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Dying Trajectories and Prognostication

Prognostication, as an art, refers to prediction and communication about future health. Prognostication relates not only to predicting death, but other outcome states such as what percentage of patients with a cancer of a certain stage on initial presentation will eventually develop metastatic disease or what the course of a chronic illness is likely to be. As Nicholas Christakis points out, of three traditional domains of medicine (diagnosis, therapy, and prognosis), prognosis has received relatively little attention in modern medical training and research.¹ While prognostication is important in all aspects of medical decision making, here let us first focus on prognosis relative to dying. It is easier to predict when death will occur for patients with some illnesses than for others. Proper prognosis at the end of life enables better decision making about care options and planning for patients and families. The definitive text on prognosis as it relates to palliative care is Death Foretold: Prophecy and Prognosis in Medical Care by Christakis.¹

Predicting Death: The Search for the Holy Grail of Prognosis

We all desire and fear certainty. A desire for certainty arises, I think, in response to apparent chaos in the world.^2,3 However, fear arises because not all that is certain is good. Certainty also negates ambiguity and possibility, wherein people find hope that they can alter a problematic future. Therefore, an intense desire arises for some magic formula that will erase such uncertainty. While people may lament their lack of control over “bad” outcomes such as death, the ability to predict and know the future represents a form of control if the future unfolds as predicted. Many studies have been devoted to a search for certainty in predicting death, often with little to show for it.

Prediction of death is not linear. That is, we are not necessarily better at predicting death in an ultrashort time frame (seconds to hours) than we are in a long time frame (months to years). Rather, it is like predicting the weather. In California we are good at long-range predictions for a dry summer and wet winter. We are good at predicting that rain will fall on a certain day two to five days beforehand but cannot make such a prediction a month beforehand. In the ultrashort range it defies our abilities to predict exactly when the next raindrop will hit a finger. Similarly, we are fairly good at predicting that a patient is at a high risk of dying over a matter of several months. For certain diseases, especially cancer, we are reasonably good at predicting death over a matter of weeks. It is usually impossible to predict the exact moment of death.

Most “holy grail” approaches to predicting death use a grouping of diagnostic criteria and apply them at a certain point in time to establish a probability of dying at some time in the future.⁴ The major problem with these approaches is that they are usually discrete, one-time predictions. Real clinical prognostication is more iterative, a form of “fuzzy logic.”⁵ That is, the most valuable prognostic tool is to note the magnitude of change observed since the last prediction and incorporate this change into a new prediction. For most serious and chronic illnesses, the trend is the best predictor of what the future might hold. Patients whose clinical decline is rapidly accelerating will likely die sooner than those with otherwise identical clinical parameters but who decline more slowly.

Various studies have pointed out physician deficits in terms of the ability to predict time of death.^6,7,8 The general bias in physicians is to be overly optimistic about prognosis by a factor of twofold to fivefold, although errors at the other extreme have also been observed. Christakis and Lament, in a study of 343 physicians, studied their ability to predict time of death for 468 patients referred to hospice. They found that only 20% of predictions were accurate (within 33% of actual survival), with 63% of predictions overly optimistic and 17% overly pessimistic. Accuracy was calculated by dividing predicted by observed life expectancy. Values falling between .67 and 1.33 were considered accurate. Median life expectancy was 24 days from referral to hospice. On average physicians overestimated life expectancy by a factor of 5. It is interesting to note that 67% of the patients had cancer, usually considered to have a relatively predictable dying trajectory. In this study cancer patients were the most likely to have overly optimistic predictions.⁷

There are many theoretical reasons why physicians are as inaccurate as they appear to be. For a more detailed discussion of these issues the reader is referred to Christakis’s book on prognosis. I highlight here only a few key points:

Prognostication isn’t like throwing darts and seeing how close one can get to the bullseye. A very different way to look at prognosis is as a process in communication, central to relationships among clinicians, families, and patients.⁹ Families and patients can be greatly helped by an accurate prognosis, but this is not the only relevant issue. Prognostication can be a test of the physician’s power; the more accurate the prognosis, the more powerful the “wizard-physician.” While patients and families want a powerful physician, they are often conflicted because usually part of them wants the physician to be wrong when it comes to predicting bad things like death. Perhaps if we are wrong as to when a patient will die, we might be wrong as to whether the patient really is dying. A desire for hope often conflicts with the desire for certainty in prognosis. The physician, as (perceived) keeper of prognostic wisdom, is often a target for the conflicting emotions that arise between the desire for hope and the quest for certainty. Skill is required for safe passage between these extremes.

