Impact of Quality of Life Outcomes on Clinical Practice

Impact of Quality of Life Outcomes on Clinical Practice

ABSTRACT: This paper reviews the current status of translation of quality of life outcomes from research to clinical practice. A major barrier in this process is the lack of mature results from quality of life studies in phase III randomized controlled trials. As more trials are completed, we can expect the diffusion of those results into clinical practice and decision making. Further, as quality of life measurement tools are used more widely and become more user-friendly, we can anticipate their adoption in the routine clinical assessment of patients in the oncologist's practice. [ONCOLOGY 9(Suppl):61-65, 1995]


During the past 10 to 15 years, we have experienced the phenomenal
development of a new scientific technology that allows for the
reliable and valid assessment of patient outcomes in research
and clinical practice [1-3]. The technology of patient outcomes
relies heavily on patient self-report of various aspects of health
and well-being, as well as symptoms associated with disease. The
tools for measuring these outcomes are derived from social science
research and the field of psychometrics. The science of measuring
quality of life and the criteria for evaluation of candidate tools
have been described extensively by others [4-6].

In this paper, we assume that oncologists will soon have a choice
of several validated general cancer and site-specific quality
of life (QOL) instruments for use in their practices. This may
allow future clinicians to order and utilize "quality of
life tests" much as they would order a serum glucose test
or an electrocardiogram. When this is the case, what will be the
impact of quality of life outcomes on clinical practice?

Should We Expect QOL Outcomes to Affect
Clinical Practice?

Clinical practice and treatment decisions are influenced by a
variety of factors based on the physician's training and clinical
experience, synthesis of the relevant literature, and familiarity
with consensus opinions developed by scientific bodies, treatment
guidelines, and the results of randomized clinical trials. While
clinical trials are considered the "gold standard" for
the scientific evaluation of new treatments, relatively few therapies
have been subjected to such rigorous evaluation. More important,
even when a new therapy has been demonstrated to be efficacious,
there may be very slow diffusion of the findings and limited adoption
of the effective therapy. For example, two randomized trials comparing
breast conservation therapy with mastectomy were published in
a prestigious journal during the early 1980s [7,8]; however, several
years after those results, there was only limited adoption of
breast conservation, and extraordinary geographic variation in
the use of this approach to the management of primary breast cancer

If clinicians have so much difficulty changing their clinical
practice in the face of data from well-controlled scientific studies
that use familiar biomedical outcomes (eg, survival and recurrence),
how can we expect them to accept without question the findings
from quality of life studies, whose measures may be unfamiliar
and whose outcomes are at times contrary to clinical opinion?

Sources of QOL Outcome Data in Oncology

We have only recently begun to see the systematic incorporation
of QOL outcomes in randomized clinical trials [11,12]. This process
began over a decade ago in European and Canadian trials, and has
been gradually introduced into US treatment trials during the
past 5 years. Some pharmaceutical companies began to incorporate
QOL measures into their studies because of their potential importance
in the drug approval process [13]. Many current phase III trials
within the US cooperative groups include QOL assessment; however,
it will be many years before these studies mature and the QOL
data are examined. Therefore, at present, only a limited amount
of QOL outcome information from randomized clinical trials is
available for clinicians. Quality of life data from some phase
III trials may be useful to clinicians in their practice, eg,
the observation that patients being treated with chemotherapy
for metastatic breast cancer had a better quality of life if treated
on a continuous rather than intermittent schedule [14] (discussed
below). As current phase III trials mature, this important source
of QOL data will expand rapidly.

Much of the information that we have about QOL in cancer patients
comes from well-designed research studies. Although not phase
III randomized clinical trials, these studies have the advantage
of being comprehensive and often use multiple tools for QOL assessment-something
that is not feasible within the clinical trial setting. The major
advantage of a research study is the adequacy of support staff
for collection of QOL data, which usually results in less missing
data. Limitations of research studies include the lack of treatment
randomization and use of volunteer samples. Nevertheless, the
high quality and comprehensiveness of data from research studies
make it possible to draw important conclusions about the quality
of life impact of some therapies.

For example, in a descriptive study of a community-based sample
of women treated for early-stage breast cancer, Ganz et al [15]
found no significant differences in the major dimensions of health-related
quality of life between women choosing mastectomy and those choosing
breast conservation (discussed below). The clinical implications
of these findings are that we can describe these two surgical
treatments for breast cancer in terms of their impact on survival
and quality of life, and use this information to help patients
make treatment decisions.

