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Health Values of Hospitalized Patients 80 Years or Older

Health Values of Hospitalized Patients 80 Years or Older Abstract Context.— Health values (utilities or preferences for health states) are often incorporated into clinical decisions and health care policy when issues of quality vs length of life arise, but little is known about health values of the very old. Objective.— To assess health values of older hospitalized patients, compare their values with those of their surrogate decision makers, investigate possible determinants of health values, and determine whether health values change over time. Design.— A prospective, longitudinal, multicenter cohort study. Setting.— Four academic medical centers. Participants.— Four hundred fourteen hospitalized patients aged 80 years or older and their surrogate decision makers who were interviewed and understood the task. Main Outcome Measures.— Time–trade-off utilities, reflecting preferences for current health relative to a shorter but healthy life. Results.— On average, patients equated living 1 year in their current state of health with living 9.7 months in excellent health (mean [SD] utility, 0.81 [0.28]). Although only 126 patients (30.7%) rated their current quality of life as excellent or very good, 284 (68.6%) were willing to give up at most 1 month of 12 in exchange for excellent health (utility ≥0.92). At the other extreme, 25 (6.0%) were willing to live 2 weeks or less in excellent health rather than 1 year in their current state of health (utility ≤0.04). Patients were willing to trade significantly less time for a healthy life than their surrogates assumed they would (mean difference, 0.05; P=.007); 61 surrogates (20.3%) underestimated the patient's time–trade-off score by 0.25 (3 months of 12) or more. Patients willing to trade less time for better health were more likely to want resuscitation and other measures to extend life. Time–trade-off score correlated only modestly with quality-of-life rating (r=0.28) and inversely with depression score (r=−0.27), but there were few other clinical or demographic predictors of health values. When patients who survived were asked the time–trade-off question again at 1 year, they were willing to trade less time for better health than at baseline (mean difference, 0.04; P=.04). Conclusion.— Very old hospitalized patients who could be interviewed were able, in most cases, to have their health values assessed using the time–trade-off technique. Most patients were unwilling to trade much time for excellent health, but preferences varied greatly. Because proxies and multivariable analyses cannot gauge health values of elderly hospitalized patients accurately, health values of the very old should be elicited directly from the patient. LIFE EXPECTANCY has increased dramatically even among elderly adults over the past few generations. Currently, an 80-year-old man can expect to live 7 years and an 80-year-old woman, 9.1 years.1 But because the elderly disproportionately have both acute and chronic illnesses, quality of life has assumed increasing importance. There are 2 approaches to assessing health-related quality of life. The health status approach describes functioning and well-being in 1 or more domains, such as physical functioning, mental health, social function, role function, pain, vitality, and health perception.2-5 Health status has been assessed in the elderly for over 30 years, most often with measures of ability to perform activities of daily living (such as bathing, dressing, eating, toileting, and transferring)6,7 and, increasingly, with brief, multidimensional surveys.3 The other approach to assessing health-related quality of life, known as utility, preference, or value assessment, ascertains the desirability of a state of health.2,8 One such health value measure is the time trade-off, which quantifies a person's preference for quality vs quantity of life. Health values are used in individual clinical decision making as well as in health policy formulation.2,5,8,9 In clinical decision making, they can provide a general sense of how patients feel about quality vs quantity of life, and they can be used in clinical decision analyses to optimize treatment for an individual.2,10 Examples of using health values in clinical practice include helping couples decide whether to have amniocentesis11 and deciding when a patient with human immunodeficiency virus infection should start zidovudine therapy.12 In addition, utility assessment has demonstrated that clinical guidelines for management of ischemic heart disease may not conform to preferences of affected patients.13 Utilities are most commonly used as quality-of-life adjustments to life expectancy in the calculation of quality-adjusted life years (QALYs), which, in turn, are used in clinical decision analyses and, in conjunction with cost estimates, in cost-effectiveness (cost-utility) analyses. While many analysts8,14 advocate using QALYs for such purposes, not all agree.15-17 La Puma and Lawlor16 have raised ethical concerns over the use of QALYs in health policy formulation, one being that the "old-old" cannot or will not provide health values and are thus disenfranchised. Although much work has been done in geriatric health status assessment, little is known about the health values of the very old. Health value assessment in the elderly is particularly important because elderly patients receive fewer invasive procedures and less resource-intensive hospital care than younger patients, even when differences in severity of illness and preferences for life-extending care are taken into account.18 Whether this different approach is with the assent of the patient or caregivers is not clear. Given the importance of health values, if in fact health values cannot be ascertained from frail elderly patients, it would be important to know whether their health care proxies could provide accurate estimates or whether health values can be predicted on the basis of health status or other variables. Another important issue is whether health values change over time. Methods Study Participants The Hospitalized Elderly Longitudinal Project (HELP) was a prospective study of the prognoses, preferences, and decision making of hospitalized patients aged 80 years or older, their surrogate decision makers, and their physicians. The HELP study took place from January 1993 to November 1994 at 4 academic medical centers (HELP was related closely to a concurrent study, the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments project19). To qualify, patients had to be at least 80 years old and hospitalized. Patients were excluded if they did not speak English; were foreign nationals admitted specifically for a medical procedure; had the acquired immunodeficiency syndrome (AIDS); had sustained multiple trauma; were admitted