The prognostic value of health-related quality-of-life data in predicting survival in glioblastoma cancer patients: results from an international randomised phase III EORTC Brain Tumour and Radiation Oncology Groups, and NCIC Clinical Trials Group study.

This is one of the few studies that have explored the value of baseline symptoms and health-related quality of life (HRQOL) in predicting survival in brain cancer patients. Baseline HRQOL scores (from the EORTC QLQ-C30 and the Brain Cancer Module (BN 20)) were examined in 490 newly diagnosed glioblastoma cancer patients for the relationship with overall survival by using Cox proportional hazards regression models. Refined techniques as the bootstrap re-sampling procedure and the computation of C-indexes and R(2)-coefficients were used to try and validate the model. Classical analysis controlled for major clinical prognostic factors selected cognitive functioning (P=0.0001), global health status (P=0.0055) and social functioning (P<0.0001) as statistically significant prognostic factors of survival. However, several issues question the validity of these findings. C-indexes and R(2)-coefficients, which are measures of the predictive ability of the models, did not exhibit major improvements when adding selected or all HRQOL scores to clinical factors. While classical techniques lead to positive results, more refined analyses suggest that baseline HRQOL scores add relatively little to clinical factors to predict survival. These results may have implications for future use of HRQOL as a prognostic factor in cancer patients.

Development and validation of a risk score predicting substantial weight gain over 5 years in middle-aged European men and women.

BACKGROUND Identifying individuals at high risk of excess weight gain may help targeting prevention efforts at those at risk of various metabolic diseases associated with weight gain. Our aim was to develop a risk score to identify these individuals and validate it in an external population. METHODS We used lifestyle and nutritional data from 53°758 individuals followed for a median of 5.4 years from six centers of the European Prospective Investigation into Cancer and Nutrition (EPIC) to develop a risk score to predict substantial weight gain (SWG) for the next 5 years (derivation sample). Assuming linear weight gain, SWG was defined as gaining ≥ 10% of baseline weight during follow-up. Proportional hazards models were used to identify significant predictors of SWG separately by EPIC center. Regression coefficients of predictors were pooled using random-effects meta-analysis. Pooled coefficients were used to assign weights to each predictor. The risk score was calculated as a linear combination of the predictors. External validity of the score was evaluated in nine other centers of the EPIC study (validation sample). RESULTS Our final model included age, sex, baseline weight, level of education, baseline smoking, sports activity, alcohol use, and intake of six food groups. The model's discriminatory ability measured by the area under a receiver operating characteristic curve was 0.64 (95% CI = 0.63-0.65) in the derivation sample and 0.57 (95% CI = 0.56-0.58) in the validation sample, with variation between centers. Positive and negative predictive values for the optimal cut-off value of ≥ 200 points were 9% and 96%, respectively. CONCLUSION The present risk score confidently excluded a large proportion of individuals from being at any appreciable risk to develop SWG within the next 5 years. Future studies, however, may attempt to further refine the positive prediction of the score.

A clinical prognostic model for the identification of low-risk patients with acute symptomatic pulmonary embolism and active cancer.

BACKGROUND: Physicians need a specific risk-stratification tool to facilitate safe and cost-effective approaches to the management of patients with cancer and acute pulmonary embolism (PE). The objective of this study was to develop a simple risk score for predicting 30-day mortality in patients with PE and cancer by using measures readily obtained at the time of PE diagnosis. METHODS: Investigators randomly allocated 1,556 consecutive patients with cancer and acute PE from the international multicenter Registro Informatizado de la Enfermedad TromboEmbólica to derivation (67%) and internal validation (33%) samples. The external validation cohort for this study consisted of 261 patients with cancer and acute PE. Investigators compared 30-day all-cause mortality and nonfatal adverse medical outcomes across the derivation and two validation samples. RESULTS: In the derivation sample, multivariable analyses produced the risk score, which contained six variables: age > 80 years, heart rate ≥ 110/min, systolic BP < 100 mm Hg, body weight < 60 kg, recent immobility, and presence of metastases. In the internal validation cohort (n = 508), the 22.2% of patients (113 of 508) classified as low risk by the prognostic model had a 30-day mortality of 4.4% (95% CI, 0.6%-8.2%) compared with 29.9% (95% CI, 25.4%-34.4%) in the high-risk group. In the external validation cohort, the 18% of patients (47 of 261) classified as low risk by the prognostic model had a 30-day mortality of 0%, compared with 19.6% (95% CI, 14.3%-25.0%) in the high-risk group. CONCLUSIONS: The developed clinical prediction rule accurately identifies low-risk patients with cancer and acute PE.

