A Decade of Changes in Preferences for Life-Sustaining Treatments Among Terminally Ill Patients With Cancer

Background: Changes over time in preferences for life-sustaining treatments (LSTs) at end of life (EOL) in different patient cohorts are not well established, nor is the concept that LST preferences represent more than 2 groups (uniformly prefer/not prefer). Purpose: The purpose of this study was to explore heterogeneity and changes in patterns of LST preferences among 2 independent cohorts of terminally ill patients with cancer recruited a decade apart. Methods: Preferences for cardiopulmonary resuscitation, intensive care unit care, cardiac massage, intubation with mechanical ventilation, intravenous nutritional support, nasogastric tube feeding, and dialysis were surveyed among 2,187 and 2,166 patients in 2003–2004 and 2011–2012, respectively. Patterns and changes in LST preferences were examined by multigroup latent class analysis. Results: We identified 7 preference classes: uniformly preferring, uniformly rejecting, uniformly uncertain, favoring nutritional support but rejecting other treatments, favoring nutritional support but uncertain about other treatments, favoring intravenous nutritional support with mixed rejection of or uncertainty about other treatments, and preferring LSTs except intubation with mechanical ventilation. Probability of class membership decreased significantly over time for the uniformly preferring class (15.26%–8.71%); remained largely unchanged for the classes of uniformly rejecting (41.71%–40.54%) and uniformly uncertain (9.10%–10.47%), and favoring nutritional support but rejecting (20.68%–21.91%) or uncertain about (7.02%–5.47%) other treatments, and increased significantly for the other 2 classes. The LST preferences of Taiwanese terminally ill patients with cancer are not a homogeneous construct and shifted toward less-aggressive treatments over the past decade. Conclusions: Identifying LST preference patterns and tailoring interventions to the unique needs of patients in each LST preference class may lead to the provision of less-aggressive EOL care.

Background

Eliciting and honoring patients' treatment preferences are recognized as essential to high-quality end-of-life (EOL) care,1 whereas ignoring patients' preferences is considered a medical error.2 However, physicians tend to discuss prognosis and goals/preferences for EOL care with patients with advanced cancer late in their illness trajectory or not at all.3 A shift in this paradigm has been advocated to earlier, thorough assessments of patients' EOL care goals/preferences and tailoring care to individual needs throughout the dying process, thus personalizing EOL care.3 Furthermore, trends in EOL cancer care over recent decades have become increasingly aggressive and costly in the United States,4 Canada,5 and Taiwan,6 threatening national economies and the long-term sustainability of health care systems.7,8 One potential target for controlling such skyrocketing costs is patients' unrealistic expectations and preferences for life-sustaining treatments (LSTs).9,10 Understanding and addressing these expectations and preferences will facilitate patients' realistic appraisals of the risks and benefits of LSTs at EOL.

EOL care preferences of individual seriously/terminally ill patients have been extensively studied,1116 and the stability of these preferences over time is well established.17 However, few studies have explored changes in attitudes toward EOL care among different public or patient cohorts recruited at different times, with the exception of reports from the United States,18 Britain,19 Italy,20 the Netherlands,21 and Singapore,22 on the impact of social and political changes. Three studies1820 focused on changes in public attitudes toward individual rights for life-and-death decisions,18 prognostic disclosure and pain relief,20 and comfort in discussing death and medical needs at EOL.19 Two studies explored changes in awareness of impending death21 or prognostic disclosure among patients with advanced cancer.22 Investigations could not be found on changes over time in EOL care preferences among different public or patient cohorts.

Furthermore, although it is well-known that patients' preference weights for treatment benefits versus risks are heterogenous,23 the concept that EOL care preferences can represent more than 2 groups (uniformly prefer/not prefer) is not well established, except as reported by 2 publications.16,24 The question remains regarding whether LST preferences at EOL represent a homogeneous phenomenon dichotomized into 2 groups (preferring/not preferring all treatments examined) or represent more than 2 preference groups. Accurate and parsimonious classification of patients' LST preferences can inform the development of clinical interventions and treatments tailored to the needs of patients in distinct classes. Therefore, the purposes of this study were to explore heterogeneity and changes over a decade in patterns of LST preferences for 2 independent cohorts of Taiwanese terminally ill patients with cancer. These aims reflect 2 hypotheses: (1) LST preferences at EOL are not a homogeneous construct, and (2) these preferences change over time in response to social and political changes.

