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Background: Breast cancer (BC) is a significant health concern among European women, with the highest prevalence rates among all cancers. Existing BC prediction models account for major risks such as hereditary, hormonal and reproductive factors, but research suggests that adherence to a healthy lifestyle can reduce the risk of developing BC to some extent. Understanding the influence and predictive role of lifestyle variables in current risk prediction models could help identify actionable, modifiable, targets among high-risk population groups. Purpose: To systematically review population-based BC risk prediction models applicable to European populations and identify lifestyle predictors and their corresponding parameter values for a better understanding of their relative contribution to the prediction of incident BC. Methods: A systematic review was conducted in PubMed, Embase and Web of Science from January 2000 to August 2021. Risk prediction models were included if (i) developed and/or validated in adult cancer-free women in Europe, (ii) based on easily ascertained information, and (iii) reported models' final predictors. To investigate further the comparability of lifestyle predictors across models, estimates were standardised into risk ratios and visualised using forest plots. Results: From a total of 49 studies, 33 models were developed and 22 different existing models, mostly from Gail (22 studies) and Tyrer-Cuzick and co-workers (12 studies) were validated or modified for European populations. Family history of BC was the most frequently included predictor (31 models), while body mass index (BMI) and alcohol consumption (26 and 21 models, respectively) were the lifestyle predictors most often included, followed by smoking and physical activity (7 and 6 models respectively). Overall, for lifestyle predictors, their modest predictive contribution was greater for riskier lifestyle levels, though highly variable model estimates across different models. Conclusions: Given the increasing BC incidence rates in Europe, risk models utilising readily available risk factors could greatly aid in widening the population coverage of screening efforts, while the addition of lifestyle factors could help improving model performance and serve as intervention targets of prevention programmes. [ABSTRACT FROM AUTHOR]

Despite poor cardiovascular outcomes, there are no dedicated, validated risk stratification tools to guide investigation or treatment in type 2 myocardial infarction. The goal of this study was to derive and validate a risk stratification tool for the prediction of death or future myocardial infarction in patients with type 2 myocardial infarction. The T2-risk score was developed in a prospective multicenter cohort of consecutive patients with type 2 myocardial infarction. Cox proportional hazards models were constructed for the primary outcome of myocardial infarction or death at 1 year using variables selected a priori based on clinical importance. Discrimination was assessed by area under the receiving-operating characteristic curve (AUC). Calibration was investigated graphically. The tool was validated in a single-center cohort of consecutive patients and in a multicenter cohort study from sites across Europe. There were 1,121, 250, and 253 patients in the derivation, single-center, and multicenter validation cohorts, with the primary outcome occurring in 27% (297 of 1,121), 26% (66 of 250), and 14% (35 of 253) of patients, respectively. The T2-risk score incorporating age, ischemic heart disease, heart failure, diabetes mellitus, myocardial ischemia on electrocardiogram, heart rate, anemia, estimated glomerular filtration rate, and maximal cardiac troponin concentration had good discrimination (AUC: 0.76; 95% CI: 0.73-0.79) for the primary outcome and was well calibrated. Discrimination was similar in the consecutive patient (AUC: 0.83; 95% CI: 0.77-0.88) and multicenter (AUC: 0.74; 95% CI: 0.64-0.83) cohorts. T2-risk provided improved discrimination over the Global Registry of Acute Coronary Events 2.0 risk score in all cohorts. The T2-risk score performed well in different health care settings and could help clinicians to prognosticate, as well as target investigation and preventative therapies more effectively. (High-Sensitivity Troponin in the Evaluation of Patients With Suspected Acute Coronary Syndrome [High-STEACS]; NCT01852123) [Display omitted] [ABSTRACT FROM AUTHOR]

