• Paul Glare, Kiran Virik, Mark Jones, Malcolm Hudson, Steffen Eychmuller, John Simes, and Nicholas Christakis.
• Department of Palliative Care, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia. paul@email.cs.nsw.gov.au
• BMJ. 2003 Jul 26;327(7408):195-8.

ObjectiveTo systematically review the accuracy of physicians' clinical predictions of survival in terminally ill cancer patients.Data SourcesCochrane Library, Medline (1996-2000), Embase, Current Contents, and Cancerlit databases as well as hand searching.Study SelectionStudies were included if a physician's temporal clinical prediction of survival (CPS) and the actual survival (AS) for terminally ill cancer patients were available for statistical analysis. Study quality was assessed by using a critical appraisal tool produced by the local health authority.Data SynthesisRaw data were pooled and analysed with regression and other multivariate techniques.Results17 published studies were identified; 12 met the inclusion criteria, and 8 were evaluable, providing 1563 individual prediction-survival dyads. CPS was generally overoptimistic (median CPS 42 days, median AS 29 days); it was correct to within one week in 25% of cases and overestimated survival by at least four weeks in 27%. The longer the CPS the greater the variability in AS. Although agreement between CPS and AS was poor (weighted kappa 0.36), the two were highly significantly associated after log transformation (Spearman rank correlation 0.60, P < 0.001). Consideration of performance status, symptoms, and use of steroids improved the accuracy of the CPS, although the additional value was small. Heterogeneity of the studies' results precluded a comprehensive meta-analysis.ConclusionsAlthough clinicians consistently overestimate survival, their predictions are highly correlated with actual survival; the predictions have discriminatory ability even if they are miscalibrated. Clinicians caring for patients with terminal cancer need to be aware of their tendency to overestimate survival, as it may affect patients' prospects for achieving a good death. Accurate prognostication models incorporating clinical prediction of survival are needed.

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