Machine learning can identify newly diagnosed patients with CLL at high risk of infection

Rudi Agius, Christian Brieghel, Michael A. Andersen, Alexander T. Pearson, Bruno Ledergerber, Alessandro Cozzi-Lepri, Yoram Louzoun, Christen L. Andersen, Jacob Bergstedt, Jakob H. Stemann, Mette Jørgensen, Man-Hung Eric Tang, Magnus Fontes, Jasmin Bahlo, Carmen D. Herling, Michael Hallek, Jens Lundgren, Cameron Ross MacPherson, Jan Larsen and Carsten U. Niemann ()
Additional contact information
Rudi Agius: Technical University of Denmark
Christian Brieghel: Rigshospitalet, Copenhagen University Hospital
Michael A. Andersen: Rigshospitalet, Copenhagen University Hospital
Alexander T. Pearson: University of Chicago
Bruno Ledergerber: University of Zurich
Alessandro Cozzi-Lepri: University College London
Yoram Louzoun: Bar-Ilan University
Christen L. Andersen: Rigshospitalet, Copenhagen University Hospital
Jacob Bergstedt: Institut Pasteur
Jakob H. Stemann: Rigshospitalet, Copenhagen University Hospital
Mette Jørgensen: Rigshospitalet, Copenhagen University Hospital
Man-Hung Eric Tang: Rigshospitalet, Copenhagen University Hospital
Magnus Fontes: Rigshospitalet, Copenhagen University Hospital
Jasmin Bahlo: University Hospital
Carmen D. Herling: University Hospital
Michael Hallek: University Hospital
Jens Lundgren: Rigshospitalet, Copenhagen University Hospital
Cameron Ross MacPherson: Rigshospitalet, Copenhagen University Hospital
Jan Larsen: Technical University of Denmark
Carsten U. Niemann: Rigshospitalet, Copenhagen University Hospital

Nature Communications, 2020, vol. 11, issue 1, 1-17

Abstract: Abstract Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop the CLL Treatment-Infection Model (CLL-TIM) that identifies patients at risk of infection or CLL treatment within 2 years of diagnosis as validated on both internal and external cohorts. CLL-TIM is an ensemble algorithm composed of 28 machine learning algorithms based on data from 4,149 patients with CLL. The model is capable of dealing with heterogeneous data, including the high rates of missing data to be expected in the real-world setting, with a precision of 72% and a recall of 75%. To address concerns regarding the use of complex machine learning algorithms in the clinic, for each patient with CLL, CLL-TIM provides explainable predictions through uncertainty estimates and personalized risk factors.

Date: 2020
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DOI: 10.1038/s41467-019-14225-8

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