Letters

Cystatin C is a 13-kDa protein and one of the competitive lysosomal cysteine protease inhibitors. Cystatin C is freely filtered by the glomerulus and has an advantage over creatinine when estimating glomerular filtration rate in that its production is not dependent on muscle mass.2 In comparison, serum creatinine, the primary tool for evaluation of kidney function in clinical practice, can be affected by extrarenal factors including age, body weight, nutritional status, race/ethnicity, and sex. Hence, cystatin C measurement is a more accurate estimate of glomerular filtration rate than creatinine-based equations. Cystatin C and change in cystatin C are potent prognostic markers in acute heart failure, 3 and change in cystatin C was a secondary end point in another trial.4 Given the potential advantages of cystatin C as a more specific marker of renal function, this novel end point was used as the primary safety end point in the ROSE trial. Patients with acute heart failure and renal dysfunction are at risk for inadequate decongestion and worsening renal function, both factors associated with adverse clinical outcomes.5 Hence, the objective of the ROSE trial was to define if lowdose dopamine or low-dose nesiritide, when added to diuretic therapy, would enhance decongestion and preserve renal function in patients with acute heart failure and renal dysfunction. By design, the study population had at least moderate renal dysfunction. However, subgroup analysis did not demonstrate a differential treatment effect according to estimated glomerular filtration rate. This suggests that in this population with acute heart failure and renal dysfunction, the severity of renal dysfunction did not affect the response to treatment. Because patients with normal renal function were not studied in the ROSE trial, we are unable to state how they would respond to low-dose dopamine or nesiritide. Horng H. Chen, MBBCh Margaret M. Redfield, MD

4. Bart BA, Goldsmith SR, Lee KL, et al; Heart Failure Clinical Research Network. Ultrafiltration in decompensated heart failure with cardiorenal syndrome. N Engl J Med. 2012;367(24):2296-2304. 5. Ronco C, Cicoira M, McCullough PA. Cardiorenal syndrome type 1: pathophysiological crosstalk leading to combined heart and kidney dysfunction in the setting of acutely decompensated heart failure. J Am Coll Cardiol. 2012;60(12):1031-1042.

Lag Time to Benefit for Preventive Therapies To the Editor: In their Viewpoint, Dr Lee and colleagues 1 explained why clinicians and guideline panels should move beyond age as a crude marker for life expectancy and compare life expectancy with the earliest time when benefits exceed harms. They also noted that “unlike magnitude of benefit, measures of lag time to benefit are rarely reported.”1 One method to estimate the time until benefits exceed harms for a patient undergoing a particular intervention that has short-term harms and long-term benefits is the “payoff time,” which can be approximated by dividing a patient’s probability of being harmed due to the intervention by the reduction in mortality attributable to the intervention.2,3 The payoff time can be viewed as a patient-centered version of “lag time to benefit” because it takes into account individual patient characteristics that may amplify or attenuate the procedure’s harms, benefits, or both. It has been implemented to inform decision making for a variety of conditions, including colorectal cancer screening,4 prophylactic carotid artery surgery,5 prophylactic aortic aneurysm surgery, and tight glucose control for patients with diabetes, at sites ranging from Veterans Affairs hospitals to safety-net hospitals to tertiary care facilities. If appropriate decision support tools are incorporated into the next generation of electronic medical records, use of these approaches could be greatly facilitated, simultaneously reducing harm to patients and unnecessary health expenditures. R. Scott Braithwaite, MD, MS

Author Affiliations: Mayo Clinic, Rochester, Minnesota. Corresponding Author: Horng H. Chen, MBBCh, Mayo Clinic, 200 First St SW, Rochester, MN 55905 ([email protected]).

Author Affiliation: Department of Population Health, NYU School of Medicine, New York, New York.

Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Chen reported serving on the board of Zumbro Discovery (as cofounder); receiving grants to his institution from the National Heart, Lung, and Blood Institute; serving on the board and receiving grants from Scios Inc; receiving royalties from Nile Therapeutics, Anexon Inc, and UpToDate; and reported that his institution has licensed patents to Nile Therapeutics and Anexon Inc for designer natriuretic peptides. Dr Redfield reported receiving royalties from Anexon; payment for educational presentations from the Heart Failure Society of America; and unpaid advisory committee membership for Novartis.

Corresponding Author: R. Scott Braithwaite, MD, MS, NYU School of Medicine, 550 First Ave, New York, NY 10016 ([email protected]).

