Opinion

VIEWPOINT

Sei J. Lee, MD, MAS Division of Geriatrics, University of California, San Francisco. Rosanne M. Leipzig, MD, PhD Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, New York. Louise C. Walter, MD Division of Geriatrics, University of California, San Francisco.

Incorporating Lag Time to Benefit Into Prevention Decisions for Older Adults Prevention holds the promise of maintaining good health by testing, diagnosing, and treating conditions before they cause symptoms. However, prevention can harm as well as help when tests or treatments for asymptomatic conditions cause immediate complications. “Lag time to benefit” is defined as the time between a preventive intervention (when complications and harms are most likely) to the time when improved health outcomes are seen.1 Just as different interventions have different magnitudes of benefit, different preventive interventions have different lag times to benefit, ranging from 6 months for statin therapy for secondary prevention to more than 10 years for prostate cancer screening.2 Many standardized measures such as relative risk, odds ratio, and absolute risk reduction quantify the magnitude of benefit (“How much will it help?”). However, the measures and methodologies to calculate a lag time to benefit (“When will it help?”) are underdeveloped and often not reported. This Viewpoint will describe how guidelines are already using age as a crude marker for life expectancy and show how explicitly accounting for life expectancy could improve prevention decisions. To help clinicians apply this framework, the Viewpoint will outline ways to determine lag time to benefit and life expectancy, highlighting how online life expectancy calculators (eg, http://eprognosis.ucsf.edu) may facilitate prediction of life expectancy. The Viewpoint also will demonstrate how this framework could be applied for a hypothetical patient during a Medicare Annual Wellness visit.

When Will It Help?

Corresponding Author: Sei J. Lee, MD, MAS, Division of Geriatrics, University of California, San Francisco, 4150 Clement St, Bldg 1, Room 220F, San Francisco, CA 94121 (sei [email protected]). jama.com

For older adults, the question “When will it help?” is just as important as “How much will it help?” If an older adult’s life expectancy is substantially shorter than the lag time to benefit for a preventive intervention, administering that intervention exposes them to the immediate risks of the intervention with little likelihood of surviving long enough to benefit.3 In addition, the factors associated with limited life expectancy, such as increased age, comorbidities, and functional limitations, are strong risk factors for complications and adverse effects of interventions, further increasing the chances that prevention would harm rather than help these patients. Many guidelines now recommend targeting preventive interventions such as colorectal and prostate cancer screening to patients whose life expectancy is greater than the lag time to benefit.4 Further, treatment for many chronic asymptomatic conditions in older adults also has immediate risks and delayed benefits. For example, treatment for hypertension can quickly lead to orthostatic hypotension

and falls, but decreased cardiovascular outcomes occur many months or years later. Glycemic treatment for diabetes can cause immediate hypoglycemia, with the hope of preventing vascular complications many years in the future. Given immediate risks and delayed benefits, treatments for asymptomatic conditions should also be targeted to older patients whose life expectancy is greater than the lag time to benefit. Juxtaposing an older patient’s life expectancy and the lag time to benefit may help clinicians identify which patients are more likely to be helped by a preventive intervention and which patients are more likely to be harmed. A general approach involves the following: (1) estimate the patient’s life expectancy; (2) estimate the preventive intervention’s lag time to benefit; (3A) if life expectancy is much greater than lag time to benefit, the intervention may help and should generally be recommended; (3B) if life expectancy is much less than lag time to benefit, the intervention is more likely to harm and generally should not be recommended; (3C) if life expectancy and lag time to benefit are roughly equivalent, the benefits vs harms of the preventive intervention are a “close call,” and patient preferences (eg, the degree of importance placed on the potential benefits and harms) should play the dominant role in decision making.

Moving Beyond Age as a Crude Marker for Life Expectancy Many guidelines use age as the main criterion for recommending preventive interventions, with the specific age threshold determined by the average life expectancy for the selected age group. For example, the US Preventive Services Task Force recommends routine colorectal cancer screening for older adults aged 50 to 75 years.4 One reason for the threshold of 75 years is that the average life expectancy for 75-year-old US adults (11.1 years in 2000)5 is similar to the lag time to benefit for colorectal cancer screening (10.3 years).6 However, older adults of the same age display substantial heterogeneity in their life expectancies. For example, although the average life expectancy for a 75year-old is 11.1 years, 75-year-olds in the healthiest quartile will live 15.7 years, whereas those in the sickest quartile will live only 5.9 years.3 Featuring age cutoffs prominently in guidelines while not emphasizing the life expectancy rationale focuses attention on age rather than the other key components of life expectancy, such as comorbidities or functional limitations. Focusing on age rather than life expectancy can lead to poor prevention decisions. For example, a 70-yearold man with oxygen-dependent lung disease and reJAMA December 25, 2013 Volume 310, Number 24

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stricted mobility would be within the age range at which routine colorectal cancer screening is recommended, but he has a limited life expectancy and is unlikely to benefit from colorectal cancer screening. Conversely, an 80-year-old man who walks 9 holes for golf weekly would not be within the age range at which colorectal cancer screening is recommended but has a good chance of surviving to benefit from screening. Mortality indexes that incorporate comorbid conditions and functional status along with age are more accurate than age alone and could help clinicians improve prediction of life expectancy, resulting in improved prevention decisions.2 For example, mortality indexes that account for severe lung disease and functional limitations would accurately identify the 70-year-old man as having a life expectancy less than 10 years, while predicting that the 80-yearold man would have a life expectancy greater than 10 years. Thus, moving beyond age and explicitly accounting for life expectancy by using mortality indexes (or otherwise accounting for key predictors of life expectancy) would allow for more individualized decision making.