for hospice care, to the psychiatry service, or for an elective operation; were admitted to a hospital ward after transfer from another hospital; died or were discharged within 72 hours of hospitalization; or were scheduled for discharge within 72 hours of admission. For most patients, a surrogate was identified.20 Interviews and Instruments For each patient, severity of acute illness was ascertained using the acute physiology score (APS) component of the Acute Physiology and Chronic Health Evaluation III prognostic scoring system.21 Patients who were not intubated, were able to communicate, and were able to pass a cognitive screening test22 were eligible to be interviewed. Trained interviewers interviewed patients and surrogates approximately 4 days and 12 months after study entry. The health value measure used was the time–trade-off score.22-24 Patients and their surrogates were independently asked in a systematic fashion whether the patient would prefer living only 1 year in the patient's current state of health or less time in excellent health, until an indifference point was ascertained. The time–trade-off score was then calculated as the fraction of a year in excellent health that was equivalent to a year of current health. For example, if the patient were indifferent about choosing between living 12 months in their current state of health or living only 9 months in excellent health, their time–trade-off utility would equal 9 divided by 12 or 0.75. Possible scores ranged from 0.04 (equivalent to indifference between 2 weeks in excellent health and 1 year in current health) to 1.0. After the respondent completed the time–trade-off question, the interviewer judged whether the respondent understood the task; respondents who did not understand were excluded. Health status instruments included (1) a global quality-of-life question, in which the respondent was asked to rate the patient's quality of life as excellent, very good, good, fair, or poor22; (2) a revised measure of dependence in activities of daily living over the previous 2 weeks6,22,25; (3) a revised version of the Duke Activity Status Index,22,26,27 which assesses ability to perform strenuous activities; (4) a shortened version of the Profile of Mood States,28,29 which assesses anxiety and depression; and (5) a measure of frequency and severity of pain.19 In addition, we asked questions concerning preferences regarding cardiopulmonary resuscitation (CPR); willingness to tolerate each of 6 potentially lifelong adverse outcomes—pain, mechanical ventilation, tube feeding, coma, confusion, and living in a nursing home; preferences for care focused on extending life as much as possible, even if it means having more pain and discomfort, vs care focused on relieving pain and discomfort as much as possible, even if it means not living as long; and perceived prognosis for surviving for 2 and 12 months and for functioning independently in 2 and 12 months.22,30 We assessed the effect of the patient's illness on family members in terms of assistance needed and savings depleted.28,31 For the Duke Activity Status Index, missing items were imputed from surrogate responses for 5 patients.32 Statistical Analysis Means are expressed as mean (SD), and medians are given with 25th and 75th percentiles. Continuous variables were compared using the Wilcoxon rank sum test. Within-patient changes over time were assessed with the Wilcoxon signed rank test. Concordance between patients' and their surrogates' time–trade-off scores was assessed using the Wilcoxon signed rank test. Univariate associations between the time–trade-off and the health status measures, measures of perceived prognosis, willingness to tolerate adverse outcomes, and demographic variables were assessed using Spearman correlation coefficients. Because time–trade-off scores were not normally distributed, we used multivariable ordinal logistic regression to identify significant predictors of time–trade-off scores at day 4 and month 12. Variables significantly associated with time–trade-off scores in univariate analyses (when P<.10) and variables found to have been related to time–trade-off scores in the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments study33 were entered into the model and retained through backward elimination, if P was less than .05. A summary statistic for the ordinal logistic regression models is Somers D, which measures the models' ability to predict time–trade-off utilities. For a binary outcome (high vs low utilities) the statistic is a linear function of the area under the receiver operating characteristic (ROC) curve (D=2×[area under ROC−0.5]). Within-patient changes in time–trade-off scores were compared with changes in other measures using the Kruskal-Wallis test and Spearman correlation coefficients. Analyses were performed using SAS statistical software (SAS Institute, Inc, Cary, NC). Results Patients and Interviews The HELP study enrolled 1266 patients. Many patients could not be interviewed: 25 patients (2.0%) were comatose or intubated or both; 272 (21.5%) were unable to communicate for other reasons; 204 (16.1%) failed the cognitive screening test; 29 (2.3%) died or were discharged within 72 hours; and 2 (0.2%) were ineligible for other reasons. Of the 734 eligible for interview, 622 (84.7%) participated. Of those 622, 62 (10.0%) terminated the interview before the time–trade-off question was asked. Of the remaining 560 patients, 43 (7.7%) refused to answer the time–trade-off question, answered "don't know," or had missing or incomplete answers. Of the 517 patients completing the time–trade-off question at their initial interview, interviewers provided judgments regarding the patient's understanding in 475 cases (91.9%); 414 (87.2%) were judged to have understood the task. Those 414 patients formed our main analytic sample (Table 1). In-hospital and 12-month mortality rates and APS scores were significantly higher (higher APS scores indicate more severe illness) for the 852 excluded patients than for the 414 who completed the time–trade-off question. Among the 414 patients, 319 also had time–trade-off questions completed by their surrogate at day 4. For the 319 surrogates' interviews, interviewers' judgments regarding understanding of the time–trade-off question were available for 311 (97.5%), and the interviewer believed that 300 (96.5%) of those surrogates understood the task. Compared with patients who had a matching surrogate interview, patients without a surrogate interview were similar in age, sex, and level of education attained and had similar APS and Duke Activity Status Index scores but slightly more dependencies in activities of daily living (1.08 vs 0.84 dependencies; P=.02). Of the patients alive at month 12, 176 (52%) completed and understood a follow-up time–trade-off assessment. Health Values The mean (SD) time–trade-off score for the 414 