Development and validation of a bedside score to predict early death in cancer of unknown primary patients

Abstract

Background: We have investigated predictors of 90-day-mortality in a large cohort of non-specific cancer of unknown primary patients. Methods: Predictors have been identified by univariate and then logistic regression analysis in a single-center cohort comprising 429 patients (development cohort). We identified four predictors that produced a predictive score that has been applied to an independent multi-institutional cohort of 409 patients (validation cohort). The score was the sum of predictors for each patient (0 to 4). Results: The 90-day-mortality-rate was 33 and 26% in both cohorts. Multivariate analysis has identified 4 predictors for 90- day-mortality: performance status.1 (OR = 3.03, p = 0.001), at least one co-morbidity requiring treatment (OR = 2.68, p = 0.004), LDH> 1.5 × the upper limit of normal (OR = 2.88, p = 0.007) and low albumin or protein levels (OR = 3.05, p = 0.007). In the development cohort, 90-day-mortality-rates were 12.5%, 32% and 64% when the score was [0-1], 2 and [3-4], respectively. In the validation cohort, risks were 13%, 25% and 62% according to the same score values. Conclusions: We have validated a score that is easily calculated at the beside that estimates the 90-days mortality rate in non-specific CUP patients. This could be helpful to identify patients who would be better served with palliative care rather than aggressive chemotherapy. © 2009 Penel et al.