Methods

To examine changes over time in patterns of LST preferences among Taiwanese terminally ill patients with cancer, we analyzed data from 2 nationwide studies conducted a decade apart.25,26 Both studies used the same methods for subject recruitment, instruments, and data collection, with the second study following up the first.

Study Design and Sample

Patients who were recruited by convenience from 24 and 23 hospitals throughout Taiwan that provided most cancer care were cross-sectionally surveyed in 2003–200425 and 2011–2012,26 respectively. Adult patients with cancer were referred by their oncologist, who declared them terminally ill when their disease continued to progress and was unresponsive to current curative treatments. Detailed procedures for referring, recruiting, and interviewing eligible patients can be found elsewhere.25,26 The study was approved by the study hospitals' Institutional Review Boards. All participants signed written informed consent.

Measure

Preferences for LSTs were evaluated using an adapted interview protocol (see supplemental eAppendix 1, available with this article at JNCCN.org).14,27 Interview questions elicited preferences for cardiopulmonary resuscitation (CPR) when life was in danger, intensive care unit (ICU) care, cardiac massage, intubation with mechanical ventilation, intravenous nutritional support, nasogastric tube feeding, and dialysis. Notably, cardiac massage and intubation with mechanical ventilation could be provided independently in Taiwan for clinical or cultural considerations.28 For each LST, patients were asked whether they (1) wanted the treatment, (2) did not want the treatment, or (3) were undecided.

Statistical Analyses

Characteristics and LST preferences of the 2 study cohorts were compared by chi-square tests. Changes in patterns of LST preferences were studied using multiple-group latent class analysis (LCA) that incorporated 2 waves of data from 2003–2004 and 2011–2012. In this approach, LST preferences were treated as a pattern or set of treatments (“latent classes”) rather than individual treatments. LCA divides individuals into mutually exclusive probabilistic classes based on shared characteristics that discriminate among members of each class.29 Multiple-group LCA, an extension of LCA, simultaneously estimates class probabilities (the number and relative size of classes) and conditional probabilities across patient cohorts in a single model. Conditional probability reflects the probability that an individual in a given class will want, not want, or be undecided about each LST.30

Models were estimated sequentially with PROC (procedure; SAS code) LCA in the SAS program.31 First, we conducted separate exploratory LCAs in each cohort to account for possible variations in the most favorable number of classes across cohorts. In each step, the number of specified classes was increased monotonically. Model fit to the data was evaluated by the Akaike information criterion (AIC),32 Bayesian information criterion (BIC),33 sample-size-adjusted BIC (adjusted BIC),34 and consistent AIC (CAIC).35 Lower AIC, BIC, adjusted BIC, and CAIC scores indicate a better model fit. These criteria and the substantive clinical meaningfulness of the latent class results were factored in deciding the optimal number of classes.

Next, we conducted an omnibus nested-model comparison30 to examine the equivalence of class profiles across different patient cohorts.36 When results indicated that EOL care preference profiles of some classes differed significantly across cohorts, we identified which classes differed. Once the final model was estimated, individuals were assigned to classes based on their most probable class membership. Changes in class probabilities (relative class sizes) across cohorts were also examined. Given our large nationwide samples, significance of all analyses was set at a P value of .005 or less.

Results

Sample Characteristics

Of 3,125 and 2,764 eligible patients with cancer in 2003–200425 and 2011–2012,26 respectively, 2,187 and 2,166 were recruited (69.98% and 78.37% participation rate), respectively. The primary reasons for patients declining to participate were being too weak (n=776 [82.73%] and n=445 [74.42%]) or uninterested (n=159 [16.95%] and n=146 [24.42%]), respectively. Characteristics of patients who did and did not participate could not be compared because of restricted access to information about those who refused to participate in each study.

