A framework for tailoring clinical guidelines to comorbidity at the point of care

Arch Intern Med. 2007 Nov 26;167(21):2361-5. doi: 10.1001/archinte.167.21.2361.

Abstract

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Colonoscopy
  • Colorectal Neoplasms / diagnosis
  • Colorectal Neoplasms / epidemiology
  • Comorbidity
  • Female
  • Guidelines as Topic*
  • HIV Infections / diagnosis
  • HIV Infections / epidemiology
  • Heart Failure / diagnosis
  • Humans
  • Life Expectancy
  • Male
  • Mass Screening / methods*
  • Middle Aged
  • Point-of-Care Systems / statistics & numerical data*
  • Risk Factors
  • Survival Analysis
  • Time Factors