Aims: The 2021 European Society of Cardiology prevention guidelines recommend the use of (lifetime) risk prediction models to aid decisions regarding initiation of prevention. We aimed to update and systematically recalibrate the LIFEtime-perspective CardioVascular Disease (LIFE-CVD) model to four European risk regions for the estimation of lifetime CVD risk for apparently healthy individuals., Methods and Results: The updated LIFE-CVD (i.e. LIFE-CVD2) models were derived using individual participant data from 44 cohorts in 13 countries (687 135 individuals without established CVD, 30 939 CVD events in median 10.7 years of follow-up). LIFE-CVD2 uses sex-specific functions to estimate the lifetime risk of fatal and non-fatal CVD events with adjustment for the competing risk of non-CVD death and is systematically recalibrated to four distinct European risk regions. The updated models showed good discrimination in external validation among 1 657 707 individuals (61 311 CVD events) from eight additional European cohorts in seven countries, with a pooled C-index of 0.795 (95% confidence interval 0.767-0.822). Predicted and observed CVD event risks were well calibrated in population-wide electronic health records data in the UK (Clinical Practice Research Datalink) and the Netherlands (Extramural LUMC Academic Network). When using LIFE-CVD2 to estimate potential gain in CVD-free life expectancy from preventive therapy, projections varied by risk region reflecting important regional differences in absolute lifetime risk. For example, a 50-year-old smoking woman with a systolic blood pressure (SBP) of 140 mmHg was estimated to gain 0.9 years in the low-risk region vs. 1.6 years in the very high-risk region from lifelong 10 mmHg SBP reduction. The benefit of smoking cessation for this individual ranged from 3.6 years in the low-risk region to 4.8 years in the very high-risk region., Conclusion: By taking into account geographical differences in CVD incidence using contemporary representative data sources, the recalibrated LIFE-CVD2 model provides a more accurate tool for the prediction of lifetime risk and CVD-free life expectancy for individuals without previous CVD, facilitating shared decision-making for cardiovascular prevention as recommended by 2021 European guidelines., Competing Interests: Conflict of interest: Due to the very long author list, these will be provided per author using ICMJE forms at the revision stage, if applicable., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Purpose: To externally evaluate the performance of two European risk prediction models, for identifying patients at high-risk of medication harm, in an Australian hospital setting. Methods: This was a secondary analysis of a pre-existing cohort study described in a recently published study by Falconer et al. (Br J Clin Pharmacol 87(3):1512–1524, 2021) describing the development of a predictive risk model for inpatient medication harm. We retrospectively extracted relevant variables using the electronic health records of general medical and geriatric patients admitted to a quaternary hospital, in Brisbane, over 6 months from July to December 2017. This dataset was used to externally evaluate the two European models, The Brighton Adverse Drug Reaction Risk (BADRI) model by Tangiisuran et al. and a risk model developed by Trivalle et al. The variables were entered into both models and the patients' risk of medication harm was calculated, and compared with actual patient outcomes. Predictive performance was evaluated by measuring area under the receiver operative characteristic (AuROC) curves. Results: The Australian patient cohort included 1982 patients (median age 74 years), of which 136 (7%) patients experienced ≥ 1 medication harm event(s). External evaluation of the two European models identified that both the BADRI and the Trivalle models had reduced predictive performance in an Australian patient cohort, compared with their original studies (AuROC of 0.63 [95% CI: 0.58–0.68] and 0.60 [95% CI: 0.55–0.65], respectively). Conclusion: Neither model demonstrated sufficient discrimination to warrant further evaluation in our local setting. This is likely a result of variations between the development and the validation cohorts, and the change in healthcare systems over time, and highlights the need for an up-to-date and context-specific risk prediction model. [ABSTRACT FROM AUTHOR]

Background: Head and neck cancer (HNC) incidence is on the rise, often diagnosed at late stage and associated with poor prognoses. Risk prediction tools have a potential role in prevention and early detection., Methods: The IARC-ARCAGE European case-control study was used as the model development dataset. A clinical HNC risk prediction model using behavioral and demographic predictors was developed via multivariable logistic regression analyses. The model was then externally validated in the UK Biobank cohort. Model performance was tested using discrimination and calibration metrics., Results: 1926 HNC cases and 2043 controls were used for the development of the model. The development dataset model including sociodemographic, smoking, and alcohol variables had moderate discrimination, with an area under curve (AUC) value of 0.75 (95% CI, 0.74-0.77); the calibration slope (0.75) and tests were suggestive of good calibration. 384 616 UK Biobank participants (with 1177 HNC cases) were available for external validation of the model. Upon external validation, the model had an AUC of 0.62 (95% CI, 0.61-0.64)., Conclusion: We developed and externally validated a HNC risk prediction model using the ARCAGE and UK Biobank studies, respectively. This model had moderate performance in the development population and acceptable performance in the validation dataset. Demographics and risk behaviors are strong predictors of HNC, and this model may be a helpful tool in primary dental care settings to promote prevention and determine recall intervals for dental examination. Future addition of HPV serology or genetic factors could further enhance individual risk prediction., (© 2024 The Author(s). Head & Neck published by Wiley Periodicals LLC.)

