Rethinking Fundamental Assumptions: SUPPORT's Implications for Future Reform
Explores the possibility that improved individual, patient-level decision-making is not the most effective strategy for improving end-of-life care and that improving routine practices may be more effective.
Joanne Lynn, MD, MA, MS
BACKGROUND: The intervention in SUPPORT, the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments, was ineffective in changing communication, decision-making and treatment patterns despite evidence that counseling and information were delivered as planned. The previous paper, in this volume, shows that modest alterations in the intervention design probably did not explain the lack of substantial effects.
OBJECTIVE: To explore the possibility that improved individual, patient-level decision-making is not the most effective strategy for improving end-of-life care and that improving routine practices may be more effective.
DESIGN: This paper reflects our efforts to synthesize findings from SUPPORT and other sources in order to explore our conceptual models, their consistency with the data and their leverage for change.
RESULTS: Many of the assumptions underlying the model of improved decision-making are problematic. Furthermore, the results of SUPPORT suggest that implementing an effective intervention based on a normative model of shared decision-making can be quite difficult. Practice patterns and social expectations may be strong influences in shaping patients' courses of care. Innovations in system function, such as quality improvement or changing the financing incentives, may offer more powerful avenues for reform.
CONCLUSIONS: SUPPORT's intervention may have failed to have an impact because strong psychological and social forces underlie present practices. System-level innovation and quality improvement in routine care may offer more powerful opportunities for improvement. J Am Geriatr Soc 48:S214-S221, 2000.
The Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments . SUPPORT . has been at once productive and perplexing, especially with regard to its finding that an intervention intended to improve decision-making was completely ineffectual. In a companion article in this volume, we report and assess others' explanations of why the intervention did not succeed. We conclude that those explanations cannot adequately account for the intervention's failure. Clearly, the intervention did not improve communication about preferences and outcomes or practices concerning patient decision-making. Perhaps the SUPPORT intervention was not forceful enough or clever enough to persuade physicians to work with their patients to make better decisions. Alternatively, however, the complete ineffectiveness of the intervention could point to a different interpretation.
SUPPORT built on the widespread perception, both then and now, that (1) each patient's course of care results from the interaction between each patient's illness and the decisions about his or her care; (2) those decisions largely require physicians; and (3) improved individual, patient-level decision-making will enhance both process and outcomes for patients. Improved decision-making requires incorporating the patient's ratings of the desirability of various courses of action. In turn, the physician needs to know the patient's evaluation of the merits of alternatives, requiring effective communication between patient and physician.
However, we contend here that the course of care may be determined largely by pre-existing routine, that patient centered decision-making is often difficult to implement, and that improving the experience of patients might best be achieved by changing institutional and professional routines.
We will present the essential elements of this argument, acknowledging from the outset that our data cannot prove our contentions. Our aim is to show that a shared decision making model is not always the key goal, that another conceptual framework might offer greater opportunity for improvement, and that SUPPORT'S findings are consistent with this alternative model. We begin by characterizing the "decision making model" that underlay the SUPPORT intervention. We then examine its potential shortcomings and propose an alternative "patterned care model" that is consistent with our data. Finally, we will outline the implications of that model for reform.
SUPPORTS CONCEPTUAL MODEL-SHARED DECISION-MAKING
At the time we designed SUPPORT, most research on outcomes focused on survival and prognosis for survival, and most studies used a simple model relating baseline status to outcome, e.g:
Baseline characteristics (disease and physiology) =>
Likelihood of survival to a particular time or event (1)
The SUPPORT team recognized a number of shortcomings in this model, including that it overlooked a "black box" of decisions and treatments between the baseline and the outcomes:
Baseline characteristics =>
Decisions and Treatments => Outcomes (2)
The intervention was motivated by the observation that, if one started from the same baseline but had the benefit of better decisions, one might achieve better outcomes.
Baseline characteristics =>
Usual Decisions and Treatments=> Usual Outcomes (3)
Baseline characteristics =>
Better Decisions and Treatments => Better Outcomes (4)
Medical, nursing, legal, and ethical literature, both then and now, assumed that decisions drive treatments and arise from decision-making that can be deficient and that can be improved. Those authorities generally espoused a model of shared decision-making in which good decision-making is marked by the patient's authority to decide and his or her understanding of likely outcomes. In that model, achieving better outcomes requires enhancing participants' understanding of outcomes and preferences before making and implementing shared decisions, reflecting patients' informed preferences and physician counseling. The SUPPORT intervention did not involve formal decision analysis but did follow the decision analysis paradigm: the best course to implement is the one with the highest product of (1) the probability of achieving a set of outcomes and (2) the patient's assessment of the desirability of those outcomes. Toward that end, the SUPPORT intervention aimed to facilitate these key components of decision-making:
- Understanding of likely outcomes by each patient (and
family) and physician;
- Understanding of current patient preferences by each
patient (and family) and physician;
- Utilization of preferences expressed previously by
the patient if a patient becomes incompetent; and
- Counseling each patient (and family) so they can make
choices aligned with patient preferences.
Many commentators have not realized that SUPPORT tried
to improve decision-making, not end-of-life care per se. The observant leader
may question why pain relief was a measured outcome of the project. In Phase I,
the project uncovered a disturbingly high prevalence of serious pain affecting
patients from all sites and involving most of the diseases. As investigators and
clinicians, we felt obliged to address this issue, but we were limited by having
planned a randomized trial. Addressing pain comprehensively would have
contaminated the control group and would thus complicate evaluation of the
intervention's effect on decision making. Furthermore, treating pain
comprehensively would have diverted resources away from improving decision
making. After consultation with advisors, we decided that addressing pain only
among the intervention patients would be a suitable compromise. That strategy
would allow SUPPORT to shed light on pain management, provide an entree into the
clinical setting for the SUPPORT intervention nurses, and respond to the
substantial rates of pain among patients. Thus, the intervention added a focus
on pain relief by assessing pain, encouraging patients to seek better pain
relief, and having the SUPPORT nurse ensure access to existing pain relief
services. However, the intervention's primary aim remained to improve the
decision-making process, which in turn was expected to improve decisions and
treatments and thus improve outcomes.
INADEQUACIES IN THE SHARED DECISION-MAKING MODEL
We suggest that there may have been serious inadequacies
in this model regarding how the course of care comes to be and, therefore, how
an intervention might succeed. The shared decision-making model of patient care
is predicated on several assumptions, among which are the following:
- Patients' preferences about outcomes are stable (for
long enough to act on), important to them and expressible upon inquiry.
Instead, we found that patients' preferences often evolved as they confronted
situations and that their preferences were often quite labile and difficult to
articulate completely. This was the case, in part, because people find it very
hard to imagine themselves in a future state with unfamiliar symptoms and more
severe illness and debilitation and to imagine or evaluate the desirability of
death. With unstable and poorly articulated patient preferences, it is
difficult to ascertain which course of action would maximize a patient's
- All parties concerned would have to recognize the
need to decide. Instead, as the patient's body failed, situations resolved
themselves through predictable, routine behaviors. Indeed, certain expected
behaviors were called "decisions" even though there was no consideration of
any alternative. Do-not-resuscitate (DNR) "decisions" near the time of death
were a prominent example: by the time questions about cardiopulmonary
resuscitation were raised, DNR was likely to be a foregone conclusion in most
participants' minds. With no actual decision to make, there would be no change
with an intervention based on improving decision-making.
- Whoever is the responsible decision-maker would have
to be willing and able to take responsibility for the resulting outcomes.
Instead, we found that people often delayed or dodged making a choice, perhaps
because the task was unfamiliar or because they feared subsequent regret. If
no one wants to accept responsibility for making a decision, there may be less
motivation to weigh alternatives, and an intervention based on improved
decision-making might be harder to implement.
- All responsible decision-makers would have to be
willing and able to deal with information in a rational way, to advance the
patent health-related interests, and to consider all of the important
outcomes, costs and benefits. Instead, we found multiple distorting elements
that precluded considering these issues as dispassionately as the expected
utility calculation for a decision analytic model requires. For example,
patients and providers were often willing to let things unfold as they usually
do (rather than to "rock the boat"). Patients were often concerned with how
they would be seen by others or how their relationship with others would be
remembered. Many people (patients and providers) did not want to talk about
death, or they dealt with life, death and disease in non-rational terms. For
example, some patients framed their experience in fatalistic or magical ways
that would not be easy to incorporate in a decision analytic model.
We now discuss each of these four elements in more
Decision-Making Model Assumption: Patient Preferences
are Stable, Important and Expressible
The model used in SUPPORT and, indeed, normative
decision-making models in general, assume that patients can articulate
preferences and make choices congruent with them. In formal decision analysis,
the utility of each outcome is multiplied by its likelihood of occurrence, and
this product is compared with other options. Even if formal decision analysis is
not performed, ascertaining a patient's relative preferences for various
outcomes should be part of the decision-making procedure."
Unfortunately, the preferences of patients confronting
end-of-life care often evolve or are constructed during the situation rather
than accessed from a stable set of settled priorities." Preferences are often
not fixed characteristics that people can report easily in a reliable and valid
manner; preferences are often highly dependent on 13.14 For example, McNeil et
al. "have shown that patient preference for radiation versus surgical treatment
for cancer is influenced by whether the potential outcomes are expressed in
terms of mortality or survival. These two variables are perfectly inversely
related, so the patient's preference should be unaffected by the semantic
framing. However, because patients' preferences are actually affected, a
physician may find it very difficult to have confidence in an analysis based on
a patient's preference assessed with only one phrasing. Semantic framing is not
the only way in which preferences can be shown to be liable. Many SUPPORT
patients were willing to answer questions such as whether they would prefer to
die rather than live with some adverse condition.
However, most had not had that condition, many who were
currently facing the condition had avoided contemplating it, and virtually no
one who said that he or she would rather die than endure a particular outcome
actually sought to die when that outcome occurred. Patients and families often
seemed to be at a loss in attempting to express their preferences, often
checking with people around them for cues as to how one should feel and what
responses were conventional."
A second problem, which complicates assessing patients'
preferences for outcomes of end-of-life care, pertains to the relationship
between predicted utility and experienced utility. Predicted utility represents
one's beliefs about the utility of some future outcome. Experienced utility is
the actual utility of the experience associated with that outcome when it
transpires." In end-of-life care, the patients usually have very little
experience with the potential outcomes. Hence, a given patient's predicted
utility may not be congruent with his or her eventual experienced utility, even
though the predicted utility serves as the basis for the patients'
decision-making. If the predicted utility were routinely different from the
experienced utility, patients' interests would not be well served by ensuring
their ability to make and enforce plans for future states. Finally, people near
death may well have quite different values from persons who are not near
Practitioners working with people who are dying often
describe patients who say that their "quality of life is very poor" but also
that, "this is the best time of my life," or "these last few weeks have been so
very valuable." Patients may be referring to the meaningfulness they have found
in religion, relationships or contemplating the mystery of life. They may now
see a value to their lives that was missing before or rediscover long-held
tenets and commitments.'" In any case, they often attribute great worth to an
existence that would seem to be of little value when evaluated by others in the
usual ways. Although clinicians caring for terminally ill patients see this
phenomenon often, it is not likely that a patient can anticipate such a
substantial shift in this or other values when still remote from the
Decision-Making Model Assumption: A Decision
Opportunity Will Be Recognized
In order for decision analysis to be applied, one must
be aware that there is a decision to be faced. If no need for a decision is
perceived at a particular juncture, then probabilities and utilities are not
germane. For example, if a physician tells a patient that his or her disease is
normally treated in a particular way, then the patient may never consider
probabilities and utilities, because there seems to be no decision to be made.
