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. von Stemann; Mette Jørgensen; Man Hung Eric Tang; Magnus Fontes; Jasmin Bahlo; Carmen D. Herling; Michael Hallek; Jens Lundgren; Cameron Ross MacPherson; Jan Larsen; Carsten U. Niemann

doi:10.1038/s41467-019-14225-8

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. von Stemann, Mette Jørgensen, Man Hung Eric Tang, Magnus Fontes, Jasmin Bahlo, Carmen D. Herling, Michael Hallek, Jens Lundgren, Cameron Ross MacPherson, Jan Larsen, Carsten U. Niemann

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

97 Scopus citations

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.

Original language	English
Article number	363
Journal	Nature Communications
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41467-019-14225-8
State	Published - 1 Dec 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).

Funding

This work is in part supported by funding from the Novo Nordisk Foundation, grant NNF16OC0019302 and the Danish National Research Foundation grant 126. This work was initiated during a Modeling Immune System and Pathogen Camp (MispCamp). The authors thank the Danish hematology centers that participated with data submission to the Danish National CLL Registry. The following physicians contributed to data collection and represent the Danish Hematology centers participating in the Danish National CLL Registry: Christian Hartmann Geisler, Lisbeth Enggaard, Christian Bjørn Poulsen, Peter de Nully Brown, Henrik Frederiksen, Olav Jonas Bergmann, Elisa Jacobsen Pulczynski, Robert Schou Pedersen, Ilse Christiansen, and Linda Højberg Nielsen. C.N. has received consultancy fees and/or travel grants from Janssen, Abbvie, Novartis, Roche, Gilead, Sunesis, Acerta, AstraZeneca, and CSL Behring, research support from Abbvie, AstraZeneca, and Janssen, outside this project. C.D.H. received research funding and travel support from Roche. The other authors declare no competing interests.

Funders	Funder number
National Institute of Dental and Craniofacial Research	K08DE026500
AstraZeneca
Novartis
Roche
Gilead Sciences
AbbVie
CSL Behring
Janssen Pharmaceuticals
Danmarks Grundforskningsfond	126
Novo Nordisk Fonden	NNF16OC0019302

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Agius, R., Brieghel, C., Andersen, M. A., Pearson, A. T., Ledergerber, B., Cozzi-Lepri, A., Louzoun, Y., Andersen, C. L., Bergstedt, J., von Stemann, J. H., Jørgensen, M., Tang, M. H. E., Fontes, M., Bahlo, J., Herling, C. D., Hallek, M., Lundgren, J., MacPherson, C. R., Larsen, J., & Niemann, C. U. (2020). Machine learning can identify newly diagnosed patients with CLL at high risk of infection. Nature Communications, 11(1), Article 363. https://doi.org/10.1038/s41467-019-14225-8

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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.",

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Agius, R, Brieghel, C, Andersen, MA, Pearson, AT, Ledergerber, B, Cozzi-Lepri, A, Louzoun, Y, Andersen, CL, Bergstedt, J, von Stemann, JH, Jørgensen, M, Tang, MHE, Fontes, M, Bahlo, J, Herling, CD, Hallek, M, Lundgren, J, MacPherson, CR, Larsen, J & Niemann, CU 2020, 'Machine learning can identify newly diagnosed patients with CLL at high risk of infection', Nature Communications, vol. 11, no. 1, 363. https://doi.org/10.1038/s41467-019-14225-8

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AU - Agius, Rudi

AU - Brieghel, Christian

AU - Andersen, Michael A.

AU - Pearson, Alexander T.

AU - Ledergerber, Bruno

AU - Cozzi-Lepri, Alessandro

AU - Louzoun, Yoram

AU - Andersen, Christen L.

AU - Bergstedt, Jacob

AU - von Stemann, Jakob H.

AU - Jørgensen, Mette

AU - Tang, Man Hung Eric

AU - Fontes, Magnus

AU - Bahlo, Jasmin

AU - Herling, Carmen D.

AU - Hallek, Michael

AU - Lundgren, Jens

AU - MacPherson, Cameron Ross

AU - Larsen, Jan

AU - Niemann, Carsten U.

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AB - 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.

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

Abstract

Bibliographical note

Funding

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