Aims: Atrial fibrillation (AF) and concomitant cardiometabolic disease processes interact and combine to lead to adverse events, such as stroke, heart failure, myocardial infarction, and cardiovascular death. Circulating biomolecules provide quantifiable proxies for cardiometabolic disease processes. The aim of this study was to test whether biomolecule combinations can define phenotypes in patients with AF., Methods and Results: This pre-specified analysis of the EAST-AFNET 4 biomolecule study assigned patients to clusters using polytomous variable latent-class analysis based on baseline concentrations of 13 precisely quantified biomolecules potentially reflecting ageing, cardiac fibrosis, metabolic dysfunction, oxidative stress, cardiac load, endothelial dysfunction, and inflammation. In each cluster, rates of cardiovascular death, stroke, or hospitalization for heart failure or acute coronary syndrome, the primary outcome of EAST-AFNET 4, were calculated and compared between clusters over median 5.1 years follow-up. Findings were independently validated in a prospective cohort of 748 patients with AF (BBC-AF; median follow-up 2.9 years).Unsupervised biomolecule analysis assigned 1586 patients (71 years old, 46% women) into four clusters. The highest risk cluster was dominated by elevated bone morphogenetic protein 10, insulin-like growth factor-binding protein 7, N-terminal pro-B-type natriuretic peptide, angiopoietin 2, and growth differentiation factor 15. Patients in the lowest risk cluster showed low concentrations of these biomolecules. Two intermediate-risk clusters differed by high or low concentrations of C-reactive protein, interleukin-6, and D-dimer. Patients in the highest risk cluster had a five-fold higher cardiovascular event rate than patients in the low-risk cluster. Early rhythm control was effective across clusters (Pinteraction = 0.63). Sensitivity analyses and external validation in BBC-AF replicated clusters and risk gradients., Conclusion: Biomolecule concentrations identify cardiometabolic subphenotypes in patients with AF at high and low cardiovascular risk., Competing Interests: Conflict of interest: W.C. is now an employee of ICF (www.icf.com). Both W.C. and V.R.C. performed the work described here while employed by the University of Birmingham. A.J.C. receives personal funds from Acesion, Incarda, Menarini, Milestone, Sanofi, Bayer, Anthos, Daiichi Sankyo, Pfizer, Abbott, Biosense Webster, Biotronik, Boston Scientific, Medtronic, and Johnson & Johnson. P.T.E. receives sponsored research support from Bayer AG, IBM Research, Bristol Myers Squibb, Pfizer, and Novo Nordisk; P.T.E. has also served on advisory boards or consulted for Bayer AG. H.J.G.M.C. discloses advisory board fees from InCarda Therapeutics, Roche Diagnostics, Daiichi Sankyo, Sanofi, Acesion, and Atricure. Speaker fee from Medtronic. U.S. received consultancy fees or honoraria from Università della Svizzera Italiana (USI, Switzerland) and Roche Diagnostics (Switzerland). U.S. was supported by a grant from EP Solutions Inc. (Switzerland) and is co-founder and shareholder of YourRhythmics BV, a spin-off company of the University of Maastricht. C.M. has received speaker fees from AstraZeneca, Novartis, Boehringer Ingelheim/Lilly, Bayer, Pfizer, Sanofi, Aventis, Apontis, and Abbott outside of this work. C.M. has participated in advisory boards for Boehringer Ingelheim and Novo Nordisk. R.B.S. has received lecture fees and advisory board fees from BMS/Pfizer and Bayer outside of this work. L.F. received institutional research grants by EU 633196 (CATCH-ME) and EU 965286 (MAESTRIA). British Heart Foundation (AA/18/2/34218), German Center for Cardiovascular Research, supported by the German Ministry of Education and Research (DZHK), and several biomedical companies active in the field of research. L.F. is listed as inventor on two issued patents held by the employing institution (Atrial Fibrillation Therapy WO 2015140571, Markers for Atrial Fibrillation WO 2016012783). P.K. received research support for basic, translational, and clinical research projects from the European Union, British Heart Foundation, Leducq Foundation, Medical Research Council (UK), and German Center for Cardiovascular Research, from several drug and device companies active in AF, and has received honoraria from several such companies in the past, but not in the last 3 years. P.K. is listed as an inventor on two issued patents held by the institution (Atrial Fibrillation Therapy WO 2015140571, Markers for Atrial Fibrillation WO 2016012783)., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Introduction: Women with migration background present specific challenges related to risk stratification and care of gestational diabetes mellitus (GDM). Therefore, this study aims to investigate the role of ethnic origin on the risk of developing GDM in a multiethnic European cohort., Methods: Pregnant women were included at a median gestational age of 12.9 weeks and assigned to the geographical regions of origin: Caucasian Europe ( n  = 731), Middle East and North Africa countries (MENA, n  = 195), Asia ( n  = 127) and Sub-Saharan Africa (SSA, n  = 48). At the time of recruitment maternal characteristics, glucometabolic parameters and dietary habits were assessed. An oral glucose tolerance test was performed in mid-gestation for GDM diagnosis., Results: Mothers with Caucasian ancestry were older and had higher blood pressure and an adverse lipoprotein profile as compared to non-Caucasian mothers, whereas non-Caucasian women (especially those from MENA countries) had a higher BMI and were more insulin resistant. Moreover, we found distinct dietary habits. Non-Caucasian mothers, especially those from MENA and Asian countries, had increased incidence of GDM as compared to the Caucasian population (OR 1.87, 95%CI 1.40 to 2.52, p  < 0.001). Early gestational fasting glucose and insulin sensitivity were consistent risk factors across different ethnic populations, however, pregestational BMI was of particular importance in Asian mothers., Discussion: Prevalence of GDM was higher among women from MENA and Asian countries, who already showed adverse glucometabolic profiles at early gestation. Fasting glucose and early gestational insulin resistance (as well as higher BMI in women from Asia) were identified as important risk factors in Caucasian and non-Caucasian patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Kotzaeridi, Monod, Linder, Eppel, Seidel, Feichtinger, Mosimann, Filippi, Wegener, Henrich, Tura and Göbl.)