Detailed characteristics of the 2 study cohorts can be found in Table 1. The 2 cohorts did not differ significantly in characteristics, except for diagnosis. More patients with lung cancer and hematologic malignancies were recruited in 2003–2004 than in 2011–2012.

Preferences for LSTS

More than half the participants (52.92%–68.11% and 51.90%–76.54% for the 2003–2004 and 2011–2012 cohorts, respectively) rejected the LSTs examined in this study, except intravenous nutritional support (Table 2). Intravenous nutritional support was preferred by 61.35% to 62.03% of participants, whereas other LSTs were preferred by 16.09% to 31.53% and 9.89% to 28.12% of the 2003–2004 and 2011–2012 cohorts, respectively. Among the 2 cohorts, 10.06% to 18.82% and 10.95% to 25.82% of participants were undecided, respectively. The 2 study cohorts differed significantly in all LST preferences except intravenous nutritional support.

Multi-Group LCA of Patterns of LST Preferences

Evaluation of the BIC, CAIC, and clinical meaningfulness supported the selection of a 7-class solution as optimal and parsimonious for both groups (model fit indexes for the 1- through 9-class solutions of LST preferences by separate latent class modeling for each study cohort are found in supplemental eAppendix 2). These 7 classes were labeled uniformly preferring, uniformly rejecting, uniformly uncertain (classes 1–3), favoring nutritional support but rejecting (class 4) or uncertain about (class 5) other treatments, favoring intravenous nutritional support with mixed rejection of or uncertainty about other treatments (class 6), and preferring LSTs except intubation with mechanical ventilation (class 7).

Changes in Patterns of LST Preferences Between the 2003–2004 and 2011–2012 Cohorts

For conditional probabilities and sizes of the 7 classes of LST preferences estimated simultaneously by study cohort, see Figures 1 and 2 and Table 3. Patients comprising the 3 “uniform-response” classes (classes 1–3) uniformly preferred (conditional probabilities: 95.70%–99.98%), rejected (conditional probabilities: 68.01%–99.86%), or were uncertain about (conditional probabilities: 84.63%–99.58%) all LST items. Omnibus nested-model comparisons indicated that the conditional probabilities for classes 1 through 3 were homogenous across cohorts (Figures 1 and 2 and supplemental eAppendix 2, last 4–7

Table 1

Comparison of Characteristics for the 2003–2004 and 2011–2012 Cohorts

Table 1
rows). Furthermore, class probability significantly decreased from 15.26% to 8.71% for the uniformly preferring class (class 1; Table 3), but remained largely unchanged for the uniformly rejecting (class 2; 41.71%–40.54%) and uniformly uncertain (class 3; 9.10%–10.47%) classes between the 2003–2004 and 2011–2012 cohorts, respectively (supplemental eAppendix 2, last 3 rows).

Across the 2 cohorts, the 2 classes favoring nutritional support but rejecting (class 4) or uncertain about (class 5) other treatments represented patients who preferred nasogastric tube feeding and intravenous nutritional support but did not want or were undecided about wanting CPR, ICU care, cardiac massage, intubation with mechanical ventilation, and dialysis at EOL (Figures 1 and 2). Participants in the class favoring intravenous nutritional support with mixed rejection of or uncertainty about other treatments (class 6) generally declined CPR, ICU care, cardiac massage, and intubation with mechanical ventilation but were unsure about wanting nasogastric tube feeding and dialysis. In both cohorts, participants in the class preferring LSTs except intubation with mechanical ventilation (class 7) consistently declined intubation with mechanical ventilation.

