Predicting the survival of terminal cancer patients is a difficult task. To better understand this difficulty, we divide prognostication into two distinct elements: foreseeing and foretelling. Foreseeing is a physician’s silent cognitive estimate about a patient’s illness. Foretelling is the physician’s communication of that prediction to the patient or significant others. In this article, we review the impact of each element of prognosis on physicians’ overall prognostic accuracy. We show that physicians often make unwitting, large, and generally optimistic errors in foreseeing patients’ prognoses. They also may make more conscious, but equally large, optimistic errors in foretelling prognoses to patients. The net effect is that patients may become twice removed from the truth about their illness, both times toward a falsely optimistic prognosis. We also describe the possible consequences of these optimistic prognostic errors. Finally, we review techniques that may improve physicians’ prognostic accuracy. We conclude that part of the challenge of providing humane, compassionate end-of-life care to cancer patients may involve accurately foreseeing and foretelling their prognoses.
A prognosis is a physician’s prediction about a patient’s future. This prediction may be divided into two distinct elements: foreseeing and foretelling. Foreseeing is a physician’s silent, cognitive estimate about a patient’s illness. Foretelling is the physician’s 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 own patients.
For example, in a paper from the National Surgical Adjuvant Breast and Bowel Project’s 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 individual’s 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 physician’s 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 physician prognostication.
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-Hodgkin’s lymphoma (NHL) to predict a given patient’s 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 patient’s 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 patient’s performance status of 90 simply means that they cannot die of their cancer in the next 3 months
1. Fisher B, Costantino JP, Wickerham DL, et al: Tamoxifen for prevention of breast cancer: Report of the National Surgical Adjuvant Breast and Bowel Project P-1 study. J Natl Cancer Inst 90:1371-1388, 1998.
2. Gail MH, Brinton LA, Byar DP, et al: Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 81:1879-1886, 1989.
3. Arribas R, Capella G, Tortola S, et al: Assessment of genomic damage in colorectal cancer by DNA fingerprinting: Prognostic applications. J Clin Oncol 15(10):3230-3240, 1997.
4. Gaynor J, Chapman D, Little C, et al: A cause-specific hazard rate analysis of prognostic factors among 199 adults with acute lymphoblastic leukemia: The Memorial experience since 1969. J Clin Oncol 6:1014-1030, 1988.
5. Noguchi M, Earashi M, Minami M, et al: Effects of piroxicam and esculetin on the MDA-MB-231 human breast cancer cell line. Prostaglandins, Leukotrienes, and Essential Fatty Acids 53(5):325-329, 1995.
6. Janisch L, Mick R, Schilsky RL, et al: Prognostic factors for survival in patients treated in phase I clinical trials. Cancer 74(7):1965-1973, 1994.
7. Evans C, McCarthy M: Prognostic uncertainity in terminal care: Can the Karnofsky index help? Lancet 1(8439):1204-1206, 1985.
8. Forster LE, Lynn J: Predicting life span for applicants to inpatient hospice. Arch Intern Med 148:2540-2543, 1988.
9. Heyse-Moore LH, Johnson-Bell VE: Can doctors accurately predict the life expectancy of patients with terminal cancer? Pall Med 1:165-166, 1987.
10. Maltoni M, Nanni O, Derni S, et al: Clinical prediction of survival is more accurate than the Karnofsky performance status in estimating life span of terminally ill cancer patients. Eur J Cancer 6:764-766, 1994.
11. Oxenham D, Cornbleet MA: Accuracy of prediction of survival by different professional groups in hospice. Palliat Med 12:117-118, 1998.
12. Parkes EM: Accuracy of predictions of survival in later stages of cancer. Br Med J 2:29-31, 1972.
13. Mackillop WJ, Quirt CF: Measuring the accuracy of prognostic judgements in oncology. J Clin Epidemiol 50(1):21-29, 1997.
14. Shipp M, Harrington D, Anderson J, et al: A predictive model for aggressive non-Hodgkin’s lymphoma. N Engl J Med 329:987-994, 1993.
15. Dawes RM, Faust D, Meehl PE: Clinical vs actuarial judgement. Science 243:1668-1674, 1989.
16. Lee KL, Pryor DB, Harrell FE, et al: Predicting outcome in coronary disease. Statistical models vs expert clinicians. Am J Med 80:553-560, 1986.
17. Christakis NA: Timing of referral of terminally ill patients to an outpatient hospice. J Gen Intern Med 9:314-320, 1994.
18. Bruera E, Miller MJ, Kuehn N, et al: Estimate of survival of patients admitted to a palliative care unit: A prospective study. J Pain Symptom Manage 7:82-86, 1992.
19. Reuben DB, Mor V, Hiris J: Clinical symptoms and length of survival in patients with terminal cancer. Arch Intern Med 148:1586-1591, 1988.