• Multivariable risk prediction algorithms are useful for making clinical decisions. • PredictA algorithm is an International algorithm developed in Europe to predict the risk of anxiety disorders. • The performance of PredictA algorithm is not known in North America. • PredictA algorithm has acceptable discrimination, but the calibration capacity was poor. • The use of PredictA in the US general population for predicting individual risk of generalized anxiety and/or panic disorders is not encouraged. Multivariable risk prediction algorithms are useful for making clinical decisions and health planning. While prediction algorithms for new onset of anxiety disorders in Europe and elsewhere have been developed, the performance of these algorithms in the Americas is not known. The objective of this study was to validate the PredictA algorithm for new onset of anxiety and/or panic disorders in the US general population. Longitudinal study design was conducted with approximate 2-year follow-up data from a total of 24 626 individuals who participated in Wave 1 and 2 of the US National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) and who did not have generalized anxiety disorder (GAD) and panic disorder in the past year at Wave 1. The PredictA algorithm was directly applied to the selected participants. Among the participants, 5.4% developed GAD and/or panic disorder over two years. The PredictA algorithm had a discriminative power (C -statistics = 0.62, 95%CI: 0.61; 0.64), but poor calibration (p < 0.001) with the NESARC data. The observed and the mean predicted risk of GAD and/or panic disorders in the NESARC were 5.3% and 3.6%, respectively. Particularly, the observed and predicted risks of GAD and/or panic disorders in the highest decile of risk score in the NESARC participants were 13.3% and 10.4%, respectively. The PredictA algorithm has acceptable discrimination, but the calibration with the NESARC data was poor. The PredictA algorithm is likely to underestimate the risk of GAD/panic disorders in the US population. Therefore, the use of PredictA in the US general population for predicting individual risk of GAD and/or panic disorders is not encouraged. [ABSTRACT FROM AUTHOR]

Aims: To validate the modified World Health Organization (mWHO) risk classification in advanced and emerging countries, and to identify additional risk factors for cardiac events during pregnancy.Methods and Results: The ongoing prospective worldwide Registry Of Pregnancy And Cardiac disease (ROPAC) included 2742 pregnant women (mean age ± standard deviation, 29.2 ± 5.5 years) with established cardiac disease: 1827 from advanced countries and 915 from emerging countries. In patients from advanced countries, congenital heart disease was the most prevalent diagnosis (70%) while in emerging countries valvular heart disease was more common (55%). A cardiac event occurred in 566 patients (20.6%) during pregnancy: 234 (12.8%) in advanced countries and 332 (36.3%) in emerging countries. The mWHO classification had a moderate performance to discriminate between women with and without cardiac events (c-statistic 0.711 and 95% confidence interval (CI) 0.686-0.735). However, its performance in advanced countries (0.726) was better than in emerging countries (0.633). The best performance was found in patients with acquired heart disease from developed countries (0.712). Pre-pregnancy signs of heart failure and, in advanced countries, atrial fibrillation and no previous cardiac intervention added prognostic value to the mWHO classification, with a c-statistic of 0.751 (95% CI 0.715-0.786) in advanced countries and of 0.724 (95% CI 0.691-0.758) in emerging countries.Conclusion: The mWHO risk classification is a useful tool for predicting cardiac events during pregnancy in women with established cardiac disease in advanced countries, but seems less effective in emerging countries. Data on pre-pregnancy cardiac condition including signs of heart failure and atrial fibrillation, may help to improve preconception counselling in advanced and emerging countries. [ABSTRACT FROM AUTHOR]

BACKGROUND: Approximately 20% of patients with stage II colorectal cancer will experience a relapse. Current clinical-pathologic stratification factors do not allow clear identification of these high-risk patients. ColoPrint (Agendia, Amsterdam, The Netherlands, http://www.agendia.com) is a gene expression classifier that distinguishes patients with low or high risk of disease relapse. METHODS: ColoPrint was developed using whole-genome expression data and validated in several independent validation cohorts. Stage II patients from these studies were pooled (n = 416), and ColoPrint was compared with clinical risk factors described in the National Comprehensive Cancer Network (NCCN) 2013 Guidelines for Colon Cancer. Median follow-up was 81 months. Most patients (70%) did not receive adjuvant chemotherapy. Risk of relapse (ROR) was defined as survival until first event of recurrence or death from cancer. RESULTS: In the pooled stage II data set, ColoPrint identified 63% of patients as low risk with a 5-year ROR of 10%, whereas high-risk patients (37%) had a 5-year ROR of 21%, with a hazard ratio (HR) of 2.16 (p = .004). This remained significant in a multivariate model that included number of lymph nodes retrieved and microsatellite instability. In the T3 microsatellite-stable subgroup (n = 301), ColoPrint classified 59% of patients as low risk with a 5-year ROR of 9.9%. High-risk patients (31%) had a 22.4% ROR (HR: 2.41; p = .005). In contrast, the NCCN clinical high-risk factors were unable to distinguish high- and low-risk patients (15% vs. 13% ROR; p = .55). CONCLUSION: ColoPrint significantly improved prognostic accuracy independent of microsatellite status or clinical variables, facilitating the identification of patients at higher risk who might be considered for additional treatment. [ABSTRACT FROM AUTHOR]

