Derivation and assessment of risk prediction models using case-cohort data

Background Case-cohort studies are increasingly used to quantify the association of novel factors with disease risk. Conventional measures of predictive ability need modification for this design. We show how Harrell’s C-index, Royston’s D, and the category-based and continuous versions of the net reclassification index (NRI) can be adapted. Methods We simulated full cohort and case-cohort data, with sampling fractions ranging from 1% to 90%, using covariates from a cohort study of coronary heart disease, and two incidence rates. We then compared the accuracy and precision of the proposed risk prediction metrics. Results The C-index and D must be weighted in order to obtain unbiased results. The NRI does not need modification, provided that the relevant non-subcohort cases are excluded from the calculation. The empirical standard errors across simulations were consistent with analytical standard errors for the C-index and D but not for the NRI. Good relative efficiency of the prediction metrics was observed in our examples, provided the sampling fraction was above 40% for the C-index, 60% for D, or 30% for the NRI. Stata code is made available. Conclusions Case-cohort designs can be used to provide unbiased estimates of the C-index, D measure and NRI

Spatial primes produce dissociated inhibitory effects on saccadic latencies and trajectories.

In masked priming, a briefly presented prime can facilitate or inhibit responses to a subsequent target. In most instances, targets with an associated response that is congruent with the prime direction speed up reaction times to the target (a positive compatibility effect; PCE). However, under certain circumstances, slower responses for compatible primes are obtained (a negative compatibility effect; NCE). NCEs can be found when a long pre-target delay is used. During the delay, inhibition is assumed to take place, and therefore an effect on saccade trajectories may also be expected. In a previous study, we found the effects of inhibition on response times and trajectories to be dissociated, but this experiment varied the timing of several aspects of the stimulus sequence and it is therefore unclear what caused the dissociation. In the present study, we varied only one aspect of the timing, but replicated the dissociation. By varying just the pre-target delay, we found a PCE for a short delay, and an NCE for a long delay, but saccade trajectories deviated away from prime directions in both conditions. This suggests dissociated inhibitory effects of primes on response times and saccade trajectories

Atmospheric Boundary Layer Impacts on Wind Farms

Increasing demand for renewable energy sources has meant that wind power is becoming a more crucial source of energy, leading to larger wind farms. It is currently unknown whether wind farms impact the boundary layer. This thesis aims to improve our understanding of the impact from wind farms. To do this, numerical simulations are carried out in BLASIUS and WRF with an existing Wind Farm Parametrisation (WFP) being implemented in BLASIUS. Neutral boundary layer simulations are carried out in BLASIUS, with different velocities, height and capping inversion strengths. It is found that decreases in boundary layer height increase the impact from the wind farm, where the height is between 715 m and 992 m for turbines with a hub height of 95 m. Increases in velocity increase the vertical advection of horizontal momentum upstream of the turbines and greater deceleration of momentum in the wind farm. Non-dimensional analysis found jumps in the inversion layer above the wind farm for Fr < 1, and increases in the pressure perturbations for low Z flows. Comparisons are made between BLASIUS and a linear model for wind farms in neutral boundary layers. The drag term in the linear model is overestimated and should be modified to account for the logarithmic velocity profile near the surface. The assumptions made in the linear model do not inhibit its representation of the velocity and pressure perturbations within the boundary layer. The impact of a wind farm on a sea breeze is investigated using WRF simulations. It is found that a wind farm at the coast does not impact the propagation of the sea breeze but does impact the land breeze. This is due to the turbulent boundary layer which the wake is in, causing a fast decay of the wake. The land breeze propagates through the wind farms and is directly impacted

Extrusion Processing of Biomass By-Products for Sustainable Food Production

The sustainability of the food supply chain is gaining increasing attention in the quest to balance economic, environmental, and social dimensions. A key opportunity to enhance food system sustainability is by addressing food waste through upcycling strategies to generate higher value, functional foods. Extrusion is a food manufacturing technology that is emerging as a promising option for the incorporation of various types of biomass by-products, such as fruit and vegetable pomace, brewer’s spent grain, bagasse, and oil press cake. In this chapter, we present an overview of the latest research conducted on incorporating biomass by-products into extruded food products, with an emphasis on the challenges and opportunities associated with this approach. A meta-analysis study was conducted regarding a key challenge for product quality when incorporating by-products, which is the reduction in radial expansion index of expanded snack and breakfast cereal products. To highlight future opportunities, two case studies illustrate successful examples of by-product incorporation for commercial extruded food products, while emerging protein sources from waste-consuming insects were also explored. Overcoming these challenges and leveraging opportunities can contribute to a more sustainable food system through the integration of by-products into value-added extruded foods

Assessing risk prediction models using individual participant data from multiple studies.

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous

Equalization of four cardiovascular risk algorithms after systematic recalibration: Individual-participant meta-analysis of 86 prospective studies

© 2018 The Author(s). Published by Oxford University Press on behalf of the European Society of Cardiology. Aims There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after \u27recalibration\u27, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied. Methods and results Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at \u27high\u27 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29-39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22-24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms. Conclusion Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need

World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

© 2019 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Background: To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. Methods: In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40–80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. Findings: Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell\u27s C indices ranging from 0·685 (95% CI 0·629–0·741) to 0·833 (0·783–0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40–64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. Interpretation: We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. Funding: World Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research

Quantifying the contribution of established risk factors to cardiovascular mortality differences between Russia and Norway.

