Annals of Oncology Advance Access originally published online on October 16, 2006
Annals of Oncology 2007 18(6):971-976; doi:10.1093/annonc/mdl343
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© 2006 European Society for Medical Oncology
reviews |
Predicting prognosis in patients with advanced cancer
1 Division of Mental Health, St George's University of London, Cranmer Terrace, London SW17 ORE
2 Trinity Hospice, 30 Clapham Common North Side, London SW4 0RN, UK
* Correspondence to: Dr P. C. Stone, Division of Mental Health, St George's University of London, Cranmer Terrace, London SW17 ORE. Tel: +44 20 8725 0145; Fax: +44 20 8725 2161; E-mail: p.stone{at}sgul.ac.uk
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Abstract |
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 Top Abstract introductionmethodsclinician estimates of prognosisprognostic scoring systemsdiscussionReferences |
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Background: Patients with advanced cancer and their carers frequently wish to know how long they can expect to live. Improved prognostication would enable patients and their carers to be better prepared for their impending death, and would allow clinicians to make better informed decisions about place of care. However, clinician estimates of survival are inaccurate and systematically overoptimistic. Recently, attempts have been made to improve upon clinician estimates of survival by devising prognostic scales incorporating clinical information with biochemical and haematological results.
Design: A descriptive and critical review of palliative prognostic scales, on the basis of the recommendations of the European Association of Palliative Care prognosis working group (2005) supplemented by an Ovid Medline search 1966–March 2006 using the key words ‘prognosis’, ‘neoplasms’, ‘palliative care’ and ‘terminal care’.
Results: This paper reviews the advantages and limitations of the palliative prognostic score, the palliative prognostic index, the Chuang prognostic scale, the terminal cancer prognostic score and the poor prognostic indicator.
Conclusions: All the currently available prognostic scales have limitations, but nonetheless offer an improvement on unadjusted clinician estimates of survival. Further research is required to systematically develop a prognostic scale on the basis of all the known prognostic variables in patients with advanced cancer.
Key words: neoplasms, palliative care, prognosis, survival, terminal care, theoretical models
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introduction |
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TopAbstractintroductionmethodsclinician estimates of prognosisprognostic scoring systemsdiscussionReferences |
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Patients with advanced cancer often want to know how long they have left to live [1]. Steinhauser et al. [2] have investigated the preferences of patients nearing the end of life and have reported that overwhelmingly issues of ‘preparation’ are felt to be important. Patients report that they want to have time to express their wishes both verbally and in writing, to name someone to make decisions, to put their financial affairs in order and to make their funeral preparations. In order to allow terminally ill patients the opportunity to focus their energies on to these matters, it is important to provide them with a reasonably accurate estimate of prognosis. Additional benefits of accurate prognostic information include helping patients and clinicians to make decisions regarding the appropriateness of palliative radiotherapy or chemotherapy [3], providing patients with the opportunity to think about where they want to be cared for and allowing them time to take practical steps to prepare for their own deaths. Better prognostic information may also improve patient's access to certain resources (e.g. funding of care under National Health Service Continuing Care Act). In some countries, admission to government-funded palliative care units depends on a prognosis of <6 months [4].
