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Short-term time-to-event model of response to treatment following the GIMEMA protocol for Acute Myeloid Leukaemia

Acute Myeloid Leukaemia (AML) is a serious condition that may require aggressive systemic treatment. As a consequence of this it is important to characterize quantitatively response to treatment, differentiating patients across a range of clinical and laboratory indicators. This study follows the disease progression for a cohort of n=509 patients diagnosed with AML “de novo” and treated according to a strict protocol defined by the “Gruppo Italiano Malattie Ematologiche dell'Adulto” (GIMEMA). This protocol involves an induction therapy with health assessment typically within 60--90 days and three possible outcomes: complete remission (CR), resistance to induction therapy (Res) and induction death (ID). Accordingly, a time-to-event model with competing risks using the framework of partial logistic artificial neural networks with automatic relevance determination (PLANNCR-ARD) is applied. This results show a stratification of the mortality risk following therapy.