Prediction of docetaxel toxicity in older cancer patients: a Bayesian network approach.

Abstract

Chemotherapy is an essential therapy in the fight against cancer. Polypathology and polymedication are often encountered in elderly patients, making this population especially at risk for adverse drug reactions, and particularly with cytotoxic drugs. The objective of this study was to build a model to predict high-grade toxicity in elderly patients treated with docetaxel. Data from the trial TAX-108 have been used to create the model. The variable to predict was the occurrence of grade 3 or 4 toxicity. The explanatory variables entered in the model were anthropometric and biological characteristics of patients at inclusion; fragility criteria (SMAF, CIRS-G, performance status); location of the primary tumor; chemotherapy history, radiotherapy or surgery; weekly dose of docetaxel, cumulative dose administered. A Bayesian network model was developed using a global search procedure and an Expectation-Maximization algorithm. A 10-fold cross-validation was performed. A toxicity of grade 3 or higher was observed in 54% of patients. The variables providing the most information were the primary site (19.4%), the dose per course (17.5%), and albuminemia (13.1%). The area under the curve of the model obtained after cross-validation was 74±1.4%. The model built allows classifying correctly 71.21±0.9% of patients in our sample in the cross-validation procedure. The sensitivity and specificity of the model were 75 and 67%, respectively, and the positive and negative predictive values were 73 and 69%. The encouraging results from this first study show that Bayesian networks could help assess the benefit-risk ratio of chemotherapy in elderly patients.