• Jaime Feliu, Ana María Jiménez-Gordo, Rosario Madero, José Ramón Rodríguez-Aizcorbe, Enrique Espinosa, Javier Castro, Jesús Domingo Acedo, Beatriz Martínez, Alberto Alonso-Babarro, Raquel Molina, Juan Carlos Cámara, María Luisa García-Paredes, and Manuel González-Barón.
• Department of Medical Oncology, University Hospital La Paz, Paseo de la Castellana, Madrid 261-28046, Spain. jfeliu.hulp@salud.madrid.org
• J. Natl. Cancer Inst. 2011 Nov 2;103(21):1613-20.

BackgroundDetermining life expectancy in terminally ill cancer patients is a difficult task. We aimed to develop and validate a nomogram to predict the length of survival in patients with terminal disease.MethodsFrom February 1, 2003, to December 31, 2005, 406 consecutive terminally ill patients were entered into the study. We analyzed 38 features prognostic of life expectancy among terminally ill patients by multivariable Cox regression and identified the most accurate and parsimonious model by backward variable elimination according to the Akaike information criterion. Five clinical and laboratory variables were built into a nomogram to estimate the probability of patient survival at 15, 30, and 60 days. We validated and calibrated the nomogram with an external validation cohort of 474 patients who were treated from June 1, 2006, through December 31, 2007.ResultsThe median overall survival was 29.1 days for the training set and 18.3 days for the validation set. Eastern Cooperative Oncology Group performance status, lactate dehydrogenase levels, lymphocyte levels, albumin levels, and time from initial diagnosis to diagnosis of terminal disease were retained in the multivariable Cox proportional hazards model as independent prognostic factors of survival and formed the basis of the nomogram. The nomogram had high predictive performance, with a bootstrapped corrected concordance index of 0.70, and it showed good calibration. External independent validation revealed 68% predictive accuracy.ConclusionsWe developed a highly accurate tool that uses basic clinical and analytical information to predict the probability of survival at 15, 30, and 60 days in terminally ill cancer patients. This tool can help physicians making decisions on clinical care at the end of life.

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