Stability of Preferences for End-of-Life Treatment After 3 Years of Follow-up: The Johns Hopkins Precursors Study | End of Life | JAMA Internal Medicine | JAMA Network
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1.
Institute of Medicine, Crossing the Quality Chasm: A New Health System for the 21st Century.  Washington, DC National Academies Press2001;
2.
Singer  PAMartin  DKKelner  M Quality end-of-life care: patients' perspectives.  JAMA 1999;281 (2) 163- 168PubMedGoogle ScholarCrossref
3.
Lynn  JTeno  JM Perceptions by family members of the dying experience of older and seriously ill patients.  Ann Intern Med 1997;126 (2) 97- 106PubMedGoogle ScholarCrossref
4.
Ahronheim  JCMorrison  RSBaskin  SAMorris  JMeier  DE Treatment of the dying in the acute care hospital: advanced dementia and metastatic cancer.  Arch Intern Med 1996;156 (18) 2094- 2100PubMedGoogle ScholarCrossref
5.
Hanson  LCDanis  MGarrett  J What is wrong with end-of-life care? opinions of bereaved family members.  J Am Geriatr Soc 1997;45 (11) 1339- 1344PubMedGoogle Scholar
6.
Gallo  JJStraton  JKlag  M  et al.  Life-sustaining treatments: what do physicians want and do they express their wishes to others?  J Am Geriatr Soc 2003;51 (7) 961- 969PubMedGoogle ScholarCrossref
7.
Straton  JBWang  NYMeoni  L  et al.  Physical functioning, depression, and preferences for treatment at the end of life: the Johns Hopkins Precursors Study.  J Am Geriatr Soc 2004;52 (4) 577- 582PubMedGoogle ScholarCrossref
8.
Klag  MJHe  JMead  LAFord  DEPearson  TALevine  DM Validity of physicians' self-reports of cardiovascular disease risk factors.  Ann Epidemiol 1993;3 (4) 442- 447PubMedGoogle ScholarCrossref
9.
Thomas  CB Observations on some possible precursors of essential hypertension and coronary artery disease.  Bull Johns Hopkins Hosp 1951;89 (6) 419- 441PubMedGoogle Scholar
10.
Klag  MJWang  NMeoni  L  et al.  Coffee intake and risk of hypertension: the Johns Hopkins Precursors Study.  Arch Intern Med 2002;162 (6) 657- 662PubMedGoogle ScholarCrossref
11.
Klag  MJFord  DMead  L  et al.  Serum cholesterol in young men and subsequent cardiovascular disease.  N Engl J Med 1993;328 (5) 313- 318PubMedGoogle ScholarCrossref
12.
Emanuel  LLEmanuel  E The medical directive: a new comprehensive advance care document.  JAMA 1989;261 (22) 3288- 3293PubMedGoogle ScholarCrossref
13.
Fischer  GSAlpert  HStoeckle  JEmanuel  L Can goals of care be used to predict intervention preferences in an advance directive?  Arch Intern Med 1997;157 (7) 801- 807PubMedGoogle ScholarCrossref
14.
Emanuel  LLEmanuel  EJStoeckle  JDHummel  LRBarry  MJ Advance directives: stability of patients' treatment choices.  Arch Intern Med 1994;154 (2) 209- 217PubMedGoogle ScholarCrossref
15.
Ware  JE How to Score the Revised MOS Short-Form Health Survey Scale (SF-36).  Boston, MA Health Institute, New England Medical Center Hospitals1988;
16.
Stewart  ALedWare  JEed Measuring Functioning and Well-being.  Durham, NC Duke University Press1993;
17.
McHorney  CA Measuring and monitoring general health status in elderly persons: practical and methodological issues in using the SF-36 health survey.  Gerontologist 1996;36 (5) 571- 583PubMedGoogle ScholarCrossref
18.
Stewart  ALHays  RDWare  JE The MOS short-form general health survey: reliability and validity in a patient population.  Med Care 1988;26 (7) 724- 735PubMedGoogle ScholarCrossref
19.
Stewart  ALGreenfield  SHays  RD  et al.  Functional status and well-being of patients with chronic conditions: results from the Medical Outcomes Study.  JAMA 1989;262 (7) 907- 913PubMedGoogle ScholarCrossref
20.
Wells  KBStewart  AHays  RD  et al.  The functioning and well-being of depressed patients: results from the Medical Outcomes Study.  JAMA 1989;262 (7) 914- 919PubMedGoogle ScholarCrossref
21.
Stadnyk  KCalder  JRockwood  K Testing the measurement properties of the Short Form-36 Health Survey in a frail elderly population.  J Clin Epidemiol 1998;51 (10) 827- 835PubMedGoogle ScholarCrossref
22.
Lyons  RAPerry  ILittlepage  B Evidence for the validity of the Short-form 36 Questionnaire (SF-36) in an elderly population.  Age Ageing 1994;23 (3) 182- 184PubMedGoogle ScholarCrossref
23.
Singleton  NTurner  A Measuring patients' views of their health: SF 36 is suitable for elderly patients.  BMJ 1993;307 (6896) 126- 127PubMedGoogle ScholarCrossref
24.
Berwick  DMMurphy  JMGoldman  PAWare  JEBarsky  AJWeinstein  MC Performance of a five-item mental health screening test.  Med Care 1991;29 (2) 169- 176PubMedGoogle ScholarCrossref