Table 2

Comparison of Preferences for Life-Sustaining Treatments Among the 2003–2004 and 2011–2012 Cohorts

Table 2

Omnibus nested-model comparisons indicated that the conditional probabilities for classes 4 through 7 partially varied across cohorts (Figures 1 and 2; supplemental eAppendix 2, last 4–7 rows). Furthermore, although the class probability of favoring nutritional support but rejecting (class 4) or uncertain about (class 5) other treatments remained largely unchanged (20.68%–21.91% and 7.02%–5.47%, respectively; Table 3), the class probabilities of being in classes 6 and 7 changed significantly between cohorts (supplemental eAppendix 2, last 3 rows). These 2 classes favoring intravenous nutritional support with mixed rejection of or uncertainty about other treatments and favoring LSTs except intubation with mechanical ventilation increased significantly across cohorts (from 4.31% to 7.64% and from 1.93% to 5.28%, respectively).

Discussion

We identified 7 distinct classes that were partially homogeneous in conditional probabilities (probability of wanting, not wanting, or being undecided about a LST, given class membership), with varying class probabilities (relative class size) across the 2 cohorts. The largest proportion of Taiwanese terminally ill

Figure 1
Figure 1

Conditional probabilities for the 7 classes of preferences for life-sustaining treatments, 2003–2004 cohort (n=2,187).

Abbreviations: CM, cardiac massage; CPR, cardiopulmonary resuscitation; ICU, intensive care unit; ITMV, intubation with mechanical ventilation; IVNS, intravenous nutritional support; NGTF, nasogastric tube feeding.

Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 13, 12; 10.6004/jnccn.2015.0179

Figure 2
Figure 2

Conditional probabilities for the 7 classes of preferences for life-sustaining treatments, 2011–2012 cohort (n=2,166).

Abbreviations: CM, cardiac massage; CPR, cardiopulmonary resuscitation; ICU, intensive care unit; ITMV, intubation with mechanical ventilation; IVNS, intravenous nutritional support; NGTF, nasogastric tube feeding.

Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 13, 12; 10.6004/jnccn.2015.0179

patients with cancer consistently declined all LSTs (40.54%–41.71%), possibly reflecting the influence of Taiwan's government37 and nongovernmental organizations,38 which have actively promoted the hospice movement in Taiwan since 2004 (for more details, see later discussion). The goal of these efforts was to improve the quality of EOL care, with an emphasis on avoiding LSTs that do not benefit patients at EOL. Another major category of LST preferences in our study was definitely favoring nutritional support only at EOL, with 32.01% to 35.02% of participants in classes 4 to 6. Taiwanese people believe that a person who dies hungry will become a “starving soul” or “hungry ghost/spirit” in hell.39 Furthermore, providing food, hydration, and nutrition is recognized by Asian populations as necessary not only to stave off physical deterioration but also for humanistic EOL care by not abandoning the care of dying patients and fulfilling filial piety.39,40 Therefore, nutritional support is highly preferred at EOL in Taiwan.

Approximately one-tenth of participants in both cohorts were undecided about all LST preferences at EOL (class 3). Decisional uncertainty may reflect patients' lack of knowledge and understanding about the clinical situation or complex medical decisions9,10 and inability to project their preferences into the future.15 Therefore, decisional uncertainty is an important indicator of the need to clarify patients' understanding, expectations, and values before any advance care planning can be appropriately implemented.

Our results confirm our first hypothesis that the preferences for multifaceted LSTs are not a homogeneous construct in terminally ill patients with cancer. The seven LSTs we examined constitute LSTs with different degrees of aggressiveness and intrusiveness. LST preferences reflect patients' values and understanding of the risks and benefits associated with each treatment choice, consistent with evidence that such values and understanding are heterogenous.23 Therefore, LST preferences are not uniform across all LSTs. Only when health care professionals accurately recognize each patient's unique pattern of LST preferences can they provide individualized EOL care consistent with those preferences.1

Table 3

Sizes of the 7 Classes of Preferences for Life-Sustaining Treatments Across 2 Study Cohorts