In situations in which there are no data from clinical trials
or the research literature, clinicians must rely on the clinical
interview. Unfortunately, few clinicians have been trained to
incorporate questions about health-related quality of life into
their usual patient interviews. Instead, physicians usually focus
on physical symptoms and findings. Even under these circumstances,
more subjective symptoms such as pain are often ignored. Clinical
interviews are seldom multidimensional (unlike a QOL instrument)
and are usually unsystematic with regard to psychosocial concerns
[16,17]. The incorporation of several open-ended questions that
address physical, emotional, and social functioning could enhance
the standard clinical interview. Other tools, such as short screening
instruments (eg, the Duke Health Profile [18] or the Dartmouth
COOP charts [19]), could facilitate the clinical identification
of QOL concerns of patients in the clinical setting. Unfortunately,
most of these tools have been evaluated only in general medical
settings and have not been tested with oncology patients.

Limitations of Existing Data Sources

Despite the expanding interest in measurement of QOL outcomes
in cancer clinical trials, there are few data available to guide
the clinician. Although many trials with QOL endpoints are in
progress, these are fewer than might be hoped, due to the complexity
of appending these studies to clinical trials and the limitation
of resources available to conduct these additional studies [20].
The National Cancer Institute of Canada has been an exception
in requiring the use of QOL endpoints in all of its phase III
clinical trials [21]. Thus far, this policy has been implemented
successfully and with high rates of compliance to data collection.
As these trials mature, we can expect valuable information to
emerge that will likely have an impact on clinical care.

As with treatment trials, QOL outcomes data may sometimes be counterintuitive
(eg, lack of significant differences between mastectomy and breast
conservation therapy), and thus, as with treatment trials, it
is important to confirm QOL study results. Only the regular and
systematic measurement of QOL in all phase III treatment trials
will permit clinicians to determine which therapies have the best
survival, response, and QOL outcomes.

Quality of life endpoints are particularly critical for the evaluation
of therapies in advanced metastatic disease (eg, non-small-cell
lung cancer), where survival gains of treatment are modest and
treatment toxicity is considerable. However, a major limitation
of QOL evaluation in lung cancer trials has been the high rate
of missing QOL data due to death and deterioration [22,23]. There
is keen interest in developing statistical methods for dealing
with missing QOL data, typically from the sickest patients, in
longitudinal studies [23].

Other Methodological Problems

There are other methodological problems that need to be addressed
prior to the clinical application of QOL outcomes study results.
Several popular generic and cancer-specific tools are now being
widely used in clinical and research studies [24]. Although statistical
comparison tests can have adequate power to detect changes between
groups of patients, the clinical meaning of individual changes
in scores in the clinical setting is uncertain. For example, what
does a 2-point change in physical functioning on a particular
QOL instrument mean in an individual patient when assessed at
two different points in time? That is to say, what is the clinical
significance for the individual patient, and could this merely
represent an error in measurement? Furthermore, will two different
instruments that measure physical functioning show the same direction
of change? These questions are not currently answerable for most
of the commonly used QOL instruments. As noted by Guyatt et al,
instruments that are reliable and valid in the cross-sectional
evaluation of a sample may not be responsive to change over time

Another parallel problem is the lack of head-to-head comparisons
of different instruments simultaneously in the same patient population.
This type of information would be useful to examine the relative
strengths and limitations of some of the leading QOL tools, as
well as to develop some form of common scoring system across several
measures. This last question is being addressed by Cella in an
ongoing research program (personal communication).

An example of the value of such comparisons comes from a recent
study that evaluated long-term survivors of early-stage prostate
cancer. Litwin et al [26] found that a generic health-related
QOL tool did not discriminate between healthy controls and patients
who received either surgery or radiation therapy. Two cancer-specific
measures did a bit better, but it was only a very specific symptom
measure that distinguished between the patients and normal healthy
men [26]. Thus, the sensitivity and specificity of psychometrically
validated QOL tools may not be robust enough to detect subtle
differences in functioning among patient groups. Furthermore,
these tools have not been tested for monitoring the QOL of individual
cancer patients in the clinical setting.


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