Genetic risk prediction for CHD: will we ever get there or are we already there?
|
|
- Jayson Short
- 5 years ago
- Views:
Transcription
1 Genetic risk prediction for CHD: will we ever get there or are we already there? Themistocles (Tim) Assimes, MD PhD Assistant Professor of Medicine Stanford University School of Medicine WHI Investigators meeting May 7, 2015 Disclosure: CRA with Telomere Diagnostics Inc.
2 Initial GWAS: Complex diseases have more complex genetic architectures then expected not the best situation for risk prediction Adapted from Thanassoulis, G. and R.S. Vasan, Genetic cardiovascular risk prediction: will we get there? Circulation, (22): p
3 Most widely used and recognized test of discrimination C statistic AKA Receiver-operating-curve (AUC) Sensitivity vs 1-Specificity Probability among a randomly selected case and control, that the case will have a higher modelbased predicted probability of an event 0.5 = chance 1.0 = perfect standard metric for binary outcomes Limitations Big differences in risk = small differences in risk Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29 36.
4 AUC tough to budge when its already reasonably good Cook NR. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clin Chem 2008;54:17-23.
5 Pitfall of relying only on AUC: some TRFs would not be included in current scores + indicates the addition of each variable separately to the model with age, SBP, smoking only Adapted from Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007;115:
6 General concept of reclassification Who moves and to where with new model? But what if you moved a subject inappropri ately? E.g. move case into a lower category of risk Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007;115:
7 New discrimination tests NRI and cnri introduced in 2008 Consider absolute predicted risk of individuals ( reclassification ) 2 category Net reclassification index (NRI) clinical NRI (cnri) The NRI for the intermediate category of risk only US Preventative Services Task Force endorsed concept of reclassification Pencina MJ, D'Agostino RB, Sr., D'Agostino RB, Jr., Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157-72; discussion Cook, N.R., Comments on 'Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond' by M. J. Pencina et al., Statistics in Medicine. Stat Med, (2): p Helfand M, Buckley DI, Freeman M, et al. Emerging risk factors for coronary heart disease: a summary of systematic reviews conducted for the U.S. Preventive Services Task Force. Ann Intern Med 2009;151:
8 NRI for adding HDL to Framingham AUC: (without HDL), (with HDL), ΔAUC p-value= Adapted from Pencina MJ, D'Agostino RB, Sr., D'Agostino RB, Jr., Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157-72; discussion
9 Bias in the cnri Clinical NRI widely used but found to be biased Overall NRI could be negative and the clinical NRI highly positive (including several GRS papers) Correction for Clinical NRI correction Wiped out signal in 2 high profile CVD risk predictions papers Paynter, N.P. and N.R. Cook, A Bias-Corrected Net Reclassification Improvement for Clinical Subgroups. Med Decis Making, 2012
10 cnri bias in CHD risk prediction Ripatti S, Tikkanen E, Orho-Melander M, Havulinna AS, Silander K, Sharma A, et al. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet Oct 23;376(9750): PubMed PMID:
11 Questioning the utility of the NRI NRI is not a proportion Only the NRI events and NRI nonevents Combining as a simple sum or not appropriate and potentially misleading If one weights overall prevalence of events and non-events, NRI can easily move from positive to negative territory look at each separately and consider clinical consequences 3 category NRI doesn t consider large jumps in risk any differently than small jumps Kerr KF, Wang Z, Janes H, McClelland RL, Psaty BM, Pepe MS. Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology Jan;25(1): PubMed PMID: Leening MJ, Vedder MM, Witteman JC, Pencina MJ, Steyerberg EW. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide. Ann Intern Med Jan 21;160(2): PubMed PMID:
12 Large and Small Values for NRI >0 Are Undefined Kerr, K.F., et al., Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology, (1): p
13 Questioning that statistical properties of NRI >0 high false positive rate even with independent test set Can Make Uninformative New Markers Appear Predictive Especially if models not well calibrated But not the case for AUC or for likelihood ratio testing Kerr, K.F., et al., Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology, (1): p Pepe, M.S., H. Janes, and C.I. Li, Net risk reclassification p values: valid or misleading? J Natl Cancer Inst, (4): p. dju041