The trick seems to be to communicate what is and is not known about a patient’s prognosis in a manner that strengthens the relationship between physician and patient/family while meeting informational needs. It may be impossible to protect patients and families from bad news, but skilled communication may enable them to make better decisions and thereby maintain some sense of control. Usually, it is best to give ranges of time for prognoses—hours to days, days to weeks, weeks to months, or months to years. You may need to explain that you are not trying to be vague, but that our ability to predict death is imperfect at best. Rather than set yourself up for a fall with a prediction that may go wrong, your relationship may actually be strengthened by empathizing with the desire for greater certainty. In revealing your humanity and imperfection, you will have found common ground with the patient and family in the face of the mystery of death.^*

You may need to educate them on exactly how you go about telling when someone is about to die by saying, for example, “It helps to know how similar patients with similar illnesses have done, and my prior experience is useful. But most important now, more important than any blood test, is following the trend for your loved one. How he/she progresses over time will help us the most in getting a better reading on when death will come. We’ll keep in touch with you as we see how things are changing.” Such a statement reflects the iterative process of prognosis and invites ongoing discussion, strengthening the relationship.

“How much time do I have, Doc?” It is often a mistake to take this question too literally. Requests for prognosis may relate to very practical needs, such as whether to reschedule a meeting or a trip or a common desire by a loved one to be at the bedside when the person dies. It may be very helpful to inquire why someone is asking just then about prognosis rather than assuming you know the answer. This helps frame your response. What they may want, in fact, is a concrete recommendation as to whether the family should be called or a change in plans made. You might want to give more concrete advice on this while admitting uncertainty about the exact timing of death.

The concept of a dying trajectory^10,11,12 was first suggested by Glaser and Strauss¹³ in 1965 and refers to the change in health status over time as a patient approaches death.^† It is usually plotted (retrospectively for an individual) with time on the X axis and health status on the Y axis. Sudden death has the simplest graph, a rectangle with a straight line down from a state of being healthy to death. The concept of dying trajectories has been helpful in understanding patterns of advanced illness and dying for different disease processes, which, in turn, have implications for care needs, decision making, and prognostication. Individuals will, of course, vary in their personal dying trajectories; however, it is remarkable how similar dying trajectories can be for patients with similar disease processes. Here I will discuss certain common trajectories and their implications.

The person found suddenly and unpredictably dead is technically beyond the care of the physician (Fig. 2.1). However, the survivors are not. It is often the physician who must notify others and provide initial support. Physicians also must provide health care to those who have been so bereaved (see section on bereavement in Chapter 7). People may die suddenly and peacefully but more commonly sudden death results from accidents or violence, especially in the young. The hallmark of this trajectory is lack of preparation for dying. Usually, those who die, unless already very ill or of advanced age, will not have prepared for their death. Even if details such as wills and funeral plans have been made, they have often not had a chance to settle matters in their minds or say goodbye, nor have their loved ones. In addition to the shock of sudden separation and bereavement, survivors may have regrets or feel guilty that they did something wrong or should have done or said something different. Bereavement needs are intense, especially for those bereaved of the young or through violence. While grief counselors may be mobilized during disasters such as mass shootings, incredibly, many healthcare systems have not established standards of care or rudimentary support systems for those in need during traumatic deaths. It seems odd, indeed unconscionable, that the provision of grief support is standard for hospice deaths, which are often the best predicted and least traumatic, but no such standard exists for emergency rooms.¹⁴

Cancer Deaths

Solid tumor cancers of very different tumor types and locations often follow very similar dying trajectories (Fig. 2.2). In fact, the dying trajectory of cancer is one of the most predictable trajectories. Most patients with metastatic cancer remain quite functional until approximately five to six months before their deaths. Their health statuses then tend to slowly decline until the rate of decline begins to accelerate rapidly two to three months before death.¹⁵ In Teno’s study one month prior to death more than 50 percent of the 1655 cancer patients studied had difficulty getting out of a bed or chair. Prior to the period of decline in the last few months of life, cancer patients may have various symptom needs such as a need for pain management but tend to remain at high functional levels. During the rapid decline phase patients start to “take to their beds.” A good rule of thumb is that a patient with advanced cancer who has taken to bed without a correctable cause will usually die in a matter of weeks to a few months. (Usually means usually. I have treated many patients who are exceptions to this rule of thumb.) The caveat about correctable causes is important. Treating certain very responsive cancers with chemotherapy can get people out of bed. Treating complications such as certain infections sometimes makes a difference. This rapid decline in functional status in the last few weeks to months of life correlates highly with admission to and life expectancy in hospice. Christakis in 1996 identified a median survival of 36 days for 6451 patients followed on the Medicare hospice benefit. Eighty percent of these patients had cancer.⁷

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