patients at their initial interview was 0.81 (0.28) (median [25th, 75th percentile], 0.92 [0.83, 1.0]). This indicates that, on average, patients equated living 1 year in their current state of health with living 9.7 months (0.81×12 months) in excellent health. But time–trade-off scores varied widely from patient to patient (Figure 1): 169 (40.8%) had utilities of 1.0, meaning that they were unwilling to give up any time in exchange for a shorter life in excellent health, and another 115 (27.8%) had utilities of 0.92, meaning that they were willing to give up only 1 month of 12 ([1-0.92]×12 months) in exchange for excellent health. Thus, more than two thirds of the patients (284 [68.6%]) were willing to forgo at most 1 month of 12. At the other extreme, 25 (6.0%) had utilities of 0.04, indicating that they preferred living 2 weeks or less in excellent health to living 1 year in their current state of health. Patients Compared With Their Surrogates Time–trade-off scores given by surrogates, who were asked to answer as they thought the patient would, also varied widely. For the 300 patient-surrogate pairs, the mean (SD) patient utility was 0.80 (0.30) (median [25th, 75th], 0.92 [0.83, 1.0]) and was higher than the mean surrogate utility by 0.05 (0.38) (median difference, 0 [0, 0.17]; P=.007); 61 (20.3%) of surrogates underestimated the patient's time–trade-off score by 0.25 (3 months of 12) or more. The correlation between patients' and their paired surrogates' health values was modest (r=0.36). Relationship of Heath Values to Other Measures Time–trade-off utilities were related to patients' preferences for CPR (Table 2). Patients who desired CPR had a mean (SD) time–trade-off score at day 4 of 0.86 (0.23) (median [25th, 75th], 0.92 [0.83, 1.0]);whereas, patients who preferred not to undergo CPR had slightly lower mean scores of 0.75 (0.34) (median [25th, 75th], 0.92 [0.63, 1.0]; P<.001). Higher time–trade-off scores were also related to patients' preferences for care that focused on extending life: patients who preferred care that focused on extending life had higher time–trade-off scores (ie, they were not willing to trade away as much time) than patients who preferred care that focused on relieving pain and discomfort. Health values correlated only modestly (r=0.28) with overall quality of life (Figure 2). A total of 126 patients (30.7%) rated their current quality of life as excellent or very good. For the patients with utilities of 1.0, indicating an unwillingness to trade any time in current health for a shorter but healthier life, only 29 (17.3%) considered their quality of life to be excellent as is; 39 (23.2%) rated it as very good, 61 (36.3%) as good, 32 (19.0%) as fair, and 7 (4.2%) as poor. For the patients with utilities of 0.04 or less, 2 (8.0%) rated their quality of life as excellent, 3 (12.0%) as very good, 6 (24.0%) as good, 3 (12.0%) as fair, and 11 (44.0%) as poor. Health values correlated modestly (and inversely) with level of depression (r=−0.27); but they correlated poorly if at all with other health status measures, willingness to tolerate the 6 adverse outcomes, and perceived prognosis for survival and independent functioning (Table 3). Among the demographic variables, time–trade-off scores were not related to age, sex, race, or level of education. In a multivariable analysis, on average, patients who preferred treatment that extended life were more likely to report high time–trade-off scores (odds ratio [OR], 2.8; 95% confidence interval [CI], 1.8-4.4); in addition, health values were positively related to quality of life (OR, 1.2 for each level of better quality of life; 95% CI, 1.0-1.5) but were inversely related to level of depression (OR, 0.6 for each level of more severely depressed mood; 95% CI, 0.4-0.7; Somers D=0.343). Health Values 1 Year Later For the 176 patients who completed the time–trade-off questions at both day 4 and month 12, scores increased over the year by an average of 0.04, from 0.84 to 0.88 (SD of change, 0.30; median, 0; 25th percentile, −0.08; 75th percentile, 0.08; P=.04). This means that, a year after hospitalization, on average, the time patients would give up in their current state of health to be able to be in excellent health had declined by 2 weeks. As at the initial interview, time–trade-off scores were higher among those who desired CPR and among those who preferred life prolongation over relief of pain and discomfort (Table 2); utilities were not significantly related to the degree of impact of the illness on the family. At the 12-month interview, 60 patients (34.5%) rated their quality of life as excellent or very good, but time–trade-off scores correlated only moderately (r=0.24) with quality of life. In a multivariable analysis, on average, patients who preferred treatment that extended life were more likely to report high time–trade-off scores (OR, 4.6; 95% CI, 2.1-10.0). Time–trade-off scores were positively related to quality of life (OR, 1.6 for each level of better quality of life; 95% CI, 1.2-2.1; Somers D=0.378). Change in time–trade-off scores was weakly correlated with changes in quality of life, physiologic reserve, and mental health (r=0.13-0.21). Comment Would an elderly person who is frail and ill prefer living as long as possible over a shorter but healthy life? This study ascertained the health values vis-à-vis quantity vs quality of life of a cohort of 414 hospitalized patients aged 80 to 98 years. On average, patients indicated a fairly strong "will to live": 40.8% were unwilling to exchange any time in their current state of health for a shorter life in excellent health, and 27.8% were willing to give up at most 1 month of 12 in return for excellent health. The variance was large, however, with 6.0% of patients willing to live 2 weeks or less in excellent health rather than 1 year in their current state of health. One year later, surviving patients who could be interviewed had higher utilities than during the index hospitalization, but again there was widespread variation. The matter of how elderly patients weigh quantity and quality of life, then, is highly individualistic. Importantly, our study demonstrated that the majority of patients who could be interviewed understood and completed the task at hand. This finding, coupled with the findings that surrogates could not accurately gauge patients' health values and that health values could not be predicted from demographic or clinical variables, signifies that, when possible, health values should be ascertained directly from patients. We are unaware of other studies of time–trade-off utilities focusing solely on hospitalized very old patients. In the Beaver Dam Health Outcomes Study, mean time–trade-off scores for patients older than 75 years with any of a variety of chronic conditions were very similar (0.79-0.84, depending on age and sex) to those we report (mean, 