14 Concerns with testing the nulls for NRI H0 : NRI = 0, z-statistic has never been validated t t For 2-category NRI event or NRI non-event at a given risk threshold cannot reject H 0 : NRI event = 0 and H0 : NRI non-event = 0 on the basis of Y being a risk factor. Tests not yet established for these nulls Kerr, K.F., et al., Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology, (1): p Pepe, M.S., H. Janes, and C.I. Li, Net risk reclassification p values: valid or misleading? J Natl Cancer Inst, (4): p. dju041
15 Back to the AUC?? recent insights on testing whether a new model is better than the old one EQUIVALENT NULL HYPOTHESES H 0 : risk (X,Y) = risk (X) H 0 : AUC (X,Y) = AUC (X) Recommend standard regression statistics No need to test null > 1 x Superior power with Highly developed likelihoodbased tests Avoid inconsistent results from inference that has not been worked out as well for other methods Pepe, M.S., et al., Testing for improvement in prediction model performance. Stat Med, (9): p
16 Back to the AUC?? Testing the AUC Much more work needed re: properties of tests Delong or resampling based tests do not adjust for variability in est. regression coefficients VERY CONSERVATIVE (low power) - even after bootstrap Is this why AUC is insensitive to improvements in prediction performance? Pepe, M.S., et al., Testing for improvement in prediction model performance. Stat Med, (9): p
17 Performance of GRS for CHD today Many examples of robust and relatively consistent association with GRS using ~45-50 GWAS SNPs Cohort GRS RR (95% CI) Comparison # events ARIC 1.29 ( ) Per SD GRS 620 Finnish Cohorts 1.27 ( ) Per SD GRS Swedish Cohorts 1.54 ( ) 4 th quartile to 1 st 781 Goldstein, B.A., et al., Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example. Front Genet, : p. 254 Tikkanen, E., et al., Genetic risk prediction and a 2-stage risk screening strategy for coronary heart disease, in Arterioscler Thromb Vasc Biol p Ganna, A., et al., Multilocus Genetic Risk Scores for Coronary Heart Disease Prediction. Arterioscler Thromb Vasc Biol, (9): p
18 Then what? Need to estimate the extent of improvement Big debate as to how to quantify improvement One strong recommendation: net benefit (NB) Good news, if you reject H 0 : risk (X,Y) = risk (X) You also reject H 0 : NB (X,Y) (t) = NB (X) (t) where t is risk threshold Test of equality of decision curves Pepe, M.S., et al., Testing for improvement in prediction model performance. Stat Med, (9): p Vickers, A.J. and E.B. Elkin, Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making, (6): p
19 Net benefit analysis of treating with rosuvastatin in the JUPITER trial Dorresteijn, J.A., et al., Estimating treatment effects for individual patients based on the results of randomised clinical trials. BMJ, : p. d5888.
20 Comparison of HRs for the last CHD risk factor added to the model Goldstein, Salfati, Yang, Assimes, under preparation
21 My optimistic viewpoint for genetic risk prediction in CHD ++ Markers reproducible and stable ++ Safe and effective interventions We have already overcome initial analytic challenges GRS with robust association comparable to other risk factors, will continue to improve Net benefit likely present when it comes to statin Rx clinical trial to test GRS? Value to just having a better calibrated model to convey risk? the main impediment to implementation is cost genotyping / sequencing Technical genetic data & lgorithms into EHRs
Outline of Part III. SISCR 2016, Module 7, Part III. SISCR Module 7 Part III: Comparing Two Risk Models
SISCR Module 7 Part III: Comparing Two Risk Models Kathleen Kerr, Ph.D. Associate Professor Department of Biostatistics University of Washington Outline of Part III 1. How to compare two risk models 2.
More informationNet Reclassification Risk: a graph to clarify the potential prognostic utility of new markers
Net Reclassification Risk: a graph to clarify the potential prognostic utility of new markers Ewout Steyerberg Professor of Medical Decision Making Dept of Public Health, Erasmus MC Birmingham July, 2013
More informationQuantifying the added value of new biomarkers: how and how not
Cook Diagnostic and Prognostic Research (2018) 2:14 https://doi.org/10.1186/s41512-018-0037-2 Diagnostic and Prognostic Research COMMENTARY Quantifying the added value of new biomarkers: how and how not
More informationSISCR Module 4 Part III: Comparing Two Risk Models. Kathleen Kerr, Ph.D. Associate Professor Department of Biostatistics University of Washington
SISCR Module 4 Part III: Comparing Two Risk Models Kathleen Kerr, Ph.D. Associate Professor Department of Biostatistics University of Washington Outline of Part III 1. How to compare two risk models 2.
More informationDiscrimination and Reclassification in Statistics and Study Design AACC/ASN 30 th Beckman Conference
Discrimination and Reclassification in Statistics and Study Design AACC/ASN 30 th Beckman Conference Michael J. Pencina, PhD Duke Clinical Research Institute Duke University Department of Biostatistics
More informationDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta GA, USA.
