***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: 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 Aims To evaluate the 2013 American Heart Association (AHA)-American College of Cardiology (ACC)-Atherosclerotic Cardiovascular Disease ( ASCVD ) risk score among four different race/ethnic groups and to ascertain which factors are most associated with risk overestimation by the AHA-ACC-ASCVD score . Methods and results The Multi-Ethnic Study of Atherosclerosis ( MESA ) , a prospect i ve community-based cohort , was used to examine calibration and discrimination of the AHA-ACC-ASCVD risk score in 6441 White , Black , Chinese , and Hispanic Americans ( aged 45 - 79 years and free of known ASCVD at baseline ) . Using univariable and multivariable absolute risk regression , we modelled the impact of individual risk factors on the discordance between observed and predicted 10-year ASCVD risk . Overestimation was observed in all race/ethnic groups in MESA and was highest among Chinese ( 252 % for women and 314 % for men ) and lowest in White women ( 72 % ) and Hispanic men ( 67 % ) . Higher age , Chinese race/ethnicity ( when compared with White ) , systolic blood pressure ( treated and untreated ) , diabetes , alcohol use , exercise , lipid-lowering medication , and aspirin use were all associated with more risk overestimation , whereas family history was associated with less risk overestimation in a multivariable model ( all P < 0.05 ) . Conclusion The AHA-ACC-ASCVD risk score overestimates ASCVD risk among men , women , and all four race/ethnic groups evaluated in a modern American primary prevention cohort . Clinicians treating patients similar to those in MESA , particularly older individuals and those with factors associated with more risk overestimation , may consider interpreting absolute ASCVD risk estimates with caution Background Prognostic models often show poor performance when applied to independent validation data sets . We illustrate how treatment use in a validation set can affect measures of model performance and present the uses and limitations of available analytical methods to account for this using simulated data . Methods We outline how the use of risk-lowering treatments in a validation set can lead to an apparent overestimation of risk by a prognostic model that was developed in a treatment-naïve cohort to make predictions of risk without treatment . Potential methods to correct for the effects of treatment use when testing or validating a prognostic model are discussed from a theoretical perspective .. Subsequently , we assess , in simulated data sets , the impact of excluding treated individuals  and the use of inverse probability weighting ( IPW ) on the estimated model discrimination ( c-index ) and calibration ( observed : expected ratio and calibration plots ) in scenarios with different patterns and effects of treatment use . Results Ignoring the use of effective treatments in a validation data set leads to poorer model discrimination and calibration than would be observed in the untreated target population for the model . Excluding treated individuals provided correct estimates of model performance only when treatment was r and omly allocated , although this reduced the precision of the estimates . IPW followed by exclusion of the treated individuals provided correct estimates of model performance in data sets where treatment use was either r and om or moderately associated with an individual 's risk when the assumptions of IPW were met , but yielded incorrect estimates in the presence of non-positivity or an unobserved confounder . Conclusions When validating a prognostic model developed to make predictions of risk without treatment , treatment use in the validation set can bias estimates of the performance of the model in future targeted individuals , and should not be ignored . When treatment use is r and om , treated individuals can be excluded from the analysis . When treatment use is non-r and om , IPW followed by the exclusion of treated individuals is recommended , however , this method is sensitive to violations of its assumptions BACKGROUND The objective of this study was to examine the association of Joint National Committee ( JNC-V ) blood pressure and National Cholesterol Education Program ( NCEP ) cholesterol categories with coronary heart disease ( CHD ) risk , to incorporate them into coronary prediction algorithms , and to compare the discrimination properties of this approach with other noncategorical prediction functions . METHODS AND RESULTS This work was design ed as a prospect i ve , single-center study in the setting of a community-based cohort . The patients were 2489 men and 2856 women 30 to 74 years old at baseline with 12 years of follow-up . During the 12 years of follow-up , a total of 383 men and 227 women developed  CHD , which was significantly associated with categories of blood pressure , total cholesterol , LDL cholesterol , and HDL cholesterol ( all P<.001 ) . Sex-specific prediction equations were formulated to predict CHD risk according to age , diabetes , smoking , JNC-V blood pressure categories , and NCEP total cholesterol and LDL cholesterol categories . The accuracy of this categorical approach was found to be comparable to CHD prediction when the continuous variables themselves were used . After adjustment for other factors , approximately 28 % of CHD events in men and 29 % in women were attributable to blood pressure levels that exceeded high normal ( > or = 130/85 ) . The corresponding multivariable-adjusted attributable risk percent associated with elevated total cholesterol ( > or = 200 mg/dL ) was 27 % in men and 34 % in women . CONCLUSIONS Recommended guidelines of blood pressure , total cholesterol , and LDL cholesterol effectively predict CHD risk in a middle-aged white population sample . A simple coronary disease prediction algorithm was developed using categorical variables , which allows physicians to predict multivariate CHD risk in patients without overt CHD Background In Europe , for primary prevention of cardiovascular diseases ( CVD ) , the Systematic COronary Risk Evaluation ( SCORE ) risk charts for high-risk and low-risk regions ( SCORE-high and SCORE-low , respectively ) are used . For the Dutch ‘ Clinical Practice Guideline for Cardiovascular Risk Management ’ an adapted SCORE risk chart ( SCORE-NL ) was developed in collaboration with the SCORE group . We evaluated these three SCORE equations using Dutch risk factor and mortality data . Design Prospect i ve cohort study with 10-year follow-up . Methods Baseline data were collected between 1987 and 1997 in 32 885 persons aged 37.5–62.5 years .  Vital status was checked and causes of death were obtained from Statistics Netherl and s. On the basis of the level of risk factors , the expected number of CVD deaths was calculated by applying the three SCORE equations and compared with the observed number . Results The observed CVD mortality was three-fold higher in men ( n = 242 ; 1.6 % ) than in women ( n = 83 ; 0.5 % ) . On the basis of SCORE-NL , 8.5 % of the men and 0.8 % of the women had a CVD mortality risk of 5 % or more . The ratio of the observed-to-expected number of CVD deaths was 0.75 for men and 0.55 for women using SCORE-NL , 0.54 and 0.56 using SCORE-high , and 1.11 and 0.95 using SCORE-low . Conclusion At the population level , SCORE-low predicts the number of CVD deaths well , whereas both SCORE-NL and SCORE-high overestimate the number of CVD deaths by a factor 1.5–2 BACKGROUND The aims of this paper are to derive a 10-year coronary risk predictive equation for adult Italian men , and to assess its accuracy in comparison with the Framingham Heart Study ( FHS ) and PROCAM study equations . METHODS The CUORE study is a prospect i ve fixed-cohort study . Eleven cohorts , from the north and the centre-south of Italy , had been investigated at baseline between 1982 and 1996 , adopting MONICA methods to measure risk factors . Among this sample of 6865 men , aged 35 - 69 years and free of coronary heart disease ( CHD ) at baseline , 312 first fatal and non-fatal major coronary events occurred in 9.1 years median follow-up . Calibration , as the difference between 10-year predicted and actual risk , and discrimination , as the ability of the risk functions to separate high-risk from low-risk subjects , have been assessed to compare accuracy of the FHS , the PROCAM , and the CUORE study equations . RESULTS The best CUORE equation includes age , total cholesterol , systolic blood pressure , cigarette smoking , HDL-cholesterol , diabetes mellitus , hypertension drug treatment , and family history of CHD ( area under the ROC curve = 0.75 ) . The uncalibrated estimates of the 10-year risk in this CUORE follow-up data were 0.093 and 0.109 higher ( P < 0.05 ) from the Framingham and PROCAM risk scores , respectively , than the Kaplan-Meier estimate for CUORE , indicating risk overestimates for both equations . St and ard recalibration techniques improved accuracy of the FHS equation only . PROCAM overestimates were prominent in the higher risk deciles . With an alternative method for recalibration better risk estimates were obtained , but a cohort study was needed to obtain a properly calibrated risk equation . CONCLUSIONS The CUORE Project predictive equation showed better accuracy of the FHS and PROCAM equations , overcoming frequently reported risk overestimates . The CUORE equation may be adopted to identify men with high coronary risk in Italy OBJECTIVES To compare different methods to h and le treatment when developing a prognostic model that aims to produce accurate probabilities of the outcome of individuals if left untreated . STUDY DESIGN AND SETTING Simulations were performed based on two normally distributed predictors , a binary outcome , and a binary treatment , mimicking a r and omized trial or an observational study . Comparison was made between simply ignoring treatment ( SIT ) , restricting the analytical data set to  untreated individuals ( AUT ) , inverse probability weighting ( IPW ) , and explicit modeling of treatment ( MT ) . Methods were compared in terms of predictive performance of the model and the proportion of incorrect treatment decisions . RESULTS Omitting a genuine predictor of the outcome from the prognostic model decreased model performance , in both an observational study and a r and omized trial . In r and omized trials , the proportion of incorrect treatment decisions was smaller when applying AUT or MT , compared to SIT and IPW . In observational studies , MT was superior to all other methods regarding the proportion of incorrect treatment decisions . CONCLUSION If a prognostic model aims to produce correct probabilities of the outcome in the absence of treatment , ignoring treatments that affect that outcome can lead to suboptimal model performance and incorrect treatment decisions . Explicitly , modeling treatment is recommended Objective The objectives of this study were to develop a coronary heart disease ( CHD ) risk model among the Korean Heart Study ( KHS ) population and compare it with the Framingham CHD risk score . Design A prospect i ve cohort study within a national insurance system . Setting 18 health promotion centres nationwide between 1996 and 2001 in Korea . Participants 268 315 Koreans between the ages of 30 and 74 years without CHD at baseline . Outcome measure Non-fatal or fatal CHD events between 1997 and 2011 . During an 11.6-year median follow-up , 2596 CHD events ( 1903 non-fatal and 693 fatal ) occurred in the cohort . The optimal CHD model was created by adding high-density lipoprotein (HDL)-cholesterol , low-density lipoprotein (LDL)-cholesterol and triglycerides to the basic CHD model , evaluating using the area under the receiver operating characteristic curve ( ROC ) and continuous net reclassification index ( NRI ) . Results The optimal CHD models for men and women included HDL-cholesterol ( NRI=0.284 ) and triglycerides 
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Overestimation was most pronounced for high-risk individuals and European population s. Further , discriminative performance was better in women for all models .
There was considerable heterogeneity in the c-statistic between studies , likely due to differences in population characteristics .