Personalized estimates of benefit from preventive care guidelines: a proof of concept.
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.


METHODS
Mathematical model.


METHODS
USPSTF recommendations and supporting evidence and National Vital Statistics Reports.


METHODS
Nonpregnant adults.


METHODS
Lifetime.


CONCLUSIONS
Individual.


METHODS
USPSTF grade A and B recommendations.


METHODS
Personalized gain in life expectancy associated each recommendation.


RESULTS
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
Robust to variation of model inputs and satisfied face validity criteria.


CONCLUSIONS
Expected adherence rates and quality of life were not considered.


CONCLUSIONS
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.
New York University School of Medicine, New York, New York, USA. glentaksler@gmail.com
Glen B
GB
Taksler
Melanie
M
Keshner
Angela
A
Fagerlin
Negin
N
Hajizadeh
R Scott
RS
Braithwaite
D000328:Adult; D000367:Age Factors; D006801:Humans; D008017:Life Expectancy; D008390:Markov Chains; D008403:Mass Screening / Q000592:standards; D008962:Models, Theoretical; D017410:Practice Guidelines as Topic / Q000592:standards; D011314:Preventive Health Services / Q000592:standards; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012737:Sex Factors; D014481:United States