Andrew J. Vickers, PhD; Ethan Basch, MD; Michael W. Kattan, PhD
Grant Support: In part by a grant from the National Cancer Institute (P50-CA92629 SPORE).
Potential Financial Conflicts of Interest: None disclosed.
Requests for Single Reprints: Andrew J. Vickers, PhD, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021; e-mail, firstname.lastname@example.org.
Current Author Addresses: Drs. Vickers and Basch: Department of Epidemiology and Biostatistics and Department of Medicine, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021.
Dr. Kattan: Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH 44195.
The act of diagnosis requires that patients be placed in a binary category of either having or not having a certain disease. Accordingly, the diseases of particular concern for industrialized countries—such as type 2 diabetes, obesity, or depression—require that a somewhat arbitrary cut-point be chosen on a continuous scale of measurement (for example, a fasting glucose level >6.9 mmol/L [>125 mg/dL] for type 2 diabetes). These cut-points do not adequately reflect disease biology, may inappropriately treat patients on either side of the cut-point as 2 homogenous risk groups, fail to incorporate other risk factors, and are invariable to patient preference. This article discusses risk prediction as an alternative to diagnosis: Patient risk factors (blood pressure, age) are combined into a single statistical model (risk for a cardiovascular event within 10 years) and the results are used in shared decision making about possible treatments. The authors compare and contrast the diagnostic and risk prediction approaches and attempt to identify the types of medical problem to which each is best suited.
Vickers AJ, Basch E, Kattan MW. Against Diagnosis. Ann Intern Med. ;149:200–203. doi: 10.7326/0003-4819-149-3-200808050-00010
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Published: Ann Intern Med. 2008;149(3):200-203.
Cardiology, Coronary Risk Factors, Diabetes, Endocrine and Metabolism, Hematology/Oncology.
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Print ISSN: 0003-4819 | Online ISSN: 1539-3704
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