A More Intuitive Interpretation of the Area Under the ROC Curve A. Cecile J.W. Janssens, PhD Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta GA, USA. Corresponding
More informationCoronary heart disease (CHD) is a complex disorder with the
Genetic Risk Prediction and a 2-Stage Risk Screening Strategy for Coronary Heart Disease Emmi Tikkanen, Aki S. Havulinna, Aarno Palotie, Veikko Salomaa, Samuli Ripatti Objective Genome-wide association
More informationEvaluation of incremental value of a marker: a historic perspective on the Net Reclassification Improvement
Evaluation of incremental value of a marker: a historic perspective on the Net Reclassification Improvement Ewout Steyerberg Petra Macaskill Andrew Vickers For TG 6 (Evaluation of diagnostic tests and
More informationSystematic reviews of prognostic studies 3 meta-analytical approaches in systematic reviews of prognostic studies
Systematic reviews of prognostic studies 3 meta-analytical approaches in systematic reviews of prognostic studies Thomas PA Debray, Karel GM Moons for the Cochrane Prognosis Review Methods Group Conflict
More informationIt s hard to predict!
Statistical Methods for Prediction Steven Goodman, MD, PhD With thanks to: Ciprian M. Crainiceanu Associate Professor Department of Biostatistics JHSPH 1 It s hard to predict! People with no future: Marilyn
More informationThe Potential of Genes and Other Markers to Inform about Risk
Research Article The Potential of Genes and Other Markers to Inform about Risk Cancer Epidemiology, Biomarkers & Prevention Margaret S. Pepe 1,2, Jessie W. Gu 1,2, and Daryl E. Morris 1,2 Abstract Background:
More informationKey Concepts and Limitations of Statistical Methods for Evaluating Biomarkers of Kidney Disease
Key Concepts and Limitations of Statistical Methods for Evaluating Biomarkers of Kidney Disease Chirag R. Parikh* and Heather Thiessen-Philbrook *Section of Nephrology, Yale University School of Medicine,
More informationAssessment of performance and decision curve analysis
Assessment of performance and decision curve analysis Ewout Steyerberg, Andrew Vickers Dept of Public Health, Erasmus MC, Rotterdam, the Netherlands Dept of Epidemiology and Biostatistics, Memorial Sloan-Kettering
More informationCVD risk assessment using risk scores in primary and secondary prevention
CVD risk assessment using risk scores in primary and secondary prevention Raul D. Santos MD, PhD Heart Institute-InCor University of Sao Paulo Brazil Disclosure Honoraria for consulting and speaker activities
More informationSubclinical atherosclerosis in CVD: Risk stratification & management Raul Santos, MD
Subclinical atherosclerosis in CVD: Risk stratification & management Raul Santos, MD Sao Paulo Medical School Sao Paolo, Brazil Subclinical atherosclerosis in CVD risk: Stratification & management Prof.
More informationRisk modeling for Breast-Specific outcomes, CVD risk, and overall mortality in Alliance Clinical Trials of Breast Cancer
Risk modeling for Breast-Specific outcomes, CVD risk, and overall mortality in Alliance Clinical Trials of Breast Cancer Mary Beth Terry, PhD Department of Epidemiology Mailman School of Public Health
More informationSISCR Module 7 Part I: Introduction Basic Concepts for Binary Biomarkers (Classifiers) and Continuous Biomarkers
SISCR Module 7 Part I: Introduction Basic Concepts for Binary Biomarkers (Classifiers) and Continuous Biomarkers Kathleen Kerr, Ph.D. Associate Professor Department of Biostatistics University of Washington
More informationBy targeting interventions to high-risk population
Editorial Genetic Risk Prediction for CKD: A Journey of a Thousand Miles Related Article, p. 19 By targeting interventions to high-risk population subgroups, tools that provide quantitative estimates of
More informationRisk prediction equations are used in various fields for
Annals of Internal Medicine Academia and Clinic Advances in Measuring the Effect of Individual Predictors of Cardiovascular Risk: The Role of Reclassification Measures Nancy R. Cook, ScD, and Paul M Ridker,
More informationA SAS Macro to Compute Added Predictive Ability of New Markers in Logistic Regression ABSTRACT INTRODUCTION AUC
A SAS Macro to Compute Added Predictive Ability of New Markers in Logistic Regression Kevin F Kennedy, St. Luke s Hospital-Mid America Heart Institute, Kansas City, MO Michael J Pencina, Dept. of Biostatistics,
More informationCRP for the Clinician
CRP for the Clinician J. C. Kaski, D.Sc., M.D., D.M (Hons), F.E.S.C., F.R.C.P., F.A.C.C. F.A.H.A Professor of Cardiovascular Science Head, Cardiovascular Sciences Research Centre St George s, University