Such situations are quite common. While the course of care might be
characterized, especially in retrospect, as resulting from a choice, it may be
that the patient and everyone else involved really perceived it as simply "what
is done." Joe Cruzan, commenting on his search to have artificial feeding
stopped for his daughter Nancy said that the events felt more like a "wagon
picking up speed going downhill." SUPPORT patients may have felt the same way -
as if they were riders on the roller coaster of illness and their care system's
responses. They did not often note that coming to the hospital was itself a
decision or that there might exist alternatives to the "usual course of
The same description may apply to physicians'
decision-making. Klein has promulgated the concept of "recognition-primed
decision-making" to describe decision-making in situations involving
firefighters who must make quick life-and-death decisions when they themselves
are in conditions of extreme danger. Klein reports that firefighters do not
ponder probabilities and utilities; they would have neither the time nor the
inclination to use such information even if it were readily available. Instead
firefighters attempt to characterize each situation. When they recognize a fire
as a particular type, they then take the course of action that such a fire calls
for. This recognition-primed decision-making does not explicitly employ the two
analytic inputs to normative responses.
Even a poor prognostic estimate provided to the
physician by the SUPPORT investigators will not be relevant to the daughter's
decision-making. Decision-making based on rule-following is evaluated by how
closely one follows the rule, not by expected utility. Is forgoing major surgery
for one's parent seen as an appropriate decision for a loving clild? If not, and
the child is motivated by rule following, then that option will not be
considered seriously or its consideration will not be on the rational grounds of
expected utility in terms of health outcomes.
The goal of SUPPORT was to improve individual,
patient-level decision-making because we and others thought that enhanced
decision-making would lead to better treatments and outcomes. Guiding the
SUPPORT intervention were certain assumptions about decision-making: patients
would be able to articulate preferences that would be stable and important; all
parties would acknowledge that decisions were needed at particular junctures;
patients or their surrogates would step forward and assume responsibility for
the decision and its outcome; and decision-makers would appropriately
incorporate all relevant information, including prognostic estimates and patient
We now conclude that these assumptions were naive. We do
not deny that appropriately considering prognostic estimates and patients'
utilities could lead to better treatments and outcomes. We suggest, however,
that people tend not to consider end-of-life care in these terms, and the
SUPPORT intervention failed to encourage decision-makers to do so. Hence,
improving end-of-life care may require means other than enhanced, shared
decision-making. We suggest that to achieve better treatments and outcomes, one
should consider improving practice patterns directly rather than relying on
TOWARD AN ALTERNATIVE MODEL
A more fundamental re-interpretation of SUPPORT may help
achieve better outcomes. Many have claimed that the culture of medicine is
antithetical to good care at the end-of-life. In this view, improving
patient-physician communication cannot ameliorate shortcomings in care;
improvement requires restructuring the entire environment. Many of these
commentators point to the physicians' drive to use technology to fight disease,
even if against the wishes of patients. As Callahan put it, medical culture and
the healthcare environment encourage heroic and aggressive treatment, which
engenders deafness to patients' wishes and a tendency to discount prognostic
information. Marshall's pointed to physicians' desire to preserve life (take
action, cure disease, overcome helplessness incurred by illness) rather than to
cultivate sensitivity to emotional aspects of a person's approaching death. In
accord with Marshall's assessment, Annas contended that the pressure to use
technology leaves the physician with little time for conversation with families
or patients. He contends that such attitudes are so pervasive in hospitals that
"the only realistic way to improve the care of dying patients in the short run
is to get them out of the hospital, and to keep them from going to the hospital,
at the end of their life." Both Dugan and Godec speculated that financial
incentives often motivate hospitals and doctors to provide invasive
interventions to dying patients whether they want them or not. "By contrast,
relieving pain and allowing patients to die in peace and dignity are not
significantly compensable in the US medical system. In essence, then, these
critiques charge that, in a medical (hospital) culture that values technology
over discussion, the SUPPORT intervention was too limited in scope to yield real
change. These critics seem to agree that rather than working within the existing
structure (the status quo), a more systematic, far-reaching, and deep-seated
plan to restructure the hospital environment is needed. As Brody puts it, "The
system is so entrenched, so powerful and (not coincidentally) so rich that
present-day reformers who would try to alter it usually find the task much more
difficult than they anticipated. This happened to the SUPPORT investigators."
Insofar as these critics envision rescuing dying persons
from a hostile environment by discharging them from the hospital, they disregard
SUPPORT'S findings regarding the difficulty of identifying this class of "dying"
persons. SUPPORT-like patients with very poor prognoses ordinarily die quickly,
often deliberately forgoing aggressive treatment. SUPPORT patients very near
death usually did not undergo CPR; the overall rate of CPR was 11% by family
reports and 14% by medical record review for those who died in SUPPORT
hospitals. However, individuals just a little farther from death usually have
quite uncertain prognoses. During what turned out to be their last week, most
persons had a prognosis of at least a 50% likelihood to live two months or
more. Even among individuals with progressive chronic illness such as colon
cancer or congestive heart failure, the remaining life is often comfortable and
valued. Thus, very few SUPPORT patients could readily be classified as "dying"
in the sense that they - or anyone else - would have found it appropriate to
accept death and be treated for symptoms at home. Instead, they were thought to
have a substantial chance to leave the hospital and do well for a while if
treatment were successful. Most of our patients were in the "middle muddle" of
prognoses, bad enough to be at risk of death but good enough to hope for longer
survival with the appropriate treatment. Thus, one cannot expect to improve on
SUPPORTS findings simply by splitting off the dying and treating them somewhere
Nevertheless, the observation that the hospital or
intensive care environment would need pervasive change for the SUPPORT
intervention to succeed presents some considerations. First, it signals that the
course of care arises from number of arrangements, many of which are not well
articulated, most of which did not arise specifically from considerations of the
patient's good, and few of which are expected to be responsive to different
decisions by different patients. Second, the course of care is a function of the
characteristics of the region's healthcare system. Third, these arrangements may
be hard to change because they are generally efficient, predictable, and
comforting to all involved. Most involved seem to cherish the comfort of
predictable patterns, the efficiency of well-rehearsed behaviors and the
reassurance that they are not taking on substantial responsibility for novel
Indeed, the narratives written by the intervention
nurses in SUPPORT used some illuminating phrases. For example, the nurses
repeatedly mentioned the "readiness" of patients or families when considering
the opportunity to "make decisions." When we sought what the patient or family
member was to be "ready" for, it seemed that they were getting "ready" for a
change in course that signaled acknowledgement of impending death or some other
undesired outcome. The nurses' claim seemed almost like etiquette: that it was
gracious and humane to give patients and families the time to recognize what was
in store. The patient's failing course would eventually force the issue, but the
appearance of participants making choices was still valued, even if it only
meant agreeing sooner rather than later that death or another undesired outcome
In another example, we found many situations in which
the patient or family member explains his or her motives as "trying to be a good
_______," with the blank being father, daughter or some other social role.
Although the process may have the same end result, reassurance that one is
behaving virtuously is different from seeking the best expected utility for the
patient's health-related outcomes. Family members were often unsure about what
constituted reliable landmarks as they sought to define their roles. The
guidance of their physician was often highly valued, but such guidance could be
unintelligible if it spoke to utility rather than virtue or expected behavior.
The SUPPORT nurse's guidance was probably also trusted, but it was subject to
the same mismatch of language and, in addition, the potential for suspicion of
an unfamiliar party providing counsel. In any case, if the patients and family
members were looking for reassurance that their behavior would be seen as
virtuous, accommodation to the usual pattern of care is one useful way to
accomplish that. Myriad cues provide reassurance to a family member who is
"going with the flow," and, conversely, myriad cues raise doubt in a family
member who is "doing something differently."
We contend that the course of care for each patient
operates more as a glide path in which all parties concerned watch the patient
for signs that they should act in certain ways. When the patient becomes more
difficult to arouse and acknowledges that the end is near, family and care
providers feel that it "is time" for a DNR "discussion," which acts as the
symbol that death cannot be averted. The family's refusal of a DNR order is,
then, just a delay and is accepted as long as there is likely to be time for
reconsideration. Most families eventually follow the script and "make a decision
for DNR," which may be more an acknowledgement of the patient's physical decline
than a decision.
The "default option," or what is "usually done," is very
powerful. If the default option at a particular hospital is a certain pattern of
aggressive care, then the salience and timing of various "decisions" will
reflect that milieu. What keeps patients and families from deviating from strong
glide paths? Patients and families may well be comforted by the assurance that
they are good people that arises from following the expected pathways.
Deviations foster such disincentives as anticipatory regret or worry about being
a "good daughter."
The information available is not usually so revealing
that it compels deviating from the usual glide path. Prognoses are "middling,"
and patient preferences are not firm or well known. A patient with a strong
viewpoint can be accommodated, but the usual patient and family will "go along,"
trusting that the course of care has been honed over many patients. Patients may
well trust that the usual course of care will serve patients "like me" better
than anything that would result from trying to customize each choice to reflect
their own values.
For SUPPORT patients with fatal illnesses, the
intervention not only did not improve provider-patient communication; it also
simply did not affect the assumptions about the appropriateness of the default
option, whatever it may have been. Perhaps patients rarely had strong and
relevant preferences; they usually had muddled prognoses for survival; their
quality of life before hospitalization was generally acceptable, and rarely did
alternatives seem clearly better or worse to the participants than usual care.
The individual, patient-level decision-making model is constrained by system
factors that are clearly beyond the control of the patient, such as availability
of supportive care at home. The five SUPPORT sites differed dramatically in
rates of dying at home versus in the hospital. Indeed, such variations were
pervasive in the
Medicare population generally, and differences across
regions (from 29% to 66%) correlated strongly with hospital bed supply and
patterns of utilization (r2 = 0.83)." In the SUPPORT hospital that had the
highest rate of out-of-hospital death, physicians usually followed their
patients at home or in nursing home or hospice settings, employers were
generally willing to grant leave for family caregivers, and active hospice
services were readily available. In contrast, the hospital with the highest rate
of in-hospital deaths had very few physicians who followed patients at home, had
many family members faced with losing their jobs if they became primary
caregivers and had underdeveloped, competitive and largely unfamiliar hospice
and home-care services. These phenomena show the SUPPORT decision-making model
to be naively optimistic. No patient could simply choose to have their care
system behave like the first care system just described, nor, for that matter,
like the last one. The range of choices was often limited to the timing of DNR
orders, with virtually everyone (patient, family, physicians and others)
agreeing that resuscitation at the time of death would not serve the patient.
This is a weak choice, although alternatives with greater impact were largely
determined by the care system.