0.81); wide individual-to-individual variation in utilities was also seen.34 Compared with previously studied younger patients, the mean time–trade-off utility of our elderly cohort was slightly lower than that of survivors of myocardial infarction (0.87)35 and slightly higher than that of patients with the acquired immunodeficiency syndrome (0.79)36 and of seriously ill patients (0.73).33 The findings that time–trade-off scores do not correlate well with health status,13,36-39 are higher than surrogates believe,33 and increase over time33 are by no means unique to the current study, however. There are several possible explanations for the abundance of high time–trade-off scores. First, patients may have been unwilling to trade much time for "excellent health" because they thought that their health was excellent at the time; yet, when asked directly, only 13.2% rated it as excellent, and only 17.3% of patients with utilities of 1.0 rated their health as excellent. Second, we used a short time horizon (1 year) in the time–trade-off scenarios. It is possible that, if presented with a longer life expectancy in current health, patients would be willing to trade away a larger proportion for a shorter but healthy life. Also, the sequence of the time–trade-off questions or noise in the instrument could have affected the results.40 Further study of such issues and of other health value measures8 in elderly patients, sick or healthy, is warranted. Findings from this study are relevant for decision making in both clinical practice and policy making.2,5,10,13 For decision making at the individual patient level, time–trade-off utilities can be used in a general sense to gauge the patient's "will to live" or, more precisely, as quality-of-life weights in calculating QALYs for use in decision analyses assessing the risks and benefits of various diagnostic or therapeutic options.2,11,41,42 For decisions involving the allocation of health care resources, QALYs form the denominator of cost-effectiveness (cost-utility) analyses for calculating the incremental costs per incremental QALY gained for various programs, which may in turn be compared with each other.2,14 But we should be cautious about promulgating health policy that neglects to incorporate the wishes of individual patients. A recent study of patients with angina by Nease and colleagues13 that also found wide interpatient variation in health values (for their angina) concluded that guidelines for managing ischemic heart disease should be based on individual patients' preferences rather than symptom severity. Similarly, with wide variation in the health values of the very old, there is a risk that guidelines developed for their care will not conform to their preferences. In summary, health values, as measured by the time trade-off, of very old hospitalized patients who can be interviewed (1) can be elicited in most cases; (2) indicate that patients are unwilling to trade much time in their current health state for excellent health; (3) correlate with few other measures; (4) are higher than surrogates believe; and (5) rise over 1 year among surviving patients who could be reinterviewed. Because health values vary from patient to patient, when possible, health values should be ascertained directly from the patient. References 1. Manton KG, Vaupel JW. Survival after the age of 80 in the United States, Sweden, France, England, and Japan. N Engl J Med.1995;333:1232-1235.Google Scholar 2. Tsevat J, Weeks JC, Guadagnoli E. et al. Using health-related quality of life information: clinical encounters, clinical trials, and health policy. J Gen Intern Med.1994;9:576-582.Google Scholar 3. Ware Jr JE, Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care.1996;34:220-233.Google Scholar 4. Bergner M. Quality of life, health status, and clinical research. Med Care.1989;27(suppl):S148-S156.Google Scholar 5. Patrick DL, Erickson P. Health Status and Health Policy: Quality of Life in Health Care Evaluation and Resource Allocation. New York, NY: Oxford University Press; 1992. 6. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged: the index of ADL: a standard measure of biological and psychosocial function. JAMA.1963;185:914-919.Google Scholar 7. Stuck AE, Aronow HU, Steiner A. et al. A trial of annual in-home comprehensive geriatric assessments for elderly people living in the community. N Engl J Med.1995;333:1184-1189.Google Scholar 8. Torrance GW, Feeny D. Utilities and quality-adjusted life years. Int J Technol Assess Health Care.1989;5:559-575.Google Scholar 9. Guyatt GH, Naylor CD, Juniper S, Heyland DK, Jaeschke R, Cook DJ.The Evidence-Based Medicine Working Group. Users' guide to the medical literature, XII: how to use articles about health-related quality of life. JAMA.1997;277:1232-1237.Google Scholar 10. Kassirer JP. Incorporating patients' preferences into medical decisions. N Engl J Med.1994;330:1895-1896.Google Scholar 11. Pauker SP, Pauker SG. The amniocentesis decision: ten years of decision analytic experience. Birth Defects.1987;23:151-169.Google Scholar 12. Lenderking WR, Gelber RD, Cotton DJ. et al. Evaluation of the quality of life associated with zidovudine treatment in asymptomatic human immunodeficiency virus infection. N Engl J Med.1994;330:738-743.Google Scholar 13. Nease Jr RF, Kneeland T, O'Connor GT. et al. Variation in patient utilities for outcomes of the management of chronic stable angina: implications for clinical practice guidelines. JAMA.1995;273:1185-1190.Google Scholar 14. Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-effectiveness in Health and Medicine. New York, NY: Oxford University Press; 1996. 15. Kolata G. Ethicists struggle to judge the ‘value' of life. New York Times.November 24, 1992:C3.Google Scholar 16. La Puma J, Lawlor EF. Quality-adjusted life-years: ethical implications for physicians and policymakers. JAMA.1990;263:2917-2921.Google Scholar 17. Hadorn DC. Setting health care priorities in Oregon: cost-effectiveness meets the rule of rescue. JAMA.1991;265:2218-2225.Google Scholar 18. Hamel MB, Phillips RS, Teno JM. et al. Seriously ill hospitalized adults: do we spend less on older patients? J Am Geriatr Soc.1996;44:1043-1048.Google Scholar 19. The Writing Group for the SUPPORT Investigators. A controlled trial to improve care for seriously ill hospitalized adults: the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT). JAMA.1995;274:1591-1598.Google Scholar 20. Murphy DJ, Knaus WA, Lynn J. Study population in SUPPORT: patients (as defined by disease categories and mortality projections), surrogates, and physicians. J Clin Epidemiol.1990;43(suppl):11S-28S.Google Scholar 21. Knaus WA, Wagner DP, Draper EA. et al. The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults. Chest.1991;100:1619-1636.Google Scholar 22. Appendices. J Clin Epidemiol.1990;43(suppl):109S-123S.Google Scholar 23. Torrance GW, Thomas WH, Sackett