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Palliative Medicine
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Predicting survival in terminal cancer patients: clinical observation or quality-of-life evaluation?

Franco Toscani

Cinzia Brunelli

Istituto di Ricerca in Medicina Palliativa ‘L. Maestroni’, Cremona

Guido Miccinesi

CSPO, Istituto Scientifico della Regione Toscana, Firenze

Massimo Costantini

Servizio di Epidemiologia Clinica e Sperimentazioni Controllate, Istituto Nazionale per la Ricerca sul Cancro, Genova

Michele Gallucci

Unitá di Cure Palliative e Terapia del Dolore, Desio

Marcello Tamburini

Servizio di Psicologia, Istituto Nazionale Tumori, Milano

Eugenio Paci

CSPO, Istituto Scientifico della Regione Toscana, Firenze

Paola Di Giulio

Dip. Sanitá Pubblicae Microbiogia, Facoltá di Medicina e Chirurgia, Universitaá degli Studi di Torino, Torino

Carlo Peruselli

Unitá Operativa di Cure Palliative, Ospedale di Biella

Introduction: This study compares the relative prognostic power of clinical variables and quality-of-life (QoL) measures in a population of terminal cancer patients. Methods: A prospective cohort study in 58 Italian Palliative Care Units. Of the 601 randomly selected terminal cancer patients, 574 were followed until death in order to compare clinical and QoL variables (using the Therapy Impact Questionnaire (TIQ) as predictors of survival, and assess whether their combined implementation makes prediction more accurate. Results: The clinical variables most strongly associated with survival were dyspnoea, cachexia, Katz's ADL, oliguria, dysphagia, dehydration, liver and acute kidney failure and delirium (hazard ratios (HR) ranging from 2.10 to 3.01). Only the first four kept their strength once introduced in the Cox model (HRs ranging from 1.95 to 2.22). In the TIQ primary scale the strongest predictors were physical wellbeing, fatigue, functional status and cognitive status (HRs ranging from 1.42 to 1.71), but only fatigue showed an independent prognostic relevance (90% of selection). In the TIQ global scales, the Physical Symptom Index showed a stronger association with survival (HR 1.71) than the Therapy Impact Index (HR 1.47). The former marginally improved the prognostic power of the model when added to clinical variables. Internal validation confirmed that the results were not spurious. Conclusions: In terminal cancer patients, clinical variables are better predictors of survival than QoL. The large residual variability not accounted for by the model (: {approx} 70%) suggests that survival is also influenced by factors unlikely to be identified in a survey.

Key Words: clinical observation • end-of-life • quality of life • terminal patients' survival

Palliative Medicine, Vol. 19, No. 3, 220-227 (2005)
DOI: 10.1191/0269216305pm1000oa
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