How can we think about such change? Certainly, merely
signing advance directives would not accomplish much," and neither would
encouraging patients one by one to demand change. Instead, change could be
conceptualized more as a public health issue: How could care be organized to
serve seriously ill people best? This question is very different from SUPPORTS
framework, which might be characterized as: How can each patient's course of
care reflect that patient's likely outcomes and informed preferences?
Playing out the contrast between the patterned care
model, which leads to a public health approach, and the shared decision-making
paradigm has substantial implications for reform of end-of-life care. First, we
have to acknowledge the limited role that patient self-determination is likely
to have, not because self-determination is unimportant to patients but because
the care system is not able to respond adequately." Instead, although
accommodation can be made for a few patients with unusual, and unusually strong,
convictions, the usual course of care will remain the usual course of care.
Unlike the major reforms in obstetric care in the 1970s, in which enough
patients were able to "vote with their feet" and refuse obstetric care not
meeting their demands it seems unlikely that patients as sick as those in
SUPPORT would be well served by refusing to go to the hospital.
Fortunately, however, the power of the "usual course of
care" may also point toward avenues for improvement. For years, the legal
profession tried to persuade Americans to write a will. Eventually, most states
resolved most of the problem by changing the law concerning what happens to an
estate with no will Most people have rather similar preferences, and those were
codified into law. Now, for most modest estates, not much turns on whether a
will was written. Perhaps those who want reform in health care will do well to
think in the same ways. Rather than trying to have patients, one by one, state
their preferences and shape their own health care, reform should first center on
ensuring that persons who do not explicitly state their wishes still get
treatment that is close to what most people would want. There would be room for
customizing, but perhaps most of what can be achieved depends on getting the
default glide path to be much more congruent with what serves most people
That default glide path arises from several forces, a
particularly worthy one being evidence about which practices tend to serve the
population best. One of the questions for reform is to ensure that the routine
practices give the time and attention to individual decision-making at the
junctures where doing so serves the patient population best. Rather than aiming
to make customized decisions on every issue, a good default glide path would
encourage decision-making primarily when the issue is important and carries
quite divergent outcomes, the desirability of which is evaluated quite
differently by affected patients. Thus, the care system might operate as if on
auto-pilot to treat acute respiratory failure but might build in a substantial
opportunity for considering a decision to reintubate a person struggling with
advanced emphysema. The optimum default glide path would be defined, in a sense,
by having the best overall utility for a population of people with that
For example, this approach would take into consideration
that patients with cancer generally want their pain treated promptly and
appropriately. Reform would make competent pain management the usual course.
Then, routine assessment of pain, prompt and pre-emptive drug and non-drug
interventions and patient and family education about how to adjust treatments
would become high priorities. The occasional patient who wants no opioids or who
wants to try unusual approaches could still be accommodated in such a care
system, but the person who is just "going with the flow," or is confused or
uncertain, will nonetheless be assured pain relief.
In another example, the person who has very poor cardiac
function and widely metastatic cancer would be advised that CPR is
contraindicated and, therefore, will be withheld unless that person has a strong
reason to want it. In order to solicit an early DNR order, however, the current
symbolic uses of DNR would require attention. Perhaps other actions could
symbolize that a person is now close to death: e.g., calling the family
together, advising to consider "last words," and so forth. Perhaps there would
need to be overt disarticulation of the symbolism of DNR, with physicians and
other providers educating families and patients about the best uses and merits
of CPR and its commonplace uselessness in some circumstances. In addition, one
might set out to establish different and more useful rituals. Such suggestions
would need to be tried out and assessed. The heart of strategic reform in
end-of-life care may be widespread, carefully evaluated, well networked
improvement in the routine functioning of care systems. Rather than assuming, as
SUPPORT did, that utility maximization for each patient can be accomplished by
close adherence to a decision analytic model at the individual level, reformers
might do better to adopt a model of continuous quality improvement at the
Default paths can be structured that maximize utility
summing over all patients, still allowing for customization of care for those
patients who wish to do so. Such an approach requires repeated efforts at
improvement, with evaluation and redirection. A number of recent reports show
remarkable effects of such strategies, which stand in striking contrast to the
utter failure of the SUPPORT intervention.
Innovation, evaluation and system change are not how our
financing system, medical ethics and case law have construed reform for
end-of-life care. Instead, social policy has construed patient care as resulting
from decision-making concerning each patient. While Medicare pays for each
service to each individual patient. Medicare generates no measures of overall
quality of care for very sick populations.
While medical ethics addresses the individual's right to
choose, there is strikingly little authoritative guidance on what choices the
care system should make available and what the course of care should be in the
absence of choice. Case law has added to this emphasis by posing issues as "the
right to refuse treatment" or the "right to choose." General welfare, costs,
social meanings or system function have little role in litigation arising from a
particular patient's care.
The intervention in SUPPORT did not yield any measurable
improvements in decision-making for seriously ill hospitalized adults. Most of
the explanations offered seem unconvincing for such a thoroughly ineffective
effort. However, we now question the fundamental assumption: that the course of
care for the seriously ill hospitalized patient is the result of individual,
patient-level decision-making that could be improved with better counseling and
information. Instead, the course of care may well be shaped largely by how the
care system is organized and by the interpersonal meanings ascribed to various
cues and signals that shape the predictable patterns of care. Research might be
more fruitful if it shed light on the psychological, sociological and
anthropological factors that shape the course of care. Future interventions may
be more effective if they address system change and quality improvement.
The authors acknowledge our debt to the thousands of
patients, family members, and physicians who participated in the SUPPORT
project, to the dozens of nurses and data collectors who made it work, and to
the investigators and colleagues who have encouraged us to learn as much as
possible from the project. We are deeply indebted as well to the project's
sponsor, The Robert Wood Johnson Foundation, which not only provided the
resources for SUPPORT but has also launched a major national campaign to direct
public attention and clinical service innovation toward problems affecting the
1. The SUPPORT Principal Investigators. A controlled
trial to improve care for seriously ill hospitalized patients: The study to
understand prognoses and preferences for outcomes and risks of treatment
2. Lynn J, DeVries KO, Arkes H et al. Ineffectiveness of
the SUPPORT Intervention: Review of explanations. J Am Geriatr Soc
3. Making Health Care Decisions. The President's
Commission for the Study of Bioethical Problems in Medicine and Biomedical and
Behavioral Research. US Government Printing Office, Washington, DC, October
4. New York State Task Force on Life and the Law. Do Not
Resuscitate Orders: The Proposed Legislation and Report of the New York State
Task Force on Life and the Law. 1986.
5. American Geriatrics Society Ethics Committee The care
of dying patients. A position statement from AGS. J Am Geriatr Soc
6. American Medical Association, Guidelines for the
appropriate use of do-not-resuscitate orders. JAMA 1991;265:18ff8-1871.
7. ACP Ethics Committee, American College of Physicians
Ethics Manual, 3"* Ed. Ann Intern Med 1992:117:947-960.
8. Committee on Ethics in End-of-life Decision-Making.
Understanding the goals of care. American College of Obstetrics and Gynecology
Committee Opinion 19951156:1-6.
9. Kasper JF, Mulley AG Jr. Wennberg JE. Developing
shared decision-making programs to improve the quality of health care. QRB
10. Destnens NA, Vu AW. Pain and suffering in seriously
ill hospitalized patients. J Am Geriatr Soc 2000;48(suppl):S183-186.
11. Tsevat J, Cook F, Green ML et al. Health values of
the seriously ill. Ann Intern Med 1995,122:514-520.
12. Slovic P. The construction of preferences. Am
13. Forrow L. The green eggs and ham phenomenon.
Hastings Cent Rep 1994; 24(special supplement):S29-32.
14. Callahan D. Once again, reality: Now where do we go?
Hastings Cent Rep 1995;25(special supplement):S33-36.
15. McNeil BJ, Pauker SG, Sox HC Jr. Tversky A. On the
elicitation of preferences for alternative therapies. N EnglJ Med
16. Drought, TS, Koenig BA, Raffin TA, Advance
directives. Changing our expectations (editorial). Chest 1996;110;589-591.
17. Kahneman D, Snell J. Predicting a change in taste:
Do people know what they will like? J Behav Decis Making 199215:187-200.
18. Byock 1. Dying Well. New York: RiverHead Books,
19. Cruzan V. Director, Missouri Department of Health,
497 U.S. 261 (1990).
20. Klein GA. A recognition-primed decision (RPD) model
of rapid decision making. In: Klein GA, Orasanu J, Calderwood R. Zsambok C, eds.
Decision Making in Action: Models and Methods. Norwood, NJ: Ablex, 1993, pp
21. Hammond KR. Human judgment and social policy:
Irreducible Uncertainty, Inevitable Error, Unavoidable Justice. New York: Oxford
University Press, 1996.
22. Hern HE Jr. Koenig BA, Moore LJ, Marshall PA. The
difference that culture can make in end-of-life decision-making. Camb Q Health
23. Paris JJ, MuirJ, Cameron, Reardon FE. Ethical and
legal issues in intensive care. J Intensive Care Med 1997;12:298-309.
24. Samuelson W, Zeckhauser R. Status quo bias in
decision making. J Risk Uncertainty 1988:1:7-59.
25. Prilchard RS, Fisher ES, Teno JM et al. Influence of
patient preferences and local health system characteristics on the place of
death. J Am Geriatr Soc 1998:46:1242-1250.
26. Landman J. Regret: The persistence of the possible.
Oxford: Oxford University Press, 1993.
27. Bell-DE. Regret in decision making under
uncertainty. Operat Res 1982:30: 961-981.
28. Bell DE- Disappointment in decision making under
uncertainty. Operat Res 1984:32:1-27.
29. Loomes G, Sugden R. Regret theory: An alternative
theory of rational choice under uncertainty. Econ J 1982:92:805-824.
30. Landman J. Regret and elation following action and
inaction: Affective responses to positive versus negative outcomes. Personality
Soc Psychol Bull 1987:13:524-536.
31. Spranca M, Minsk E, Baron J. Omission and commission
in judgment and choice. J Exp Social Psychol 1991:27:76-105.
32. Hamm RM, Scheid DC, Smith WR, Tape TG. Opportunities
for applying psychological theory to improve medical decision making. Two case
histories. In G.B. Chapman, F. Sonnenberg, eds. Decision Making in Health Care:
Theory, Psychology, and Applications. New York: Cambridge University Press, in
33. Pauker SG, Kassirer JP. Decision analysis. New Engi
34. Stiggelbout AM, Kiebert GM, Kievit J et al. The
utility of the time trade-off method in cancer patients: Feasibility and
proportional trade-off. J Clin Epidemiol 1995:48:1207-1214.
35. Stiggelbout AM, de Hae; JCJM, Kieben GM et al.
Tradeoff between quality and quantity of life: Development of the QQ
questionnaire for cancer patient attitude. Med Decis Making 1996:16:184-192.
36. Chapman GB, Elstein AS, Andrews A et at. Prostate
cancer patients' utility for health states: How it looks depends on where you
stand. Med Decis Making 1998:18:278-286.
37. Glasziou PP, Bromwich S, Siroes RJ. Quality of life
six months after myocardial infarction treated with thrombolytic therapy. Med J
Aust 1994:161: 532-536.
38. March JG. A Primer of Decision Making: How Things
Happen. New York: FrA Press, 1994.
