Some Elements of Prognosis in Terminal Cancer
Some Elements of Prognosis in Terminal Cancer
A prognosis is a physicians
prediction about a patients future. This prediction may be
divided into two distinct elements: foreseeing and foretelling.
Foreseeing is a physicians silent, cognitive estimate about a
patients illness. Foretelling is the physicians
communication of that prediction to the patient. In this article, we
will consider both aspects of prognostication as they relate to
physicians care of cancer patients as they near death. We will
review the findings on the significant inaccuracy of physicians
predictions and then offer hypotheses to explain the sources of this
inaccuracy. We will describe techniques that may improve
physicians prognostic accuracy. Through this review, we hope to
show that part of the challenge of providing humane, compassionate
end-of-life care to cancer patients may entail accurately foreseeing
and foretelling their prognoses.
Because the success of novel anticancer therapies is measured
primarily by their ability to extend life, prognosis is a central
element of oncologic research. Technologic advances now allow cancer
patients to be scrutinized even to the level of gene expression for
factors that may explain a comparatively long or short survival.
Typically, researchers create statistical models that integrate such
factors to predict outcomes, and published results may assist
physicians in making predictions and treatment decisions about their
For example, in a paper from the National Surgical Adjuvant Breast
and Bowel Projects first prevention trial (NSABP P-01), Fisher
and colleagues developed a risk model, a variant of the Gail model,
that integrates a number of proven breast cancer prognostic factors
(age, age at menses, age at first parity, personal history of breast
disease and/or biopsy, family history of breast disease, and race) to
quantify an individuals lifetime risk of developing the
disease.[1,2] They used the model to select individuals at high risk
of developing breast cancer, and then randomized those individuals to
receive daily tamoxifen (Nolvadex) or placebo. Models, such as this
one, that rely on multivariate regression analysis are found in all
aspects of cancer research, including translational and basic science research.[3-5]
Although prognosis is a central element of oncologic research, such
formal and explicit prognostication is seldom required in the
clinical care of cancer patients. There are at least two situations
in the care of advanced cancer patients, however, in which
physicians need to formally foresee the prognosis: (1)
enrollment into experimental phase I (dose-determining) chemotherapy
protocols; and (2) referral to hospice programs. Both settings have
discrete eligibility requirements pertaining to survival.
Typically, to be considered for entry into phase I trials, patients
must have an estimated survival of at least 2 to 3 months, and for
entry into a hospice program, patients must have an estimated
survival of at most 6 months. Because of these formal requirements,
physicians ability to determine fine gradations in survival
among cancer patients in their last 6 months of life may mean the
difference between aggressive and palliative care.
How Good Are Physicians at Prognostication?
How good are physicians at determining which patients are in their
last 6 months of life? Janisch and colleagues analyzed survival data
from 349 advanced cancer patients after enrollment in phase I
therapies. They found that the median survival was 6.5 months,
well above the requisite 2 months described in their eligibility
requirements. Overall, approximately 10% of patients died within 2
months, although very few of those with a Karnofsky performance
status greater than 70 died before 2 months. Given the low clinical
response rates associated with phase I therapies, it is unlikely that
survival was enhanced by the therapies themselves. Therefore, results
from this study suggest that physicians who enroll patients in phase
I protocols are generally able to predict which patients have more
than 2 to 3 months to live. An alternate explanation is that other
eligibility requirements, such as performance status and laboratory
tests, select patients with more than 2 to 3 months to live,
obviating the need for the input of physicians. Since the study was
not designed to test the prognostic accuracy of physicians, however,
it is difficult to draw strong conclusions about the actual role of
Within the palliative oncology literature, a few studies were
specifically designed to determine physicians accuracy in
predicting the survival of cancer patients admitted to hospice
programs.[7-12] Investigators in these studies measured
physicians prognostic accuracy by comparing patients
observed survival to their predicted survival. Results of the
studies, summarized in Table 1,
show that, in aggregate, physicians overall survival estimates
tended to be incorrect by a factor of approximately two, always in
the optimistic direction.[7-10,12]
Another method for measuring physicians prognostic accuracy is
to determine the percentage of patients dying within a calculated
interval surrounding their predicted date of death. For example,
Parkes identifies extreme errors in prediction by noting that
pessimistic errors occur when patients live at least twice as long as
their predicted survival and optimistic errors occur when patients
live less than half as long as their predicted survival.