OBJECTIVES Validation studies of European system for cardiac operative risk evaluation II (EuroSCORE II) have been limited to European datasets. Therefore, the aims of this study were to assess the performance of EuroSCORE II in a large multicentre US database, and compare it with the Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM). In addition, implications for patient selection for transcatheter aortic valve implantation (TAVI) were explored. METHODS EuroSCORE II and the STS-PROM were calculated for 50 588 patients from a multi-institutional statewide database of all cardiac surgeries performed since 2003. Model performance was assessed using the area under the receiver operator curve (AUC), observed vs expected (O:E) ratios and calibration plots. Analyses were performed for isolated coronary artery bypass grafting (CABG) (n = 40 871), aortic valve replacement (AVR) (n = 4107), AVR + CABG (n = 3480), mitral valve (MV) replacement (n = 1071) and MV repair (n = 1059). RESULTS The overall in-hospital mortality rate was 2.1%. EuroSCORE II was outperformed by the STS-PROM in the overall cohort with regard to discrimination (AUC = 0.77 vs 0.81, respectively; P < 0.001) and calibration (O:E = 0.68 vs 0.80, respectively). Discrimination for CABG was worse with EuroSCORE II (AUC = 0.77 vs STS-PROM: 0.81, P < 0.001). For other procedures discrimination was similar: AVR (AUC = 0.71 vs STS-PROM: 0.74, P = 0.40), AVR + CABG (AUC = 0.72 vs STS-PROM: 0.74, P = 0.47), MV repair (AUC = 0.82 vs STS-PROM: 0.86, P = 0.55) and MV replacement (AUC = 0.78 vs STS-PROM: 0.79, P = 0.69). Calibration of EuroSCORE II was worse for CABG (O:E = 0.68 vs STS-PROM: 0.80), similar in AVR + CABG (O:E = 0.76 vs STS-PROM: 0.70) and MV repair (O:E = 0.64 vs STS-PROM: 0.67), while EuroSCORE II may be more accurate in AVR (O:E = 0.96 vs STS-PROM: 0.76). Performance of both models improved when only recent cases (after 1 January 2008) were used. Ongoing TAVI trials aimed at patients with an estimated 4–10% risk of mortality are enrolling patients with mean estimated risks of 6.2% (EuroSCORE II) or 6.0% (STS-PROM), and an actual mortality rate of 4.6% (EuroSCORE II) or 4.8% (STS-PROM). CONCLUSIONS In a large US multicentre database, the STS-PROM performs better than EuroSCORE II for CABG. However, EuroSCORE II is a reasonable alternative in low-risk CABG patients and in those undergoing other cardiac surgical procedures. Clinical trials and physicians that use these scores recruit and treat patients who are at a lower risk than anticipated. This potentially leads to overtreatment with an investigational device. Decision-making should not solely be based on risk scores, but should comprise multidisciplinary heart team discussions. [ABSTRACT FROM AUTHOR]

Increased height is a known independent risk factor for atrial fibrillation (AF). However, whether genetic determinants of height influence risk is uncertain. In this candidate gene study, we examined the association of 209 height-associated single-nucleotide polymorphisms (SNPs) with incident AF in 3,309 persons of European descent from the Cardiovascular Health Study, a prospective cohort study of older adults (aged ≥65 years) enrolled in 1989–1990. After a median follow-up period of 13.2 years, 879 participants developed incident AF. The height-associated SNPs together explained approximately 10% of the variation in height (P = 6.0 × 10−8). Using an unweighted genetic height score, we found a nonsignificant association with risk of AF (per allele, hazard ratio = 1.01, 95% confidence interval: 1.00, 1.02; P = 0.06). In weighted analyses, we found that genetically predicted height was strongly associated with AF risk (per 10 cm, hazard ratio = 1.30, 95% confidence interval: 1.03, 1.64; P = 0.03). Importantly, for all models, the inclusion of actual height completely attenuated the genetic height effect. Finally, we identified 1 nonsynonymous SNP (rs1046934) that was independently associated with AF and may warrant future study. In conclusion, we found that genetic determinants of height appear to increase the risk of AF, primarily via height itself. This approach of examining SNPs associated with an intermediate phenotype should be considered as a method for identifying novel genetic targets. [ABSTRACT FROM PUBLISHER]

Aims: The aim of this study was to develop, validate, and illustrate an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40-69 years in Europe., Methods and Results: We derived risk prediction models using individual-participant data from 45 cohorts in 13 countries (677 684 individuals, 30 121 CVD events). We used sex-specific and competing risk-adjusted models, including age, smoking status, systolic blood pressure, and total- and HDL-cholesterol. We defined four risk regions in Europe according to country-specific CVD mortality, recalibrating models to each region using expected incidences and risk factor distributions. Region-specific incidence was estimated using CVD mortality and incidence data on 10 776 466 individuals. For external validation, we analysed data from 25 additional cohorts in 15 European countries (1 133 181 individuals, 43 492 CVD events). After applying the derived risk prediction models to external validation cohorts, C-indices ranged from 0.67 (0.65-0.68) to 0.81 (0.76-0.86). Predicted CVD risk varied several-fold across European regions. For example, the estimated 10-year CVD risk for a 50-year-old smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and HDL-cholesterol of 1.3 mmol/L, ranged from 5.9% for men in low-risk countries to 14.0% for men in very high-risk countries, and from 4.2% for women in low-risk countries to 13.7% for women in very high-risk countries., Conclusion: SCORE2-a new algorithm derived, calibrated, and validated to predict 10-year risk of first-onset CVD in European populations-enhances the identification of individuals at higher risk of developing CVD across Europe., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2021. For permissions, please email: journals.permissions@oup.com.)