Table 3

The EOL care preferences of Taiwanese terminally ill patients with cancer shifted toward fewer aggressive LSTs. First and foremost, the class probability of the uniformly preferring class (class 1) decreased significantly (from 15.26% to 8.71%). Second, the probability of being in class 6 (favoring intravenous nutritional support with mixed rejection of or uncertainty about other LSTs) almost doubled, from 4.31% to 7.64%, with a shift from uncertainty to definitely rejecting receiving ICU care (Figures 1 and 2). Third, although the probability of being in class 7 (favoring LSTs except intubation with mechanical ventilation) increased significantly, from 1.93% to 5.28%, participants in the 2011–2012 cohort were more likely to reject CPR in addition to intubation with mechanical ventilation—the 2 most aggressive LSTs. This shift toward fewer preferences for aggressive LSTs over time may reflect recent efforts of Taiwan's government to promote hospice and palliative care, supporting our second hypothesis, and echoes the successes of social and political innovations from the United States,18 Britain,19 Italy,20 and Singapore.22 Since 2004, Taiwan's government has launched multiple nationwide projects to facilitate dissemination of hospice philosophy and palliative care services. As a result, the number of hospice programs increased substantially from 2004 to 2012: 49 to 77 for hospice home care, 27 to 50 for inpatient hospice units, and 8 to 69 for hospital-based palliative care teams.37

However, we cannot eliminate the possibility that the observed shift in LST preferences toward less aggressiveness may have been attributable to the impact of fewer patients diagnosed with hematologic malignancies in the 2011–2012 cohort, despite a report that cancer diagnosis is not associated with EOL care preferences.14 Patients with hematologic malignancies tend to have a treatment goal of curability. Furthermore, the potentially reversible nature of sporadic events, such as infection, may lead these patients and their physicians to prefer to do everything possible to promote patient survival. These preferences may decrease the likelihood of letting nature take its course, and shift the goal of care from cure with LST support to palliating symptom distress only. Indeed, patients with hematologic malignancies are more likely to receive chemotherapy,41,42 CPR,43 and ICU care42,44 at EOL. Future studies should directly explore the impact of diagnosis on changes in patterns of EOL care preferences.

The strength of this study lies in its use of nationwide samples to characterize the heterogeneity and changes in the patterns of LST preferences among terminally ill patients with cancer at EOL from 2 identical studies conducted over a recent decade. However, representativeness of the target population and generalizability of our findings may have been compromised by convenience sampling. Nonetheless, the validity of our findings is strengthened by the similarity of our participants' gender, age, and disease categories to those of patients with cancer who died in Taiwan in 2012,45 except that patients with hepatic-pancreatic cancer were underrepresented. The restricted access to information for terminally ill patients with cancer who refused to participate in our study limited our ability to examine their characteristics and to detect any nonrespondent biases. One could argue that patients who prefer aggressive LSTs might have chosen not to participate in our study given the increasing societal and medical emphasis on palliative care and avoiding LSTs at EOL, thereby biasing our results. Trends in LST preferences warrant continued monitoring. Our findings need to be replicated for terminally ill patients with cancer with different cultural and societal characteristics in other countries. Our investigation into the patterns of EOL care preferences was limited to the 7 LST preferences assessed in the study. Including other EOL care items (eg, vasopressors, hospice care) could alter the patterns found. Our cross-sectional design might not have captured fluctuations in patients' preferences as their death approached nor the extent of agreement between preferred and actual LSTs received before death.

Conclusions

Our study identified 7 distinct classes of LST preferences among terminally ill Taiwanese patients with cancer and found a shift in patients' EOL care preferences toward less aggressiveness, in contrast to the increasing trend in aggressiveness of EOL care actually received by these patients.6 Such a paradox between preferred and actual LSTs likely reflects the low proportion of physician-patient EOL care discussions in Taiwan.46 Without appropriate physician-patient discussions, physicians often misunderstand patients' EOL care preferences47 and provide more aggressive EOL care than patients prefer.48 Our findings can be used by clinicians and policymakers to target resources toward timely initiation of EOL care discussions and the development of individualized treatment plans for patients facing highly emotion-laden EOL care decisions. For example, patients in the classes of uniformly preferring, uniformly uncertain, favoring nutritional support but uncertain about other LSTs, and preferring LSTs except intubation with mechanical ventilation may require more nuanced interventions, because they are at high risk of receiving potentially futile treatments before death, given current clinical practice norms. These patients may need intensive and personalized education about the efficacy of LSTs in prolonging life, which may not be needed by patients in the classes of uniformly rejecting LSTs and favoring nutritional support but rejecting other LSTs. With appropriate interventions tailored to the unique needs of terminally ill patients with cancer in each class identified in this study, optimal EOL care may be provided to enable a death that agrees with patients' wishes1 and to avoid potentially futile aggressive EOL care.

