Personalized Estimates of Benefit From Preventive Care Guidelines: A Proof of Concept
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VIEW CORRECTIONPublication: Annals of Internal Medicine
Volume 159, Number 3
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Abstract
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 with 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.
Primary Funding Source:
New York University School of Medicine.
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Supplemental Material
Supplement 1. Detailed Methods for the Personalized Preventive Care Model in Figure 1
Supplement 3. Detailed Results of Sensitivity and Face Validity Analyses
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Published In
Annals of Internal Medicine
Volume 159 • Number 3 • 6 August 2013
Pages: 161 - 168
History
Published online: 6 August 2013
Published in issue: 6 August 2013
Keywords
Copyright
© 2013 American College of Physicians.
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Personalized Estimates of Benefit From Preventive Care Guidelines: A Proof of Concept. Ann Intern Med.2013;159:161-168. [Epub 6 August 2013]. doi:10.7326/0003-4819-159-3-201308060-00005
Personalized Estimates of Benefit From Preventive Care Guidelines: A Proof of Concept. Ann Intern Med.2013;159:161-168. [Epub 6 August 2013]. doi:10.7326/0003-4819-159-3-201308060-00005
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Reply to Dr. Matthys
(1) Owens DK, Goldhaber-Fiebert JD. Prioritizing guideline-recommended interventions. Ann Intern Med. 2013;159:223-4.
Personalized estimates of benefit from preventive care guidelines: a proof of concept.
Dear editor,
Prioritization of preventive interventions for patients with comorbidities via a mathematical model is interesting and actual (1,2), and is more in line when taking the patient’s preferences and expectations into account. Prioritization might be an alternative for the total cardiovascular risk approach (Framingham, SCORE…). Hereby it is known that in most people, atherosclerotic cardiovascular disease is the product of a number of risk factors, (3) where interaction may play an important role.In that sense we were surprised to read in the legend of the central fig 2 ‘adherence to several recommendations may change life expectancy by less than the sum of individual recommendations’: in this context we cannot agree with this statement because it is not scientifically proven and intuitively, one could assume that the reverse seems even more possible: adherence to more recommendations may change life expectancy by more than the sum of individual recommendations. Further, we must not forget that the most effective approaches have been shown to be multilevel—targeting more than one factor with more than one intervention. (3,4) A single factor approach might be expected to have limited effectiveness if the factors determining adherence interact and potentiate each other’s influence, as they are likely to do. (3) Anyway, apart from aforementioned comments, the shift of focus from population burden of preventable morbidity and mortality to individual priorities makes this article very refreshing.
1. Taksler GB, Keshner M, Fagerlin A, Hajizadeh N, Braithwaite RS. Personalized estimates of benefit from preventive care guidelines: a proof of concept. Ann Intern Med. 2013;159:161-8.
2. Owens DK, Goldhaber-Fiebert JD. Prioritizing guideline-recommended interventions. Ann Intern Med. 2013;159:223-4.
3. ESC/EAS Guidelines for the management of dyslipidemias: the Task Force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and the European Atherosclerosis Society (EAS). Reiner Z, Catapano AL, De Backer G et al. ESC Committee for Practice Guidelines (CPG) 2008-2010 and 2010-2012 Committees. Eur Heart J. 2011; 32:1769-818.
4. Wood DA, Kotseva K, Connolly S, Jennings C, Mead A, Jones J et al. Nurse-coordinated multidisciplinary, family-based cardiovascular disease prevention programme (EUROACTION) for patients with coronary heart disease and asymptomatic individuals at high risk of cardiovascular disease: a paired, cluster-randomized controlled trial. EUROACTION Study Group. Lancet. 2008;371:1999-2012.