title: Clinical implications of referral bias in the diagnostic performance of exercise testing for coronary artery disease.
abstract: BACKGROUND
  Exercise testing with echocardiography or myocardial perfusion imaging is widely used to risk-stratify patients with suspected coronary artery disease. However, reports of diagnostic performance rarely adjust for referral bias, and this practice may adversely influence patient care. Therefore, we evaluated the potential impact of referral bias on diagnostic effectiveness and clinical decision-making.
  
  
  RESULTS
  Searching PubMed and EMBASE (1990-2012), 2 investigators independently evaluated eligibility and abstracted data on study characteristics and referral patterns. Diagnostic performance reported in 4 previously published meta-analyses of exercise echocardiography and myocardial perfusion imaging was adjusted using pooled referral rates and Bayesian methods. Twenty-one studies reported referral patterns in 49 006 patients (mean age 60.7 years, 39.6% women, and 0.8% prior history of myocardial infarction). Catheterization referral rates after normal and abnormal exercise tests were 4.0% (95% CI, 2.9% to 5.0%) and 42.5% (36.2% to 48.9%), respectively, with odds ratio for referral after an abnormal test of 14.6 (10.7 to 19.9). After adjustment for referral, exercise echocardiography sensitivity fell from 84% (80% to 89%) to 34% (27% to 41%), and specificity rose from 77% (69% to 86%) to 99% (99% to 100%). Similarly, exercise myocardial perfusion imaging sensitivity fell from 85% (81% to 88%) to 38% (31% to 44%), and specificity rose from 69% (61% to 78%) to 99% (99% to 100%). Summary receiver operating curve analysis demonstrated only modest changes in overall discriminatory power but adjusting for referral increased positive-predictive value and reduced negative-predictive value.
  
  
  CONCLUSIONS
  Exercise echocardiography and myocardial perfusion imaging are considerably less sensitive and more specific for coronary artery disease after adjustment for referral. Given these findings, future work should assess the comparative ability of these and other tests to rule-in versus rule-out coronary artery disease.
authors: Department of Population Health, New York University School of Medicine, New York, NY.
authors: Joseph A
authors: JA
authors: Ladapo
authors: Saul
authors: S
authors: Blecker
authors: Michael R
authors: MR
authors: Elashoff
authors: Jerome J
authors: JJ
authors: Federspiel
authors: Dorice L
authors: DL
authors: Vieira
authors: Gaurav
authors: G
authors: Sharma
authors: Mark
authors: M
authors: Monane
authors: Steven
authors: S
authors: Rosenberg
authors: Charles E
authors: CE
authors: Phelps
authors: Pamela S
authors: PS
authors: Douglas
mesh_terms: D000368:Aged; D019540:Area Under Curve; D001499:Bayes Theorem; D015982:Bias; D003324:Coronary Artery Disease / Q000175:diagnosis*; D003661:Decision Support Techniques; D025401:Echocardiography, Stress; D005080:Exercise Test; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D055414:Myocardial Perfusion Imaging; D016017:Odds Ratio; D010818:Practice Patterns, Physicians'; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012017:Referral and Consultation; D012720:Severity of Illness Index
keywords: coronary artery disease; diagnostic performance; echocardiography; exercise testing; myocardial perfusion imaging