See JNCCN.org for supplemental online content.

The authors have disclosed that they have no financial interests, arrangements, affiliations, or commercial interests with the manufacturers of any products discussed in this article or their competitors. This work was funded by the Bureau of Health Promotion, Department of Health, Taiwan, (DOH 9911020C), with partial support from National Health Research Institute (NHRI-EX104-10208PI).

References

  • 1.

    Field MJ, Cassell C, eds. Approaching Death: Improving Care at the End of Life. Washington, DC: National Academy Press; 1997:4.

  • 2.

    Allison TA, Sudore RL. Disregard of patients' preferences is a medical error: comment on “Failure to engage hospitalized elderly patients and their families in advance care planning.” JAMA Intern Med 2013;173:787.

    • Search Google Scholar
    • Export Citation
  • 3.

    Peppercorn JM, Smith TJ, Helft PR. American society of clinical oncology statement: toward individualized care for patients with advanced cancer. J Clin Oncol 2011;29:755760.

    • Search Google Scholar
    • Export Citation
  • 4.

    Earle CC, Landrum MB, Souza JM. Aggressiveness of cancer care near the end of life: is it a quality-of-care issue? J Clin Oncol 2008;26:38603866.

    • Search Google Scholar
    • Export Citation
  • 5.

    Ho TH, Barbera L, Saskin P. Trends in the aggressiveness of end-of-life cancer care in the universal health care System of Ontario, Canada. J Clin Oncol 2011;29:15871591.

    • Search Google Scholar
    • Export Citation
  • 6.

    Tang ST, Wu SC, Hung YN. Trends in quality of end-of-life care for Taiwanese cancer patients who died in 2000-2006. Ann Oncol 2009;20:343348.

    • Search Google Scholar
    • Export Citation
  • 7.

    Luengo-Fernandez R, Leal J, Gray A, Sullivan R. Economic burden of cancer across the European Union: a population-based cost analysis. Lancet Oncol 2013;14:11651174.

    • Search Google Scholar
    • Export Citation
  • 8.

    Sullivan R, Peppercorn J, Sikora K. Delivering affordable cancer care in high-income countries. Lancet Oncol 2011;12:933980.

  • 9.

    Cox CE, Martinu T, Sathy SJ. Expectations and outcomes of prolonged mechanical ventilation. Crit Care Med 2009;37:28882894.

  • 10.

    Heyland DK, Frank C, Groll D. Understanding cardiopulmonary resuscitation decision making: perspectives of seriously ill hospitalized patients and family members. Chest 2006;130:419428.

    • Search Google Scholar
    • Export Citation
  • 11.

    Fried TR, Bradley EH, Towle VR, Allore H. Understanding the treatment preferences of seriously ill patients. N Engl J Med 2002;346:10611066.

  • 12.

    Rose JH, O'Toole EE, Dawson NV. Perspectives, preferences, care practices, and outcomes among older and middle-aged patients with late-stage cancer. J Clin Oncol 2004;22:49074917.

    • Search Google Scholar
    • Export Citation
  • 13.

    Barnato AE, Anthony DL, Skinner J. Racial and ethnic differences in preferences for end-of-life treatment. J Gen Inter Med 2009;24:695701.

  • 14.

    Wright AA, Mack JW, Kritek PA. Influence of patients' preferences and treatment site on cancer patients' end-of-life care. Cancer 2010;116:46564663.

    • Search Google Scholar
    • Export Citation
  • 15.

    Sudore RL, Schillinger D, Knight SJ, Fried TR. Uncertainty about advance care planning treatment preferences among diverse older adults. J Health Commun 2010;15(Suppl 2):159171.