25.
McHorney  CAWare  JRaczek  A The MOS 36-Item Short-Form Health Survey (SF-36), II: psychometric and clinical tests of validity in measuring physical and mental health constructs.  Med Care 1993;31 (3) 247- 263PubMedGoogle ScholarCrossref
26.
Vermunt  JKMagidson  J Latent class analysis. Bryman  AedsLiao  TFedsLewis-Beck  MSeds The SAGE Encyclopedia of Social Sciences Research Methods. Thousand Oaks, CA SAGE Publications2004;Google Scholar
27.
Reboussin  BAReboussin  DMLiang  KYAnthony  JC Latent transition modeling of progression of health-risk behavior.  Multivariate Behav Res 1998;33 (4) 457- 478Google ScholarCrossref
28.
Chung  HPark  YLanza  ST Latent transition analysis with covariates: pubertal timing and substance use behavior in adolescent females.  Stat Med 2005;24 (18) 2895- 2910PubMedGoogle ScholarCrossref
29.
Dempster  APLaird  NMRubin  DB Maximum likelihood from incomplete data via the EM algorithm with discussion.  J R Stat Soc B 1977;391- 38Google Scholar
30.
Schwarz  G Estimating the dimension of a model.  Ann Stat 1978;6 (2) 461- 464Google ScholarCrossref
31.
Velicer  WFMartin  RACollins  LM Latent transition analysis for longitudinal data.  Addiction 1996;91 ((suppl)) S197- S209PubMedGoogle ScholarCrossref
32.
Jansen  SJKievit  JNooij  MStiggelbout  A Stability of patients' preferences for chemotherapy: the impact of experience.  Med Decis Making 2001;21 (4) 295- 306PubMedGoogle ScholarCrossref
33.
Menon  ASCampbell  DRuskin  PHebel  JR Depression, hopelessness, and the desire for life-saving treatments among elderly medically ill veterans.  Am J Geriatr Psychiatry 2000;8 (4) 333- 342PubMedGoogle ScholarCrossref
34.
Lee  MGanzini  L The effect of recovery from depression on preferences for life-sustaining therapy in older patients.  J Gerontol 1994;49 (1) M15- M21PubMedGoogle ScholarCrossref
35.
Blank  KRobison  JDoherty  EPrigerson  HDuffy  JSchwartz  HI Life-sustaining treatment and assisted death choices in depressed older patients.  J Am Geriatr Soc 2001;49 (2) 153- 161PubMedGoogle ScholarCrossref
36.
Danis  MGarrett  JHarris  RPatrick  DL Stability of choices about life-sustaining treatments.  Ann Intern Med 1994;120 (7) 567- 573PubMedGoogle ScholarCrossref
37.
Rosenfeld  KEWenger  NSPhillips  RS  et al.  Factors associated with change in resuscitation preference of seriously ill patients.  Arch Intern Med 1996;156 (14) 1558- 1564PubMedGoogle ScholarCrossref
38.
Eggar  RSpencer  AAnderson  DHiller  L Views of elderly patients on cardiopulmonary resuscitation before and after treatment for depression.  Int J Geriatr Psychiatry 2002;17 (2) 170- 174PubMedGoogle ScholarCrossref
39.
Hakim  RBTeno  JMHarrell  FE  et al. SUPPORT Investigators, Factors associated with do-not-resuscitate orders: patients' preferences, prognoses, and physicians' judgments.  Ann Intern Med 1996;125 (4) 284- 293PubMedGoogle ScholarCrossref
40.
Walker  RMSchonwetter  RSKramer  DRRobinson  BE Living wills and resuscitation preferences in an elderly population.  Arch Intern Med 1995;155 (2) 171- 175PubMedGoogle ScholarCrossref
41.
Fried  TRVan Ness  PHByers  ALTowle  VRO'Leary  JRDubin  JA Changes in preferences for life-sustaining treatment among older persons with advanced illness.  J Gen Intern Med 2007;22 (4) 495- 501PubMedGoogle ScholarCrossref
42.
Fried  TRByers  ALGallo  WT  et al.  Prospective study of health status preferences and changes in preferences over time in older adults.  Arch Intern Med 2006;166 (8) 890- 893PubMedGoogle ScholarCrossref
43.
McParland  ELikourezos  AChichin  ECastor  TParis  BE Stability of preferences regarding life-sustaining treatment: a two-year prospective study of nursing home residents.  Mt Sinai J Med 2003;70 (2) 85- 92PubMedGoogle Scholar
44.
Ditto  PHJacobson  JASmucker  WDDanks  JHFagerlin  A Context changes choices: a prospective study of the effects of hospitalization on life-sustaining treatment preferences.  Med Decis Making 2006;26 (4) 313- 322PubMedGoogle ScholarCrossref
45.
Carmel  SMutran  EJ Stability of elderly persons' expressed preferences regarding the use of life-sustaining treatments.  Soc Sci Med 1999;49 (3) 303- 311PubMedGoogle ScholarCrossref
46.
Rosenfeld  KEWenger  NSKagawa-Singer  M End-of-life decision making: a qualitative study of elderly individuals.  J Gen Intern Med 2000;15 (9) 620- 625PubMedGoogle ScholarCrossref
Original Investigation
October 27, 2008

