Predicting survival in patients with advanced disease
Introduction
Diagnosis, treatment and prognosis are the three great clinical skills in medicine,1 but prognosis diminished in importance during the 20th century as effective treatments became available for many previously fatal conditions.2 Progress in palliative care over the past 40 years has encouraged a renaissance of prognostication as a clinical skill. Oncologists and palliative care clinicians need to be proficient at prognosis for various reasons:
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To provide patients and families with information for goal setting including priorities and expectations of care.3, 4, 5, 6, 7
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To assist with clinical decision making.8, 9
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To compare similar patients with regard to outcomes.10
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To establish patients’ eligibility for care programmes.8, 11
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For the design and analysis of clinical trials.
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For policy making.6, 7, 8
Like the stages of cancer, prognosis can provide a common language for health care professionals involved in end of life care.
Despite the importance of prognosis as part of good end of life care, modern clinicians are often averse to predicting medical outcomes, particularly death. A survey of American physicians found that most respondents felt poorly trained for prognostication and faced difficulty in both formulating and communicating a prognosis.12 They also found forecasting stressful, and worried about being judged poorly by patients and colleagues when predictions were incorrect. As a result they developed a number of coping strategies, including avoidance, optimism and vagueness.13 Substantial progress in five aspects of the contemporary understanding of prognosis may help improve this difficult situation.
Firstly, prognosis is a much broader concept than just predicting survival. It is rightly defined as the ‘relative probabilities of the various outcomes of the natural history of a disease’.14 A useful taxonomy for the domains of prognosis is ‘the 5D’s of prognostication’:15
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Disease progression/recurrence.
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Death.
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Disability/discomfort.
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Drug toxicity.
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Dollars (costs of health care).
All of these domains are relevant to palliative care, although predicting survival is the primary focus of this article. Examples of day-to-day prognostic questions faced by palliative care practitioners are shown in Table 1.
Secondly, prognostication in far advanced cancer is based on different predictive factors than prognosis in early stage disease. The diagnostic, pathological and treatment-related prognostic factors important in early stage cancer are less relevant in patients with advanced progressive disease than functional status, the anorexia–cachexia syndrome, systemic inflammation, lymphopaenia, poor quality of life and psychosocial factors.
Thirdly, the modern concept of ‘death trajectories’ makes predicting survival in far advanced cancer easier to conceptualise.16 The typical ‘cancer’ trajectory involves a gradual decline in health status over a period of months or years, with an accelerated decline in the final weeks to months (Fig. 1). The challenge for clinicians caring for advanced cancer patients is identifying when the accelerated, irreversible decline is occurring rather than an acute and reversible event.
Fourthly, proponents of prognostication need to counter criticism of survival predictions being too inaccurate to be helpful. Of course there are many uncertainties when predicting future outcomes, especially when considering the complex dynamic of the human body and the multiple interactions between the human body and illness. However, the accuracy of survival predictions depends on the type of prediction being made, with probabilistic predictions (the percentage chance of surviving to a certain time) (50–75% accuracy) being superior to traditional temporal predictions (the estimate of time the patient will survive), accurate only 25% of the time.3, 17, 18, 19, 20, 21 As Table 2 shows, the accuracy of probabilistic cancer survival predictions is comparable with that of other medical predictions and meteorological forecasting.
Lastly, prognosis is often misunderstood as a static phenomenon, reinforced by the research studies focusing on one point in time (e.g. survival after admission to hospital or referral to hospice). The illness trajectory changes over time, so that as the illness evolves, new issues must be considered and the prognosis should be revised.
Section snippets
Performance status
Almost 150 different variables have been evaluated for their ability to predict survival (Table 3).22, 23, 24 Of all of them, performance status has been the most extensively studied and consistently shows an association with survival duration. A Karnofsky Performance Scale (KPS) score <50% is associated with a short survival,6, 7, 22, 25, 26, 27 although it accounts for only a small amount of the variability in observed survival.64 Patient-rated KPS scores can provide independent prognostic
Formulating a survival prediction
There are two components to the clinical act of prognostication. The first is formulating the prediction (i.e. foreseeing). The second is communicating the prediction to the patient, family or other medical professionals (i.e. foretelling). Both foreseeing and foretelling are areas for research and quality improvement.67
The two approaches to formulate the prognosis are (a) the clinical prediction of survival (CPS) and (b) the use of statistical tools. Research commencing within the field of
Communicating a prognosis
Both formulating and communicating prognoses to patients with advanced cancer are complex and difficult processes. Episodes of poor communication are often vividly remembered. However, communication is a skill and can be honed. It is incumbent on all health professionals to ensure that they are trained adequately to break this prototypical example of ‘bad news’.
Conclusion
Prognostication remains a challenging topic. In the past 20 years, much research has been undertaken to identify the ways of improving the accuracy and precision of clinicians’ estimates and many tools are now available to improve prognostication. Research is now needed to show how these tools aid clinical decision making.
Whilst we are now in a better position to give the patient ‘x % chance of surviving for y weeks/months’, we are not yet able to propose any of the existing tools as the ideal
Conflict of interest statement
None declared.
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