39. Annas GJ. How we die. Hastings Cent Rep 1995;25
(special supplement): S12-14.
40. Brody H. The best system in the world. Hastings Cent
Rep 1995;25(special supplement's 18-21.
41. Dugan DO. SUPPORT. Asking different questions.
Making the rounds in healthcare. Faith Ethics 1996:1:1-2.
42. Codec MS. The SUPPORT Project and Improving Care for
Seriously III Patients. JAMA (letter) 1996:275:1228.
43. Lo B. End-of-life care after termination of SUPPORT.
Hastings Cent Rep 1995;25(special supplement):S6-8.
44. Lo B. Improving care near the end of life: Why is it
so hard? JAMA 1995; 272:1634-1636.
45. Marshal PA The SUPPORT Study: Who's talking?
Hastings Cent Rep 1995; 25(special supplement):S9-ll.
46. Miller FG, Fins JJ. A proposal to restructure
hospital care for dying patients. New Engi J Med 1996:334:1740-1742.
47. Moskowin EH, Nelson JL. The best laid plans.
Hastings Cent Rep 1995; 25(special supplement):S3-5.
48. Sachs GA. The SUPPORT Project and Improving Care for
Seriously III Patients. JAMA (letter) 1996:275:1229.
49. Schneider CE. From consumer choice to consumer
welfare. Hastings Cent Rep 1995;25(special supplement):S25-28.
50. Solomon MZ. The enormity of the task: SUPPORT and
changing practice. Hastings Cem Rep 1995;25(special supplement):S28-32.
51. Solomon MZ, Jennings B, Dickey N et al. The SUPPORT
Project and Improving Care for Seriously III Patients. JAMA (letter)
52. Berwick DM The SUPPORT Project. Lessons for action.
Hastings Cent Rep 1995;25(special supplement):S21-22.
53. Lynn J, Harrell F Jr. Cohn F et al. Prognoses of
seriously ill hospitalized patients on the days before death: Implications for
patient care and public policy. NHoriz 1997:5:56-61.
54. Lynn J. An 88-year-old woman facing the end of life.
55. Teno JM, Murphy D, Lynn J et al, for the SUPPORT
Investigators. Prognosis-based futility guidelines: Does anyone win? J Am
Geriatr Soc 1994:42:1202-1207.
56. Lynn J, Teno JM, Phillips RS et al. Perceptions by
family members of the dying experience of older and seriously ill patients. Ann
Intern Med 1997; 126:97-106.
57. Goodlin S, Zhong Z, Lynn J et al. Factors associated
with use of cardiopulmonary resuscitation in seriously ill hospitalized adults.
JAMA 1999:282: 2333-2339.
58. Lynn J, Harrell FE Jr. Cohn F et al. Defining the
"Terminally Bl:" Insights from SUPPORT. Duq Law Rev 1996:25:311-336.
59. Jaagosild P, Dawson NV, Thomas C et al. Outcomes of
acute exacerbation of severe congestive heart failure: Quality of life, resource
use, and survival. Arch Intern Med 1998:158:1081-1089.
60. Murphy P, Kreling B, Kathtyn E et al. Description of
the SUPPORT Intervention. J Am Geriatr Soc 2000;48(suppl):S154-161.
61. Teno JM, Lynn J, Wenger N, Phillips RS et al.
Advance directives for seriously ill hospitalized patients: Effectiveness with
the patient selfdetermination act and the SUPPORT Intervention. J Am Geriatr Soc
62. DuPen SL, DuPen AR, Polissa N et aL Implementing
guidelines for cancer pain management. Results of a randomized controlled
clinical trial. J Clin Oncol 1999:17:361-370. .
63. Lynn J, Shuster JL, The Center to Improve Care of
the Dying, and The Institute for Healthcare Improvement. Improving Care at the
End of Life-. A Sourcebook for Managers and Providers. New York: Oxford
University Press, 1999, in press.
64. The Quality Letter for Health Care Leaders, Vol. 10,
No. 10, October 1998. Canitol Publications- a division of Asoen Publishers.
Inc.. Alexandria, VA.
The Collected Reports from SUPPORT and HELP: An
Annotated Bibliography of Manuscripts in print as of December 31, 1999, and
SUPPORT Prognostic Formulas
Nancy Freeborne, MPH, PA-C
"The Study to Understand Prognoses and Preferences and
Risks of Treatment, SUPPORT, enabled a variety of researchers, including those
from five clinical sites and the National Coordinating Center at George
Washington University, to observe and comment on many aspects of the care and
outcomes of very sick hospitalized patients. An allied project, the Hospitalized
Elderly Longitudinal Project, HELP, studied the course of hospitalized persons
aged 80 years or older.
SUPPORTS basic approach and methods were outlined in a
special edition of a journal that included data collection instruments such as
questionnaires and medical record abstraction forms: Murphy DJ, Cluff LE, eds.
SUPPORT: Study to Understand Prognoses and Preferences for Outcomes and Risks of
Treatments, Study Design. J Clin Epidemiol 1990;43:iS-123S.
Public Use Data regarding SUPPORT is available from the
Inter-University Consortium for Political and Social Research
The SUPPORT Physiology Score and Prognostic Models are
presented in Appendix B. The original prognostic model had a typographical error
in the formula which is corrected in this notation. A national public opinion
poll concerning experiences with end-of-life care is reported in Appendix A.
Before the collection of articles published in a
supplement to the Journal of the American Geriatric Society, one overview and
interpretation was published by a lead investigator: LynnJ. Unexpected returns:
Insights from SUPPORT. In: Isaacs SL et aL, eds. To Improve Health and Health
Care: The Robert Wood Johnson Foundation Anthology (JosseBass Health Series).
New York: Josse-Bass, 1997, pp 161186.
Following below are citations to all of the articles
published from SUPPORT and HELP before December 31,1999, along with brief
summaries of their approach and findings.
These are listed alphabetically by first author. Despite
having a common database, the reader will find that the research topics are
varied and that many journals are represented.
SUPPORT ARTICLES: IN PRINT AS OF DECEMBER 31, 1999
1. Arkes HR, Dawson NV, Speroff T et al. and the SUPPORT
Investigators. The covariance decomposition of the probability score and its use
in evaluating prognostic estimates. Med Decis Making 1995;15:120-131.
The covariance decomposition of the mean probability
score has not previously been used to evaluate medical judgment. Authors
analyzed prognostic estimates made by three groups within SUPPORT: physicians,
their patients, and patients' surrogate decision-makers. The decomposition
reveals that while the physicians have the best overall estimation performance,
their levels of bias and scatter are not always
Superior to those of the other groups. This is because
physicians' prognostic estimates are pessimistic, and the patients' estimates
are generally optimistic (thereby achieving low scatter). Authors conclude that
the decomposition is a useful tool with which to analyze medical judgment.
2. Connors AF Jr., Dawson NV, Thomas C et al. for the
SUPPORT Investigators. Outcomes following acute exacerbation of severe chronic
obstructive lung disease. Arn J Respir Crit Care Med 1996;154:959-967.
A cohort of patients with exacerbation of COPD (n =
1,016) were studied for 6 months. Mortality was high at 60 days, 180 days, I
year, and 2 years (20%, 33%, 43%, and 49%, respectively) despite a relatively
low index hospitalization mortality rate (11%). At 6 months, only 26% of the
cohort were alive and able to report a good, very good, or excellent quality of
3. Connors AF, Speroff T, Dawson NV et al. for the
SUPPORT Investigators. The effectiveness of right heart catheterization in the
initial care of critically ill patients.JAMA 1996;276:889-897.
Investigators reviewed records of SUPPORT patients
having undergone right heart catheterization (RHC) and compared them with
comparable matched controls. This observational study suggested that patients
with RHC had an increased 30-day mortality. Also, the mean cost per hospital
stay was $49,300 in patients with RHC and $35,700 in patients without RHC.
4. Cooper GS, Bellamy P, Dawson NV et al. A prognostic
model for patients with end-stage liver disease. Gastroenierology
Charts were reviewed on patients with end-stage liver
disease in Phase 1 (295) and Phase II (243). Cumulative incidence of death was
30% at 30 days and 50% at 6 months. In 295 Phase I patients, time until death
was associated independently (P < .01) with five factors measured on study
Day 3: renal insufficiency, cognitive dysfunction, ventilatory insufficiency,
age >= 65 years, and prothrombin time >= 16 seconds. A model based on
these risk factors stratified 243 patients in Phase II into three groups, with
cumulative incidences of death at 30 days of 12%, 40%, and 74%.
5. Covinsky KE, Goidman L, Cook EF et al. The impact of
serious illness on patients' families. JAMA 1994;272:1839-1844.
Seriously ill patients and their family members (n =
2129) were interviewed about the impact of the patient's illness on the family.
Severe care-giving and financial burdens were common. One-third (34%) of
patients required substantial care-giving help from a family member. For 20% of
the patients, a family member had to quit work or make another major life change
because of the time required to care for the patient. Loss of most or all of the
family savings was reported by 31%; 29% reported loss of the major source of
family income and 17% reported that the illness required a major change in
family plans. Factors associated independently with the loss of family savings
included poor functional status, lower family income, and young age.
6. Covinsky KE, Landefeld S, Teno J et al. for the
SUPPORT Investigators. Is economic hardship on the families of the seriously ill
associated with patient and surrogate care preferences? Arch Intern Med
Two months after index hospitalization, patients and
surrogates (n == 3158) were interviewed about whether the patient's preference
for care focused on maximizing comfort or on maximizing life-expectancy. Answers
were compared with responses about whether the illness had caused financial
hardship within the family. Respondents who reported family financial stress as
a result of the illness were significantly more likely to express a preference
for comfort care over life-extending care in two out of three age strata (42% vs
32% in those aged <45 years; 69% vs 62% in those aged 265 years). However,
economic hardship on the family did not affect either the frequency or direction
of patient-surrogate disagreements about whether the goal of care should be
focused on maximizing comfort or prolonging life (73.7% agreement between
patients and surrogates in those not reporting economic hardship, and 72%
agreement in those reporting economic hardship).
7. Covinsky KE, Wu AW, Landefeld CS et al. Health status
versus quality of life in older patients: Does the distinction matter? Am J Med
Two months after a hospitalization, 493 cognitively
intact patients older than age 80 were asked to describe their health status
using scales that described four domains (physical capacity, limitations in
performing activities of daily living, psychological distress and pain).
Patients were also asked to rate their quality of life. Each of the four health
status scales was significantly correlated with patients' global quality of
life. However, for a substantial number of patients, scores on the health status
scales did not accurately reflect their global quality of life. The results
suggest that whereas health status may be a marker of the quality of life for
populations of patients, assumptions about the quality of life of individual
patients should not be based on their health status alone.
8. Desbiens NA, Wu AW, Broste SK et al. for the SUPPORT
Investigators. Pain and satisfaction with pain control in seriously ill
hospitalized adults: Findings from the SUPPORT research investigations. Crit
Care Med 1996:24:1952-1961.
A total of 5176 of 9105 patients or surrogates were
interviewed about their experiences with pain while in the study. Nearly 50% of
patients reported pain. About 15% reported extremely severe pain or moderately
severe pain occurring at least half the time, and nearly 15% were dissatisfied
with pain control.