Predicting global change impacts on plant species' distributions: Future challenges

Given the rate of projected environmental change for the 21st century, urgent adaptation and mitigation measures are required to slow down the on-going erosion of biodiversity. Even though increasing evidence shows that recent human-induced environmental changes have already triggered species' range shifts, changes in phenology and species' extinctions, accurate projections of species' responses to future environmental changes are more difficult to ascertain. This is problematic, since there is a growing awareness of the need to adopt proactive conservation planning measures using forecasts of species' responses to future environmental changes. There is a substantial body of literature describing and assessing the impacts of various scenarios of climate and land-use change on species' distributions. Model predictions include a wide range of assumptions and limitations that are widely acknowledged but compromise their use for developing reliable adaptation and mitigation strategies for biodiversity. Indeed, amongst the most used models, few, if any, explicitly deal with migration processes, the dynamics of population at the "trailing edge" of shifting populations, species' interactions and the interaction between the effects of climate and land-use. In this review, we propose two main avenues to progress the understanding and prediction of the different processes A occurring on the leading and trailing edge of the species' distribution in response to any global change phenomena. Deliberately focusing on plant species, we first explore the different ways to incorporate species' migration in the existing modelling approaches, given data and knowledge limitations and the dual effects of climate and land-use factors. Secondly, we explore the mechanisms and processes happening at the trailing edge of a shifting species' distribution and how to implement them into a modelling approach. We finally conclude this review with clear guidelines on how such modelling improvements will benefit conservation strategies in a changing world. (c) 2007 Rubel Foundation, ETH Zurich. Published by Elsevier GrnbH. All rights reserved.

Propagation velocity kinetics and repolarization alternans in a free-behaving sheep model of pacing-induced atrial fibrillation.

AIMS: Experimental models have reported conflicting results regarding the role of dispersion of repolarization in promoting atrial fibrillation (AF). Repolarization alternans, a beat-to-beat alternation in action potential duration, enhances dispersion of repolarization when propagation velocity is involved. METHODS AND RESULTS: In this work, original electrophysiological parameters were analysed to study AF susceptibility in a chronic sheep model of pacing-induced AF. Two pacemakers were implanted, each with a single right atrial lead. Right atrial depolarization and repolarization waves were documented at 2-week intervals. A significant and gradual decrease in the propagation velocity at all pacing rates and in the right atrial effective refractory period (ERP) was observed during the weeks of burst pacing before sustained AF developed when compared with baseline conditions. Right atrial repolarization alternans was observed, but because of the development of 2/1 atrioventricular block with far-field ventricular interference, its threshold could not be precisely measured. Non-sustained AF was not observed at baseline, but appeared during the electrical remodelling in association with a decrease in both ERP and propagation velocity. CONCLUSION: We report here on the feasibility of measuring ERP, atrial repolarization alternans, and propagation velocity kinetics and their potential in predicting susceptibility to AF in a free-behaving model of pacing-induced AF using the standard pacemaker technology.

Predicting species distributions for conservation decisions.

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of 'translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.

Predicting Bacteremia in Children With Cancer and Fever in Chemotherapy-induced Neutropenia: Results of the Prospective Multicenter SPOG 2003 FN Study.

