for SAH. Attributable risks of SAH associated with current smoking and elevated SBP ( similar to 140 mm Hg) were 29% and 19%, respectively. There were no significant associations between the risk of SAH and cholesterol, BMI, or drinking alcohol. The strength of the associations of the common cardiovascular risk factors with the risk of SAH did not differ much between Asian and Australasian regions. Conclusions - Cigarette smoking and SBP are the most important risk factors for SAH in the Asia-Pacific region.

Cardiovascular disease and PPARdelta: Targeting the risk factors

Metabolism, in part, is regulated by the peroxisome proliferator-activated receptors (PPARs). The PPARs act as nutritional lipid sensors and three mammalian PPAR subtypes designated PPARalpha (NR1C1), PPARgamma (NR1C3) and PPARdelta (NR1C2) have been identified. This subgroup of nuclear hormone receptors binds DNA and controls gene expression at the nexus of pathways that regulate lipid and glucose homeostasis, energy storage and expenditure in an organ-specific manner. Recent evidence has demonstrated activation of PPARdelta in the major mass peripheral tissue (ie, adipose and skeletal muscle). It enhances glucose tolerance, insulin-stimulated glucose disposal, lipid catabolism, energy expenditure, cholesterol efflux and oxygen consumption. These effects positively influence the blood-lipid profile. Furthermore, PPARdelta activation produces a predominant type I/slow twitch/oxidative muscle fiber phenotype that leads to increased endurance, insulin sensitivity and resistance to obesity. PPARdelta has rapidly emerged as a potential target in the battle against dyslipidemia, insulin insensitivity, type II diabetes and obesity, with therapeutic efficacy in the treatment of cardiovascular disease risk factors. GW-501516 is currently undergoing phase II safety and efficacy trials in human volunteers for the treatment of dyslipidemia. The outcome of these clinical trials are eagerly awaited against a background of conflicting reports about cancer risks in genetically predisposed animal models. This review focuses on the potential pharmacological utility of selective PPARdelta agonists in the context of risk factors associated with metabolic and cardiovascular disease.

Using extreme value theory to measure value-at-risk for daily electricity spot prices

The recent deregulation in electricity markets worldwide has heightened the importance of risk management in energy markets. Assessing Value-at-Risk (VaR) in electricity markets is arguably more difficult than in traditional financial markets because the distinctive features of the former result in a highly unusual distribution of returns-electricity returns are highly volatile, display seasonalities in both their mean and volatility, exhibit leverage effects and clustering in volatility, and feature extreme levels of skewness and kurtosis. With electricity applications in mind, this paper proposes a model that accommodates autoregression and weekly seasonals in both the conditional mean and conditional volatility of returns, as well as leverage effects via an EGARCH specification. In addition, extreme value theory (EVT) is adopted to explicitly model the tails of the return distribution. Compared to a number of other parametric models and simple historical simulation based approaches, the proposed EVT-based model performs well in forecasting out-of-sample VaR. In addition, statistical tests show that the proposed model provides appropriate interval coverage in both unconditional and, more importantly, conditional contexts. Overall, the results are encouraging in suggesting that the proposed EVT-based model is a useful technique in forecasting VaR in electricity markets. (c) 2005 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

Using epidemiological models to estimate the health effects of diet behaviour change: the example of tailored fruit and vegetable promotion

Objective: To explore the use of epidemiological modelling for the estimation of health effects of behaviour change interventions, using the example of computer-tailored nutrition education aimed at fruit and vegetable consumption in The Netherlands. Design: The effects of the intervention on changes in consumption were obtained from an earlier evaluation study. The effect on health outcomes was estimated using an epidemiological multi-state life table model. input data for the model consisted of relative risk estimates for cardiovascular disease and cancers, data on disease occurrence and mortality, and survey data on the consumption of fruits and vegetables. Results: if the computer-tailored nutrition education reached the entire adult population and the effects were sustained, it could result in a mortality decrease of 0.4 to 0.7% and save 72 to 115 life-years per 100000 persons aged 25 years or older. Healthy life expectancy is estimated to increase by 32.7 days for men and 25.3 days for women. The true effect is likely to lie between this theoretical maximum and zero effect, depending mostly on durability of behaviour change and reach of the intervention. Conclusion: Epidemiological models can be used to estimate the health impact of health promotion interventions.

Whose socioeconomic status influences a woman's obesity risk: her mother's, her father's, or her own?

Background Evidence on the relative influence of childhood vs adulthood socioeconomic conditions on obesity risk is limited and equivocal. The objective of this study was to investigate associations of several indicators of mothers', fathers', and own socioeconomic status, and intergenerational social mobility, with body mass index (BMI) and weight change in young women. Methods This population-based cohort study used survey data provided by 8756 women in the young cohort (aged 18-23 years at baseline) of the Australian Longitudinal Study on Women's Health. In 1996 and 2000, women completed mailed surveys in which they reported their height and weight, and their own, mother's, and father's education and occupation. Results Multiple linear regression models showed that both childhood and adulthood socioeconomic status were associated with women's BMI and weight change, generally in the hypothesized (inverse) direction, but the associations varied according to socioeconomic status and weight indicator. Social mobility was associated with BMI (based on father's socioeconomic status) and weight change (based on mother's socioeconomic status), but results were slightly less consistent. Conclusions Results suggest lasting effects of childhood socioeconomic status on young women's weight status, independent of adult socioeconomic status, although the effect may be attenuated among those who are upwardly socially mobile. While the mechanisms underlying these associations require further investigation, public health strategies aimed at preventing obesity may need to target families of low socioeconomic status early in children's lives.