    • Search Google Scholar
    • Export Citation
  • 16.

    Heyland DK, Barwich D, Pichora D. Failure to engage hospitalized elderly patients and their families in advance care planning. JAMA Intern Med 2013;173:778787.

    • Search Google Scholar
    • Export Citation
  • 17.

    Auriemma CL, Nguyen CA, Bronheim R. Stability of end-of-life preferences: a systematic review of the evidence. JAMA Intern Med 2014;174:10851092.

    • Search Google Scholar
    • Export Citation
  • 18.

    Montero DM. End-of-life issues in the United States after Terri Schiavo: implications for social work practice. Adv Soc Work 2011;12:164180.

    • Search Google Scholar
    • Export Citation
  • 19.

    Shucksmith J, Carlebach S, Whittaker V. Dying: discussing and planning for end of life. British Social Attitudes: the 30th Report. 2013. Available at: http://www.dyingmatters.org/sites/default/files/BSA30_Full_Report.pdf. Accessed May 1, 2015.

    • Search Google Scholar
    • Export Citation
  • 20.

    Di Mola G, Crisci MT. Attitudes towards death and dying in a representative sample of the Italian population. Palliat Med 2001;15:372378.

    • Search Google Scholar
    • Export Citation
  • 21.

    Lokker ME, van Zuylen L, Veerbeek L. Awareness of dying: it needs words. Support Care Cancer 2012;20:12271233.

  • 22.

    Kao YH, Goh CR. The practice of nondisclosure of advanced cancer diagnosis in Singapore: a continuing challenge. Singapore Med J 2013;54:255258.

    • Search Google Scholar
    • Export Citation
  • 23.

    Kravitz RL, Duan N, Braslow J. Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. Milbank Q 2004;82:661687.

    • Search Google Scholar
    • Export Citation
  • 24.

    Maida V, Peck J, Ennis M. Preferences for active and aggressive intervention among patients with advanced cancer. BMC Cancer 2010;10:592.

  • 25.

    Tang ST, Liu TW, Lai MS. Concordance of preferences for end-of-life care between terminally-ill cancer patients and their family caregivers in Taiwan. J Pain Symptom Manage 2005;30:510518.

    • Search Google Scholar
    • Export Citation
  • 26.

    Tang ST, Liu TW, Chow JM. Associations between accurate prognostic understanding and end-of-life care preferences and its correlates among Taiwanese terminally ill cancer patients surveyed in 2011-2012. Psycho-Oncol 2014;23:780787.

    • Search Google Scholar
    • Export Citation
  • 27.

    Carmel S, Mutran EJ. Stability of elderly persons' expressed preferences regarding the use of life-sustaining treatments. Soc Sci Med 1999;49:303311.

    • Search Google Scholar
    • Export Citation
  • 28.

    Huang YC, Huang SJ, Ko WJ. Going home to die from surgical intensive care units. Intensive Care Med 2009;35:810815.

  • 29.

    Muthén BO, Muthén LK. Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. Alcohol Clin Exp Res 2000;24:882891.

    • Search Google Scholar
    • Export Citation
  • 30.

    McCutcheon AL. Latent class analysis. Beverly Hills, CA: Sage Publications; 1987.

  • 31.

    PROC LCA & PROC LTA [computer program]. Version 1.3.0. University Park, PA: The Methodology Center, Penn State; 2013. Retrieved from http://methodology.psu.edu.

    • Search Google Scholar
    • Export Citation
  • 32.

    Akaike H. Factor analysis and AIC. Psychometrika 1987;52:317332.

  • 33.

    Schwarz G. Estimating the dimension of a model. Ann Stat 1978;6:461464.

  • 34.

    Sclove LS. Application of a model-selection criteria to some problems in multivariate analysis. Psychometrika 1987;52:333343.

  • 35.

    Bozdogan H. Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika 1987;52:345370.

    • Search Google Scholar
    • Export Citation
  • 36.

    Muthén LK, Muthén BO. Mplus User's Guide. 6th ed. Los Angeles, CA: Muthén & Muthén; 2010.