DL. A utility maximization model for evaluation of health care programs. Health Serv Res.1972;7:118-133.Google Scholar 24. Tsevat J, Dawson NV, Matchar DB. Assessing quality of life and preferences in the seriously ill using utility theory. J Clin Epidemiol.1990;43(suppl):73S-77S.Google Scholar 25. Landefeld CS, Phillips RS, Bergner M. Patient characteristics in SUPPORT: functional status. J Clin Epidemiol.1990;43(suppl):37S-39S.Google Scholar 26. Phillips RS, Goldman L, Bergner M. Patient characteristics in SUPPORT: activity status and cognitive function. J Clin Epidemiol.1990;43(suppl):33S-36S.Google Scholar 27. Hlatky MA, Boineau RE, Higginbotham MB. et al. A brief self-administered questionnaire to determine functional capacity (the Duke Activity Status Index). Am J Cardiol.1989;64:651-654.Google Scholar 28. Oye RK, Landefeld CS, Jayes RL. Outcomes in SUPPORT. J Clin Epidemiol.1990;43(suppl):83S-88S.Google Scholar 29. Shacham S. A shortened version of the Profile of Mood States. J Pers Assess.1983;47:305-306.Google Scholar 30. Coulton CJ. Decision making in SUPPORT: patient perceptions and preferences. J Clin Epidemiol.1990;43(suppl):51S-54S.Google Scholar 31. Covinsky KE, Goldman L, Cook EF. et al. The impact of serious illness on patients' families. JAMA.1994;272:1839-1844.Google Scholar 32. Wu AW, Damiano AM, Lynn J. et al. Predicting future functional status for seriously ill hospitalized adults: the SUPPORT prognostic model. Ann Intern Med.1995;122:342-350.Google Scholar 33. Tsevat J, Cook EF, Green ML. et al. Health values of the seriously ill. Ann Intern Med.1995;122:514-520.Google Scholar 34. Fryback DG, Dasbach EJ, Klein R. et al. The Beaver Dam Health Outcomes Study: initial catalog of health-state quality factors. Med Decis Making.1993;13:89-102.Google Scholar 35. Tsevat J, Goldman L, Lamas GA. et al. Functional status versus utilities in survivors of myocardial infarction. Med Care.1991;29:1153-1159.Google Scholar 36. Tsevat J, Solzan JG, Kuntz KM. et al. Health values of patients infected with human immunodeficiency virus: relationship to mental health and physical functioning. Med Care.1996;34:44-57.Google Scholar 37. Llewellyn-Thomas HA, Sutherland HJ, Tritchler DL. et al. Benign and malignant breast disease: the relationship between women's health status and health values. Med Decis Making.1991;11:180-188.Google Scholar 38. Revicki DA. Relationship between health utility and psychometric health status measures. Med Care.1992;30(suppl):MS274-MS282.Google Scholar 39. Bosch JL, Hunink MGM. The relationship between descriptive and valuational quality-of-life measures in patients with intermittent claudication. Med Decis Making.1996;16:217-225.Google Scholar 40. Froberg DG, Kane RL. Methodology for measuring health-state preferences, III: population and context effects. J Clin Epidemiol.1989;42:585-592.Google Scholar 41. Plante DA, Kassirer JP, Zarin DA, Pauker SG. Clinical decision consultation service. Am J Med.1986;80:1169-1176.Google Scholar 42. Weinstein MC, Fineberg HV. Clinical Decision Analysis. Philadelphia, Pa: WB Saunders Co; 1980. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA American Medical Association

Health Values of Hospitalized Patients 80 Years or Older

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American Medical Association
Copyright
Copyright © 1998 American Medical Association. All Rights Reserved.
ISSN
0098-7484
eISSN
1538-3598
DOI
10.1001/jama.279.5.371
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Abstract

Abstract Context.— Health values (utilities or preferences for health states) are often incorporated into clinical decisions and health care policy when issues of quality vs length of life arise, but little is known about health values of the very old. Objective.— To assess health values of older hospitalized patients, compare their values with those of their surrogate decision makers, investigate possible determinants of health values, and determine whether health values change over time. Design.— A prospective, longitudinal, multicenter cohort study. Setting.— Four academic medical centers. Participants.— Four hundred fourteen hospitalized patients aged 80 years or older and their surrogate decision makers who were interviewed and understood the task. Main Outcome Measures.— Time–trade-off utilities, reflecting preferences for current health relative to a shorter but healthy life. Results.— On average, patients equated living 1 year in their current state of health with living 9.7 months in excellent health (mean [SD] utility, 0.81 [0.28]). Although only 126 patients (30.7%) rated their current quality of life as excellent or very good, 284 (68.6%) were willing to give up at most 1 month of 12 in exchange for excellent health (utility ≥0.92). At the other extreme, 25 (6.0%) were willing to live 2 weeks or less in excellent health rather than 1 year in their current state of health (utility ≤0.04). Patients were willing to trade significantly less time for a healthy life than their surrogates assumed they would (mean difference, 0.05; P=.007); 61 surrogates (20.3%) underestimated the patient's time–trade-off score by 0.25 (3 months of 12) or more. Patients willing to trade less time for better health were more likely to want resuscitation and other measures to extend life. Time–trade-off score correlated only modestly with quality-of-life rating (r=0.28) and inversely with depression score (r=−0.27), but there were few other clinical or demographic predictors of health values. When patients who survived were asked the time–trade-off question again at 1 year, they were willing to trade less time for better health than at baseline (mean difference, 0.04; P=.04). Conclusion.— Very old hospitalized patients who could be interviewed were able, in most cases, to have their health values assessed using the time–trade-off technique. Most patients were unwilling to trade much time for excellent health, but preferences varied greatly. Because proxies and multivariable analyses cannot gauge health values of elderly hospitalized patients accurately, health values of the very old should be elicited directly from the patient. LIFE EXPECTANCY has increased dramatically even among elderly adults over the past few generations. Currently, an 80-year-old man can expect to live 7 years and an 80-year-old woman, 9.1 years.1 But because the elderly disproportionately have both acute and chronic illnesses, quality of life has assumed increasing importance. There are 2 approaches to assessing health-related quality of life. The health status approach describes functioning and well-being in 1 or more domains, such as physical functioning, mental health, social function, role function, pain, vitality, and health perception.2-5 Health status has been assessed in the elderly for over 30 years, most often with measures of ability to perform activities of daily