9. Desbiens NA, Mueller-Rizner N, Connors AF, Wenger NS.
The relationship of nausea and dyspnea to pain in seriously ill patients. Pain
1997;71:149-156. A cohort of patients (n = 1556) were interviewed approximately
8 days after study admission about the frequency and severity of their nausea,
dyspnea and pain.
Ordinal logistic regression was used to test the
independent association of pain with nausea and dyspnea. With increasing levels
of nausea and dyspnea, patients reported more pain.
10. Desbiens NA, Mueller-Rizner N, Connors AF Jr, et al.
for the HELP Investigators. Pain in the oldest-old during hospitalization and up
to one year later. J Am Geriatr Soc 1997;45:1167-1172.
Patients 80 years and older or their surrogates were
interviewed about level of pain during hospitalization (n = 806 patients), at 2
months (n = 614 patients), and at 12 months (n = 416 patients). In this cohort
of oldest-old hospitalized patients, 45.8% complained of pain, and 19.0%
reported extremely severe pain or moderately severe pain occurring at least half
of the time. Pain levels appeared to be equal to those of SUPPORT patients who
were younger and sicker.
11. Desbiens NA, Wu AW, Aizola C et al.for the SUPPORT
Investigators. Pain during hospitalization is associated with continued pain six
months later in survivors of serious illness. ArnJ Med 1997;102:269-276.
Patients or surrogates were interviewed about patient
level of pain during hospitalization. Investigators used separate ordinal
logistic regressions to compare levels of pain over time. Patients who reported
high levels of pain during hospitalization were likely to report high levels
12. Desbiens NA, Wu AW, Masui Y et al. and the SUPPORT
Investigators. Patient empowerment and feedback does not decrease pain in
seriously ill hospitalized adults. Pain 1998;75:237-246.
Of 4804 Phase n patients, 2652 received the care of an
intervention nurse who assessed pain) educated patients and families about pain
control, empowered patients to expect pain relief, informed patients' nurses and
physicians about level of pain, and suggested or used other pain management
resources. Interviewers assessed patient pain level from interviews with
patients or surrogates at baseline and 2 and 6 months later. Overall, 50.9% of
patients reported some pain. Intervention patients did not report a significant
improvement in level of pain compared to control patients.
13. Desbiens NA, Mueller-Rizner BS, Hamel MB, Connors AF
Jr. for the SUPPORT Investigators. Preference for comfort care does not affect
the pain experience of seriously ill patients. J Pain Symptom Manage 1998;
Patients (n = 2820) responded to interview questions
about willingness to live permanently in pain, and choice of a course of
treatment aimed at extending life or relieving pain.
They answered these questions at an early interview time
(between study days 2 and 6) and at a later time (between days 8 and 10 in Phase
I, and 8 and 12 in Phase II). Of the 2820 patients, 1388 (49.2%) reported that
they preferred a course of care aimed at relieving pain, and 1426 (50.6%) were
very unwilling or would rather die than be in permanent pain. At the later
interview, 655 (23.2%) reported that they had extremely severe or moderately
14. FitzGerald JD, Wenger NS, Califf RM et al. for the
SUPPORT Investigators, functional status among survivors of in-hospital
cardiopulmonary resuscitation. Arch Intern Med 1997;157:72-76.
Functional status data were available both before and
after CPR for 162 SUPPORT patients. Investigators performed logistic regression
using specific variables to evaluate predictors of worse functional status 2
months after CPR. Of the 162 patients, 91 (56%) had preserved or improved
functional status and 71 (44%) had worse functional status after CPR. Patients
with worse functional status were extremely disabled and less likely to survive
to hospital discharge or to 6 months.
15. Galanos AN, Pieper CP, Kussin PS et al. for the
SUPPORT Investigators. Relationship of body mass index to subsequent mortality
among seriously ill hospitalized patients. Crit Care Med 1997;25:19621966
Body mass index (BMI) was calculated for 3002 Phase I
patients. Investigators reported that even when controlling for multiple disease
states and physiologic variables and removing from analysis all patients with
significant prior weight loss, a BMI <= 15th percentile remained a
significant and independent predictor of mortality.
16. Goodlin SJ, Zhong Z, LynnJ et al. factors associated
with use of cardiopulmonary resuscitation in seriously ill hospitalized adults.
JAMA 1999; 282:2333-2339.
Investigators performed a secondary analysis of SUPPORT
and HELP data to identify factors associated with use of cardiopulmonary
resuscitation(CPR). Of 10,281 SUPPORT and HELP patients, 2505 experienced
cardiopulmonary arrest and 514 of these received CPR. Multivariable analysis
revealed that use of CPR was more likely in men (odds ratio (OR) 1.39; 95%
confidence interval (Cl), 1.121.73), younger patients (OR per 10-year increase
0.90; 95%d, 0.84-0.96), blacks (OR 1.76; 95%CI, 1.33-2.34), patients whose
reported preferences were for CPR (OR = 2.60; 95%CI, 1.91-3.55), who reported
better quality of life (OR 1.49; 95%CI, 1.10-2.03), or who had higher physician
estimates for 2-month survival (OR per 10% increase 1.14; 95%CI, 1.09-1.19). The
likelihood of having CPR varied by hospital site and diagnosis. Patients with
congestive heart failure had a much higher likelihood of having CPR (adjusted OR
3.31; 95%CI, 2.12-5.15) compared with patients with acute respiratory failure or
multiple organ system failure.
17. Haidet P, Hamel MB, Davis RB et al. for the SUPPORT
Investigators. Outcomes, preferences for resuscitation, and physician-patient
communication among patients with metastatic colorectal cancer. Am J Med
Researchers studied a cohort of 520 patients with
metastatic colon cancer. Patients were interviewed about functional status,
whether any weight loss had occurred, anxiety, depression, quality of life and
the patient's estimates for their probability of 2- and 6- month survival.
Patients or surrogates were interviewed about patient CPR preference and whether
they had discussed their preference with their doctor. Quality of life and
functional status were high at study entry and remained so at 2 and 6 months. Of
the 339 patients or surrogates who expressed a preference about CPR, 127 (37%)
noted a preference not to have CPR; of these, 69 did not have do-not-resuscitate
18. Hakim RB, Teno JM, Harrell FE Jr. et al. for the
SUPPORT Investigators. Factors associated with do-not resuscitate orders:
Patients' preferences, prognoses and physicians judgment. Ann Intern Med
Patients or surrogates (n = 6802) were interviewed about
patients' cardiopulmonary resuscitation preferences and answers were correlated
with disease severity and timing of DNR orders. Investigators concluded that
patients' preferences for cardiopulmonary resuscitation and poor short-term
prognosis were associated with timing of DNR order. Increased age was frequently
associated with DNR order regardless of prognosis.
19. Hamel MB, Goidman L, Teno JM et al. Identification
of comatose patients at high risk for death or severe disability. JAMA
A total of 596 of the SUPPORT patients had non-traumatic
coma. Primary causes of coma were cardiac arrest (31%) or cerebral infarction or
intra-cerebral hemorrhage (36%). On the third day after study enrollment, five
clinical variables were independently associated with 2-month mortality.
Mortality at 2 months for patients with four or five of the following risk
factors was 97%: abnormal brain stem response, absent verbal response, absent
withdrawal response to pain, creatinine level greater than or equal to 1.5
mg/dL, and aged 70 years or older.
20. Hamel MB, Phillips RS, Teno JM et al for the SUPPORT
Investigators. Seriously ill hospitalized adults: Do we spend less on older
patients? J Am Geriatr Soc 1996;44:1043-1048.
To assess resource utilization, records were reviewed
for 4301 patients using a modified version of the Therapeutic Intervention
Scoring System (TISS), for performance of three invasive procedures (major
surgery, dialysis, and right heart catheterization) and for estimated hospital
costs. Results were compared in patients aged older and younger than SO.
Investigators noted that older patients were less likely than younger patients
to undergo major surgery (3% vs 8%), dialysis (1% vs 8%), and right heart
catheterization (17% vs 34%). Older patients had median estimated hospital costs
that were lower than those of patients who were younger ($9,422 vs $22,339).
These differences persisted after adjustment, suggesting that the results were
not related to differences in patients' severity of illness or preferences for
21. Hamel MB, Phillips RS, Davis RB et al. for the
SUPPORT Investigators. Outcomes and cost-effectiveness of initiating dialysis
and continuing aggressive care in seriously ill hospitalized adults. Ann Intern
Records were reviewed to identify patients who had begun
dialysis while in SUPPORT. Charts of this cohort (490 patients) were reviewed
and investigators determined that the median survival after dialysis initiation
was 32 days. Only 27% of patients were alive 6 months after dialysis was
Patients who survived had a median of one dependency in
activities of daily living, and 62% of survivors rated their quality of life as
"good" or better. The overall estimated cost per quality-adjusted life-year
(QALY) saved by providing dialysis was $128,200, which is well above $50,000 per
QALY, an amount cited commonly for cost-effective care. However, the cost per
QALY for the 94 patients with the best prognosis was $61,900.
22. Hamel MB, Teno JM, Goldman L et al. Patient age and
decisions to withhold life-sustaining treatments from seriously ill,
hospitalized adults. Ann Intern Med 1999; 130:116-125.
Among SUPPORT patients for whom each treatment issue
arose, decisions were made to withhold ventilator support for 30% (1600 of
3571), to withhold surgery for 13% (375 of 2982) and to withhold dialysis for
29% (380 of 1298). Analysis revealed that decisions were more common in older
age groups even after adjustment for socio-demographic characteristic,
prognoses, baseline function, patients' preferences for life-extending care and
physicians' understanding of patients' preferences for life-extending care.
23. Hamel MB, Davis RB, Teno JM et al. Older age,
aggressiveness of care, and survival for seriously ill hospitalized adults. Ann
Intern Med 1999;131:721-728.
Investigators reviewed the records of the 9105 SUPPORT
patients to test the hypothesis that older age is associated with less
aggressive treatment and higher short-term mortality caused by serious illness.
Authors noted that mean patient age was 63, 44% of patients were female, and 16%
were black. They found that adjusted estimates of age specific 6-month mortality
rates were 44% for 55-year-old patients, 48% for 65-year-old patients, 53% for
75-year-old patients, and 60% for 85-year-old patients. Acute physiology and
patients' diagnoses were more highly correlated with prognosis than patient
24. Harrell FE, Jr., Lee KL, Mark DB. Multivariable
prognostic models: Issues in developing models, evaluating assumptions and
adequacy and measuring and reducing errors. Stat Med 1996;15:361-387.
Authors discuss an easily interpretable index of
predictive discrimination and methods for assessing calibration of predicted
survival probabilities. Since multivariable regression models are used often in
clinical outcome studies, authors review the drawbacks to having poorly fitted
or over-fitted regression models and recommend a modeling strategy that avoids
many of the problems discussed.
25. Hiltimen EF, Puopolo AL, Marks GK et al. The nurse's
role in end-of-life treatment discussions: Preliminary report from the SUPPORT
project. J Cardiovasc Nurs 1995;9:68-77.