Clinicians are very poor at estimating prognosis [5]. In order to improve clinician prognostic estimates, some groups have attempted to identify specific predictors of survival and combine these variables into prognostic scales or scores [6, 7]. There are, however, a number of methodological problems with many of the published studies [8]. Ideally, a prognostic study should have a prospective design and include an inception cohort of patients. Unfortunately, defining an ‘inception cohort’ in palliative care studies is difficult as there is no consensus as to which patients should be considered to be ‘palliative’ or ‘terminal’. If the population is too heterogeneous (in terms of diagnosis, stage, age or other factors), then it may be unrealistic to be able to expect to develop any useful prognostic model. In contrast, if the population is too homogeneous, then lack of variability in key putative prognostic factors will also limit the ability to generate a regression model. A frequent problem with palliative care studies generally is attrition due to death or morbidity; however, in prognostic studies a high death rate ought to be a methodological advantage. As a statistical ‘rule of thumb’, it is recommended that when a prognostic regression model is being built, the number of events (i.e. deaths) in the sample should be at least 10 times the number of potential prognostic variables included in the model [9]. Assessing a prognostic model on the set in which it is developed is known to give an overoptimistic estimate of the predictive validity, particularly where large numbers of candidate variables are being considered. One recommended approach [8] is to divide the dataset developing the prognostic model in one part, the training dataset, before testing it in a validation set. Very few prognostic scores have been validated using this two-stage approach. Other methodological pitfalls in published prognostic indicator studies include excessive loss of patients to follow-up, a failure to randomly select patients for inclusion in studies and a failure to fully define or accurately measure proposed prognostic variables [7].
This paper summarises the results of recent studies that have attempted to improve upon or replace clinician estimates of survival and discusses their limitations and advantages.
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methods |
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The European Association for Palliative Care (EAPC) recently published evidence-based clinical recommendations on the use of prognostic factors in palliative care [7]. One of the key recommendations was that ‘clinicians make use of some easily applicable prognostic scores to make a rapid prediction capable of identifying classes of patients with significantly different life expectancies’. The review identified four prognostic scales: the Palliative Prognostic Score (PaP), the palliative prognostic index (PPI), the terminal cancer prognostic (TCP) score and the Bruera poor prognostic indicator. The EAPC recommendations were on the basis of a literature review undertaken before November 2004. A subsequent search of the literature by us (Ovid Medline 1966–March 2006 using the key words ‘prognosis’, ‘neoplasms’, ‘palliative care’ and ‘terminal care’) has revealed one further prognostic scale [the Chuang prognostic score (CPS)] published after the EAPC report was written, but included in this review for completeness. It is likely that most practising oncologists (and indeed palliative care physicians) will be unfamiliar with these scales and with how to use and interpret them. The purpose of this current review is to describe the structure and utility of these scales, to discuss their limitations and to delineate the need for future research in this area.
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clinician estimates of prognosis |
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TopAbstractintroductionmethodsclinician estimates of prognosisprognostic scoring systemsdiscussionReferences |
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The simplest way to predict survival is to ask the treating physician to estimate how long he or she expects the patient to survive. Glare et al. [5] has reviewed studies investigating the accuracy of clinicians' prognostic estimates. He reported that physicians' estimates are generally inaccurate, in only 61% of cases was expected survival accurate to within 4 weeks of actual survival. Moreover, physicians are systematically overoptimistic in their assessments. The cause of this optimism is not clear. In an interesting study, Lamont and Christakis [10] asked physicians to make an ‘honest’ appraisal of how long they expected patients to survive (this was called the ‘formulated’ prognosis). They also asked physicians what they would have said if the patient had demanded to know his/her prognostic estimate (the ‘communicated’ survival). The authors reported that physicians' ‘communicated’ survival estimates were significantly overoptimistic. This was not unexpected, since many physicians may be reluctant to be too negative in their assessments and so may tend to give patients the ‘benefit of the doubt’. However, the authors reported that even physicians' private ‘formulated’ survival estimates were also systematically overoptimistic. The median ‘communicated’ survival in this study was 90 days; the median ‘formulated’ survival was 75 days, but the median ‘actual’ survival was only 25 days. There have been several suggestions as to why clinicians are overoptimistic: they include the observations that diagnosing dying is difficult and that physicians may want to preserve hope [11]. One study has indicated that more experienced doctors and doctors who have not known the patients concerned for a long period of time may be better at assessing prognosis [12]. In order to try and improve the accuracy of prognostic estimates, physicians often use clinical data to complement their own impression of a patient's condition. A number of studies have attempted to identify which of these factors are most predictive of survival, and to bring the different clinical variables together into prognostic scores.