living (such as bathing, dressing, eating, toileting, and transferring)6,7 and, increasingly, with brief, multidimensional surveys.3 The other approach to assessing health-related quality of life, known as utility, preference, or value assessment, ascertains the desirability of a state of health.2,8 One such health value measure is the time trade-off, which quantifies a person's preference for quality vs quantity of life. Health values are used in individual clinical decision making as well as in health policy formulation.2,5,8,9 In clinical decision making, they can provide a general sense of how patients feel about quality vs quantity of life, and they can be used in clinical decision analyses to optimize treatment for an individual.2,10 Examples of using health values in clinical practice include helping couples decide whether to have amniocentesis11 and deciding when a patient with human immunodeficiency virus infection should start zidovudine therapy.12 In addition, utility assessment has demonstrated that clinical guidelines for management of ischemic heart disease may not conform to preferences of affected patients.13 Utilities are most commonly used as quality-of-life adjustments to life expectancy in the calculation of quality-adjusted life years (QALYs), which, in turn, are used in clinical decision analyses and, in conjunction with cost estimates, in cost-effectiveness (cost-utility) analyses. While many analysts8,14 advocate using QALYs for such purposes, not all agree.15-17 La Puma and Lawlor16 have raised ethical concerns over the use of QALYs in health policy formulation, one being that the "old-old" cannot or will not provide health values and are thus disenfranchised. Although much work has been done in geriatric health status assessment, little is known about the health values of the very old. Health value assessment in the elderly is particularly important because elderly patients receive fewer invasive procedures and less resource-intensive hospital care than younger patients, even when differences in severity of illness and preferences for life-extending care are taken into account.18 Whether this different approach is with the assent of the patient or caregivers is not clear. Given the importance of health values, if in fact health values cannot be ascertained from frail elderly patients, it would be important to know whether their health care proxies could provide accurate estimates or whether health values can be predicted on the basis of health status or other variables. Another important issue is whether health values change over time. Methods Study Participants The Hospitalized Elderly Longitudinal Project (HELP) was a prospective study of the prognoses, preferences, and decision making of hospitalized patients aged 80 years or older, their surrogate decision makers, and their physicians. The HELP study took place from January 1993 to November 1994 at 4 academic medical centers (HELP was related closely to a concurrent study, the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments project19). To qualify, patients had to be at least 80 years old and hospitalized. Patients were excluded if they did not speak English; were foreign nationals admitted specifically for a medical procedure; had the acquired immunodeficiency syndrome (AIDS); had sustained multiple trauma; were admitted for hospice care, to the psychiatry service, or for an elective operation; were admitted to a hospital ward after transfer from another hospital; died or were discharged within 72 hours of hospitalization; or were scheduled for discharge within 72 hours of admission. For most patients, a surrogate was identified.20 Interviews and Instruments For each patient, severity of acute illness was ascertained using the acute physiology score (APS) component of the Acute Physiology and Chronic Health Evaluation III prognostic scoring system.21 Patients who were not intubated, were able to communicate, and were able to pass a cognitive screening test22 were eligible to be interviewed. Trained interviewers interviewed patients and surrogates approximately 4 days and 12 months after study entry. The health value measure used was the time–trade-off score.22-24 Patients and their surrogates were independently asked in a systematic fashion whether the patient would prefer living only 1 year in the patient's current state of health or less time in excellent health, until an indifference point was ascertained. The time–trade-off score was then calculated as the fraction of a year in excellent health that was equivalent to a year of current health. For example, if the patient were indifferent about choosing between living 12 months in their current state of health or living only 9 months in excellent health, their time–trade-off utility would equal 9 divided by 12 or 0.75. Possible scores ranged from 0.04 (equivalent to indifference between 2 weeks in excellent health and 1 year in current health) to 1.0. After the respondent completed the time–trade-off question, the interviewer judged whether the respondent understood the task; respondents who did not understand were excluded. Health status instruments included (1) a global quality-of-life question, in which the respondent was asked to rate the patient's quality of life as excellent, very good, good, fair, or poor22; (2) a revised measure of dependence in activities of daily living over the previous 2 weeks6,22,25; (3) a revised version of the Duke Activity Status Index,22,26,27 which assesses ability to perform strenuous activities; (4) a shortened version of the Profile of Mood States,28,29 which assesses anxiety and depression; and (5) a measure of frequency and severity of pain.19 In addition, we asked questions concerning preferences regarding cardiopulmonary resuscitation (CPR); willingness to tolerate each of 6 potentially lifelong adverse outcomes—pain, mechanical ventilation, tube feeding, coma, confusion, and living in a nursing home; preferences for care focused on extending life as much as possible, even if it means having more pain and discomfort, vs care focused on relieving pain and discomfort as much as possible, even if it means not living as long; and perceived prognosis for surviving for 2 and 12 months and for functioning independently in 2 and 12 months.22,30 We assessed the effect of the patient's illness on family members in terms of assistance needed and savings depleted.28,31 For the Duke Activity Status Index, missing items were imputed from surrogate responses for 5 patients.32 Statistical Analysis Means are expressed as mean (SD), and medians are given with 25th and 75th percentiles. Continuous variables were compared using the Wilcoxon rank sum test. Within-patient changes over time were assessed with the Wilcoxon signed rank test. Concordance between patients' and their surrogates' time–trade-off scores was assessed using the Wilcoxon