ObjeCtiveTo provide an overview of prediction models for risk of cardiovascular disease (CVD) in the general population.Design Systematic review. Data sOurCesMedline and Embase until June 2013.eligibility Criteria fOr stuDy seleCtiOn Studies describing the development or external validation of a multivariable model for predicting CVD risk in the general population. results 9965 references were screened, of which 212 articles were included in the review, describing the development of 363 prediction models and 473 external validations. Most models were developed in Europe (n=167, 46%), predicted risk of fatal or non-fatal coronary heart disease (n=118, 33%) over a 10 year period (n=209, 58%). The most common predictors were smoking (n=325, 90%) and age (n=321, 88%), and most models were sex specific (n=250, 69%). Substantial heterogeneity in predictor and outcome definitions was observed between models, and important clinical and methodological information were often missing. The prediction horizon was not specified for 49 models (13%), and for 92 (25%) crucial information was missing to enable the model to be used for individual risk prediction. Only 132 developed models (36%) were externally validated and only 70 (19%) by independent investigators. Model performance was heterogeneous and measures such as discrimination and calibration were reported for only 65% and 58% of the external validations, respectively. COnClusiOnsThere is an excess of models predicting incident CVD in the general population. The usefulness of most of the models remains unclear owing to methodological shortcomings, incomplete presentation, and lack of external validation and model impact studies. Rather than developing yet another similar CVD risk prediction model, in this era of large datasets, future research should focus on externally validating and comparing head-to-head promising CVD risk models that already exist, on tailoring or even combining these models to local settings, and investigating whether these models can be extended by addition of new predictors. IntroductionCardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, 1 accounting for approximately one third of all deaths. 2 Prevention of CVD requires timely identification of people at increased risk to target effective dietary, lifestyle, or drug interventions. Over the past two decades, numerous prediction models have been developed, which mathematically combine multiple predictors to estimate the risk of developing CVD-for example, the Framingham, 3-5 SCORE, 6 and QRISK 7-9 models. Some of these prediction models are included in clinical guidelines for therapeutic management 10 11 and are increasingly advocated by health policymakers. In the United Kingdom, electronic health patient record systems now have QRISK2 embedded to calculate 10 year CVD risk.Several reviews have shown that there is an abundance of prediction models for a wide range of CVD outcomes. 12-14 However, the most comprehensive review 12 includes models published ...

Purpose The Cancer Esophagus Gefitinib trial demonstrated improved progression-free survival with the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor gefitinib relative to placebo in patients with advanced esophageal cancer who had disease progression after chemotherapy. Rapid and durable responses were observed in a minority of patients. We hypothesized that genetic aberration of the EGFR pathway would identify patients benefitting from gefitinib. Methods A prespecified, blinded molecular analysis of Cancer Esophagus Gefitinib trial tumors was conducted to compare efficacy of gefitinib with that of placebo according to EGFR copy number gain (CNG) and EGFR, KRAS, BRAF, and PIK3CA mutation status. EGFR CNG was determined by fluorescent in situ hybridization (FISH) using prespecified criteria and EGFR FISH-positive status was defined as high polysomy or amplification. Results Biomarker data were available for 340 patients. In EGFR FISH-positive tumors (20.2%), overall survival was improved with gefitinib compared with placebo (hazard ratio [HR] for death, 0.59; 95% CI, 0.35 to 1.00; P = .05). In EGFR FISH-negative tumors, there was no difference in overall survival with gefitinib compared with placebo (HR for death, 0.90; 95% CI, 0.69 to 1.18; P = .46). Patients with EGFR amplification (7.2%) gained greatest benefit from gefitinib (HR for death, 0.21; 95% CI, 0.07 to 0.64; P = .006). There was no difference in overall survival for gefitinib versus placebo for patients with EGFR, KRAS, BRAF, and PIK3CA mutations, or for any mutation versus none. Conclusion EGFR CNG assessed by FISH appears to identify a subgroup of patients with esophageal cancer who may benefit from gefitinib as a second-line treatment. Results of this study suggest that anti-EGFR therapies should be investigated in prospective clinical trials in different settings in EGFR FISH-positive and, in particular, EGFR-amplified esophageal cancer.