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prognostic scoring systems |
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TopAbstractintroductionmethodsclinician estimates of prognosisprognostic scoring systemsdiscussionReferences |
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the PaP score
Pirovano et al. [13] conducted a prospective multicentre study involving 519 patients with advanced solid tumours who were no longer considered suitable for primary treatment. Data were collected on a wide range of demographic and disease-related variables including blood results and measures of symptom severity. On multiple regression analysis, six variables (clinician prediction of survival, Karnofsky performance status, anorexia, dyspnoea, total white blood count and lymphocyte percentage) were found to be independently predictive of survival. The PaP score is generated by applying a ‘weighted’ scoring system to each of these variables. Total scores can range from 0 to 17.5 and can be used to divide patients into three prognostic categories (Table 1).
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This scale has now been evaluated in several other studies [14, 15]. In the first of these [14], the PaP score was calculated in 451 patients who were participating in an Italian palliative care programme. The score subdivided patients into three risk groups (group A had a 86.6% probability of survival at 30 days, group B had a 51.6% probability and group C had a 16.9% probability). These results are very similar to those in the original study and support the effectiveness of the PaP as a prognostic indicator. The second study [15] involved 100 terminally ill hospitalised patients in Australia. The percentage survival at 30 days for the three risk groups was 66%, 54% and 5%, respectively. This indicates that although the PaP score was good at predicting which patients had the poorest survival, it was less good at distinguishing between patients with ‘good’ or ‘intermediate’ prognosis. The PaP score has also been used in hospitalised patients with advanced cancer under the care of an oncologist (as opposed to patients under the care of a palliative care programme) [16]. In this setting, despite the population being quite different from that used in the original validation study, the PaP score was still able to divide patients into three groups. Group A had a 98% chance of survival at 1 month, group B had a 61% chance of survival and group C had a 25% chance of survival.
Although the PaP score is an improvement on ‘unadjusted’ clinician estimates, it still has a number of limitations. In clinical practice, the occasions when one would most like to use a prognostic index are precisely those occasions when one is most uncertain of the patient's survival. It is therefore a potential drawback to have to rely on a score which gives such a large weighting to clinician estimates (the clinician estimate accounts for 
50% of the total PaP score). The scale was not developed for use in patients with renal carcinoma, multiple myeloma or other haematological malignancies and cannot therefore be used in these groups. Moreover, confusion/delirium was not one of the factors included in the original multivariate model although altered cognition is recognised as a common problem at the end of life and is associated with a worse prognosis [6, 7]. Caraceni et al. [17] have reported that delirium and the PaP score are independently predictive of survival in patients with advanced cancer, indicating that the PaP score could be improved by incorporating clinical information about mental state into its scoring algorithm.
the PPI
Morita et al. [18] collected data on performance status and the presence or absence of 21 symptoms in 150 patients admitted to a hospice. Using this ‘training’ set of data, they identified five variables (performance status, oral intake, oedema, dyspnoea at rest and delirium) that were independently predictive of survival. The partial score for these variables is shown in Table 2. The total PPI score was calculated from the sum of the partial scores and could range from 0 to 15. Patients were stratified into three groups depending on their PPI score (group A, PPI 
2.0; group B, 2.0 < PPI 4.0; group C, PPI > 4.0). Using a PPI of >4 as a cut-off, 6-week survival was predicted with a positive predictive value (PPV) of 0.86 and a negative predictive value (NPV) of 0.70. The scale was then tested in an independent cohort of 95 prospectively recruited patients admitted to the same hospice and the predictive value of the scoring system was confirmed (for patients with PPI score >4, 6-week survival was predicted with PPV 0.83 and NPV 0.71).
