Publication
A systematic review of physicians' survival predictions in terminally ill cancer patients
Journal Paper/Review - Jul 26, 2003
Glare Paul, Virik Kiran, Jones Mark, Hudson Malcolm, Simes John, Christakis Nicholas, Eychmuller Steffen
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PubMed
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Journal
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Issn Electronic
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Brief description/objective
OBJECTIVE: To systematically review the accuracy of physicians' clinical predictions of survival in terminally ill cancer patients. DATA SOURCES: Cochrane Library, Medline (1996-2000), Embase, Current Contents, and Cancerlit databases as well as hand searching. STUDY SELECTION: Studies 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 SYNTHESIS: Raw data were pooled and analysed with regression and other multivariate techniques. RESULTS: 17 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. CONCLUSIONS: Although 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.