STUDY AIM:: To develop a score predicting the risk of bacteremia in cancer patients with fever and neutropenia (FN), and to evaluate its performance. METHODS:: Pediatric patients with cancer presenting with FN induced by nonmyeloablative chemotherapy were observed in a prospective multicenter study. A score predicting the risk of bacteremia was developed from a multivariate mixed logistic regression model. Its cross-validated predictive performance was compared with that of published risk prediction rules. RESULTS:: Bacteremia was reported in 67 (16%) of 423 FN episodes. In 34 episodes (8%), bacteremia became known only after reassessment after 8 to 24 hours of inpatient management. Predicting bacteremia at reassessment was better than prediction at presentation with FN. A differential leukocyte count did not increase the predictive performance. The reassessment score predicting future bacteremia in 390 episodes without known bacteremia used the following 4 variables: hemoglobin ≥90 g/L at presentation (weight 3), platelet count <50 G/L (3), shaking chills (5), and other need for inpatient treatment or observation according to the treating physician (3). Applying a threshold ≥3, the score-simplified into a low-risk checklist-predicted bacteremia with 100% sensitivity, with 54 episodes (13%) classified as low-risk, and a specificity of 15%. CONCLUSIONS:: This reassessment score, simplified into a low-risk checklist of 4 routinely accessible characteristics, identifies pediatric patients with FN at risk for bacteremia. It has the potential to contribute to the reduction of use of antimicrobials in, and to shorten the length of hospital stays of pediatric patients with cancer and FN.

Predicting adverse events in children with fever and chemotherapy-induced neutropenia: the prospective multicenter SPOG 2003 FN study.

PURPOSE To develop a score predicting the risk of adverse events (AEs) in pediatric patients with cancer who experience fever and neutropenia (FN) and to evaluate its performance. PATIENTS AND METHODS Pediatric patients with cancer presenting with FN induced by nonmyeloablative chemotherapy were observed in a prospective multicenter study. A score predicting the risk of future AEs (ie, serious medical complication, microbiologically defined infection, radiologically confirmed pneumonia) was developed from a multivariate mixed logistic regression model. Its cross-validated predictive performance was compared with that of published risk prediction rules. Results An AE was reported in 122 (29%) of 423 FN episodes. In 57 episodes (13%), the first AE was known only after reassessment after 8 to 24 hours of inpatient management. Predicting AE at reassessment was better than prediction at presentation with FN. A differential leukocyte count did not increase the predictive performance. The score predicting future AE in 358 episodes without known AE at reassessment used the following four variables: preceding chemotherapy more intensive than acute lymphoblastic leukemia maintenance (weight = 4), hemoglobin > or = 90 g/L (weight = 5), leukocyte count less than 0.3 G/L (weight = 3), and platelet count less than 50 G/L (weight = 3). A score (sum of weights) > or = 9 predicted future AEs. The cross-validated performance of this score exceeded the performance of published risk prediction rules. At an overall sensitivity of 92%, 35% of the episodes were classified as low risk, with a specificity of 45% and a negative predictive value of 93%. CONCLUSION This score, based on four routinely accessible characteristics, accurately identifies pediatric patients with cancer with FN at risk for AEs after reassessment.

Forecasting and turning point predictions in a Bayesian panel VAR model

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model which accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for a particular type of diffuse, for Minnesota-type and for hierarchical priors. Formulas for multistep, multiunit point and average forecasts are provided. An application to the problem of forecasting the growth rate of output and of predicting turning points in the G-7 illustrates the approach. A comparison with alternative forecasting methods is also provided.