  • 37.

    Health Promotion Administration. 2013 Health Promotion Administration Annual Report. Available at: http://www.hpa.gov.tw/BHPNet/Web/Easy/FormCenterShow.aspx?No=201401140001. Accessed May 1, 2015.

    • Search Google Scholar
    • Export Citation
  • 38.

    Lai YL, Su WH. Palliative medicine and the hospice movement in Taiwan. Support Care Cancer 1997;5:348350.

  • 39.

    Chiu TY, Hu WY, Chuang RB. Terminal cancer patients' wishes and influencing factors toward the provision of artificial nutrition and hydration in Taiwan. J Pain Symptom Manage 2004;27:206214.

    • Search Google Scholar
    • Export Citation
  • 40.

    Del Río MI, Shand B, Bonati P. Hydration and nutrition at the end of life: a systematic review of emotional impact, perceptions, and decision-making among patients, family, and health care staff. Psycho-Oncol 2012;21:913921.

    • Search Google Scholar
    • Export Citation
  • 41.

    Earle CC, Landrum MB, Souza JM. Aggressiveness of cancer care near the end of life: is it a quality-of-care issue? J Clin Oncol 2008;26:38603866.

    • Search Google Scholar
    • Export Citation
  • 42.

    Hui D, Didwaniya N, Vidal M. Quality of end-of-life care in patients with hematologic malignancies: a retrospective cohort study. Cancer 2014;120:15721578.

    • Search Google Scholar
    • Export Citation
  • 43.

    Wallace SK, Ewer MS, Price KJ. Outcome and cost implications of cardiopulmonary resuscitation in the medical intensive care unit of a comprehensive cancer center. Eur J Cancer Care 2002;10:425429.

    • Search Google Scholar
    • Export Citation
  • 44.

    Barbera L, Paszat L, Chartier C. Indicators of poor quality end-of-life cancer care in Ontario. J Palliat Care 2006;22:1217.

  • 45.

    Ministry of Health and Welfare. 2012 statistics of causes of death. Available at: http://www.mohw.gov.tw/EN/Ministry/Statistic.aspx?f_list_no=474&fod_list_no=5007. Accessed May 1, 2015.

    • Search Google Scholar
    • Export Citation
  • 46.

    Tang ST, Liu LN, Liu TW. Physician-patient end-of-life care discussions: predictors and impact on preferences for life-sustaining treatments among Taiwanese cancer patients surveyed in 2011-2012. Palliat Med 2014;28:12221230.

    • Search Google Scholar
    • Export Citation
  • 47.

    Winkler EC, Reiter-Theil S, Lange-Riess D. Patient involvement in decisions to limit treatment: the crucial role of agreement between physician and patient. J Clin Oncol 2009;27:22252230.

    • Search Google Scholar
    • Export Citation
  • 48.

    Mack JW, Weeks JC, Wright AA. End-of-life discussions, goal attainment, and distress at the end of life: predictors and outcomes of receipt of care consistent with preferences. J Clin Oncol 2010;28:12031208.

    • Search Google Scholar
    • Export Citation

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Correspondence: Siew Tzuh Tang, DNSc, Chang Gung University, School of Nursing, 259 Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan, Taiwan, 333, Republic of China. E-mail: sttang@mail.cgu.edu.tw

Supplementary Materials

  • View in gallery

    Conditional probabilities for the 7 classes of preferences for life-sustaining treatments, 2003–2004 cohort (n=2,187).

    Abbreviations: CM, cardiac massage; CPR, cardiopulmonary resuscitation; ICU, intensive care unit; ITMV, intubation with mechanical ventilation; IVNS, intravenous nutritional support; NGTF, nasogastric tube feeding.

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    Conditional probabilities for the 7 classes of preferences for life-sustaining treatments, 2011–2012 cohort (n=2,166).

    Abbreviations: CM, cardiac massage; CPR, cardiopulmonary resuscitation; ICU, intensive care unit; ITMV, intubation with mechanical ventilation; IVNS, intravenous nutritional support; NGTF, nasogastric tube feeding.

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