1. Schwartzenberg S, Redfield MM, From AM, Sorajja P, Nishimura RA, Borlaug BA. Effects of vasodilation in heart failure with preserved or reduced ejection fraction implications of distinct pathophysiologies on response to therapy. J Am Coll Cardiol. 2012;59(5):442-451.

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Conflict of Interest Disclosures: The author has completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported. 1. Lee SJ, Leipzig RM, Walter LC. Incorporating lag time to benefit into prevention decisions for older adults. JAMA. 2013;310(24):2609-2610. 2. Braithwaite RS. Can life expectancy and QALYs be improved by a framework for deciding whether to apply clinical guidelines to patients with severe comorbid disease? Med Decis Making. 2011;31(4):582-595. 3. Braithwaite RS, Fiellin D, Justice AC. The payoff time: a flexible framework to help clinicians decide when patients with comorbid disease are not likely to benefit from practice guidelines. Med Care. 2009;47(6):610-617.

2. Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis. 2002;40(2):221-226.

4. Gross CP, Soulos PR, Ross JS, et al. Assessing the impact of screening colonoscopy on mortality in the medicare population. J Gen Intern Med. 2011;26(12):1441-1449.

3. Campbell CY, Clarke W, Park H, Haq N, Barone BB, Brotman DJ. Usefulness of cystatin C and prognosis following admission for acute heart failure. Am J Cardiol. 2009;104(3):389-392.

5. Yuo TH, Roberts MS, Braithwaite RS, Chang CC, Kraemer KL. Applying the payoff time framework to carotid artery disease management. Med Decis Making. 2013;33(8):1039-1050.

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To the Editor Dr Lee and colleagues1 discussed using lag time to benefit along with magnitude of benefit when considering preventive therapies in older adults. However, using time-tobenefit information from randomized clinical trials (RCTs) entails a number of challenges that warrant discussion. Time-to-benefit information influences decision making for individual older patients in various ways.2 First, the outcome with the shortest time to benefit may not be valued as much by an older person as an incremental improvement in quality of life, which may require more time (or not occur).3 Second, RCTs may not be designed to ascertain nonmortality outcomes at interim time intervals. Therefore, it is possible, even with the original data, that a meta-analysis might not be able to determine time to benefit shorter than the trial duration. With inadequate interim data, we advise caution in visually interpreting significant deviation of time-to-event curves because this approach may incorrectly assume significant risk reduction and may underestimate time to significant benefit. Valid statistical approaches need to be developed for estimation of time to benefit. Third, extrapolating from healthier populations to older persons with multimorbidity is fraught with potential for underestimation or overestimation of time to benefit. Randomized clinical trials are designed to achieve predetermined outcomes over the shortest possible time, and there may be substantial heterogeneity of effects in RCTs on the basis of differences in baseline risk.4 Last, information on time to harm is even more difficult to extrapolate. Harms are not always immediate, and there may be situations in which knowing the balance of lag time to harm and lag time to benefit, and the joint dependence of benefits and harms would help patients and clinicians to make more informed treatment decisions. We agree that future preventive trials can improve reporting of useful data on time to benefit through study designs with flexible trial duration for subgroups of patients such as older adults with multimorbidities, who are typically understudied in trials. For example, if the benefit is substantial at the end of the overall trial, but not statistically significant in a subgroup, that prespecified group could accumulate more exposure time, and therefore derive meaningful subgroup results that could be applied to patients. Holly M. Holmes, MD Lillian Min, MD, MSHS Cynthia Boyd, MD, MPH Author Affiliations: University of Texas MD Anderson Cancer Center, Houston (Holmes); Division of Geriatric Medicine, University of Michigan, Ann Arbor (Min); Division of Geriatric Medicine and Gerontology, Johns Hopkins University, Baltimore, Maryland (Boyd). Corresponding Author: Holly M. Holmes, MD, University of Texas MD Anderson Cancer Center, 1515 Holcombe Bvld, Unit 1465, Houston, TX 77030 ([email protected]). Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Holmes reported receiving a grant from the National Institute on Aging. Dr Boyd reported receiving a grant from the Paul Beeson Career Development Award Program; authoring a chapter of Up-to-Date on multimorbidity; and receiving an

honorarium from United Health Care’s Medicare Advisory Board on multimorbidity. No other disclosures were reported. 1. Lee SJ, Leipzig RM, Walter LC. Incorporating lag time to benefit into prevention decisions for older adults. JAMA. 2013;310(24):2609-2610. 2. Holmes HM, Min LC, Yee M, et al. Rationalizing prescribing for older patients with multimorbidity: considering time to benefit. Drugs Aging. 2013;30(9):655-666. 3. American Geriatrics Society Expert Panel on the Care of Older Adults With Multimorbidity. Guiding principles for the care of older adults with multimorbidity: an approach for clinicians. J Am Geriatr Soc. 2012;60(10):1-25. 4. Kent DM, Rothwell PM, Ioannidis JP, Altman DG, Hayward RA. Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal. Trials. 2010;11:85.