Surprisingly few attempts have been made to quantify the simultaneous contribution of well-established risk factors to CVD mortality differences between countries. We aimed to develop and critically appraise an approach to doing so, applying it to the substantial CVD mortality gap between Russia and Norway using survey data in three cities and mortality risks from the Emerging Risk Factor Collaboration. We estimated the absolute and relative differences in CVD mortality at ages 40-69 years between countries attributable to the risk factors, under the counterfactual that the age- and sex-specific risk factor profile in Russia was as in Norway, and vice-versa. Under the counterfactual that Russia had the Norwegian risk factor profile, the absolute age-standardized CVD mortality gap would decline by 33.3% (95% CI 25.1-40.1) among men and 22.1% (10.4-31.3) among women. In relative terms, the mortality rate ratio (Russia/Norway) would decline from 9-10 to 7-8. Under the counterfactual that Norway had the Russian risk factor profile, the mortality gap reduced less. Well-established CVD risk factors account for a third of the male and around a quarter of the female CVD mortality gap between Russia and Norway. However, these estimates are based on widely held epidemiological assumptions that deserve further scrutiny

Natriuretic peptides and integrated risk assessment for cardiovascular disease. an individual-participant-data meta-analysis

BACKGROUND: Guidelines for primary prevention of cardiovascular diseases focus on prediction of coronary heart disease and stroke. We assessed whether or not measurement of N-terminal-pro-B-type natriuretic peptide (NT-proBNP) concentration could enable a more integrated approach than at present by predicting heart failure and enhancing coronary heart disease and stroke risk assessment. METHODS: In this individual-participant-data meta-analysis, we generated and harmonised individual-participant data from relevant prospective studies via both de-novo NT-proBNP concentration measurement of stored samples and collection of data from studies identified through a systematic search of the literature (PubMed, Scientific Citation Index Expanded, and Embase) for articles published up to Sept 4, 2014, using search terms related to natriuretic peptide family members and the primary outcomes, with no language restrictions. We calculated risk ratios and measures of risk discrimination and reclassification across predicted 10 year risk categories (ie, <5%, 5% to <7·5%, and ≥7·5%), adding assessment of NT-proBNP concentration to that of conventional risk factors (ie, age, sex, smoking status, systolic blood pressure, history of diabetes, and total and HDL cholesterol concentrations). Primary outcomes were the combination of coronary heart disease and stroke, and the combination of coronary heart disease, stroke, and heart failure. FINDINGS: We recorded 5500 coronary heart disease, 4002 stroke, and 2212 heart failure outcomes among 95 617 participants without a history of cardiovascular disease in 40 prospective studies. Risk ratios (for a comparison of the top third vs bottom third of NT-proBNP concentrations, adjusted for conventional risk factors) were 1·76 (95% CI 1·56-1·98) for the combination of coronary heart disease and stroke and 2·00 (1·77-2·26) for the combination of coronary heart disease, stroke, and heart failure. Addition of information about NT-proBNP concentration to a model containing conventional risk factors was associated with a C-index increase of 0·012 (0·010-0·014) and a net reclassification improvement of 0·027 (0·019-0·036) for the combination of coronary heart disease and stroke and a C-index increase of 0·019 (0·016-0·022) and a net reclassification improvement of 0·028 (0·019-0·038) for the combination of coronary heart disease, stroke, and heart failure. INTERPRETATION: In people without baseline cardiovascular disease, NT-proBNP concentration assessment strongly predicted first-onset heart failure and augmented coronary heart disease and stroke prediction, suggesting that NT-proBNP concentration assessment could be used to integrate heart failure into cardiovascular disease primary prevention

Validation and public health modelling of risk prediction models for kidney cancer using the UK Biobank

Funder: Kidney Cancer UKFunder: Yorkshire Cancer ResearchFunder: Mark Foundation For Cancer Research; Id: http://dx.doi.org/10.13039/100014599Funder: The Rosetrees TrustFunder: Urology Foundation; Id: http://dx.doi.org/10.13039/501100007568Objectives: To externally validate risk models for the detection of kidney cancer, as early detection of kidney cancer improves survival and stratifying the population using risk models could enable an individually tailored screening programme. Methods: We validated the performance of 30 existing phenotypic models predicting the risk of kidney cancer in the UK Biobank cohort (n = 450 687). We compared the discrimination and calibration of models for men, women, and a mixed‐sex cohort. Population level data were used to estimate model performance in a screening scenario for a range of risk thresholds (6‐year risk: 0.1–1.0%). Results: In all, 10 models had reasonable discrimination (area under the receiver‐operating characteristic curve >0.60), although some had poor calibration. Modelling demonstrated similar performance of the best models over a range of thresholds. The models showed an improvement in ability to identify cases compared to age‐ and sex‐based screening. All the models performed less well in women than men. Conclusions: The present study is the first comprehensive external validation of risk models for kidney cancer. The best‐performing models are better at identifying individuals at high risk of kidney cancer than age and sex alone; however, the benefits are relatively small. Feasibility studies are required to determine applicability to a screening programme