title: Risk and the physics of clinical prediction.
abstract: The current paradigm of primary prevention in cardiology uses traditional risk factors to estimate future cardiovascular risk. These risk estimates are based on prediction models derived from prospective cohort studies and are incorporated into guideline-based initiation algorithms for commonly used preventive pharmacologic treatments, such as aspirin and statins. However, risk estimates are more accurate for populations of similar patients than they are for any individual patient. It may be hazardous to presume that the point estimate of risk derived from a population model represents the most accurate estimate for a given patient. In this review, we exploit principles derived from physics as a metaphor for the distinction between predictions regarding populations versus patients. We identify the following: (1) predictions of risk are accurate at the level of populations but do not translate directly to patients, (2) perfect accuracy of individual risk estimation is unobtainable even with the addition of multiple novel risk factors, and (3) direct measurement of subclinical disease (screening) affords far greater certainty regarding the personalized treatment of patients, whereas risk estimates often remain uncertain for patients. In conclusion, shifting our focus from prediction of events to detection of disease could improve personalized decision-making and outcomes. We also discuss innovative future strategies for risk estimation and treatment allocation in preventive cardiology.
authors.affiliation: Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address: jmcevoy1@jhmi.edu.
authors.forename: John W
authors.initials: JW
authors.lastname: McEvoy
authors.affiliation: Division of Cardiology, Cedars-Sinai Medical Center, David Geffen School of Medicine, University of California, Los Angeles, California.
authors.forename: George A
authors.initials: GA
authors.lastname: Diamond
authors.affiliation: Department of Radiology, School of Medicine, University of California, Irvine, California.
authors.forename: Robert C
authors.initials: RC
authors.lastname: Detrano
authors.affiliation: Division of Cardiology, Cedars-Sinai Medical Center, David Geffen School of Medicine, University of California, Los Angeles, California.
authors.forename: Sanjay
authors.initials: S
authors.lastname: Kaul
authors.affiliation: Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins University School of Medicine, Baltimore, Maryland.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Blaha
authors.affiliation: Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins University School of Medicine, Baltimore, Maryland.
authors.forename: Roger S
authors.initials: RS
authors.lastname: Blumenthal
authors.affiliation: Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins University School of Medicine, Baltimore, Maryland.
authors.forename: Steven R
authors.initials: SR
authors.lastname: Jones
mesh_terms: D000465:Algorithms; D002309:Cardiology / Q000379:methods*; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000517:prevention & control*; D003657:Decision Making; D014943:Global Health; D006801:Humans; D009017:Morbidity / Q000639:trends; D010825:Physics; D011322:Primary Prevention / Q000379:methods*; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors