BACKGROUND: The U.S. Preventive Services Task Force (USPSTF) makes recommendations for 60 distinct clinical services, but clinicians rarely have time to fully evaluate and implement the recommendations. OBJECTIVE: To complete a proof of concept for prioritization and personalization of USPSTF recommendations, using patient-specific clinical characteristics. DESIGN: Mathematical model. DATA SOURCES: USPSTF recommendations and supporting evidence and National Vital Statistics Reports. TARGET POPULATION: Nonpregnant adults. TIME HORIZON: Lifetime. PERSPECTIVE: Individual. INTERVENTION: USPSTF grade A and B recommendations. OUTCOME MEASURES: Personalized gain in life expectancy associated each recommendation. RESULTS OF BASE-CASE ANALYSIS: Increases in life expectancy varied more than 100-fold across USPSTF recommendations, and the rank order of benefits varied considerably among patients. For an obese man aged 62 years who smoked and had hypercholesterolemia, hypertension, and a family history of colorectal cancer, the model’s top 3 recommendations (from most to least gain in life expectancy) were tobacco cessation (adding 2.8 life-years), weight loss (adding 1.6 life-years), and blood pressure control (adding 0.8 life-year). Lower-ranked recommendations were a healthier diet, aspirin use, cholesterol reduction, colonoscopy, screening for abdominal aortic aneurysm, and HIV testing (each adding 0.1 to 0.3 life-years). For a person with the same characteristics plus uncontrolled type 2 diabetes mellitus, the model’s top 3 recommendations were diabetes control, tobacco cessation, and weight loss (each adding 1.4 to 1.8 life-years). RESULTS OF SENSITIVITY ANALYSIS: Robust to variation of model inputs and satisfied face validity criteria. LIMITATION: Expected adherence rates and quality of life were not considered. CONCLUSION: Models of personalized preventive care may illustrate how magnitude and rank order of benefit associated with preventive guidelines vary across recommendations and patients. These predictions may help clinicians to prioritize USPSTF recommendations at the patient level.
BACKGROUND: The U.S. Preventive Services Task Force (USPSTF) makes recommendations for 60 distinct clinical services, but clinicians rarely have time to fully evaluate and implement the recommendations. OBJECTIVE: To complete a proof of concept for prioritization and personalization of USPSTF recommendations, using patient-specific clinical characteristics. DESIGN: Mathematical model. DATA SOURCES: USPSTF recommendations and supporting evidence and National Vital Statistics Reports. TARGET POPULATION: Nonpregnant adults. TIME HORIZON: Lifetime. PERSPECTIVE: Individual. INTERVENTION: USPSTF grade A and B recommendations. OUTCOME MEASURES: Personalized gain in life expectancy associated each recommendation. RESULTS OF BASE-CASE ANALYSIS: Increases in life expectancy varied more than 100-fold across USPSTF recommendations, and the rank order of benefits varied considerably among patients. For an obeseman aged 62 years who smoked and had hypercholesterolemia, hypertension, and a family history of colorectal cancer, the model’s top 3 recommendations (from most to least gain in life expectancy) were tobacco cessation (adding 2.8 life-years), weight loss (adding 1.6 life-years), and blood pressure control (adding 0.8 life-year). Lower-ranked recommendations were a healthier diet, aspirin use, cholesterol reduction, colonoscopy, screening for abdominal aortic aneurysm, and HIV testing (each adding 0.1 to 0.3 life-years). For a person with the same characteristics plus uncontrolled type 2 diabetes mellitus, the model’s top 3 recommendations were diabetes control, tobacco cessation, and weight loss (each adding 1.4 to 1.8 life-years). RESULTS OF SENSITIVITY ANALYSIS: Robust to variation of model inputs and satisfied face validity criteria. LIMITATION: Expected adherence rates and quality of life were not considered. CONCLUSION: Models of personalized preventive care may illustrate how magnitude and rank order of benefit associated with preventive guidelines vary across recommendations and patients. These predictions may help clinicians to prioritize USPSTF recommendations at the patient level.
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