More informationAbstract: Heart failure research suggests that multiple biomarkers could be combined
Title: Development and evaluation of multi-marker risk scores for clinical prognosis Authors: Benjamin French, Paramita Saha-Chaudhuri, Bonnie Ky, Thomas P Cappola, Patrick J Heagerty Benjamin French Department
More informationStatistical modelling for thoracic surgery using a nomogram based on logistic regression
Statistics Corner Statistical modelling for thoracic surgery using a nomogram based on logistic regression Run-Zhong Liu 1, Ze-Rui Zhao 2, Calvin S. H. Ng 2 1 Department of Medical Statistics and Epidemiology,
More informationModule Overview. What is a Marker? Part 1 Overview
SISCR Module 7 Part I: Introduction Basic Concepts for Binary Classification Tools and Continuous Biomarkers Kathleen Kerr, Ph.D. Associate Professor Department of Biostatistics University of Washington
More informationAtherosclerotic Disease Risk Score
Atherosclerotic Disease Risk Score Kavita Sharma, MD, FACC Diplomate, American Board of Clinical Lipidology Director of Prevention, Cardiac Rehabilitation and the Lipid Management Clinics September 16,
More informationHow to Develop, Validate, and Compare Clinical Prediction Models Involving Radiological Parameters: Study Design and Statistical Methods
Review Article Experimental and Others http://dx.doi.org/10.3348/kjr.2016.17.3.339 pissn 1229-6929 eissn 2005-8330 Korean J Radiol 2016;17(3):339-350 How to Develop, Validate, and Compare Clinical Prediction
More informationBiases in clinical research. Seungho Ryu, MD, PhD Kanguk Samsung Hospital, Sungkyunkwan University
Biases in clinical research Seungho Ryu, MD, PhD Kanguk Samsung Hospital, Sungkyunkwan University Learning objectives Describe the threats to causal inferences in clinical studies Understand the role of
More informationGlucose tolerance status was defined as a binary trait: 0 for NGT subjects, and 1 for IFG/IGT
ESM Methods: Modeling the OGTT Curve Glucose tolerance status was defined as a binary trait: 0 for NGT subjects, and for IFG/IGT subjects. Peak-wise classifications were based on the number of incline
More informationAssessing Cardiovascular Disease Risk with HS-C-reactive. California Technology Assessment Forum
TITLE: Assessing Cardiovascular Disease Risk with HS-C-reactive Protein AUTHOR: Judith Walsh, M.D., MPH Professor of Medicine Division of General Internal Medicine Department of Medicine University of
More informationJohn J.P. Kastelein MD PhD Professor of Medicine Dept. of Vascular Medicine Academic Medial Center / University of Amsterdam
Latest Insights from the JUPITER Study John J.P. Kastelein MD PhD Professor of Medicine Dept. of Vascular Medicine Academic Medial Center / University of Amsterdam Inflammation, hscrp, and Vascular Prevention
More informationSupplemental Material
Supplemental Material Supplemental Results The baseline patient characteristics for all subgroups analyzed are shown in Table S1. Tables S2-S6 demonstrate the association between ECG metrics and cardiovascular
More informationRegence. Medical Policy Manual. Date of Origin: May Topic: Genetic Testing for Lipoprotein(a) Variant(s) as a Decision Aid for Aspirin Treatment
Regence Medical Policy Manual Topic: Genetic Testing for Lipoprotein(a) Variant(s) as a Decision Aid for Aspirin Treatment Date of Origin: May 2013 Section: Genetic Testing Last Reviewed Date: June 2013
More informationCentral pressures and prediction of cardiovascular events in erectile dysfunction patients
Central pressures and prediction of cardiovascular events in erectile dysfunction patients N. Ioakeimidis, K. Rokkas, A. Angelis, Z. Kratiras, M. Abdelrasoul, C. Georgakopoulos, D. Terentes-Printzios,
More information2011 ASCP Annual Meeting
Diagnostic Accuracy Martin Kroll, MD Professor of Pathology and Laboratory Medicine Boston University School of Medicine Chief, Laboratory Medicine Boston Medical Center Disclosure Roche Abbott Course
More informationRole of imaging in risk assessment models: the example of CIMT
Role of imaging in risk assessment models: the example of CIMT Diederick E. Grobbee, MD, PhD, FESC Professor of Clinical Epidemiology Julius Center for Health Sciences and Primary Care, University Medical
More informationQuantifying the Added Value of a Diagnostic Test or Marker
Clinical Chemistry 58:10 1408 1417 (2012) Review Quantifying the Added Value of a Diagnostic Test or Marker Karel G.M. Moons, 1* Joris A.H. de Groot, 1 Kristian Linnet, 2 Johannes B. Reitsma, 1 and Patrick
More informationLearning Objectives. Predicting and Preventing Cardiovascular Disease. ACC/AHA Cholesterol Guidelines Key differences vs ATP III