signed rank test. Univariate associations between the time–trade-off and the health status measures, measures of perceived prognosis, willingness to tolerate adverse outcomes, and demographic variables were assessed using Spearman correlation coefficients. Because time–trade-off scores were not normally distributed, we used multivariable ordinal logistic regression to identify significant predictors of time–trade-off scores at day 4 and month 12. Variables significantly associated with time–trade-off scores in univariate analyses (when P<.10) and variables found to have been related to time–trade-off scores in the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments study33 were entered into the model and retained through backward elimination, if P was less than .05. A summary statistic for the ordinal logistic regression models is Somers D, which measures the models' ability to predict time–trade-off utilities. For a binary outcome (high vs low utilities) the statistic is a linear function of the area under the receiver operating characteristic (ROC) curve (D=2×[area under ROC−0.5]). Within-patient changes in time–trade-off scores were compared with changes in other measures using the Kruskal-Wallis test and Spearman correlation coefficients. Analyses were performed using SAS statistical software (SAS Institute, Inc, Cary, NC). Results Patients and Interviews The HELP study enrolled 1266 patients. Many patients could not be interviewed: 25 patients (2.0%) were comatose or intubated or both; 272 (21.5%) were unable to communicate for other reasons; 204 (16.1%) failed the cognitive screening test; 29 (2.3%) died or were discharged within 72 hours; and 2 (0.2%) were ineligible for other reasons. Of the 734 eligible for interview, 622 (84.7%) participated. Of those 622, 62 (10.0%) terminated the interview before the time–trade-off question was asked. Of the remaining 560 patients, 43 (7.7%) refused to answer the time–trade-off question, answered "don't know," or had missing or incomplete answers. Of the 517 patients completing the time–trade-off question at their initial interview, interviewers provided judgments regarding the patient's understanding in 475 cases (91.9%); 414 (87.2%) were judged to have understood the task. Those 414 patients formed our main analytic sample (Table 1). In-hospital and 12-month mortality rates and APS scores were significantly higher (higher APS scores indicate more severe illness) for the 852 excluded patients than for the 414 who completed the time–trade-off question. Among the 414 patients, 319 also had time–trade-off questions completed by their surrogate at day 4. For the 319 surrogates' interviews, interviewers' judgments regarding understanding of the time–trade-off question were available for 311 (97.5%), and the interviewer believed that 300 (96.5%) of those surrogates understood the task. Compared with patients who had a matching surrogate interview, patients without a surrogate interview were similar in age, sex, and level of education attained and had similar APS and Duke Activity Status Index scores but slightly more dependencies in activities of daily living (1.08 vs 0.84 dependencies; P=.02). Of the patients alive at month 12, 176 (52%) completed and understood a follow-up time–trade-off assessment. Health Values The mean (SD) time–trade-off score for the 414 patients at their initial interview was 0.81 (0.28) (median [25th, 75th percentile], 0.92 [0.83, 1.0]). This indicates that, on average, patients equated living 1 year in their current state of health with living 9.7 months (0.81×12 months) in excellent health. But time–trade-off scores varied widely from patient to patient (Figure 1): 169 (40.8%) had utilities of 1.0, meaning that they were unwilling to give up any time in exchange for a shorter life in excellent health, and another 115 (27.8%) had utilities of 0.92, meaning that they were willing to give up only 1 month of 12 ([1-0.92]×12 months) in exchange for excellent health. Thus, more than two thirds of the patients (284 [68.6%]) were willing to forgo at most 1 month of 12. At the other extreme, 25 (6.0%) had utilities of 0.04, indicating that they preferred living 2 weeks or less in excellent health to living 1 year in their current state of health. Patients Compared With Their Surrogates Time–trade-off scores given by surrogates, who were asked to answer as they thought the patient would, also varied widely. For the 300 patient-surrogate pairs, the mean (SD) patient utility was 0.80 (0.30) (median [25th, 75th], 0.92 [0.83, 1.0]) and was higher than the mean surrogate utility by 0.05 (0.38) (median difference, 0 [0, 0.17]; P=.007); 61 (20.3%) of surrogates underestimated the patient's time–trade-off score by 0.25 (3 months of 12) or more. The correlation between patients' and their paired surrogates' health values was modest (r=0.36). Relationship of Heath Values to Other Measures Time–trade-off utilities were related to patients' preferences for CPR (Table 2). Patients who desired CPR had a mean (SD) time–trade-off score at day 4 of 0.86 (0.23) (median [25th, 75th], 0.92 [0.83, 1.0]);whereas, patients who preferred not to undergo CPR had slightly lower mean scores of 0.75 (0.34) (median [25th, 75th], 0.92 [0.63, 1.0]; P<.001). Higher time–trade-off scores were also related to patients' preferences for care that focused on extending life: patients who preferred care that focused on extending life had higher time–trade-off scores (ie, they were not willing to trade away as much time) than patients who preferred care that focused on relieving pain and discomfort. Health values correlated only modestly (r=0.28) with overall quality of life (Figure 2). A total of 126 patients (30.7%) rated their current quality of life as excellent or very good. For the patients with utilities of 1.0, indicating an unwillingness to trade any time in current health for a shorter but healthier life, only 29 (17.3%) considered their quality of life to be excellent as is; 39 (23.2%) rated it as very good, 61 (36.3%) as good, 32 (19.0%) as fair, and 7 (4.2%) as poor. For the patients with utilities of 0.04 or less, 2 (8.0%) rated their quality of life as excellent, 3 (12.0%) as very good, 6 (24.0%) as good, 3 (12.0%) as fair, and 11 (44.0%) as poor. Health values correlated modestly (and inversely) with level of depression (r=−0.27); but they correlated poorly if at all with other health status measures, willingness to tolerate the 6 adverse outcomes, and perceived prognosis for survival and independent functioning (Table 3). Among the demographic variables, time–trade-off scores were not related to age, sex, race, or level of education. In a multivariable analysis, on average, patients who preferred treatment that extended life were more likely to report high time–trade-off scores (odds ratio [OR], 2.8; 95% confidence interval [CI], 1.8-4.4); in addition, health