20. Tversky A, Kahneman D: Judgement under uncertainty: Heuristics and biases. Science 185:1124-1131, 1974.
21. Weinstein ND: Optimistic biases about personal risks. Science 246:1232-1233, 1989.
22. McKenna FP, Warburton DM, Winwood M. Exploring the limits of optimism—the case of smokers decision-making. Br J Psychol 84:389-394, 1993.
23. Segerstrom SC, McCarthy WJ, Caskey NH, et al: Optimistic bias among cigarette smokers. J Appl Soc Psychol 23(19):1606-1618, 1993.
24. Stretcher VJ, Kreuter MW, Korbrin SC: Do cigarette smokers have unrealistic perceptions of their heart attack, cancer, and stroke risks. J Behav Med 20(2):45-54, 1995.
25. Novack DH, Plumer R, Smith RL, et al: Changes in physicians’ attitudes toward telling the cancer patient. JAMA 241(9):897-900, 1979.
26. Mackillop WJ, Stewart WE, Ginsburg AD, et al: Cancer patients’ perceptions of their disease and its treatment. Br J Cancer 50:355-359, 1988.
27. Eidinger RN, Schapira DV: Cancer patients’ insight into their treatment, prognosis, and unconventional therapies. Cancer 53:2736-2740, 1984.
28. Weeks JC, Cook EF, O’Day SJ, et al: Relationship between cancer patients’ predictions of prognosis and their treatment preferences. JAMA 279(21):1709-1714, 1998.
29. Christakis NA: Prognostication and death in medical thought and practice. Ann Arbor, UMI Dissertation Services, Number 9532156, 1995.
30. Delvecchio Good MJ, Good BJ, et al: American oncology and the discourse on hope. Cult Med Psychiatry 14:59-79, 1990.
31. Miyaji NT: The power of compassion: Truth-telling among American doctors in the care of dying patients. Soc Sci Med 36(3):249-264, 1993.
32. Christakis NA, Iwashyna TJ: Attutitude and self-reported practice regarding prognostication in a national sample of internists. Arch Intern Med 158:2389-2395, 1998.
33. Christakis NA: The ellipsis of prognosis in modern medical thought. Soc Sci Med 44(3):301-315, 1997.
34. Girgis A, Sanson-Fisher RW: Breaking bad news: Consensus guidelines for medical practitioners. J Clin Oncol 13:2449-2456, 1995.
35. Christakis NA: Death Foretold: Prophecy and Prognosis and Medical Care. Chicago, University of Chicago Press, 1999.
36. Piccirillo JF, Wells CK, Sasaki CT, et al: A new clinical—severity staging system for cancer of the larynx. Five-year survival rates. Ann Otol Rhinol Laryngol 103:83-92, 1994.
37. Christakis NA, Sachs GA: The role of prognosis in clinical decision making. J Gen Intern Med 11:422-425, 1996.
38. Muers MF, Shevlin P, Brown J, et al: Prognosis in lung cancer: Physicians’ opinions compared with outcome and a predictive model. Thorax 51:894-902, 1996.
39. Knaus WA, Harrell FE, Lynn J, et al: The SUPPORT prognostic model: Objective estimates of survival for seriously ill hospitalized adults. Ann Intern Med 122:191-203, 1995.
40. Poses RM, Bekes C, Winkler RL, et al: Are two (inexperienced) heads better than one (experienced) head? Arch Intern Med 150:1874-1878, 1990.
41. Fallowfield L, Ford S, Lewis S: No news is not good news: Information preferences of patients with cancer. Psychooncology 4:197-202, 1995.
42. Reynolds PM, Sanson-Fisher RW, Poole AD, et al: Cancer and communication: Information–giving in an oncology clinic. Br Med J 282:1449-1451, 1981.
43. Blanchard CG, Labrecque MS, Ruckdeschel JC, et al: Information and decision-making preferences of hospitalized adult cancer patients. Soc Sci Med 27(11):1139-1145, 1988.
44. Sell L, Devlin B, Bourke SJ, et al: Communicating the diagnosis of lung cancer. Respir Med 87:61-63, 1993.
45. Ravdin PM, Siminoff IA, Harvey JA: Survey of breast cancer patients concerning their knowledge and expectations of adjuvant therapy. J Clin Oncol 16:515-521, 1998.
46. Siminoff IA, Fetting JH, Abeloff MD: Doctor-patient communication about breast cancer adjuvant therapy. J Clin Oncol 7:1192-1200, 1989.
47. Chan A, Woodruff RK: Communicating with patients with advanced cancer. J Palliat Care 13(3):29-33, 1997.
48. Jonsen A, Siegler M, Winslade W: Clinical Ethics, pp 53-57. New York, MacGraw-Hill, 1986.