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Nicholas Christakis

Publications and source records attributed to Nicholas Christakis.

6 recordsLinked to original sources
PubMed2005

Prognostic factors in advanced cancer patients: evidence-based clinical recommendations--a study by the Steering Committee of the European Association for Palliative Care.

PURPOSE: To offer evidence-based clinical recommendations concerning prognosis in advanced cancer patients. METHODS: A Working Group of the Research Network of the European Association for Palliative Care identified clinically significant topics, reviewed the studies, and assigned the level of evidence. A formal meta-analysis was not feasible because of the heterogeneity of published studies and the lack of minimal standards in reporting results. A systematic electronic literature search within the main available medical literature databases was performed for each of the following four areas identified: clinical prediction of survival (CPS), biologic factors, clinical signs and symptoms and psychosocial variables, and prognostic scores. Only studies on patients with advanced cancer and survival < or = 90 days were included. RESULTS: A total of 38 studies were evaluated. Level A evidence-based recommendations of prognostic correlation could be formulated for CPS (albeit with a series of limitations of which clinicians must be aware) and prognostic scores. Recommendations on the use of other prognostic factors, such as performance status, symptoms associated with cancer anorexia-cachexia syndrome (weight loss, anorexia, dysphagia, and xerostomia), dyspnea, delirium, and some biologic factors (leukocytosis, lymphocytopenia, and C-reactive protein), reached level B. CONCLUSION: Prognostication of life expectancy is a significant clinical commitment for clinicians involved in oncology and palliative care. More accurate prognostication is feasible and can be achieved by combining clinical experience and evidence from the literature. Using and communicating prognostic information should be part of a multidisciplinary palliative care approach.

Biomarkers↗
PubMed2003

A systematic review of physicians' survival predictions in terminally ill cancer patients.

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.

Analysis of Variance↗