Of staff nurses interviewed (n = 1369), 65% reported
having knowledge of their patients' CPR preference. Nurses were significantly
more likely to have discussed CPR preferences when a patient did not want CPR
(32%) than when a patient did want CPR (13%). They were also significantly more
likely to have discussed CPR when they had known the patient longer and when the
patient was conversant and cognitively intact. This article describes the
purposes of Phase II and the role of the nurse facilitator but does not
Phase II results. After 18 months of Phase II, SUPPORT
nurse activities aimed at increasing communication were focused on participating
in team rounds, family conferences and chart documentation.
26. Hiltunen EF, Medich C, Chase S et al. Family
decision making for end-of-life treatment: The SUPPORT nurse narratives. J Clin
Ethics 1999; 10:126-134.
During SUPPORT, 18 specially trained nurses had contact
with 2519 seriously ill patients and their families. Authors of this article
review narratives from these encounters, focusing on 75 incidents representing
They found that families were involved in three-quarters
of the decisions, often because of the severity of patients' illnesses. In
addition, authors noted that nurses found that the decision-making process was
often quite complex.
27. Hofmann JC, Wenger NS, Davis RB et al. for the
SUPPORT Investigators. Patient preferences for communication with physicians
about end-of-life decisions. Ann Intern Med 1997;127:l-12.
Phase II patients (n = 1832) were interviewed about
their preferences for CPR. Researchers found that of those who responded to the
question on CPR preferences (n = 1589), only 366 (23%) had discussed preferences
with their physicians. Of the 1223 patients who had not discussed their
preferences with their physician, 707 (58%) were not interested in doing so.
28. Jaagosild P, Dawson NV, Thomas C et al. Outcomes of
acute exacerbation of severe congestive heart failure: Quality of life, resource
use, and survival. SUPPORT Investigators. The Study to Understand Prognosis and
Preferences for Outcomes and Risks of Treatments. Arch Intern Med
Of the 9105 patients enrolled in SUPPORT, 1404 patients
were admitted with acute exacerbation of severe congestive heart failure.
Researchers reviewed the charts and interviewed 1390 patients or surrogates.
Analysis revealed that survival was 93.4% at discharge from the index
hospitalization, 72.9% at 180 days, and 61.5% at I year. Of patients interviewed
at 180 days, the median health rating on a scale of 0 to 100 (0 indicates death;
100, excellent health) was 60, and 59.7% were independent in their activities of
daily living. However, patients with worse functional status were more likely to
29. Kennard MJ, Speroff T, Puopolo AL et al.
Participation of nurses in decision making for seriously ill adults. Clin Nurs
In the Phase I portion of SUPPORT, a cohort of nurses (n
= 696) were interviewed about their knowledge of patients' preferences for care.
Patients, surrogates and physicians were also interviewed. Many nurses reported
having no (31%) or little (36%) knowledge of their patients' preferences. While
nurses and physicians did not think that the nurse had much influence on
decision-making for the patient, patients and surrogates were more likely to
report that conversations with nurses were helpful in deciding which type of
care the patient would receive.
30. Knaus WA, Harrell FE, Lynn Jet al. The SUPPORT
prognostic model: Objective estimates of survival for seriously ill hospitalized
adults. Ann Intern Med 1995;122:191203.
SUPPORT investigators hypothesized that accurate
prediction of risk for death might help physicians in clinical decision making.
Researchers developed a prognostic model using the following variables:
diagnosis, age, number of days in hospital before study entry, presence of
cancer, neurologic function and II physiologic measures recorded on Day 3 after
study entry. Analysis revealed that the prognostic model was generally equal to
physician clinical prediction, and that the two used together yielded more
31. Krumholz HM, Phillips RS, Hamel MB et al. for the
SUPPORT Investigators. Resuscitation preferences among patients with severe
congestive heart failure: Results from the SUPPORT project. Circulation 1998;
Of 9105 patients, 1404 were hospitalized with severe
exacerbation of congestive heart failure (New York Heart Association class IV,
or clinical heart failure and an ejection fraction < 20%). Of the 1404
patients hospitalized, 936 participated in interviews about resuscitation
preferences. Of these patients, 215 (23%) did not wish to be resuscitated in
case of cardiac arrest. Only 25% of patients had discussed their preferences
with their physician. Physicians did not perceive their patient's preference
correctly 24% of the time.
32. Layde PM, Beam CA, Broste SK et al. Surrogates'
predictions of seriously ill patients' resuscitation preferences. Arch Fam Med
Researchers compared patient preferences for CPR with
surrogate belief of CPR choice in 1226 patient-surrogate pairs. Surrogates
stated patient wishes incorrectly in 50% of the cases where the patient did not
wish to have CPR. For patients who preferred CPR, only 16% had discordance with
33. Layde PM, Broste SK, Desbiens N et al.
Generalizability of clinical studies conducted at tertiary care medical centers:
A population-based analysis. J Clin Epidemiol 1996;49:835-844
This article discusses the results of a population-based
ancillary study in the Marshfield Epidemiologic Study Area (MESA) in which one
of the SUPPORT hospitals resides. Researchers attempted to identify nursing home
residents, hospice patients and outpatients who were residents of the MESA who
would have been eligible for the main SUPPORT study if they had been
hospitalized. Reviewing their data, they were able to estimate that 400,000
patients per year would fulfill SUPPORT eligibility criteria in the United
States, but an estimated 925,000 patients each year have SUPPORT-like illnesses
and do not receive intensive care.
34. Lynn J,Johnson J, Levine RJ. The ethical conduct of
health services research: A case study of 55 institutions' applications to the
SUPPORT project. Clin Res 1994;42:310.
Fifty institutions unsuccessfully applied to be
participants in the multicenter health services research project, SUPPORT.
Authors analyze these institutions' approach to defining informed consent and
comment on perceived risks to participants: patients, surrogates, physicians.
The troubling boundary between quality improvement and research and the ethics
of human subjects protection is a special focus.
35. LynnJ, Teno JM, Harrell FE. Accurate
prognostications of death: Opportunities and challenges for clinicians.
This article describes the importance of prognostication
in seriously ill patients and makes recommendations for translating a prognostic
model into clinical practice.
36. Lynn J, Harrell FE Jr., Cohn F et al. for the
SUPPORT Investigators. Defining the "Terminally Ill:" Insights from SUPPORT.
Duquesne Law Rev 1996;2S:311-336.
This article describes three possible strategies for
defining persons who are terminally ill. Definitions of terminal illness are
helpful in developing public policy, but this article suggests that any
definition will exclude some persons with true terminal illness and include some
without such illness. Because of the inability to accurately prognosticate, the
authors conclude that it would be inappropriate to allow for physician-assisted
suicide using a standard definition of terminal illness.
37. Lynn J, Harrell F Jr., Cohn F et al. Prognoses of
seriously ill hospitalized patients on the days before death: Implications for
patient care and public policy. New Horiz 1997;5:56-61.
The median SUPPORT model-generated likelihood of
surviving 2 months was 17% the day before death and 51% I week before death in
SUPPORT. For an APACHE HI based model, the predicted probability of survival was
14% the day before death and 45% I week before death. Different diseases had
dramatically different prognoses near death.
38. Lynn J, Teno JM, Phillips RS et al. for the SUPPORT
Investigators. Perceptions by family members of the dying experience of older
and seriously ill patients. Ann Intern Mcd 1997;126:97-106.
Surrogate decision-makers of patients in SUPPORT, Phases
I and II, and HELP were interviewed after a patient's death about the
circumstances of the death. Overall, surrogates reported that 55% of patients
were conscious in the last 3 days of life and that pain, dyspnea and fatigue
were common in these patients. In 59% of the cases, patients had preferred a
treatment plan that focused on comfort but actual care was not consistent with
preferred care 10% of the time.
39. Lynn J, Zhong Z, Dawson NV et al. Physician
experience caring for dying patients and its relationship to patient outcomes. J
Palliat Care 1998;337-346.
Attending physicians (n = 765) were interviewed about
their experiences caring for dying patients. Oncologists and pulmonologists or
critical care physicians were more likely to have cared for dying patients.
Physician interview responses were compared with interviews of surrogates.
Analyses showed that physicians' experience with dying had no significant effect
on patients' experience with pain, anxiety or depressive symptoms in their last
3 days of life.
40. Marbella AM, Desbiens NA, Mueller-Rizner N, Layde
PM. Surrogates' agreement with patients' resuscitation preferences: Effect of
age, relationship and SUPPORT intervention. J Crit Care 1998;13:140-145.
This article reviews 717 Phase II patient-surrogate
pairs and their agreement or disagreement about CPR preferences. Of these, 386
were assigned to specially trained nurses who spent extra time with patients and
families explaining prognoses and treatments. Rates of agreement in
resuscitation preferences increased by 4.6% from the study Day 3 interview to
the Month 2 interview; however, there was no significant difference between
intervention and control groups.
41. Matrimore 1J, Wenger NS, Desbiens NA et al.
Surrogate and physician understanding of patients' preferences for living
permanently in a nursing home. J Am Geriatr Soc 1997:45:818-824.
Patients' willingness to live permanently in a nursing
home was measured on a 5-point scale. The following results were noted in 3262
Phase I and II patients: 7% were "very willing" to live permanently in a nursing
home, 19% "somewhat willing," 11% "somewhat unwilling," 26% "very unwilling,"
and "30% would rather die." Overall, surrogate perceptions of patient
willingness to live permanently in a nursing home were similar to patient
preferences. Physicians were able to identify patient preference correctly 67%
of the time.
42. Oliverio R, Fraulo B. SUPPORT Revisited: The Nurse
Clinician's Perspective. Holist Nurse Pract 1998;13:l-7.
This article presents personal accounts of the nurse
clinician's role in SUPPORT. The two authors, who worked as nurse clinicians at
one of the SUPPORT hospitals, describe dealing with seriously ill patients in
SUPPORT. Reflecting on their experiences, they make recommendations for
education about death and dying and for the role of nurse clinicians in the
43. Phillips RS, Hamel MB, Tcno JM et al. for the
SUPPORT Investigators. Race, resource use, and survival in seriously ill
hospitalized adults. J Gen Intern Med 1996;ll:387-396.
Investigators measured resource use in the SUPPORT
cohort. They found that, in adjusted analyses, blacks were less likely to
receive five procedures (operation, dialysis, pulmonary artery catheterization,
endoscopy, and bronchoscopy) on study Days I and 3, and blacks had lower
Therapeutic Intervention Scoring System scores, indicating lower resource use.
However, blacks had slightly better survival, adjusted for severity.
44. Phillips RS, Wenger NS, Teno JM et al. for the
SUPPORT Investigators. Choices of seriously ill patients about cardiopulmonary
resuscitation: Correlates and outcomes. Am J Med 1996;100:128-137.
A cohort of patients was interviewed about preferences
for CPR, quality of life, functional status, perceptions of prognosis and
whether patients had discussed CPR with physicians. Of those interviewed (n =
1650), 28% did not want CPR. Of these, only 48% reported having discussions with
their physicians about CPR. Overall, only 29% of patients had discussed their
preferences with their physicians.
Factors associated with not wanting CPR included:
hospital site, diagnosis, being older, being more functionally impaired and
patient perception of a worse prognosis. Patients who did not prefer CPR had a
lower intensity of care, similar inhospital mortality and higher mortality at 2
and 6 months following study entry.