Presenter Disclosure Information 10:30 11:15am Predicting and Preventing Cardiovascular Disease: Can we put the Cardiologist out of business? The following relationships exist related to this presentation:
More informationInflammation and and Heart Heart Disease in Women Inflammation and Heart Disease
Inflammation and Heart Disease in Women Inflammation and Heart Disease What is the link between een inflammation and atherosclerotic disease? What is the role of biomarkers in predicting cardiovascular
More informationBiases in clinical research. Seungho Ryu, MD, PhD Kanguk Samsung Hospital, Sungkyunkwan University
Biases in clinical research Seungho Ryu, MD, PhD Kanguk Samsung Hospital, Sungkyunkwan University Learning objectives Understand goal of measurement and definition of accuracy Describe the threats to causal
More informationDo Women Benefit From Statins for Primary Prevention?: Controversy, Challenges and Consensus
Do Women Benefit From Statins for Primary Prevention?: Controversy, Challenges and Consensus C. Noel Bairey Merz MD, FACC, FAHA Professor and Women s Guild Endowed Chair Director, Barbra Streisand Women
More informationUnified baseline and longitudinal mortality prediction in idiopathic pulmonary fibrosis
ORIGINAL ARTICLE INTERSTITIAL LUNG DISEASES Unified baseline and longitudinal mortality prediction in idiopathic pulmonary fibrosis Brett Ley 1, Williamson Z. Bradford 2, Derek Weycker 3, Eric Vittinghoff
More informationAssessment of Clinical Validity of a Breast Cancer Risk Model Combining Genetic and Clinical Information
DOI: 0.09/jnci/djq88 The Author 00. Published by Oxford University Press. Advance Access publication on October 8, 00. This is an Open Access article distributed under the terms of the Creative Com mons
More informationFasting or non fasting?
Vascular harmony Robert Chilton Professor of Medicine University of Texas Health Science Center Director of Cardiac Catheterization labs Director of clinical proteomics Which is best to measure Lower continues
More informationQUANTIFYING THE IMPACT OF DIFFERENT APPROACHES FOR HANDLING CONTINUOUS PREDICTORS ON THE PERFORMANCE OF A PROGNOSTIC MODEL
QUANTIFYING THE IMPACT OF DIFFERENT APPROACHES FOR HANDLING CONTINUOUS PREDICTORS ON THE PERFORMANCE OF A PROGNOSTIC MODEL Gary Collins, Emmanuel Ogundimu, Jonathan Cook, Yannick Le Manach, Doug Altman
More informationWhite Paper Estimating Complex Phenotype Prevalence Using Predictive Models
White Paper 23-12 Estimating Complex Phenotype Prevalence Using Predictive Models Authors: Nicholas A. Furlotte Aaron Kleinman Robin Smith David Hinds Created: September 25 th, 2015 September 25th, 2015
More informationWeek 2 Video 3. Diagnostic Metrics
Week 2 Video 3 Diagnostic Metrics Different Methods, Different Measures Today we ll continue our focus on classifiers Later this week we ll discuss regressors And other methods will get worked in later
More informationKathryn M. Rexrode, MD, MPH. Assistant Professor. Division of Preventive Medicine Brigham and Women s s Hospital Harvard Medical School
Update: Hormones and Cardiovascular Disease in Women Kathryn M. Rexrode, MD, MPH Assistant Professor Division of Preventive Medicine Brigham and Women s s Hospital Harvard Medical School Overview Review
More informationNorthwestern University Feinberg School of Medicine Calculating the CVD Risk Score: Which Tool for Which Patient?
Northwestern University Feinberg School of Medicine Calculating the CVD Risk Score: Which Tool for Which Patient? Donald M. Lloyd-Jones, MD, ScM, FACC, FAHA Senior Associate Dean Chair, Department of Preventive
More informationFamily history of premature coronary heart disease and risk prediction in the EPIC-Norfolk prospective population study
1 Department of Vascular Medicine, Academic Medical Center, Amsterdam, The Netherlands 2 Department of Cardiology, Academic Medical Center, Amsterdam, The Netherlands 3 Department of Public Health and
More informationGenetics of Arterial and Venous Thrombosis: Clinical Aspects and a Look to the Future
Genetics of Arterial and Venous Thrombosis: Clinical Aspects and a Look to the Future Paul M Ridker, MD Eugene Braunwald Professor of Medicine Harvard Medical School Director, Center for Cardiovascular
More informationThe Brier score does not evaluate the clinical utility of diagnostic tests or prediction models
Assel et al. Diagnostic and Prognostic Research (207) :9 DOI 0.86/s452-07-0020-3 Diagnostic and Prognostic Research RESEARCH Open Access The Brier score does not evaluate the clinical utility of diagnostic