Predicting Adverse Events in Children with Fever and Chemotherapy-Induced Neutropenia. Results of the Prospective Multicenter SPOG 2003 FN Study

PURPOSE To develop a score predicting the risk of adverse events (AEs) in pediatric patients with cancer who experience fever and neutropenia (FN) and to evaluate its performance. PATIENTS AND METHODS Pediatric patients with cancer presenting with FN induced by nonmyeloablative chemotherapy were observed in a prospective multicenter study. A score predicting the risk of future AEs (ie, serious medical complication, microbiologically defined infection, radiologically confirmed pneumonia) was developed from a multivariate mixed logistic regression model. Its cross-validated predictive performance was compared with that of published risk prediction rules. Results An AE was reported in 122 (29%) of 423 FN episodes. In 57 episodes (13%), the first AE was known only after reassessment after 8 to 24 hours of inpatient management. Predicting AE at reassessment was better than prediction at presentation with FN. A differential leukocyte count did not increase the predictive performance. The score predicting future AE in 358 episodes without known AE at reassessment used the following four variables: preceding chemotherapy more intensive than acute lymphoblastic leukemia maintenance (weight = 4), hemoglobin > or = 90 g/L (weight = 5), leukocyte count less than 0.3 G/L (weight = 3), and platelet count less than 50 G/L (weight = 3). A score (sum of weights) > or = 9 predicted future AEs. The cross-validated performance of this score exceeded the performance of published risk prediction rules. At an overall sensitivity of 92%, 35% of the episodes were classified as low risk, with a specificity of 45% and a negative predictive value of 93%. CONCLUSION This score, based on four routinely accessible characteristics, accurately identifies pediatric patients with cancer with FN at risk for AEs after reassessment.

Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.

BACKGROUND AND PURPOSE: Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors. METHODS: The study includes 4 population-based cohorts with 2047 first incident strokes from 22,720 initially stroke-free European origin participants aged ≥55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphisms implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with area under the curve statistics comparing the GRS with age and sex, Framingham Stroke Risk Score models, and reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke. RESULTS: In the meta-analysis, adding the GRS to the Framingham Stroke Risk Score, age and sex model resulted in a significant improvement in discrimination (all stroke: Δjoint area under the curve=0.016, P=2.3×10(-6); ischemic stroke: Δjoint area under the curve=0.021, P=3.7×10(-7)), although the overall area under the curve remained low. In all the studies, there was a highly significantly improved net reclassification index (P<10(-4)). CONCLUSIONS: The single-nucleotide polymorphisms associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared with the classical epidemiological risk factors for stroke.

Leptonic secondary emission in a hadronic microquasar model

Context.It has been proposed that the origin of the very high-energy photons emitted from high-mass X-ray binaries with jet-like features, so-called microquasars (MQs), is related to hadronic interactions between relativistic protons in the jet and cold protons of the stellar wind. Leptonic secondary emission should be calculated in a complete hadronic model that includes the effects of pairs from charged pion decays inside the jets and the emission from pairs generated by gamma-ray absorption in the photosphere of the system. Aims.We aim at predicting the broadband spectrum from a general hadronic microquasar model, taking into account the emission from secondaries created by charged pion decay inside the jet. Methods.The particle energy distribution for secondary leptons injected along the jets is consistently derived taking the energy losses into account. The spectral energy distribution resulting from these leptons is calculated after assuming different values of the magnetic field inside the jets. We also compute the spectrum of the gamma-rays produced by neutral pion-decay and processed by electromagnetic cascades under the stellar photon field. Results.We show that the secondary emission can dominate the spectral energy distribution at low energies (~1 MeV). At high energies, the production spectrum can be significantly distorted by the effect of electromagnetic cascades. These effects are phase-dependent, and some variability modulated by the orbital period is predicted.

