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A Systematic Review of Physicians' Survival Predictions in Terminally Ill Cancer Patients

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A Systematic Review of Physicians' Survival Predictions in Terminally Ill Cancer Patients A Systematic Review of Physicians' Survival Predictions in Terminally Ill Cancer Patients The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Glare, Paul, Kiran Virik, Mark Jones, Malcolm Hudson, Steffen Eychmuller, John Simes, and Nicholas Christakis. 2003. A systematic review of physicians' survival predictions in terminally ill cancer patients. British Medical Journal 327, no. 7408: 195 Published Version http://dx.doi.org/10.1136/bmj.327.7408.195 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:3637831 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Downloaded from bmj.com on 5 December 2007 Papers A systematic review of physicians’ survival predictions in terminally ill cancer patients Paul Glare, Kiran Virik, Mark Jones, Malcolm Hudson, Steffen Eychmuller, John Simes, Nicholas Christakis Abstract place prognosis as the core skill accompanying Department of diagnosis.1 The inception over the past 40 years of pal- Palliative Care, Objective To systematically review the accuracy of Royal Prince Alfred liative medicine as the study of the specialised care for Hospital, physicians’ clinical predictions of survival in patients with incurable illnesses has led to a renewed Camperdown, NSW 2050, Australia terminally ill cancer patients. interest in prognostication. Data sources Cochrane Library, Medline Paul Glare “How long do I have, doctor?” is a central question head of department (1996-2000), Embase, Current Contents, and 2 for patients with far advanced, incurable illnesses. Kiran Virik Cancerlit databases as well as hand searching. Accurate prognoses are important so that patients can research fellow Study selection Studies were included if a physician’s set appropriate goals and maximise their chances for NHMRC Clinical temporal clinical prediction of survival (CPS) and the having the kind of death that most people say they Trials Centre, actual survival (AS) for terminally ill cancer patients University of want. Accuracy of predicting survival is also a technical Sydney, Sydney, were available for statistical analysis. Study quality was prerequisite for good decision making by clinicians, for Australia assessed by using a critical appraisal tool produced by study design and analysis by researchers, and for health Mark Jones biostatistician the local health authority. service planning by administrators concerned with Data synthesis Raw data were pooled and analysed John Simes optimal end of life care. Because an accurate director with regression and other multivariate techniques. communicated or formulated prediction of survival is Results 17 published studies were identified; 12 met Department of so relevant to the decisions that patients and doctors Statistics, Macquarie the inclusion criteria, and 8 were evaluable, providing make, knowing how well clinicians can predict survival University, Sydney 1563 individual prediction-survival dyads. CPS was and whether modelling prognostic factors can add Malcolm Hudson professor generally overoptimistic (median CPS 42 days, value to clinicians’ predictions is important. median AS 29 days); it was correct to within one week Several studies have suggested that contemporary Department of in 25% of cases and overestimated survival by at least Palliative Care, doctors are inaccurate and overly optimistic when pre- Kantonsspital, four weeks in 27%. The longer the CPS the greater dicting the survival of patients with terminal cancer.3–5 St Gallen, Switzerland the variability in AS. Although agreement between The aim of this systematic review was to answer the fol- CPS and AS was poor (weighted 0.36), the two were Steffen Eychmuller lowing clinical questions related to clinical predictions medical director highly significantly associated after log transformation of survival. Do doctors overestimate or underestimate (Spearman rank correlation 0.60, P < 0.001). Department of the survival of terminally ill cancer patients on Health Care Policy, Consideration of performance status, symptoms, and average? How reliable are doctors in estimating Harvard Medical School, Boston, MA, use of steroids improved the accuracy of the CPS, survival? Do doctors’ estimates of survival provide although the additional value was small. USA information above and beyond prognostic or risk Nicholas Christakis Heterogeneity of the studies’ results precluded a factor models for outcome? We obtained individual professor comprehensive meta-analysis. patient data from studies identified by a systematic Correspondence to: Conclusions Although clinicians consistently search strategy and did a meta-analysis to answer these P Glare [email protected]. overestimate survival, their predictions are highly questions. correlated with actual survival; the predictions have gov.au discriminatory ability even if they are miscalibrated. bmj.com 2003;327:195 Clinicians caring for patients with terminal cancer Methods need to be aware of their tendency to overestimate Systematic review survival, as it may affect patients’ prospects for The systematic review followed the process described achieving a good death. Accurate prognostication in the Method for Evaluating Research and Guideline Evi- models incorporating clinical prediction of survival dence (MERGE) document developed by the local are needed. health authority.6 Introduction Search strategy Diagnosis, treatment, and prognosis are the core clini- We searched Ovid Premedline (Jan 2001) and Medline cal skills fundamental to the good practice of medicine, (1966-2000), Embase, Current Contents, Cochrane but the first half of the 20th century saw treatment dis- Library, and Cancerlit databases on 19 January 2001. BMJ VOLUME 327 26 JULY 2003 bmj.com page 1 of 6 Downloaded from bmj.com on 5 December 2007 Papers The search strategy used the exploded MeSH terms tions, we rounded AS to the nearest month neoplasms AND prognosis AND terminally ill, limited (categorised together if more than six months). We to human studies, English language, and cohort studies used one way analysis of variance to test for heterogen- publication type. We also did a free text search using eity among the individual studies. We identified groups the text words predict$, survival-analysis, incidence, of homogeneous studies by applying multiple com- cohort-studies combined with prognos$, terminally ill, parisons with Tukey’s method. and neoplas$. Next, we hand searched the references As well as CPS and AS, data for 15 patient based sections of the electronically identified articles and a prognostic factors were also available from two of the book on prognostication at the end of life7 in an studies. We analysed these additional data together in a attempt to identify any articles missed by the electronic combined data subset. We used multiple linear search. Finally, we attempted to identify unpublished regression to determine three predictive models for studies, and a medical librarian advised on accessing log(AS): (a) study and log(CPS) alone; (b) the best theses and trial registries. model allowing for study and prognostic factors only; We obtained potential papers and screened them (c) the best model allowing for study, log(CPS), and to see if they met the following preset selection criteria: prognostic factors. We included the factor “study” in (a) the study involved patients with far advanced cancer the models because the patients differed significantly (that is, patients deemed “terminal” by the authors or in status (as measured by Karnofsky performance who were referred for hospice admission); (b) the status) between the two studies. We determined the results section included a temporal survival prediction, best model by backward elimination in each case, given in days or weeks, made prospectively for each retaining only statistically significant factors. To patient by a doctor; (c) the results section provided the evaluate the effect of the health status of patients on patients’ individual survival durations; and (d) the accuracy of CPS, we regressed log(AS) against the vari- methods section provided an explanation of how the ables identified in each of the three models according date of death was determined. If the raw data for clini- to three subgroups of patients defined by Karnofsky cal prediction of survival (CPS) and actual survival (AS) performance status ( < 40, 40-50, or ≥ 60). were not retrievable from the publication directly we contacted the authors to obtain them. We excluded Results papers if these data were neither retrievable from the publication nor obtainable from the authors. Study characteristics Figure 1 shows the trial flow of the systematic review. Appraising the quality of the articles The electronic search produced 22 citations, yielding 4 8–12 Three of us (PG, KV, and SE) then re-read studies six papers of apparent relevance. The hand search 3 14–22 selected for inclusion in the review and independently identified 11 other studies. We identified one evaluated them for their quality by using the MERGE unpublished study but were unable to contact the guide for critical appraisal. As MERGE does not have a author. No registered trials or theses were identified. specific checklist for studies of prognosis, we decided We obtained all 17 relevant, published papers. After that
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