More informationBiases in clinical research. Seungho Ryu, MD, PhD Kanguk Samsung Hospital, Sungkyunkwan University
Biases in clinical research Seungho Ryu, MD, PhD Kanguk Samsung Hospital, Sungkyunkwan University Learning objectives Describe the threats to causal inferences in clinical studies Understand the role of
More informationPlacebo-Controlled Statin Trials MANAGEMENT OF HIGH BLOOD CHOLESTEROL MANAGEMENT OF HIGH BLOOD CHOLESTEROL: IMPLICATIONS OF THE NEW GUIDELINES
MANAGEMENT OF HIGH BLOOD CHOLESTEROL: IMPLICATIONS OF THE NEW GUIDELINES Robert B. Baron MD MS Professor and Associate Dean UCSF School of Medicine Declaration of full disclosure: No conflict of interest
More informationPerspectives on analysing subgroup effects of clinical trials and their meta analyses
Perspectives on analysing subgroup effects of clinical trials and their meta analyses Kit CB Roes 2011, London Perspective of treating physician Evidence based decision for the (next) patient to treat,
More informationCover Page. The handle holds various files of this Leiden University dissertation
Cover Page The handle http://hdl.handle.net/1887/35287 holds various files of this Leiden University dissertation Author: Poortvliet, Rosalinde Title: New perspectives on cardiovascular risk prediction
More informationAdvanced IPD meta-analysis methods for observational studies
Advanced IPD meta-analysis methods for observational studies Simon Thompson University of Cambridge, UK Part 4 IBC Victoria, July 2016 1 Outline of talk Usual measures of association (e.g. hazard ratios)
More informationChapter 17 Sensitivity Analysis and Model Validation
Chapter 17 Sensitivity Analysis and Model Validation Justin D. Salciccioli, Yves Crutain, Matthieu Komorowski and Dominic C. Marshall Learning Objectives Appreciate that all models possess inherent limitations
More informationA multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses
A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses Samuli Ripatti, Emmi Tikkanen, Marju Orho-Melander, Aki S Havulinna, Kaisa Silander, Amitabh Sharma,
More informationSupplementary appendix
Supplementary appendix This appendix formed part of the original submission and has been peer reviewed. We post it as supplied by the authors. Supplement to: Callegaro D, Miceli R, Bonvalot S, et al. Development
More informationSummary HTA. HTA-Report Summary
Summary HTA HTA-Report Summary Prognostic value, clinical effectiveness and cost-effectiveness of high sensitivity C-reactive protein as a marker in primary prevention of major cardiac events Schnell-Inderst
More informationSupplementary Online Content
Supplementary Online Content Seymour CW, Liu V, Iwashyna TJ, et al. Assessment of clinical criteria for sepsis: for the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).
More informationThe HEMORR 2 HAGES, ATRIA and the HAS-BLED bleeding risk prediction scores in anticoagulated atrial fibrillation patients : The AMADEUS study
The HEMORR 2 HAGES, ATRIA and the HAS-BLED bleeding risk prediction scores in anticoagulated atrial fibrillation patients : The AMADEUS study Apostolakis S 1, Lane DA 1, Buller H 2, Lip GY 1 1 University
More informationGASTROINTESTINAL. Steve Halligan & Douglas G. Altman & Susan Mallett
Eur Radiol (2015) 25:932 939 DOI 10.1007/s00330-014-3487-0 GASTROINTESTINAL Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: A discussion and proposal
More informationNet benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests
open access Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests Andrew J Vickers, 1 Ben Van Calster, 2,3 Ewout W Steyerberg 3 1 Department of Epidemiology
More informationCARDIOVASCULAR RISK ASSESSMENT ADDITION OF CHRONIC KIDNEY DISEASE AND RACE TO THE FRAMINGHAM EQUATION PAUL E. DRAWZ, MD, MHS
CARDIOVASCULAR RISK ASSESSMENT ADDITION OF CHRONIC KIDNEY DISEASE AND RACE TO THE FRAMINGHAM EQUATION by PAUL E. DRAWZ, MD, MHS Submitted in partial fulfillment of the requirements for the degree of Master
More informationDevelopment, validation and application of risk prediction models
Development, validation and application of risk prediction models G. Colditz, E. Liu, M. Olsen, & others (Ying Liu, TA) 3/28/2012 Risk Prediction Models 1 Goals Through examples, class discussion, and
More informationORIGINAL INVESTIGATION. An Empirical Evaluation of Predictive Tools for Mortality
ORIGINAL INVESTIGATION ONLINE FIRST Predicting Death An Empirical Evaluation of Predictive Tools for Mortality George C. M. Siontis, MD; Ioanna Tzoulaki, PhD; John P. A. Ioannidis, MD, DSc Background:
More informationDyslipidemia in women: Who should be treated and how?
