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A Framework for Tailoring Clinical Guidelines to Comorbidity at the Point of Care
R. Scott Braithwaite, MD, MSc;
John Concato, MD, MS, MPH;
Chung Chou Chang, PhD;
Mark S. Roberts, MD, MPP;
Amy C. Justice, MD, PhD
Arch Intern Med. 2007;167(21):2361-2365.
Background Evidence is accumulating to suggest that clinical guidelines should be modified for patients with comorbidities, yet there is no quantitative and objective approach that considers benefits together with risks.
Methods We outline a framework using a payoff time, which we define as the minimum elapsed time until the cumulative incremental benefits of a guideline exceed its cumulative incremental harms. If the payoff time of a guideline exceeds a patient's comorbidity-adjusted life expectancy, then the guideline is unlikely to offer a benefit and should be modified. We illustrate the framework by applying this method to colorectal cancer screening guidelines for 50-year-old men with human immunodeficiency virus (HIV) and 60-year-old women with congestive heart failure (CHF).
Results We estimated that colorectal cancer screening payoff times for 50-year-old men with HIV would range from 1.9 to 5.0 years and that colorectal cancer screening payoff times for 60-year-old women with CHF would range from 0.7 to 2.9 years. Because the payoff times for 50-year-old men with HIV were lower than their life expectancies (12.5-24.0 years), colorectal cancer screening may be beneficial for these patients. In contrast, because payoff times for 60-year-old women with CHF were sometimes greater than their life expectancies (0.6 to >5 years), colorectal cancer screening is likely to be harmful for some of these patients.
Conclusion Use of a payoff time calculation may be a feasible framework to tailor clinical guidelines to the comorbidity profiles of individual patients.
Author Affiliations: Section of General Internal Medicine, Veterans Affairs Connecticut Health Care System and Yale University School of Medicine, West Haven, Connecticut (Drs Braithwaite, Concato, and Justice); and Section of Decision Analysis and Clinical Systems Modeling, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (Drs Chang and Roberts).
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