Treadmill Score for Predicting Prognosis in Coronary Artery Disease
Authors: GEORGE A. DIAMOND
Journal: Annals of Internal Medicine (1987)

Letters and Corrections1 October 1987Treadmill Score for Predicting Prognosis in Coronary Artery DiseaseGEORGE A. DIAMOND, M.D.GEORGE A. DIAMOND, M.D.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-107-4-595 SectionsAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail ExcerptTo the editors: Mark and coworkers (1) recently proposed a multivariate statistical model based on only three variables that purports to simplify the complex process of prognostic assessment in patients undergoing electrocardiographic stress testing for suspected coronary artery disease. Three points need to be made regarding this model.First, the model's accuracy was not adequately defined. The population on which the model was based is likely to be highly biased because it is limited to patients referred for coronary angiography—a judgment that relies at least in part on the results of the electrocardiographic test itself (2, 3). The split-sample analysis...References1. MARKHLATKYHARRELLLEECALIFFPRYOR DMFKRD. Exercise treadmill score for predicting prognosis in coronary artery disease. Ann Intern Med. 1987;106:793-800. LinkGoogle Scholar2. HLATKYPRYORHARRELLCALIFFMARKROSATI MDFRDR. Factors affecting sensitivity and specificity of exercise electrocardiography: multivariable analysis. Am J Med. 1984;77:64-71. CrossrefMedlineGoogle Scholar3. DIAMOND G. Reverend Bayes' silent majority: an alternative factor affecting sensitivity and specificity of exercise electrocardiography. Am J Cardiol. 1986;57:1175-80. CrossrefMedlineGoogle Scholar4. HLATKYBOTVINICKBRUNDAGE MEB. Diagnostic value of cardiologists compared with probability calculations using Bayes' rule. Am J Cardiol. 1982;49:1927-31. CrossrefMedlineGoogle Scholar5. LEEPRYORHARRELL KDF. Predicting outcome in coronary disease: statistical models versus expert clinicians. Am J Med. 1986;80:553-60. CrossrefMedlineGoogle Scholar6. POLLOCKFERGUSONDIAMOND BJG. DO clinical prediction rules really mean what they say [Abstract]? Clin Res. 1987;35:358A. Google Scholar7. DIACONISEFRON PB. Computer-intensive methods in statistics. Sci Am. 1983;248:116-30. CrossrefGoogle Scholar1. WASSONSOXNEFFGOLDMAN JHRL. Clinical prediction rules: applications and methodological standards. N Engl J Med. 1985;313:793-9. CrossrefMedlineGoogle Scholar2. HARRELLLEECALIFFPRYORROSATI FKRDR. Regression modelling strategies for improved prognostic prediction. Stat Med. 1984;3:143-52. CrossrefMedlineGoogle Scholar3. MARKHLATKYHARRELLLEECALIFFPRYOR DMFKRD. Exercise treadmill score for predicting prognosis in coronary artery disease. Ann Intern Med. 1987;106:793-800. LinkGoogle Scholar4. DIACONISEFRON PB. Computer-intensive methods in statistics. Sci Am. 1983;248:116-30. CrossrefGoogle Scholar5. LEEPRYORHARRELL KDF. Predicting outcome in coronary disease: statistical models versus expert clinicians. Am J Med. 1986;80:553-60. CrossrefMedlineGoogle Scholar This content is PDF only. To continue reading please click on the PDF icon. Author, Article, and Disclosure InformationAffiliations: Cedars-Sinai Medical Center Los Angeles, CA 90048 PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited byPhysician perception of exercise electrocardiography as a prognostic test after acute myocardial infarctionCorrection: Treadmill Score for Predicting Prognosis in Coronary Artery Disease 1 October 1987Volume 107, Issue 4Page: 595-596KeywordsElectrocardiographyStable coronary artery diseaseStatistical models Issue Published: 1 October 1987 PDF downloadLoading ...