Phenotype prediction is a key goal for medical genetics. Unfortunately, most genome-wide association studies are done in European populations, which reduces the accuracy of predictions via polygenic scores in non-European populations. Here, we use population genetic models to show that human demographic history and negative selection on complex traits can result in population-specific genetic architectures. For traits where alleles with the largest effect on the trait are under the strongest negative selection, approximately half of the heritability can be accounted for by variants in Europe that are absent from Africa, leading to poor performance in phenotype prediction across these populations. Further, under such a model, individuals in the tails of the genetic risk distribution may not be identified via polygenic scores generated in another population. We empirically test these predictions by building a model to stratify heritability between European-specific and shared variants and applied it to 37 traits and diseases in the UK Biobank. Across these phenotypes, ∼30% of the heritability comes from European-specific variants. We conclude that genetic association studies need to include more diverse populations to enable the utility of phenotype prediction in all populations., (Copyright © 2021 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Background: Existing cardiovascular risk scores for patients with established cardiovascular disease (CVD) estimate residual risk of recurrent major cardiovascular events (MACE). The aim of the current study is to develop and externally validate a prediction model to estimate the 10-year combined risk of recurrent MACE and cardiovascular interventions (MACE+) in patients with established CVD., Methods: Data of patients with established CVD from the UCC-SMART cohort (N = 8421) were used for model development, and patient data from REACH Western Europe (N = 14,528) and REACH North America (N = 19,495) for model validation. Predictors were selected based on the existing SMART risk score. A Fine and Gray competing risk-adjusted 10-year risk model was developed for the combined outcome MACE+. The model was validated in all patients and in strata of coronary heart disease (CHD), cerebrovascular disease (CeVD), peripheral artery disease (PAD)., Results: External calibration for 2-year risk in REACH Western Europe and REACH North America was good, c-statistics were moderate: 0.60 and 0.58, respectively. In strata of CVD at baseline good external calibration was observed in patients with CHD and CeVD, however, poor calibration was seen in patients with PAD. C-statistics for patients with CHD were 0.60 and 0.57, for patients with CeVD 0.62 and 0.61, and for patients with PAD 0.53 and 0.54 in REACH Western Europe and REACH North America, respectively., Conclusions: The 10-year combined risk of recurrent MACE and cardiovascular interventions can be estimated in patients with established CHD or CeVD. However, cardiovascular interventions in patients with PAD could not be predicted reliably., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

Background: Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool., Methods: We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance., Results: High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004)., Conclusions: We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools.

Background: The Systematic COronary Risk Evaluation - Older Persons (SCORE-OP) algorithm is developed to assess 10-year risk of death due to cardiovascular disease (CVD) in individuals aged ≥65 years. We studied the performance of SCORE-OP in the European Prospective Investigation of Cancer Norfolk (EPIC-Norfolk) prospective population cohort., Methods: 10-year CVD mortality as predicted by SCORE-OP was compared with observed CVD mortality among individuals in the EPIC-Norfolk cohort. Persons aged 65-79 years without known CVD were included in the analysis. CVD mortality was defined as death due to ischemic heart disease, cardiac failure, cerebrovascular disease, peripheral-artery disease or aortic aneurysm. Predicted 10-year CVD mortality was calculated by the SCORE-OP algorithm, and compared to observed mortality rates. The area under the receiver operator characteristics curve (AUROC) was calculated to evaluate discriminative power. Calibration was evaluated by calculating ratios of predicted vs observed mortality and by Hosmer-Lemeshow tests., Results: A total of 6590 individuals (45.8% men), mean age 70.2 years (standard deviation 3.3) were included. The predicted mortality by SCORE-OP was 9.84% (95% confidence interval (CI) 9.76-9.92) and observed mortality was 10.2% (95% CI 9.52-11.04), ratio 0.96. AUROC was 0.63 (95% CI 0.60-0.65), and X 2 was 3.3 (p = 0.92)., Conclusion: SCORE-OP overall accurately estimates the rate of CVD mortality in a general population aged 65-79 years. However, while calibration is excellent, the discriminative power of the SCORE-OP is limited, and as such cannot be readily implemented in clinical practice for this population., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

Objectives: This study aimed to examine the prevalence of frailty coding within the Dr Foster Global Comparators (GC) international database. We then aimed to develop and validate a risk prediction model, based on frailty syndromes, for key outcomes using the GC data set., Design: A retrospective cohort analysis of data from patients over 75 years of age from the GC international administrative data. A risk prediction model was developed from the initial analysis based on seven frailty syndrome groups and their relationship to outcome metrics. A weighting was then created for each syndrome group and summated to create the Dr Foster Global Frailty Score. Performance of the score for predictive capacity was compared with an established prognostic comorbidity model (Elixhauser) and tested on another administrative database Hospital Episode Statistics (2011-2015), for external validation., Setting: 34 hospitals from nine countries across Europe, Australia, the UK and USA., Results: Of 6.7 million patient records in the GC database, 1.4 million (20%) were from patients aged 75 years or more. There was marked variation in coding of frailty syndromes between countries and hospitals. Frailty syndromes were coded in 2% to 24% of patient spells. Falls and fractures was the most common syndrome coded (24%). The Dr Foster Global Frailty Score was significantly associated with in-hospital mortality, 30-day non-elective readmission and long length of hospital stay. The score had significant predictive capacity beyond that of other known predictors of poor outcome in older persons, such as comorbidity and chronological age. The score's predictive capacity was higher in the elective group compared with non-elective, and may reflect improved performance in lower acuity states., Conclusions: Frailty syndromes can be coded in international secondary care administrative data sets. The Dr Foster Global Frailty Score significantly predicts key outcomes. This methodology may be feasibly utilised for case-mix adjustment for older persons internationally., Competing Interests: Competing interests: CP has shares in Fidelity Health, has been a consultant for Merck and the Institute for Healthcare Improvement., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background Plasma apolipoprotein C-III (apoC-III) levels are associated with coronary artery disease (CAD) risk. Objective To assess whether lipoprotein-associated apoC-III levels predict risk of CAD events. Methods apoC-III associated with apoB, apoAI, and Lp(a) (apoCIII-apoB, apoCIII-apoAI, and apoCIII-Lp(a), respectively) were measured using high-throughput chemiluminescent enzyme-linked immunoassays in 2711 subjects (1879 controls and 832 cases with CAD) in the European Prospective Investigation into Cancer and Nutrition-Norfolk prospective population study with 7.4 years of follow-up. These measures were correlated with a variety of lipid measurements and the presence of CAD. The indices of "total apoCIII-apoB" and "total apoCIII-apoAI" were derived by multiplying plasma apoB and apoAI, respectively. Results apoCIII-apoB (P =.001), apoCIII-Lp(a) (P <.001), apoCIII-apoAI (P =.005) were higher in cases vs controls; tended to correlate positively with body mass index, hsCRP, apoC-III, low-density lipoprotein (LDL) cholesterol, triglycerides, remnant cholesterol, very low density lipoprotein, LDL and high-density lipoprotein particle number and very low density lipoprotein size; but negatively with LDL and high-density lipoprotein particle size (P <.001 for all). apoCIII-apoB, apoCIII-apoAI, apoCIII-Lp(a), total apoCIII-Lp(a), and total apoCIII-apoB were predictors of CAD after adjustment of age, sex, body mass index, smoking, diabetes, hypertensive and lipid-lowering drug use, but they lost their significance after further adjustment of lipid and lipoprotein variables. Conclusions This study suggests that enzyme-linked immunoassay–measured lipoprotein-associated apoC-III markers reflect atherogenic lipid particles but do not independently predict risk of CAD events. Highlights • High-throughput enzyme-linked immunoassays were developed to measure lipoprotein-associated apolipoprotein C-III (apoC-III) levels. • apoCIII-apoB, apoCIII-Lp(a), and apoCIII-apoAI were measured in EPIC-Norfolk. • apoCIII-apoB, apoCIII-Lp(a), and apoCIII-apoAI reflect atherogenic lipid particles. • apoCIII-apoB, apoCIII-Lp(a), and apoCIII-apoAI do not independently predict risk of coronary artery disease events. • Their role in patients with prior history of coronary artery disease needs to be evaluated. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer prediction tools provide quantitative guidance for doctor-patient decision-making regarding biopsy. The widely used online Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) utilized data from the 1990s based on six-core biopsies and outdated grading systems., Objective: We prospectively gathered data from men undergoing prostate biopsy in multiple diverse North American and European institutions participating in the Prostate Biopsy Collaborative Group (PBCG) in order to build a state-of-the-art risk prediction tool., Design, Setting, and Participants: We obtained data from 15 611 men undergoing 16 369 prostate biopsies during 2006-2017 at eight North American institutions for model-building and three European institutions for validation., Outcome Measurements and Statistical Analysis: We used multinomial logistic regression to estimate the risks of high-grade prostate cancer (Gleason score ≥7) on biopsy based on clinical characteristics, including age, prostate-specific antigen, digital rectal exam, African ancestry, first-degree family history, and prior negative biopsy. We compared the PBCG model to the PCPTRC using internal cross-validation and external validation on the European cohorts., Results and Limitations: Cross-validation on the North American cohorts (5992 biopsies) yielded the PBCG model area under the receiver operating characteristic curve (AUC) as 75.5% (95% confidence interval: 74.2-76.8), a small improvement over the AUC of 72.3% (70.9-73.7) for the PCPTRC (p<0.0001). However, calibration and clinical net benefit were far superior for the PBCG model. Using a risk threshold of 10%, clinical use of the PBCG model would lead to the equivalent of 25 fewer biopsies per 1000 patients without missing any high-grade cancers. Results were similar on external validation on 10 377 European biopsies., Conclusions: The PBCG model should be used in place of the PCPTRC for prediction of prostate biopsy outcome., Patient Summary: A contemporary risk tool for outcomes on prostate biopsy based on the routine clinical risk factors is now available for informed decision-making., (Copyright © 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Background and Objective: Advances in omics open new opportunities for cancer risk prediction and risk-based screening interventions. However, implementation of risk prediction in clinical practice may impact the ethical, legal, and regulatory aspects of current cancer screening programs. In order to support decision-making, we analyzed the ethical, legal, and regulatory issues and developed a set of Points to Consider to support management of these issues., Methods: We analyzed the legal and policy frameworks applicable to breast and cervical cancer screening programs in 7 European countries. We identified the most relevant issues to be considered, and we developed considerations for their management, based on the literature, the legal and policy frameworks, and our experience with similar issues., Results: The considerations focus on five topics: (A) health services planning, (B) information and invitation, (C) consent and data/sample collection, (D) risk calculation and communication of results, and (E) storage of data and residual samples., Conclusion: Current frameworks might not be adequate to implement a risk prediction approach using omics factors due to the different characteristics of such approaches., (The Author(s). Published by S. Karger AG, Basel.)

