Prediction of clinical outcome in CLL based on recurrent gene mutations, CLL-IPI variables, and (para)clinical data.

ABSTRACT: A highly variable clinical course, immune dysfunction, and a complex genetic blueprint pose challenges for treatment decisions and the management of risk of infection in patients with chronic lymphocytic leukemia (CLL). In recent years, the use of machine learning (ML) technologies has made it possible to attempt to untangle such heterogeneous disease entities. In this study, using 3 classes of variables (international prognostic index for CLL [CLL-IPI] variables, baseline [para]clinical data, and data on recurrent gene mutations), we built ML predictive models to identify the individual risk of 4 clinical outcomes: death, treatment, infection, and the combined outcome of treatment or infection. Using the predictive models, we assessed to what extent the different classes of variables are

SUBMITTER: Parviz M 

PROVIDER: S-EPMC9631547 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature