| meta-analysis | Q815382 |
| review article | Q7318358 |
| scholarly article | Q13442814 |
| P50 | author | Thomas Meade | Q21166422 |
| John Danesh | Q22277498 | ||
| Vilmundur Gudnason | Q28050060 | ||
| Rachel Dankner | Q42845391 | ||
| Emanuele Di Angelantonio | Q43884331 | ||
| Johan Sundström | Q44445324 | ||
| Michael J. Sweeting | Q47113348 | ||
| Matthew Knuiman | Q47914631 | ||
| Veikko Salomaa | Q28050067 | ||
| Inger Njølstad | Q29999957 | ||
| Jukka T. Salonen | Q30174046 | ||
| Bruce M. Psaty | Q30429993 | ||
| Annette Peters | Q37629853 | ||
| Paul J. Nietert | Q42613329 | ||
| Uri Goldbourt | Q55647560 | ||
| Ronan Roussel | Q57090457 | ||
| Eric J Brunner | Q58438211 | ||
| Ellie Paige | Q60651418 | ||
| John Gallacher | Q61471649 | ||
| Barry R. Davis | Q64684716 | ||
| Luigi Palmieri | Q77397585 | ||
| Edoardo Casiglia | Q80487436 | ||
| Maryam Kavousi | Q82821617 | ||
| Lyle G Best | Q87224321 | ||
| Lewis Kuller | Q88077314 | ||
| Gilles R Dagenais | Q91746153 | ||
| Jan-Håkan Jansson | Q91828035 | ||
| Monique Verschuren | Q95302039 | ||
| Chiara Donfrancesco | Q101118130 | ||
| Angela M Wood | Q104505869 | ||
| Daan Kromhout | Q110636089 | ||
| Gerd Assmann | Q114425524 | ||
| Lisa Pennells | Q114453314 | ||
| P2093 | author name string | Jessica Barrett | |
| Hiroyasu Iso | |||
| Simon G Thompson | |||
| Richard A Kronmal | |||
| Stephan J L Bakker | |||
| David Couper | |||
| Peter Willeit | |||
| Shinichi Sato | |||
| Johann Willeit | |||
| Altan Onat | |||
| Agustin Gómez de la Cámara | |||
| Robert W Tipping | |||
| P2860 | cites work | Quantifying heterogeneity in a meta-analysis | Q27860672 |
| Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study | Q42633494 | ||
| Updated risk factor values and the ability of the multivariable risk score to predict coronary heart disease | Q45026853 | ||
| The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study | Q47113173 | ||
| General cardiovascular risk profile for use in primary care: the Framingham Heart Study | Q29614189 | ||
| 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines | Q29620108 | ||
| Assessing risk prediction models using individual participant data from multiple studies | Q30724002 | ||
| The Emerging Risk Factors Collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases | Q31129827 | ||
| Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies | Q33571265 | ||
| Multilevel modeling versus cross-sectional analysis for assessing the longitudinal tracking of cardiovascular risk factors over time | Q33797755 | ||
| Gender differences in the accuracy of time-dependent blood pressure indices for predicting coronary heart disease: A random-effects modeling approach | Q34505083 | ||
| Blood pressure variability and cardiovascular risk in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER). | Q34533910 | ||
| Effect of correcting for long-term variation in major coronary heart disease risk factors: relative hazard estimation and risk prediction in the Atherosclerosis Risk in Communities Study | Q35785163 | ||
| Impact of blood pressure and blood pressure change during middle age on the remaining lifetime risk for cardiovascular disease: the cardiovascular lifetime risk pooling project | Q35846109 | ||
| Within-person variability in calculated risk factors: comparing the aetiological association of adiposity ratios with risk of coronary heart disease | Q37070868 | ||
| Prediction of coronary artery disease risk based on multiple longitudinal biomarkers | Q37257959 | ||
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| Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide | Q38193133 | ||
| Utility of repeat serum cholesterol measurements for assessment of cardiovascular risk in primary prevention | Q40741950 | ||
| Underestimation of Risk Associations Due to Regression Dilution in Long-term Follow-up of Prospective Studies | Q57241619 | ||
| Triglycerides and the Risk of Coronary Heart Disease | Q57267183 | ||
| Development of predictive models for long-term cardiovascular risk associated with systolic and diastolic blood pressure | Q77513204 | ||
| Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension | Q83185409 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P433 | issue | 8 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | cardiovascular disease | Q389735 |
| P1104 | number of pages | 9 | |
| P304 | page(s) | 899-907 | |
| P577 | publication date | 2017-10-01 | |
| P1433 | published in | American Journal of Epidemiology | Q4744243 |
| P1476 | title | Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis | |
| P478 | volume | 186 |
| Q58607820 | Association of Blood Pressure Classification in Korean Young Adults According to the 2017 American College of Cardiology/American Heart Association Guidelines With Subsequent Cardiovascular Disease Events |
| Q92966103 | Association of Blood Pressure and Risk of Cardiovascular and Chronic Kidney Disease in Hong Kong Hypertensive Patients |
| Q94548136 | Blood Pressure and the Electronic Health Record: A Work in Progress |
| Q59810169 | Intake of Saturated Fatty Acids Affects Atherogenic Blood Properties in Young, Caucasian, Overweight Women Even without Influencing Blood Cholesterol |
| Q52627903 | Landmark Models for Optimizing the Use of Repeated Measurements of Risk Factors in Electronic Health Records to Predict Future Disease Risk. |
| Q92521568 | Long-Term Exposure to Elevated Systolic Blood Pressure in Predicting Incident Cardiovascular Disease: Evidence From Large-Scale Routine Electronic Health Records |
| Q90750628 | Progression of conventional cardiovascular risk factors and vascular disease risk in individuals: insights from the PROG-IMT consortium |
| Q64955034 | Trajectories of Blood Lipid Concentrations Over the Adult Life Course and Risk of Cardiovascular Disease and All-Cause Mortality: Observations From the Framingham Study Over 35 Years. |
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