Background: A simple clinical tool to predict cardiovascular disease risk does not exist for haemodialysis patients. The long-term coronary risk Framingham Heart Study Risk score (FRS), although used in this population, may be inadequate. Therefore, we developed separate risk-scores for cardiovascular mortality (CVM) and cardiovascular morbidity & mortality (CVMM) in a Fresenius Medical Care-based haemodialysis patient cohort (AROii)., Methods: Applying a modified FRS approach, we derived and internally validated two-year risk-scores in incident European adult patients randomly assigned to a development (N=4831) or a validation (N=4796) dataset. External validation was conducted in the third Dialysis Outcomes and Practice Patterns Study (DOPPS III) cohort. Additional discrimination comparing to the FRS was performed., Results: The overall two-year CVM and CVMM event rates were 5.0 and 22.6 per 100 person-years respectively. Common risk predictors included increasing age, cardiovascular disease history, primary diabetic nephropathy, low blood pressure, and inflammation. The CVM score was more predictive in AROii (c-statistic 0.72) and in DOPPS III (c-statistic 0.73-0.74) than the CVMM score (c-statistic 0.66-0.67 & 0.63 respectively). The FRS was not predictive of either CVM (c-statistic 0.54) or CVMM (c-statistic 0.56) in AROii., Conclusions: We describe novel, easy-to-apply and interpret CV risk-scores for haemodialysis patients. Our improved cardiovascular prediction performance over traditional (FRS) scores reflected its tailored development and validation in haemodialysis populations, and the integration of non-classical cardiovascular risk factors. The lower expected versus observed CVM and CVMM risk suggests the existence of novel cardiovascular risk factors in this patient population not measured in this study., (Copyright © 2016. Published by Elsevier Ireland Ltd.)

Aims: B-type natriuretic peptide (BNP) and C-reactive protein (CRP) predict atrial fibrillation (AF) risk. However, their risk stratification abilities in the broad community remain uncertain. We sought to improve risk stratification for AF using biomarker information., Methods and Results: We ascertained AF incidence in 18 556 Whites and African Americans from the Atherosclerosis Risk in Communities Study (ARIC, n=10 675), Cardiovascular Health Study (CHS, n = 5043), and Framingham Heart Study (FHS, n = 2838), followed for 5 years (prediction horizon). We added BNP (ARIC/CHS: N-terminal pro-B-type natriuretic peptide; FHS: BNP), CRP, or both to a previously reported AF risk score, and assessed model calibration and predictive ability [C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI)]. We replicated models in two independent European cohorts: Age, Gene/Environment Susceptibility Reykjavik Study (AGES), n = 4467; Rotterdam Study (RS), n = 3203. B-type natriuretic peptide and CRP were significantly associated with AF incidence (n = 1186): hazard ratio per 1-SD ln-transformed biomarker 1.66 [95% confidence interval (CI), 1.56-1.76], P < 0.0001 and 1.18 (95% CI, 1.11-1.25), P < 0.0001, respectively. Model calibration was sufficient (BNP, χ(2) = 17.0; CRP, χ(2) = 10.5; BNP and CRP, χ(2) = 13.1). B-type natriuretic peptide improved the C-statistic from 0.765 to 0.790, yielded an IDI of 0.027 (95% CI, 0.022-0.032), a relative IDI of 41.5%, and a continuous NRI of 0.389 (95% CI, 0.322-0.455). The predictive ability of CRP was limited (C-statistic increment 0.003). B-type natriuretic peptide consistently improved prediction in AGES and RS., Conclusion: B-type natriuretic peptide, not CRP, substantially improved AF risk prediction beyond clinical factors in an independently replicated, heterogeneous population. B-type natriuretic peptide may serve as a benchmark to evaluate novel putative AF risk biomarkers., (Published by Oxford University Press on behalf of the European Society of Cardiology 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Background: The European Heart SCORE model constitutes the basis for national guidelines for primary prevention and treatment of cardiovascular disease (CVD) in several European countries. The model estimates individuals' 10-year CVD mortality risks from age, sex, smoking status, systolic blood pressure, and total cholesterol level. The SCORE model, however, is not mathematically consistent and does not estimate all-cause mortality. Our aim is to modify the SCORE model to allow consistent estimation of both CVD-specific and all-cause mortality., Methods: Using a competing risk approach, we first re-estimated the cause-specific risk of dying from cardiovascular disease, and secondly we incorporated non-CVD mortality. Finally, non-CVD mortality was allowed to also depend on smoking status, and not only age and sex. From the models, we estimated CVD-specific and all-cause 10-year mortality risk, and the expected residual lifetime together with corresponding expected effects of statin treatment., Results: The modified model provided CVD-specific 10-year mortality risks similar to those of the European Heart SCORE model. Incorporation of non-CVD mortality increased 10-year mortality risks, in particular for older individuals. When non-CVD mortality was assumed unaffected by smoking status, the absolute risk reduction due to statin treatment ranged from 0.0% to 3.5%, whereas the gain in expected residual lifetime ranged from 3 to 11 months. Statin effectiveness increased for non-smokers and declined for smokers, when smoking was allowed to influence non-CVD mortality., Conclusion: The modified model provides mathematically consistent estimates of mortality risk and expected residual lifetime together with expected benefits from statin treatment.