According to this system, physicians whose patients do not fall into
either error category have made correct prognoses (although,
admittedly, this is a generous definition of correct). Table
1 also contains a summary of the results of studies using this
method of measuring physicians prognostic accuracy.[7,9,10,12]
Even with this generous definition of correct, physicians who predict
the survival of hospice patients are correct only half of the time.
Furthermore, the results show that the direction of these extreme
errors is predominantly positive.
Studies of physicians abilities to predict cancer patients
survival are not limited to patients in palliative care settings.
Physicians prognostic accuracy also has been evaluated with
greater mathematical rigor in ambulatory patients undergoing
anticancer therapy. Mackillop and Quirt measured oncologists
prognostic accuracy by asking them to first predict ambulatory
cancer patients likelihood of cure and then to estimate the
duration of survival for patients whose likelihood of cure was zero.
At the 5-year point, patients who were alive and disease-free were
termed cured; the dates of death of the incurable
patients also were determined. Although oncologists were quite
accurate in predicting cure, they had difficulty in predicting the
length of survival of incurable patients. They predicted survival
correctly for only one-third of patients, with the errors
divided almost equally between optimistic and pessimistic.
In summary, physicians asked to foresee gradations of survival in
advanced cancer patients enrolling in certain therapies (either
aggressive or palliative) are able to do so accurately much less than
half the time, and, when in error, they tend to overestimate
survival. Although clinicians appear to be adept at foreseeing the
likelihood of cure in cancer patients, they are not skilled at
foreseeing the length of survival in incurable patients.
Two factors may hinder physicians in their attempts to accurately
predict survival of advanced cancer patients: the method of
prediction used and forecaster bias.
Method of Prediction
There are two general methods of prediction: actuarial prediction and
clinical prediction. With the actuarial method, a prediction is made
using empiric data contained in life tables.
For example, an oncologist might consult Surveillance, Epidemiology,
and End Results (SEER) tables of patients with non-Hodgkins
lymphoma (NHL) to predict a given patients 5-year survival.
Alternatively, the oncologist might use the International Prognostic
Index to determine the likelihood of 5-year survival of a 45-year-old
patient with an aggressive stage IV NHL and an elevated lactate
dehydrogenase (LDH) level. Models of greater complexity may
provide the physician with greater prognostic precision.
In the clinical method, a prediction arises out of human intuition
alone, without the benefit of explicit precedent data from similar
patients or optimal weighting of patient and disease variables
through mathematical formulas.
Although the actuarial method has been shown in many disciplines,
including medicine, to be superior to the purely clinical
method,[15,16] few actuarial models are designed explicitly to aid
physicians in predicting survival of terminal cancer patients.
However, several studies have correlated performance status[7,10,17]
and symptoms (eg, dysphagia, dyspnea)[17-19] with survival of
terminal cancer patients.
A distinct reason that oncologists may be inaccurate in their
predictions about the survival of terminal cancer patients may relate
to their own biases. Within the literature on prognostication,
certain forecaster biases are well-described impediments to accurate
prediction. For oncologists, optimistic bias may be the most
germane type of forecaster bias. Optimistic bias about personal risk
occurs when a person believes that he or she is less likely than
others to experience an adverse outcome.
Optimistic bias is pervasive and well studied. A classic example is
the uniform optimism held by cigarette smokers about their
health.[22-24] In study after study, cigarette smokers rate their
personal risk of developing a smoking-related illness far lower than
the average smoker, even if they can accurately forecast the risk of
smokers in general.
In his review of optimistic bias about personal risk, Weinstein
postulates three reasons for such bias that may be applied to
physicians caring for patients. First, by employing optimistic
bias, physicians invoke denial to shield themselves from a painful
reality, perhaps, in this case, the imminence of a patients
death. Second, they may think that they are better than their peers
(ie, that they take better care of their patients), and, therefore,
may believe that their patients will live longer than a survival
curve would suggest. Third, optimistic bias may occur because of
simple cognitive errors, eg, that a terminal cancer patients
performance status of 90 simply means that they cannot die of their
cancer in the next 3 months