In Reply Dr Braithwaite notes the similarities between our proposed concept of lag time to benefit and the concept of payoff time. We absolutely agree. Both concepts recognize that for many interventions, the harms occur before the benefits. Thus, both concepts strive to determine which patients are most likely to benefit from the intervention by comparing a patient’s life expectancy with the intervention’s time to benefit. Although we presented the lag time to benefit as a fixed property of the intervention, the payoff time attempts to individualize the time to benefit by incorporating an individual patient’s risk factors, potentially leading to a more accurate, individualized estimate of the time to benefit. However, quantitative estimates of the degree of risk conferred by specific factors often rely on potentially biased observational data and subgroup analyses. The primary benefit of the lag time approach is its simplicity and transparency. Currently, we believe that simplicity and transparency are important to facilitate widespread dissemination of these concepts. Dr Holmes and colleagues highlight important challenges when trying to determine time-to-benefit information from RCTs. We agree that most trials focus on younger, healthier patients, making it difficult to extrapolate these results to older patients with multimorbidity. Furthermore, we agree that estimating time to benefit by visually determining the point of separation for time-to-event curves should generally be avoided in negative trials. Currently, many frail patients with limited life expectancy are exposed to the potential harms of prevention with little chance of benefit,1,2 suggesting that many patients and clinicians do not consider time to benefit when making prevention decisions. Thus, although there are significant complexities and challenges in accurately determining time to benefit, we believe that the methods we outlined in our Viewpoint would provide time-to-benefit estimates sufficiently accurate to improve prevention decisions. Future research should test this hypothesis. Box and Draper3 noted that “All models are wrong, but some are useful.” The comments by Braithwaite and Holmes and colleagues highlight that our conceptual model is “wrong” in that it simplifies the complex evaluation of individualized estimates of the benefits and harms of a preventive intervention into 2 factors: patient’s life expectancy and the intervention’s lag time to benefit. However, we hope our simplification is useful and leads patients and clinicians to ask “When will it help?” to ex-

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plicitly consider time to benefit, ultimately leading to betterinformed prevention decisions. Sei J. Lee, MD, MAS Rosanne M. Leipzig, MD, PhD Louise C. Walter, MD Author Affiliations: Division of Geriatrics, University of California, San Francisco (Lee, Walter); Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (Leipzig). Corresponding Author: Sei J. Lee, MD, MAS, University of California, 4150 Clement St, Bldg 1, Room 220F, San Francisco, CA 94121 ([email protected]). Conflict of Interest Disclosures: The 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 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. No other disclosures were reported. 1. Walter LC, Lindquist K, Nugent S, et al. Impact of age and comorbidity on colorectal cancer screening among older veterans. Ann Intern Med. 2009;150(7):465-473. 2. Mehta KM, Fung KZ, Kistler CE, Chang A, Walter LC. Impact of cognitive impairment on screening mammography use in older US women. Am J Public Health. 2010;100(10):1917-1923. 3. Box GE, Draper NR. Empirical Model-Building and Response Surfaces. New York, NY: John Wiley & Sons Inc; 1987:424.

CORRECTION Incorrect Unit of Measure and Omitted Grant: In the Original Investigation entitled “Blood Pressure Trajectories in Early Adulthood and Subclinical Atherosclerosis in Middle Age” published in the February 5, 2014, issue of JAMA

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(2014;311[5]:490-497. doi:10.1001/jama.2013.285122), the wrong unit was provided for measurement of coronary artery calcification. Instead of Hounsfield units (HU), the correct unit of measure is Agatston units (AU). In the accompanying Editorial entitled “Early Patterns of Blood Pressure Change and Future Coronary Atherosclerosis” (2014;311[5]:471-472. doi:10.1001/jama.2013 .285123), the same change applies. Additionally, in the Original Investigation, grant RO1 HL098445 from the National Heart, Lung, and Blood Institute was omitted from the Funding/Support section. These articles have been corrected online.

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