Analysis of clinical prognostic variables for Chronic Lymphocytic Leukemia decision-making problems

Abstract

Chronic Lymphocytic Leukemia (CLL) is a disease with highly heterogeneous clinical course. A key goal is the prediction of patients with high risk of disease progression, which could benefit from an earlier or more intense treatment. In this work we introduce a simple methodology based on machine learning methods to help physicians in their decision making in different problems related to CLL. Material and Methods: Clinical data belongs to a retrospective study of a cohort of 265 Caucasians who were diagnosed with CLL between 1997 and 2007 in Hospital Cabueñes (Asturias, Spain). Different machine learning methods were applied to find the shortest list of most discriminatory prognostic variables to predict the need of Chemotherapy Treatment and the development of an Autoimmune Disease. Results: Autoimmune disease occurrence was predicted with very high accuracy (>90%). Autoimmune disease development is currently an unpredictable severe complication of CLL. Chemotherapy Treatment has been predicted with a lower accuracy (80%). Risk analysis showed that the number of false positives and false negatives are well balanced. Conclusions: Our study highlights the importance of prognostic variables associated with the characteristics of platelets, reticulocytes and natural killers, which are the main targets of the autoimmune haemolytic anemia and immune thrombocytopenia for autoimmune disease development, and also, the relevance of some clinical variables related with the immune characteristics of CLL patients that are not taking into account by current prognostic markers for predicting the need of chemotherapy. Because of its simplicity, this methodology could be implemented in spreadsheets