Dyslipidemia in women: Who should be treated and how? Lale Tokgozoglu, MD, FACC, FESC Professor of Cardiology Hacettepe University Faculty of Medicine Ankara, Turkey. Cause of Death in Women: European
More informationPrediction of Mortality Using On-Line, Self-Reported Health Data: Empirical Test of the Realage Score
Prediction of Mortality Using On-Line, Self-Reported Health Data: Empirical Test of the Realage Score William R. Hobbs 1, James H. Fowler 2 * 1 Division of Social Sciences, University of California San
More informationThe Ideal Evaluation of a Risk Prediction Model: A Randomized Clinical Trial
1/25 The Ideal Evaluation of a Risk Prediction Model: A Randomized Clinical Trial Holly Janes Fred Hutchinson Cancer Research Center 2/25 Context Often a risk prediction model is developed to identify
More informationImaging-Guided Statin Allocation: Seeing Is Believing
Imaging-Guided Statin Allocation: Seeing Is Believing The New Paradigm in Personalized Risk Assessment & Medication Prescribing Presented by: Michael J. Blaha May 15, 2014 1 General Principles of Talk
More informationAssessing Cardiovascular Risk to Optimally Stratify Low- and Moderate- Risk Patients. Copyright. Not for Sale or Commercial Distribution
CLINICAL Viewpoint Assessing Cardiovascular Risk to Optimally Stratify Low- and Moderate- Risk Patients Copyright Not for Sale or Commercial Distribution By Ruth McPherson, MD, PhD, FRCPC Unauthorised
More informationThe index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models
Kattan and Gerds Diagnostic and Prognostic Research (2018) 2:7 https://doi.org/10.1186/s41512-018-0029-2 Diagnostic and Prognostic Research METHODOLOGY Open Access The index of prediction accuracy: an
More informationAppendix This appendix was part of the submitted manuscript and has been peer reviewed. It is posted as supplied by the authors.
Appendix This appendix was part of the submitted manuscript and has been peer reviewed. It is posted as supplied by the authors. Appendix to: Banks E, Crouch SR, Korda RJ, et al. Absolute risk of cardiovascular
More informationCurrent Cholesterol Guidelines and Treatment of Residual Risk COPYRIGHT. J. Peter Oettgen, MD
Current Cholesterol Guidelines and Treatment of Residual Risk J. Peter Oettgen, MD Associate Professor of Medicine Harvard Medical School Director, Preventive Cardiology Beth Israel Deaconess Medical Center
More informationA randomized trial of personal genomics for preventive cardiology: design and challenges. Knowles, JW
A randomized trial of personal genomics for preventive cardiology: design and challenges Knowles, JW Short title: Randomized trial of personal genomics Joshua W. Knowles MD, PhD 1, Themistocles L. Assimes
More informationNew Lipid Guidelines. PREVENTION OF CARDIOVASCULAR DISEASE IN WOMEN: Implications of the New Guidelines for Hypertension and Lipids.
PREVENTION OF CARDIOVASCULAR DISEASE IN WOMEN: Implications of the New Guidelines for Hypertension and Lipids Robert B. Baron MD MS Professor and Associate Dean UCSF School of Medicine Disclosure No relevant
More informationHigh-sensitivity Troponin T Predicts Recurrent Cardiovascular Events in Patients with Stable Coronary Heart Disease: KAROLA Study 8 Year FU
ESC Congress 2011 Paris, France, August 27-31 KAROLA Session: Prevention: Are biomarkers worth their money? Abstract # 84698 High-sensitivity Troponin T Predicts Recurrent Cardiovascular Events in Patients
More informationSanger Heart & Vascular Institute Symposium 2015
Sanger Heart & Vascular Institute Symposium 2015 Cardiovascular Update For Primary Care Physicians William E. Downey, MD FACC FSCAI Medical Director, Interventional Cardiology Sanger Heart & Vascular Institute
More informationStudy protocol v. 1.0 Systematic review of the Sequential Organ Failure Assessment score as a surrogate endpoint in randomized controlled trials
Study protocol v. 1.0 Systematic review of the Sequential Organ Failure Assessment score as a surrogate endpoint in randomized controlled trials Harm Jan de Grooth, Jean Jacques Parienti, [to be determined],
More informationSUPPLEMENTARY MATERIAL
SUPPLEMENTARY MATERIAL Supplementary Figure 1. Recursive partitioning using PFS data in patients with advanced NSCLC with non-squamous histology treated in the placebo pemetrexed arm of LUME-Lung 2. (A)
More informationCritical Appraisal Series
Definition for therapeutic study Terms Definitions Study design section Observational descriptive studies Observational analytical studies Experimental studies Pragmatic trial Cluster trial Researcher
More informationDiabetes risk scores and death: predictability and practicability in two different populations
Diabetes risk scores and death: predictability and practicability in two different populations Short Report David Faeh, MD, MPH 1 ; Pedro Marques-Vidal, MD, PhD 2 ; Michael Brändle, MD 3 ; Julia Braun,
More informationThe Framingham Coronary Heart Disease Risk Score
Plasma Concentration of C-Reactive Protein and the Calculated Framingham Coronary Heart Disease Risk Score Michelle A. Albert, MD, MPH; Robert J. Glynn, PhD; Paul M Ridker, MD, MPH Background Although
More informationFinancial Disclosures. Coronary Artery Calcification. Objectives. Coronary Artery Calcium 6/6/2018. Heart Disease Statistics At-a-Glace 2017
Coronary Artery Calcification Dharmendra A. Patel, MD MPH Director, Echocardiography Laboratory Associate Program Director Cardiovascular Disease Fellowship Program Erlanger Heart and Lung Institute UT
More informationCS2220 Introduction to Computational Biology
CS2220 Introduction to Computational Biology WEEK 8: GENOME-WIDE ASSOCIATION STUDIES (GWAS) 1 Dr. Mengling FENG Institute for Infocomm Research Massachusetts Institute of Technology mfeng@mit.edu PLANS
More informationORIGINAL INVESTIGATION. Evaluation of the Framingham Risk Score in the European Prospective Investigation of Cancer Norfolk Cohort
ORIGINAL INVESTIGATION Evaluation of the Framingham Risk Score in the European Prospective Investigation of Cancer Norfolk Cohort Does Adding Glycated Hemoglobin Improve the Prediction of Coronary Heart
More information1 Introduction. st0020. The Stata Journal (2002) 2, Number 3, pp
The Stata Journal (22) 2, Number 3, pp. 28 289 Comparative assessment of three common algorithms for estimating the variance of the area under the nonparametric receiver operating characteristic curve
More informationEnzyme Immunoassay versus Latex Agglutination Cryptococcal Antigen Assays in Adults With non-hiv-related Cryptococcosis
JCM Accepts, published online ahead of print on 24 September 2014 J. Clin. Microbiol. doi:10.1128/jcm.02017-14 Copyright 2014, American Society for Microbiology. All Rights Reserved. 1 2 3 4 5 6 7 8 9
More informationComputer Models for Medical Diagnosis and Prognostication
Computer Models for Medical Diagnosis and Prognostication Lucila Ohno-Machado, MD, PhD Division of Biomedical Informatics Clinical pattern recognition and predictive models Evaluation of binary classifiers
More informationEUROPEAN UROLOGY 58 (2010)
EUROPEAN UROLOGY 58 (2010) 551 558 available at www.sciencedirect.com journal homepage: www.europeanurology.com Prostate Cancer Prostate Cancer Prevention Trial and European Randomized Study of Screening
More informationIn the United States, cardiovascular disease accounts for. Clinical Guidelines
Annals of Internal Medicine Clinical Guidelines C-Reactive Protein as a Risk Factor for Coronary Heart Disease: A Systematic Review and Meta-analyses for the U.S. Preventive Services Task Force David I.
More informationSupplementary Online Content
Supplementary Online Content Malik S, Zhao Y, Budoff M, et al. Coronary artery calcium score for long-term risk classification in individuals with type 2 diabetes and metabolic syndrome from the Multi-Ethnic
More information70 Diagnostic Accuracy. Martin Kroll MD
7 Diagnostic Accuracy Martin Kroll MD 2 Annual Meeting Las Vegas, NV AMERICAN SOCIETY FOR CLINICAL PATHOLOGY 33 W. Monroe, Ste. 6 Chicago, IL 663 7 Diagnostic Accuracy Pathologists and laboratory professionals
More informationMost primary care patients with suspected
Excluding deep vein thrombosis safely in primary care Validation study of a simple diagnostic rule D. B. Toll, MSc, R. Oudega, MD, PhD, R. J. Bulten, MD, A.W. Hoes, MD, PhD, K. G. M. Moons, PhD Julius
More informationLifetime Risk of Cardiovascular Disease Among Individuals with and without Diabetes Stratified by Obesity Status in The Framingham Heart Study
Diabetes Care Publish Ahead of Print, published online May 5, 2008 Lifetime Risk of Cardiovascular Disease Among Individuals with and without Diabetes Stratified by Obesity Status in The Framingham Heart
More informationNovel Biomarkers in Risk Assessment and Management of Cardiovascular Disease
Novel Biomarkers in Risk Assessment and Management of Cardiovascular Disease Policy Number: 2.04.65 Last Review: 1/2018 Origination: 1/2011 Next Review: 1/2019 Policy Blue Cross and Blue Shield of Kansas
More informationThe Whitehall II study originally comprised 10,308 (3413 women) individuals who, at
Supplementary notes on Methods The study originally comprised 10,308 (3413 women) individuals who, at recruitment in 1985/8, were London-based government employees (civil servants) aged 35 to 55 years.
More information