values were positively related to quality of life (OR, 1.2 for each level of better quality of life; 95% CI, 1.0-1.5) but were inversely related to level of depression (OR, 0.6 for each level of more severely depressed mood; 95% CI, 0.4-0.7; Somers D=0.343). Health Values 1 Year Later For the 176 patients who completed the time–trade-off questions at both day 4 and month 12, scores increased over the year by an average of 0.04, from 0.84 to 0.88 (SD of change, 0.30; median, 0; 25th percentile, −0.08; 75th percentile, 0.08; P=.04). This means that, a year after hospitalization, on average, the time patients would give up in their current state of health to be able to be in excellent health had declined by 2 weeks. As at the initial interview, time–trade-off scores were higher among those who desired CPR and among those who preferred life prolongation over relief of pain and discomfort (Table 2); utilities were not significantly related to the degree of impact of the illness on the family. At the 12-month interview, 60 patients (34.5%) rated their quality of life as excellent or very good, but time–trade-off scores correlated only moderately (r=0.24) with quality of life. In a multivariable analysis, on average, patients who preferred treatment that extended life were more likely to report high time–trade-off scores (OR, 4.6; 95% CI, 2.1-10.0). Time–trade-off scores were positively related to quality of life (OR, 1.6 for each level of better quality of life; 95% CI, 1.2-2.1; Somers D=0.378). Change in time–trade-off scores was weakly correlated with changes in quality of life, physiologic reserve, and mental health (r=0.13-0.21). Comment Would an elderly person who is frail and ill prefer living as long as possible over a shorter but healthy life? This study ascertained the health values vis-à-vis quantity vs quality of life of a cohort of 414 hospitalized patients aged 80 to 98 years. On average, patients indicated a fairly strong "will to live": 40.8% were unwilling to exchange any time in their current state of health for a shorter life in excellent health, and 27.8% were willing to give up at most 1 month of 12 in return for excellent health. The variance was large, however, with 6.0% of patients willing to live 2 weeks or less in excellent health rather than 1 year in their current state of health. One year later, surviving patients who could be interviewed had higher utilities than during the index hospitalization, but again there was widespread variation. The matter of how elderly patients weigh quantity and quality of life, then, is highly individualistic. Importantly, our study demonstrated that the majority of patients who could be interviewed understood and completed the task at hand. This finding, coupled with the findings that surrogates could not accurately gauge patients' health values and that health values could not be predicted from demographic or clinical variables, signifies that, when possible, health values should be ascertained directly from patients. We are unaware of other studies of time–trade-off utilities focusing solely on hospitalized very old patients. In the Beaver Dam Health Outcomes Study, mean time–trade-off scores for patients older than 75 years with any of a variety of chronic conditions were very similar (0.79-0.84, depending on age and sex) to those we report (mean, 0.81); wide individual-to-individual variation in utilities was also seen.34 Compared with previously studied younger patients, the mean time–trade-off utility of our elderly cohort was slightly lower than that of survivors of myocardial infarction (0.87)35 and slightly higher than that of patients with the acquired immunodeficiency syndrome (0.79)36 and of seriously ill patients (0.73).33 The findings that time–trade-off scores do not correlate well with health status,13,36-39 are higher than surrogates believe,33 and increase over time33 are by no means unique to the current study, however. There are several possible explanations for the abundance of high time–trade-off scores. First, patients may have been unwilling to trade much time for "excellent health" because they thought that their health was excellent at the time; yet, when asked directly, only 13.2% rated it as excellent, and only 17.3% of patients with utilities of 1.0 rated their health as excellent. Second, we used a short time horizon (1 year) in the time–trade-off scenarios. It is possible that, if presented with a longer life expectancy in current health, patients would be willing to trade away a larger proportion for a shorter but healthy life. Also, the sequence of the time–trade-off questions or noise in the instrument could have affected the results.40 Further study of such issues and of other health value measures8 in elderly patients, sick or healthy, is warranted. Findings from this study are relevant for decision making in both clinical practice and policy making.2,5,10,13 For decision making at the individual patient level, time–trade-off utilities can be used in a general sense to gauge the patient's "will to live" or, more precisely, as quality-of-life weights in calculating QALYs for use in decision analyses assessing the risks and benefits of various diagnostic or therapeutic options.2,11,41,42 For decisions involving the allocation of health care resources, QALYs form the denominator of cost-effectiveness (cost-utility) analyses for calculating the incremental costs per incremental QALY gained for various programs, which may in turn be compared with each other.2,14 But we should be cautious about promulgating health policy that neglects to incorporate the wishes of individual patients. A recent study of patients with angina by Nease and colleagues13 that also found wide interpatient variation in health values (for their angina) concluded that guidelines for managing ischemic heart disease should be based on individual patients' preferences rather than symptom severity. Similarly, with wide variation in the health values of the very old, there is a risk that guidelines developed for their care will not conform to their preferences. In summary, health values, as measured by the time trade-off, of very old hospitalized patients who can be interviewed (1) can be elicited in most cases; (2) indicate that patients are unwilling to trade much time in their current health state for excellent health; (3) correlate with few other measures; (4) are higher than surrogates believe; and (5) rise over 1 year among surviving patients who could be reinterviewed. 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Journal

JAMA – American Medical Association

Published: Feb 4, 1998

Keywords: academic medical centers,aged adult, 80 and over,demography,depressive disorders,health policy,longevity,proxy,resuscitation,quality of life,older adult,outcome measures,surrogate decision maker

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