How to Determine Lag Time to Benefit for Preventive Interventions Unlike magnitude of benefit, measures of lag time to benefit are rarely reported. Given the importance of lag time to benefit in determining whether a preventive intervention is appropriate for an older adult, all future research on preventive interventions should report the lag time to benefit (“When will it help?”) along with the magnitude of benefit (“How much will it help?”). Further, for currently accepted preventive interventions for which further studies are unlikely, original trial data should be reanalyzed using quantitative methods to determine the lag time to benefit.6 If quantitative meta-analyzed estimates are unavailable, the lag time to benefit can be estimated by reviewing Kaplan-Meier survival curves for the intervention and control groups. The point at which the curves last separate provides a qualitative estimate of the lag time to benefit for a given intervention.

The following example illustrates an approach for applying this framework. A 75-year-old man who has hypertension (blood pressure, 135/75 mm Hg), diabetes, chronic obstructive lung disease, and difficulty walking several blocks is wondering whether he should be screened for colorectal cancer. First, determine the patient’s life expectancy. Using published general mortality indexes for older adults from a systematic review, the index proposed by Lee et al7 is identified as appropriate for this patient. Using the web calculator available at http: //eprognosis.ucsf.edu, the Lee index estimates that the patient has a 4-year mortality risk of 45%, suggesting a life expectancy of approximately 5 years. Second, determine the lag time to benefit for colorectal cancer screening and blood pressure control. A recent study quantified the lag time to benefit for screening fecal occult blood testing to be 10.3 years for an absolute risk reduction of 1 death prevented for 1000 persons screened.6 Because the lag time to benefit exceeds the patient’s life expectancy, it is unlikely that he would benefit from screening; thus, screening would not be recommended. The ADVANCE study suggests that benefits of more intensive blood pressure control in older patients with diabetes appear at 12 to 18 months.8 Given this patient’s life expectancy of 5 years, continuing more intensive blood pressure control would be recommended.

Conclusion Preventing illness through early detection and treatment is a central component of care for older adults. However, nearly all prevention exposes patients to immediate risks for the hope of improved future health outcomes. Thus, it is critical to the answer to the question “When will it help?” when individualizing preventive decisions in older adults. Although research will continue to improve the accuracy of life expectancy prediction and lag time to benefit, guidelines should move beyond age and explicitly encourage clinicians to juxtapose these 2 elements to improve the targeting of prevention.

National Institute on Aging administered by the Northern California Institute for Research and Education (K24AG041180).

ARTICLE INFORMATION Published Online: December 9, 2013. doi:10.1001/jama.2013.282612. Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Lee reported serving as a consultant for the Group Health Research Institute and receiving payment for lectures from Hill Physicians Group. Dr Leipzig reported serving as a board member for the US Preventive Services Task Force; serving as a consultant for the Donald W. Reynolds Foundation; providing expert testimony in a legal case; receiving grants or grants pending from the Donald W. Reynolds Foundation, the Hartford Foundation, and the Rudin Foundation; and receiving royalties from Focus on Healthy Aging and Springer-Geriatric Medicine. Dr Walter reported no disclosures. Funding/Support: Dr Lee was supported through the Beeson Career Development Award from the National Institute on Aging and the American Federation for Aging Research (K23AG040779) and an Early Career Award from the SD Bechtel Jr Foundation. Dr Walter was supported by the National Cancer Institute (R01CA134425) and the

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Role of the Sponsors: The funding organizations had no role in the preparation, review, or approval of the manuscript or the decision to submit the manuscript for publication. REFERENCES 1. Holmes HM, Hayley DC, Alexander GC, Sachs GA. Reconsidering medication appropriateness for patients late in life. Arch Intern Med. 2006;166(6):605-609. 2. Yourman LC, Lee SJ, Schonberg MA, Widera EW, Smith AK. Prognostic indices for older adults: a systematic review. JAMA. 2012;307(2):182-192. 3. Walter LC, Covinsky KE. Cancer screening in elderly patients: a framework for individualized decision making. JAMA. 2001;285(21):2750-2756. 4. U.S. Preventive Services Task Force. Screening for colorectal cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2008;149(9):627-637.

5. Wei R, Curtin LR, Arias E, Anderson RN. U.S. decennial life tables for 1999-2001: methodology of the United States Life Tables. Natl Vital Stat Rep. 2008;57(4):1-9. 6. Lee SJ, Boscardin WJ, Stijacic-Cenzer I, Conell-Price J, O’Brien S, Walter LC. Time lag to benefit after screening for breast and colorectal cancer: meta-analysis of survival data from the United States, Sweden, United Kingdom, and Denmark. BMJ. 2013;346:e8441. 7. Lee SJ, Lindquist K, Segal MR, Covinsky KE. Development and validation of a prognostic index for 4-year mortality in older adults. JAMA. 2006;295(7):801-808. 8. Patel A, MacMahon S, Chalmers J, et al; ADVANCE Collaborative Group. Effects of a fixed combination of perindopril and indapamide on macrovascular and microvascular outcomes in patients with type 2 diabetes mellitus (the ADVANCE trial): a randomised controlled trial. Lancet. 2007;370(9590):829-840.

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Incorporating lag time to benefit into prevention decisions for older adults.

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