***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: We adapt a recent model from the Framingham study ( Circulation 1991 ; 83 : 356 - 362 ) to predict  CHD in France for both sexes over a large age range . Calculations were based on data from the French PCV-METRA study . In France , the Paris Prospect i ve Study model could predict CHD but only for men aged 43 - 53 years . Applied to men 43 - 53 years from the PCV-METRA  , the Framingham model estimated a 5-year CHD risk ( 4 % ) lower than the risk reported in the Framingham sample , but significantly higher than the risk estimated by the French model ( 2 % ) . Differences in estimated CHD risk between the Framingham and the PCV-METRA sample s were explained for only 30 % by adjustment on major CHD risk factors ( mainly HDL-cholesterol and tobacco ) . Modifying the intercept in the Framingham model , agreement with estimated risk by the French model was improved from 29 to 80 % . By an appropriate change of the intercept , the Framingham model might be used to estimate CHD risk in other population To develop a simple , patient self-report-based coronary heart disease ( CHD ) risk score for adults without previously diagnosed CHD ( Personal Heart Early Assessment Risk Tool [ HEART ] score ) , the Atherosclerosis Risk In Communities ( ARIC ) Study , a prospect i ve cohort of subjects aged 45 to 64 years at baseline , was used to develop a measure for 10-year risk of CHD  ( n = 14,343 ) . Variables evaluated for inclusion were age , history of diabetes mellitus , history of hypercholesterolemia , history of hypertension , family history of CHD , smoking , physical activity , and body mass index . The 10-year risk of CHD events was defined as myocardial infa rct ion , fatal CHD , or cardiac procedure . The new measure was compared with the Framingham Risk Score ( FRS ) and European Systematic Coronary Risk Evaluation ( SCORE ) . The Personal HEART score for men included age , diabetes , hypertension , hypercholesterolemia , smoking , physical activity , and family history . In men , the area under the receiver-operator characteristic curve for predicting 10-year CHD for the Personal HEART score ( 0.65 ) was significantly different from that for the FRS ( 0.69 , p = 0.03 ) , but not for the European SCORE ( 0.62 , p = 0.12 ) . The Personal HEART score for women included age , diabetes , hypertension , hypercholesterolemia , smoking , and body mass index . The area under the curve for the Personal HEART score ( 0.79 ) for women was not significantly different from that for the FRS ( 0.81 , p = 0.42 ) and performed better than the European SCORE ( 0.69 , p = 0.01 ) . In conclusion , the Personal HEART score identifies 10-year risk for CHD based on self-report data , is similar in predictive ability to the FRS and European SCORE , and has the potential for easy self- assessment Objectives To investigate the performance of classic risk factors , and of some new biomarkers , in predicting cardiovascular mortality in very old people from the general population with no history of cardiovascular disease . Design The Leiden 85-plus Study ( 1997 - 2004 ) is an observational prospect i ve cohort study with 5 years of follow-up . Setting General population of the city of Leiden , the Netherl and s. Participants Population based sample of participants aged 85 years ( 215 women and 87 men ) with no history of cardiovascular disease ; no other exclusion criteria . Main measurements Cause specific mortality was registered during follow-up . All classic risk factors included in the Framingham risk score ( sex , systolic blood pressure , total and high density lipoprotein cholesterol , diabetes mellitus , smoking and electrocardiogram based left ventricular hypertrophy ) , as well as plasma concentrations of the new biomarkers homocysteine , folic acid , C reactive protein , and interleukin 6 , were assessed at baseline . Results During follow-up , 108 of the 302 participants  died ; 32 % ( 35/108 ) of deaths were from cardiovascular causes . Classic risk factors did not predict cardiovascular mortality when used in the Framingham risk score ( area under receiver operating characteristic curve 0.53 , 95 % confidence interval 0.42 to 0.63 ) or in a newly calibrated model ( 0.53 , 0.43 to 0.64 ) . Of the new biomarkers studied , homocysteine had most predictive power ( 0.65 , 0.55 to 0.75 ) . Entering any additional risk factor or combination of factors into the homocysteine prediction model did not increase its discriminative power . Conclusions In very old people from the general population with no history of cardiovascular disease , concentrations of homocysteine alone can accurately identify those at high risk of cardiovascular mortality , whereas classic risk factors included in the Framingham risk score do not . These preliminary findings warrant validation in a separate cohort Background — Present cardiovascular disease ( CVD ) risk prediction algorithms were developed for a ≤10-year follow up period . Clustering of risk factors at younger ages and increasing life expectancy suggest the need for longer-term risk prediction tools . Methods and Results — We prospect ively followed 4506 participants ( 2333 women ) of the Framingham Offspring cohort aged 20 to 59 years and free of CVD and cancer at baseline examination in 1971–1974 for the development of “ hard ” CVD events ( coronary death , myocardial infa rct ion , stroke ) . We used a modified Cox model that allows adjustment for competing risk of noncardiovascular death to construct a prediction algorithm for 30-year risk of hard CVD . Cross-vali date d survival C statistic and calibration & khgr;2 were used to assess model performance . The 30-year hard CVD event rates adjusted for the competing risk of death were 7.6 % for women and 18.3 % for men . St and ard risk factors ( male sex , systolic blood pressure , antihypertensive treatment , total and high-density lipoprotein cholesterol , smoking , and diabetes mellitus ) , measured at baseline , were significantly related to the incidence of hard CVD and remained significant when up date d regularly on follow-up . Body mass index was associated positively with 30-year risk of hard CVD only in models that did not up date risk factors . Model performance was excellent as indicated by cross-vali date d discrimination C=0.803 and calibration & khgr;2=4.25 ( P=0.894 ) . In contrast , 30-year risk predictions based on different applications of 10-year functions proved inadequate . Conclusions — St and ard risk factors remain strong predictors of hard CVD over extended follow-up . Thirty-year risk prediction functions offer additional risk burden information that complements that of 10-year functions Methods to improve coronary heart disease ( CHD ) risk prediction include incorporation of coronary artery calcium ( CAC ) within risk models and considering longer time horizons such as evaluation of lifetime cardiovascular risk ( LTR ) . We compared the accuracy of 10-year Framingham risk score ( FRS ) , LTR , and Multi-Ethnic Study of Atherosclerosis ( MESA ) arterial age-adjusted 10-year risk for prediction of incident  CHD events in men in the Prospect i ve Army Coronary Calcium Project . We studied 1,633 healthy men ( mean age 43 years , range 40 to 50 years , mean FRS 4.6 % ) with electron-beam computed tomography for CAC .  Events ( CHD death , myocardial infa rct ion , acute coronary syndrome with nonelective coronary revascularization ) were prospect ively assessed over 5.6 ± 1.5 years . Predicted risk using 10-year FRS for CHD and cardiovascular disease , LTR , and MESA arterial age were evaluated in relation with CAC and CHD events . CAC prevalence was strongly related to LTR , increasing in a grade d fashion from 10.1 % to 66.7 % across 8 categories of increasing LTR . On receiver operating characteristic analysis , MESA arterial age ( area under curve 0.78 , 95 % confidence interval 0.64 to 0.93 ) had the largest area under the curve but similar areas under the curve were observed for 10-year risk ( CHD 0.74 , 0.61 to 0.86 ; cardiovascular disease 0.70 , 0.59 to 0.82 ) , LTR ( 0.68 , 0.49 to 0.76 ) , and LTR with CAC as a covariate ( 0.76 , 0.63 to 0.89 ) . Inclusion of family history of CHD or body mass index did not improve model accuracy . In conclusion , increasing LTR was associated with increasingly prevalent CAC in this low-risk cohort and  inclusion of CAC improved the accuracy of LTR for short-term event prediction Background Risk charts that depict the absolute risk of myocardial infa rct ion ( MI ) for each combination of risk factors in individuals are convenient and beneficial tools