45. Pritchard RS, Fisher ES, Teno JM ct al. Influence of
patient preferences and local health system characteristics on the place of
death. J Am Geriatr Soc 1998; 46:1242-1250.
Researchers reviewed places of death (hospital or
non-hospital) in SUPPORT patients and compared these with those of a sample of
Medicare beneficiaries who died in 1992-1993. The percent of SUPPORT patients
dying inhospital varied from 29 to 69% across the five SUPPORT sites, and the
percent of Medicare beneficiaries dying inhospital varied from 23% to 54% across
US Hospital Referral Regions (HRRs). The likelihood of in-hospital death was
increased for residents of regions with greater hospital bed availability and
use, and the likelihood of in-hospital death was decreased in regions with
greater nursing home and hospice availability and use. This effect explained 80%
of the variation across regions. Within the HRRs, the number of acute care
hospital beds per 1000 population ranged from 1.86 in Mesa, Arizona to 5.30 in
Monroe, Louisiana (mean, 3.3).
46. Puopolo AL, Kennard MJ, Mallatratt L et al.
Preferences for cardiopulmonary resuscitation. Image. J Nurs Scholarship
This study reviews interview responses from nurses (n =
1427) regarding discussions about CPR with their patients. Nurses reported
having discussions about CPR with 13% of their patients. Discussions were more
likely if the nurse thought that the patient did not want CPR, if the nurse had
spent more time with the patient, or if the patient was in an intensive care
unit at time of study entry. Nurses understood preferences accurately for 74% of
47. Regueiro CR, Hamel MB, Davis RB et al.. A comparison
of generalist and pulmonologist care for patients hospitalized with severe
chronic obstructive pulmonary disease: resource intensity, hospital costs and
survival. Am J Med 1998; 105:366-372.
The study described in this article compared generalists
and pulmonologists in their care of the seriously ill COPD patient.
Investigators reviewed the charts of 866 patients with severe COPD. Resource
intensity was measured using a modified version of the Therapeutic Intervention
Scoring System and estimated hospital costs. Of the 866 patients studied, 512
were treated by generalist physicians, and 354 were treated by pulmonologists.
Investigators did not find any differences in resource intensity and hospital
costs in-patients treated by pulmonologists compared with generalists. Patients
treated by pulmonologists did not have better survival.
48. Roach MJ, Connors AF, Dawson NV et al. for the
SUPPORT Investigators. Depressed mood and survival in seriously ill hospitalized
adults. Arch Intern Med 1998;158:397-404.
Phase I and II patients (n = 3,529) were interviewed
using a shortened version of the Profile of Mood States depression scale.
Researchers used a stratified Cox proportional hazards model to study
association of depressed mood with worse levels of physical functioning.
Depressed mood was associated with reduced survival time even after adjusting
for patients' demographics and health status.
49. Rosenfeld KE, Wenger NS, Phillips RS et aLfor the
SUPPORT Investigators. Factors associated with change in resuscitation
preference of seriously ill patients. Arch Intern Med 1996;156:1558-1564.
Investigators interviewed patients (n = 1590) about CPR
preference on study Day 3 (baseline) and 2 months later. Overall, 80% of
patients had stable preferences over 2 months. For patients initially preferring
DNR, patients with substantial improvements in depression scores were more
likely to change to a preference for CPR at follow-up.
50. Schroeder SA. The legacy of SUPPORT. Ann Intern Med
The author discusses his thoughts on the impact of
SUPPORT. He asserts that despite the failure of the study to show improvement of
end-of-life care in the seriously ill, SUPPORT has proven itself to be a worthy
51. The SUPPORT Principal Investigators. A controlled
trial to improve care for seriously ill hospitalized patients: The study to
understand prognoses and preferences for outcomes and risks of treatments
(SUPPORT). JAMA 1995;274:1591-1598,
This article reviews the primary goals of SUPPORT and
describes the findings of the Phase I observational trial and the Phase II
clinical trial. Phase I analysis of 4301 patients revealed that communication
about CPR preference was uncommon, moderate to severe pain affected half of
dying patients able to communicate, and final hospitalizations for half of the
patients included more than 8 days in the ICU.
Also, nearly half of DNR orders were written in the last
2 days of life. In Phase II, trained nurses were provided to intervention
patients to provide prognostic information to patients and physicians, and to
elicit and document patient and family preferences and understanding of disease
prognosis and to improve pain management. Analysis revealed that intervention
patients did not show an improvement in any of the targeted outcomes.
52. Teno J, Lynn J, Phillips RS et al. for the SUPPORT
Investigators. Do formal advance directives affect resuscitation decisions and
the use of resources for seriously ill patients? J Clin Ethics 1994;5:23-30.
This analysis of advance directives reveals that 20.2%
of patients (618 out of 3058) for whom information was available had written
advance directives, 11% had living wills, 13.4% had Durable Powers of Attorney,
and 4.9% had both. The investigators noted that there was no significant
association between the existence of advance directives and decisions about
53. TenoJM, Murphy D, Lynn J et al. for the SUPPORT
Investigators. Prognosis-based futility guidelines: Does anyone win? J Am
Geriatr Soc 1994;42:1202-1207. This article estimates the savings in hospital
costs for SUPPORT patients that would be realized if cases deemed futile had
forgone or withdrawn life-sustaining treatment.
Approximately $1.2 million would have been saved if, on
study Day 3, life-sustaining treatment had been stopped or not initiated for the
115 subjects with estimated 2-month survival probability of less than or equal
to 1%. However, only 12 patients accounted for 75% of the potential savings, and
they were often young, acutely ill or posttransplant patients with good prior
54. Teno JM, Hakim RB, Knaus WA et al. for the SUPPORT
Investigators. Preferences for cardiopulmonary resuscitation: Physician-patient
agreement and hospital resources. J Gen Intern Med 1995;10:l 79-186. Patients or
surrogates (n = 2636) and matched physicians were interviewed about patients'
CPR preferences. Answers were correlated with hospital resource use. Adjusted
hospital resource consumption was lowest when the patient and physician agreed
on a do-not-resuscitate preference and highest when patients did not have a
preference and the physician believed patients wanted resuscitation in the case
of cardiopulmonary arrest.
55. Teno J, Lynn J, Wenger N for the SUPPORT
Investigators. Advance directives for seriously ill hospitalized patients'
Effectiveness with the patient self-determination act and the SUPPORT
intervention. J Am Geriatr Soc 1997;45:500-507.
Researchers reviewed interviews conducted with patients,
surrogates, and physicians about awareness, completion, and impact of Advance
Directives (ADs). The authors compared responses before (PRE) and after (POST)
the Patient Self-Determination Act (PSDA) and after the SUPPORT intervention
(POST+SUPPORT). Before the PSDA (PRE), 62% were familiar with a living will, and
21% had an Advance Directive. These rates were similar for the POST and
POST-SUPPORT cohorts, but advance directive documentation increased by 78% in
these groups. The POST patients with and without Advance Directives had no
significant differences in the rates of medical record documentation of
discussion about resuscitation, DNR orders among those who wanted to forgo
resuscitation, and attempted resuscitations at death. The POST+SUPPORT patients
had similar results.
56. Teno J, Lynn J, Connors AF, Jr. et al. for the
SUPPORT Investigators. The illusion of end-of-life resource savings with advance
directives. J Am Geriatr Soc 1997;45:513-518.
Three analyses were performed to help answer the
question of whether increasing advance directives (ADs) would lead to a
reduction in resource utilization. Investigators reviewed charts of patients
both before enactment of the Patient Self Determination Act (PSDA) and after. In
the latter group, 2652 patients received an intervention: a nurse was provided
to facilitate communication among patients, surrogates and physicians about
preferences for and outcomes of treatments. Control patients (n = 2152) received
no intervention. Researchers found increased chart documentation of existing
advance directives with both PSDA and SUPPORT. Intervention patients were more
likely to have preexisting ADs documented, but there was no corresponding change
in hospital resource use for those who died during hospitalization. Also, ADs
documented without further intervention by study Day 3 were associated with
decreased hospital resource use, but the same was not true for cases where
intervention was used to increase AD documentation. The authors concluded that
ADs are not likely to lead to a reduction in resource use.
57. Teno JM, Licks S, Lynn J et al. for the SUPPORT
investigators. Do advance directives provide instructions that direct care? J Am
Geriatr Soc 1997;45:508-512.
For the 4804 patients in Phase II, authors found 688
Advance Directives (ADs) written by 569 patients. Almost all of the documents
were witnessed (88%), and 38% were notarized. Of these directives, 453 were
durable powers of attorney for health care, 214 were living wills, and 21 were
other directives. Only 90 (13%) directives provided additional instructions for
medical care beyond naming a proxy or staring preferences of a standard living
will, only 36 (5%) gave specific instructions to guide the use of
life-sustaining treatment, and only 22 (3%) might have been applicable to the
patient's current situation.
58. Teno JM, Stevens M, Spernak S, Lynn J. Role of
written advance directives in decision making: Insights from qualitative and
quantitative data. J Gen Intern Med 1998:13:439-446.
Investigators reviewed quantitative and qualitative data
in 14 patients to better understand the role of Advance Directives (ADs) in
decision making. The 14 patients studied were selected based on the report of
having a written Advance Directive and meeting at least one of the following
criteria: comatose as defined by a Glasgow coma score of 9 or less and 2-month
prognosis for survival of 40% or less; or death during enrollment
hospitalization. Investigators extensively reviewed qualitative data: narratives
written by intervention nurse written shortly after the patient's death or
discharge from the hospital. These data suggested that advance directives played
an important role in decision-making in five of the 14 cases.
59. Teno JM. Lessons learned and not learned from the
SUPPORT project (editorial). Palliat Med 1999;13:91-93. In this editorial, the
author (a SUPPORT investigator) notes the failure of SUPPORT to enhance
communication or physician understanding of patient preferences. The author
discusses important lessons learned through SUPPORT which include: (1) seriously
ill patients are often able to communicate preferences; (2) there are
opportunities for improvement in care of the seriously ill, especially for
communication regarding preferences and pain control; (3) there may be a chance
to affect change in the care of the terminally ill by working at the local
level; and (4) we cannot assume that all seriously ill patients want less care.
More research needs to be done regarding this fundamental assumption. 60. Tsevat
J, Cook F, Green ML et al. for the SUPPORT Investigators. Health values of the
seriously ill. Ann Intern Med 1995;122:514-520.
Investigators measured time-tradeoff (TTO) utilities and
health ratings in 1438 patients. TTO utilities indicate whether seriously ill
patients would prefer a shorter, healthier life to their current state of
health. Patients' answers varied widely. 34.8% of patients were unwilling to
give up any time in their current state of health for a shorter life in
excellent health, and 9.0% were willing to live 2 weeks or less in excellent
health rather than I year in their current state of health. Health rating scales
averaged 57.8 ± 24.0 on a scale of 0 (death) to 100 (perfect health), and
patient's health values exceeded that of their paired surrogate.
61. Tsevat J, Dawson NV, Wu AW, Lynn J, Soukup JR, Cook
EF, VidaiUet H, Phillips RS, for the HELP Investigators. Health values of
hospitalized patients 80 years or older. JAMA 1998;279:371-375.