MIGCLIM: Predicting plant distribution and dispersal in a changing climate

Many studies have forecasted the possible impact of climate change on plant distribution using models based on ecological niche theory. In their basic implementation, niche-based models do not constrain predictions by dispersal limitations. Hence, most niche-based modelling studies published so far have assumed dispersal to be either unlimited or null. However, depending on the rate of climatic change, the landscape fragmentation and the dispersal capabilities of individual species, these assumptions are likely to prove inaccurate, leading to under- or overestimation of future species distributions and yielding large uncertainty between these two extremes. As a result, the concepts of "potentially suitable" and "potentially colonisable" habitat are expected to differ significantly. To quantify to what extent these two concepts can differ, we developed MIGCLIM, a model simulating plant dispersal under climate change and landscape fragmentation scenarios. MIGCLIM implements various parameters, such as dispersal distance, increase in reproductive potential over time, barriers to dispersal or long distance dispersal. Several simulations were run for two virtual species in a study area of the western Swiss Alps, by varying dispersal distance and other parameters. Each simulation covered the hundred-year period 2001-2100 and three different IPCC-based temperature warming scenarios were considered. Our results indicate that: (i) using realistic parameter values, the future potential distributions generated using MIGCLIM can differ significantly (up to more than 95% decrease in colonized surface) from those that ignore dispersal; (ii) this divergence increases both with increasing climate warming and over longer time periods; (iii) the uncertainty associated with the warming scenario can be nearly as large as the one related to dispersal parameters; (iv) accounting for dispersal, even roughly, can importantly reduce uncertainty in projections.

Predicting potential distribution of the jaguar (Panthera onca) in Mexico: identification of priority areas for conservation

Aim The jaguar, Panthera onca, is a species of global conservation concern. In Mexico, the northernmost part of its distribution range, its conservation status, is particularly critical, while its potential and actual distribution is poorly known. We propose an ensemble model (EM) of the potential distribution for the jaguar in Mexico and identify the priority areas for conservation.Location Mexico.Methods We generated our EM based on three presence-only methods (Ecological Niche Factor Analysis, Mahalanobis distance, Maxent) and considering environmental, biological and anthropogenic factors. We used this model to evaluate the efficacy of the existing Mexican protected areas (PAs), to evaluate the adequacy of the jaguar conservation units (JCUs) and to propose new areas that should be considered for conservation and management of the species in Mexico.Results Our results outline that 16% of Mexico (c. 312,000 km2) can be considered as suitable for the presence of the jaguar. Furthermore, 13% of the suitable areas are included in existing PAs and 14% are included in JCUs (Sanderson et al., 2002).Main conclusions Clearly much more should be carried out to establish a proactive conservation strategy. Based on our results, we propose here new jaguar conservation and management areas that are important for a nationwide conservation blueprint.

Predicting future distributions of mountain plants under climate change: does dispersal capacity matter?

Many studies have investigated the impacts that climate change could potentially have on the distribution of plant species, but few have attempted to constrain projections through plant dispersal limitations. Instead, most studies published so far have been using the simplification of considering dispersal as either unlimited or null. However, depending on a species' dispersal capacity, landscape fragmentation, and the rate of climatic change, these assumptions can lead to serious over- or underestimation of a species' future distribution. To quantify the discrepancies between unlimited, realistic, and no dispersal scenarios, we carried out projections of future distribution over the 21st century for 287 mountain plant species in a study area of the Western Swiss Alps. For each species, simulations were run for four dispersal scenarios (unlimited dispersal, no dispersal, realistic dispersal and realistic dispersal with long-distance dispersal events) and under four climate change scenarios. Although simulations accounting for realistic dispersal limitations did significantly differ from those considering dispersal as unlimited or null in terms of projected future distribution, using the unlimited dispersal simplification nevertheless provided good approximations for species extinctions under more moderate climate change scenarios. Overall, simulations accounting for dispersal limitations produced, for our mountainous study area, results that were significantly closer to unlimited dispersal than to no dispersal. Finally, analyzing the temporal pattern of species extinctions over the entire 21st century showed that, due to the possibility of a large number of species shifting their distribution to higher elevation, important species extinctions for our study area might not occur before the 2080-2100 time periods.