Stability of Preferences for End-of-Life Treatment After 3 Years of Follow-up: The Johns Hopkins Precursors Study

Author Affiliations

Author Affiliations: Department of Family Medicine and Community Health (Drs Wittink and Gallo) and Center for Clinical Epidemiology and Biostatistics (Dr Morales), University of Pennsylvania School of Medicine, Philadelphia; and Department of Medicine, School of Medicine, and Departments of Epidemiology and Health Policy and Management, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, Maryland (Ms Meoni and Drs Ford, Wang, and Klag).

Arch Intern Med. 2008;168(19):2125-2130. doi:10.1001/archinte.168.19.2125
Abstract

Background  Preferences for life-sustaining treatment elicited in one state of health may not reflect preferences in another state of health.

Methods  We estimated the stability of preferences for end-of-life treatment across 3 years and whether declines in physical functioning and mental health were associated with changes in preferences for end-of-life treatment.In this longitudinal cohort study of medical students in the graduating classes of 1948 to 1964 at Johns Hopkins University, 818 physicians completed the life-sustaining treatment questionnaire in 1999 and 2002 (mean age at baseline, 69 years).

Results  Although the prevalence of the 3 clusters of life-sustaining treatment preferences remained stable across the 3-year follow-up, certain physicians changed their preferences with time. The probability that physicians were in the same cluster at follow-up as at baseline was 0.41 for “most aggressive,” 0.50 for “intermediate care,” and 0.80 for “least aggressive.” Physicians without advance directives were more likely to transition to the most aggressive cluster than to the least aggressive cluster during the 3-year follow-up (odds ratio, 1.96; 95% confidence interval, 1.11-3.45). Age at baseline and decline in physical and mental health were not associated with transitions between 1999 and 2002.

Conclusion  Periodic reassessment of preferences is most critical for patients who desire aggressive end-of-life care or who do not have advance directives.

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