Of 1,266 HELP patients, 414 successfully completed
interview questions pertaining to health value. These patients were asked
whether they would prefer living only I year in their current state of health or
less time in excellent health. The mean time-trade-off score was .81 which
indicated that, on average, patients equated living I year in their current
state of health with living 9.7 months in excellent health. However,
time-trade-off scores varied widely. In 300 patient surrogate pairs, the mean
patient time-trade off scale was .80 while the mean surrogate value was .05
less. 61 (20.3%) of surrogates underestimated the patient's time-trade off score
by .25 (3 months of 12) or more.
62. Weeks JC, Cook EF, O'Day SJ, Peterson LM, Wenger N,
Reding D, Han-ell FE, Kussin P, Dawson NV, Connors AF, Jr., Lynn J, Phillips RS.
Relationship between cancer patients' predictions of prognosis and their
treatment preferences. JAMA 1998;279:1709-1714.
Phase I and II patients with stage III or IV non-small
cell lung cancer or colon cancer metastatic to the liver (n = 977) were
interviewed for estimates of their own prognoses and for their treatment
preferences. Patients who thought they had a high probability of surviving for
at least 6 months were more likely to favor life-extending therapy over comfort
care, compared to patients who thought there was at least a 10% chance that they
would not live 6 months. Patients overestimated their chances of 6-month
survival while physicians' predictions were more accurate. Patients' overly
optimistic views of their prognosis led to adverse outcomes.
63. Wenger NS, Oye RK, Bellamy PE, Lynn J, Phillips RS,
Desbiens NA, Kussin P, Youngner SJ. Prior capacity of patients lacking decision
making ability early in hospitalization: Implications for advance directive
administration. J Gen Intern Med 1994;9:539-543.
SUPPORT investigators reviewed the ability of seriously
ill patients to make decisions regarding advance directives at hospitalization.
Forty percent of patients were not considered interviewable due to reasons such
as coma, intubation, or failing a cognitive screen. However, 83% of those that
could not participate in an interview could have participated in treatment
decisions 2 weeks before hospitalization.
64. The SUPPORT Investigators: Wenger NS, Oye RK,
Desbiens NA, Phillips RS, Teno JM, Connors AF, Liu H, Zemsky MF, Kussin P. The
stability of DNR orders on hospital readmission. J Clin Ethics 1996;7:48-54.
Researchers reviewed patients' charts for DNR orders.
Of 3,710 patients with DNR orders, 543 were subsequently
readmitted to the same hospital during the six-month study period. In 157 cases
(29%), the DNR order differed from first admission to the next admission.
Multivariable analysis revealed that patients with unstable DNR orders were
younger and more likely to be non-white. Patients from one hospital, white
patients, and patients with metastatic cancer and COPD had greater stability of
65. Wenger NS, Greengold NL, Oye RK, Kussin P, Phillips
RS, Desbiens NA, Lin H, Hiatt JR, Teno JM, and Connors AF, Jr. for the SUPPORT
Investigators. Patients with DNR orders in the operating room: Surgery,
resuscitation, and outcomes. J Clin Ethics 1997:8:250-257.
Of 4,301 patients in Phase 1,1,251 were considered for
surgery. Of these, 119 (10%) had DNR orders recorded on the chart. Of the
patients considered for surgery who had DNR orders, 48% (57) underwent surgical
procedures. In contrast, 61% of patients without a DNR order who were considered
for surgery had surgery.
66. Wilson ffi. Green ML, Goidman L, TsevatJ, Cook EF,
Phillips RS, for the SUPPORT Investigators. Is experience a good teacher? How
interns and attending physicians understand patients' choices for end-of-life
care. Med Deds Making 1997:17:217-227.
Medical interns and attending physicians at one SUPPORT
hospital were interviewed about their Phase I patients' preferences for CPR and
their thoughts about patient willingness to live with a series of undesirable
outcomes. Answers were compared to responses from patients or surrogates.
Compared with interns, attending physicians had known the patients longer, had
talked with patients more frequently about prognosis, and felt they knew more
about their patients' preferences. However, attending physicians were no more
accurate than interns in predicting patients' preferences.
67. Wn AW, Damiano AM, Lynn J, Aizola C, Teno J,
Landefdd CS, Desbiens N, Tsevat J, Mayer-Oakes A, Harrell HE, Knaus WA.
Predicting future functional status for seriously ill hospitalized adults: The
SUPPORT model. Ann Intern Med 1995;122:342-350.
At 2 months after admission, one third of patients
available for assessment (n = 1746) had severe functional limitations defined as
Sickness Impact Profile scores >= to 30 or activities of daily living scores
>= 4. Researchers also noted that patients had frequent functional change:
21% who had no baseline dependencies were limited at 2 months, and 30% of those
with 4 or more baseline limitations had improved at 2 months. Using clinical and
questionnaire data, it is possible to predict future functional status with
acceptable accuracy. 68. Zhong Z., Lynn J. The Lamont/Christakis Article
Reviewed. Oncology 1999; 13: 1172-1173.
Authors discuss the suggestion by Lamont and Christakis
that physicians' survival estimates in advanced cancer patients are relatively
inaccurate and biased in the direction of excessive optimism in light of SUPPORT
findings. Authors analyzed SUPPORT data of patients with advanced cancer (colon
n = 406; lung n = 764; and multiple-organ system failure with malignancy n =
594). They compared physician estimates of patient prognosis (surviving 2 and 6
months) with prognosis estimated by the SUPPORT prognostic model. They found
that the SUPPORT model, mean physician estimates, and actual survival are
virtually identical at 2 and 6 months. Also, they noted some variation in
estimates depending on time and disease type but noted that physicians and
models always erred in the same direction.
APPENDIX A: A BRIEF REPORT
National Perspective on Dying in America: Does Place of
John M. Benson, MA,* Joel C. Cantor, ScD,** Joanne Lynn,
MD, MA, MS,*** and Joan Teno, MD, MS **** First presented at the New York
Symposium on Health Services Research, December 10, 1996.
SUPPORT (Study to Understand Prognoses and Preferences
for Outcomes and Risks of Treatment) focused on specific diagnostic categories
and enrolled patients in five major medical centers, thus potentially limiting
its generalizability. We present here results of a national telephone survey of
participants (defined below) in end-of-life decisions, aimed at assessing the
degree to which the findings of SUPPORT may be generalized beyond major teaching
Between September 8 and October 8, 1995, telephone
interviews were conducted with 502 participants in end-of-life decisions.
Respondents were screened from a national probability sample of adults, with a
screening rate of 9% and a response rate of 50%. Respondents gave affirmative
answers to two questions: "In the past two years, have you had a close family
member or other loved one who died after a serious illness?" and, if yes: "Did
you have contact with the medical staff caring for this person, or not?"
Respondents were asked about their relationship to the
decedent, the timing of the person's death, the degree to which the death was
expected, the cause and place of death, the quality of communication with
medical providers, the use of life support, the extent of patient consciousness
and pain, patient preferences for treatment focused on comfort versus extending
life and the degree to which those wishes were honored, advance directives,
family economic impact, and sociodemographic characters of the respondent and
Data were analyzed using univariate and multivariant
methods. Characteristics of survey respondents and decedents and
respondent-reported outcomes of care were compared by place of death (hospital,
home, or other place). Findings among patients who died in a hospital were
further stratified by whether the death took place in a "major medical center
affiliated with a university" or in another type of hospital. Statistical
significance was examined using chi-square tests of homogeneity and
Kruskal-Wallis tests of differences among medians. Univariate associations of
outcomes of care with place of death and type of hospital were confirmed using
multiple regression analyses, controlling for potential confounders.
Characteristics of the Sample Median age of the 502
respondents was 46 years; 57% were female; 83% were white and not Hispanic; 30%
had a high school level education, and another 58% had at least some college;
annual family income split almost evenly between those earning less than
$25,000, between $25,000 and $50,000, and more than $50,000; 35% were the
deceased person's child, 13% were the spouse, and 48% were another relative.
Decedents had a median age of 72 years; 42% died of cancer and 23% of heart
disease; 85% wanted relief of pain and discomfort (rather than extending life as
much as possible); and the death was reported to be completely unexpected 22% of
the time and expected for at least the last two months in 40% of the cases.
Place of Death
More than half (58%) of the respondents died in a
hospital. Among those died in hospital, about one in four (27%) were reported to
have died in a "major medical center affiliated with a university." Home deaths
accounted for 26%, and all other for 16%.
Place of death (hospital, home, other) was not
associated with respondent characteristics but was associated with several
decedent characteristics. Those who died at home or in other settings (including
hospice and nursing home) settings were more likely to have died of cancer (55%
of those at home, 44% for other, and 36% of hospital deaths, P < .001) and to
have had their death expected for a longer period (57% expected for at least 2
months for home deaths, 45% for other, and 31% for hospital, P < .001). Those
who died in other settings also had a significantly higher median age of death
(78 years vs 71 at home and 70 in hospital, P < .001) and a slightly more
common preference for palliation over efforts to extend life (93% for other, 79%
for home, and 85% for hospital, P = .033)
Among hospital decedents, age and preferences varied
significantly by major medical center versus other. The median age of decedents
in major medical centers was 5 years less (67 vs 72 years), and these patients
were more than twice as likely to prefer extension of life over pain relief
compared with those who died in other hospitals.(24% vs 10%, P = .025) Decedents
in major medical centers were also more likely to have been on mechanical life
support during the last 3 days of life (38% vs 23%, P = .022). Survey
respondents whose loved ones died in a major medical center were somewhat less
likely to be non-Hispanic-whites (70% vs 86%, P = .009).
Outcomes of Care
Experiences of patients and families varied little by
place of death or by type of hospital for seven outcomes examined.
Only having an advance directive (AD) was significantly
associated with place of death or type of hospital in univariate statistics.
Compared with hospital decedents, those who died in other settings (including
nursing home and hospice) were about 2.4 times as likely to have an AD; and
compared with decedents in major medical centers, those in other hospitals were
about twice as likely to have an AD. However, both of these associations became
statistically insignificant in multivariate analysis controlling for potential
confounders (decedent age, expectedness of death, respondent education, and
whether the respondent was one of the main people consulting with the medical
The other six measures of experiences near the end of
life were not associated with place of death or type of hospital in univariate
analysis. These experiences included statements by respondents that: (1)
patient/family preferences were followed (overall rate = 91% of those with valid
answers); (2) the medical staff explained the patient's condition adequately
(overall, 80%); (3) the medical staff explained treatment options adequately
(overall, 80%); (4) the patient/family had adequate influence over treatment
decisions (overall, 76%); (5) the patient (if conscious) was in severe pain most
of the time during the last 3 days of life (overall, 46%); and (6) the medical
staff did as much as the patient/family wanted to control pain (overall, 86%).
The proportion of patients who were on mechanical life support was 18%
Two of these measures became significantly associated
with type of hospital in multivariate analysis: opportunity to influence
treatment (AOR « 0.23; 95% Cl, 0.07-0.74) and presence of severe pain (AOR=
0.36; 95% Cl, 0.14-0.93). Controlling for potential confounders (enumerated
above), these outcomes were worse in other hospitals than in major medical