for primary prevention of ischemic heart disease . Although risk charts have been developed using data from North American and European cardiovascular cohort studies , there is no such chart derived from cardiovascular incidence data obtained from the Japanese population . Methods and Results We calculated and constructed risk charts that estimate the 10-year absolute risk of MI by using data from the  Jichi Medical School ( JMS ) Cohort Study —a prospect i ve cohort study which followed 12 490 participants in 12 Japanese rural communities for an average of 10.9 years . We identified 92 cases of a clinical ly-certified MI event . Color-coded risk charts were created by calculating the absolute risk associated with the following conventional cardiovascular risk factors : age , sex , smoking status , diabetes status , systolic blood pressure , and serum total cholesterol . Conclusions In health education and clinical practice , particularly in rural communities , these charts should prove useful in underst and ing the risks of MI , without the need for cumbersome calculations . In addition , they can be expected to provide benefits by improving existing risk factors in individuals Objective To derive a new cardiovascular disease risk score ( QRISK ) for the United Kingdom and to vali date its performance against the established Framingham cardiovascular disease algorithm and a newly developed Scottish score ( ASSIGN ) . Design Prospect i ve open cohort study using routinely collected data from general practice . Setting UK practice s contributing to the Q RESEARCH data base . Participants The derivation cohort consisted of 1.28 million patients , aged 35 - 74 years , registered at 318 practice s between 1 January 1995 and 1 April 2007 and who were free of diabetes and existing cardiovascular disease . The validation cohort consisted of 0.61 million patients from 160 practice s. Main outcome measures First recorded diagnosis of cardiovascular disease ( incident diagnosis between 1 January 1995 and 1 April 2007 ) : myocardial infa rct ion , coronary heart disease , stroke , and transient ischaemic attacks . Risk factors were age , sex , smoking status , systolic blood pressure , ratio of total serum cholesterol to high density lipoprotein , body mass index , family history of coronary heart disease in first degree relative aged less than 60 , area measure of deprivation , and existing treatment with antihypertensive agent . Results A cardiovascular disease risk algorithm ( QRISK ) was developed in the derivation cohort . In the validation cohort the observed 10 year risk of a cardiovascular event was 6.60 % ( 95 % confidence interval 6.48 % to 6.72 % ) in women and 9.28 % ( 9.14 % to 9.43 % ) in men . Overall the Framingham algorithm over-predicted cardiovascular disease risk at 10 years by 35 % , ASSIGN by 36 % , and QRISK by 0.4 % . Measures of discrimination tended to be higher for QRISK than for the Framingham algorithm and it was better calibrated to the UK population than either the Framingham or ASSIGN models . Using QRISK 8.5 % of patients aged 35 - 74 are at high risk ( 20 % risk or higher over 10 years ) compared with 13 % when using the Framingham algorithm and 14 % when using ASSIGN . Using QRISK 34 % of women and 73 % of men aged 64 - 75 would be at high risk compared with 24 % and 86 % according to the Framingham algorithm . UK estimates for 2005 based on QRISK give 3.2 million patients aged 35 - 74 at high risk , with the Framingham algorithm predicting 4.7 million and ASSIGN 5.1 million . Overall , 53 668 patients in the validation data set ( 9 % of the total ) would be reclassified from high to low risk or vice versa using QRISK compared with the Framingham algorithm . Conclusion QRISK performed at least as well as the Framingham model for discrimination and was better calibrated to the UK population than either the Framingham model or ASSIGN . QRISK is likely to provide more appropriate risk estimates to help identify high risk patients on the basis of age , sex , and social deprivation . It is therefore likely to be a more equitable tool to inform management decisions and help ensure treatments are directed towards those most likely to benefit . It includes 
    Output:
    
There is an excess of models predicting incident CVD in the general population .