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In a subsequent study, Morita et al. [19] studied two independent series of hospice inpatients to examine whether the PPI could improve clinician prediction of survival. In the first series (n = 150), prediction was on the basis of clinical experience alone, whereas in the second series (n = 108) the estimated prognosis was made with reference to the PPI score. In keeping with the results of the validation study, the PPI was found to predict 6-week survival with a PPV of 0.91 and a NPV of 0.67. In order to compare the accuracy of prognostication in the two series, Morita defined a ‘serious prognostic error’ to be one in which the actual survival was incorrect by at least ±28 days and was out by a factor of two. Even when combined with clinician estimates of survival, the PPI still resulted in a considerable number of serious prognostic errors (16%) although this was significantly (P < 0.028) lower than the rate of serious errors when clinician estimates were used alone (27%).
On the face of it, because the PPI does not make explicit reference to clinician estimates of survival, it appears to be more objective than the PaP. However, clinicians completing the PPI are required to judge whether or not delirium is caused ‘solely by a single medication’ and is thus potentially reversible. This can be a difficult judgement to make when patients are severely debilitated and are on multiple medications. Moreover, it is not entirely clear why ‘reversible’ delirium due to drugs should be excluded from the scoring system when other potentially reversible causes of delirium are not. There can also be a certain amount of clinical judgement required in order to determine patients' palliative performance score (a measure of performance status that also includes evaluation of ambulatory status, disease extent, self-care activities, oral intake and conscious level). Another limitation of the PPI is that, as the authors themselves state, it is probably most suitable for prediction of 3-week survival and less useful for patients with a longer prognosis. In many clinical situations, it is likely to be more helpful to know whether a patient is expected to survive for ‘months’ or ‘weeks’ (influencing decisions about whether to undergo treatments such as anti-depressants or affecting placement decisions for continuing care) than it is to know whether a patient will survive for 3 or 6 weeks.
the CPS
Chuang et al. [20] studied 356 consecutive referrals to their palliative care unit. They recorded demographic data, the severity of symptoms on admission, the presence or absence of clinical signs and the Eastern Cooperative Oncology Group (ECOG) performance status. Using these data, they constructed a prognostic scale incorporating eight variables (lung metastasis, liver metastasis, tiredness, ascites, oedema, cognitive impairment, weight loss and ECOG performance status). This scale was then validated in an independent group of 184 prospectively recruited patients. The prognostic score is calculated by adding together the ‘weighted’ partial scores for each of the eight variables (Table 3). Scores can range between 0 (best prognosis) and 8.5 (worst prognosis). Patients with a score of >3.5 on this scale are predicted to survive for <2 weeks with an accuracy of 0.61 (PPV 0.63 and NPV 0.60). It should be noted that the overall survival in this study was extremely poor, with the median survival of the first 356 patients being only 13 days. It is not clear how clinically useful it is to distinguish between patients with a prognosis of 1 or 2 weeks or how accurate the CPS would be in a population who are not so close to death.
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the TCP Score
Yun et al. [21] undertook a prospective study of 91 ‘terminal’ cancer patients diagnosed at a tertiary hospital. They collected demographic and disease-related variables and information about various symptoms (weight loss, dysphagia, pain, confusion, loss of appetite, dry mouth, nausea, vomiting, constipation, diarrhoea, dyspnoea, hiccups, dizziness, depression, anxiety and insomnia). On multivariate analysis, only three variables (anorexia, diarrhoea and confusion) were found to be independently predictive of survival. These three variables were combined in a weighted score to produce the TCP score, which was found to differentiate patients into homogenous prognostic groups (Table 4). The TCP was developed in a comparatively small sample of patients in Korea. The robustness of the regression model has not been tested in an independent cohort of patients. Considering the small sample size, the number of variables included in the multiple regression analysis was excessive and until or unless the results of this study are repeated in an independent cohort of patients, TCP should be regarded as being inadequately validated.
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Bruera's poor prognostic indicator
Bruera et al. [22] studied 47 consecutive admissions to their palliative care unit. They recorded performance status, pain, nausea, depression, anxiety, anorexia, dry mouth, dyspnoea, dysphagia, loss of weight and mini-mental-state examination. On logistic regression analysis, only dysphagia to solids or liquids, cognitive failure and weight loss >10 kg in the last 6 months retained statistical significance. These three variables were combined into a ‘poor prognostic indicator’ score, which was found to have a PPV of 0.76 and a NPV of 0.71 at estimating 4-week survival. This was one of the first prognostic indicator studies to be undertaken in patients with advanced cancer, it was a prospective study and involved consecutive admissions to the palliative care unit. However, this was an exceptionally small study. The number of variables included in the regression was far in excess of the n/10 recommended by Harrell et al. [9] as a guide to estimating sample sizes in prognostic studies. The validity of the poor prognostic indicator has never been demonstrated in an independent cohort of patients.
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discussion |
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TopAbstractintroductionmethodsclinician estimates of prognosisprognostic scoring systemsdiscussionReferences |
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For many patients, the first question after ‘What's wrong with me?’ is ‘How long have I got?’ Although clinicians are often inaccurate at answering this question, a number of prognostic scoring systems are now available to help improve their survival estimates. The PaP score is the best validated and most widely used of the currently available scales and was specifically identified as such in the recent EAPC evidence-based clinical recommendations on prognosis [7]. It is simple to use and quick to complete, it has been validated in several countries and has reliably categorised patients into three distinct prognostic groups. However, further research is required to assess the clinical importance of PaP scores and whether improved prognostic information results in important changes to clinical practice. Does better prognostic information actually result in improved access to specialist palliative care or hospice services? Do patients appreciate being given more accurate prognostic information? How would patients like to receive such information?
Although PaP is the best of the currently available prognostic scales, there is scope to produce a more robust and possibly more accurate instrument. It has already been noted that one of the limitations of the PaP is that cognitive failure (a known poor prognostic factor) is not included in the scale. The authors originally evaluated 34 variables of interest [13, 23, 24] and from these identified six factors that were independently predictive of survival. However, many factors that were known (or have since been shown) to be of prognostic significance in advanced cancer were not included in the original development phase (e.g. cognitive function, C-reactive protein, oedema etc.) and so could not be included in the regression that produced the final scale. Indeed, some of the heterogeneity in the factors included in the different prognostic scales discussed in this review undoubtedly stem from the heterogeneity of the factors that were evaluated in the original studies.
Is there some way in which the decision about which factors to evaluate in the scale development phase could be more evidence-based? Two systematic reviews have summarised all the factors that have been reported to predict survival in palliative care patients. In the first of these, Vigano et al. [6] reviewed 22 studies, and 136 different variables were examined as possible predictors of survival. Due to the nature of the publications, a quantitative meta-analysis of results could not be carried out. However, the authors were able to carry out a qualitative review. More recently, Maltoni et al. [7] identified physical and psychological symptoms and signs and biologic factors that have been reported to have prognostic significance in patients with advanced cancer. Table 5 lists all the factors identified by Vigano et al. [6] as ‘possibly’ or ‘definitely’ associated with decreased survival and the additional factors identified by Maltoni et al. [7]. As yet no study has attempted to systematically assess the relative importance of each of these factors or whether they can be used in combination to improve upon or replace clinician estimates of survival in patients with advanced cancer. However, a large multicentre study has recently been funded by Cancer Research UK and will start recruitment in 2008. It is hoped that this will provide evidence about the best combination of prognostic factors to use, whether it is possible to rely on observer ratings of symptom severity, and whether repeated measurements are more accurate at predicting survival than a single assessment. Improved prognostication is likely to lead to improvements in the care of patients at the end of life and will provide patients and their relatives/carers with better quality information in order to help them to make appropriate plans for their future.
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Received for publication March 14, 2006. Revision received June 21, 2006. Accepted for publication August 17, 2006.
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References |
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