Pinkie Chambers
Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function
Chambers, Pinkie; Watson, Matthew; Bridgewater, John; Forster, Martin D.; Roylance, Rebecca; Burgoyne, Rebecca; Masento, Sebastian; Steventon, Luke; Harmsworth King, James; Duncan, Nick; al Moubayed, Noura
Authors
Dr Matthew Watson matthew.s.watson@durham.ac.uk
Post Doctoral Research Associate
John Bridgewater
Martin D. Forster
Rebecca Roylance
Rebecca Burgoyne
Sebastian Masento
Luke Steventon
James Harmsworth King
Nick Duncan
Dr Noura Al Moubayed noura.al-moubayed@durham.ac.uk
Associate Professor
Abstract
Background
In those receiving chemotherapy, renal and hepatic dysfunction can increase the risk of toxicity and should therefore be monitored. We aimed to develop a machine learning model to identify those patients that need closer monitoring, enabling a safer and more efficient service.
Methods
We used retrospective data from a large academic hospital, for patients treated with chemotherapy for breast cancer, colorectal cancer and diffuse-large B-cell lymphoma, to train and validate a Multi-Layer Perceptrons (MLP) model to predict the outcomes of unacceptable rises in bilirubin or creatinine. To assess the performance of the model, validation was performed using patient data from a separate, independent hospital using the same variables. Using this dataset, we evaluated the sensitivity and specificity of the model.
Results
1214 patients in total were identified. The training set had almost perfect sensitivity and specificity of >0.95; the area under the curve (AUC) was 0.99 (95% CI 0.98–1.00) for creatinine and 0.97 (95% CI: 0.95–0.99) for bilirubin. The validation set had good sensitivity (creatinine: 0.60, 95% CI: 0.55–0.64, bilirubin: 0.54, 95% CI: 0.52–0.56), and specificity (creatinine 0.98, 95% CI: 0.96–0.99, bilirubin 0.90, 95% CI: 0.87–0.94) and area under the curve (creatinine: 0.76, 95% CI: 0.70, 0.82, bilirubin 0.72, 95% CI: 0.68–0.76).
Conclusions
We have demonstrated that a MLP model can be used to reduce the number of blood tests required for some patients at low risk of organ dysfunction, whilst improving safety for others at high risk.
Citation
Chambers, P., Watson, M., Bridgewater, J., Forster, M. D., Roylance, R., Burgoyne, R., Masento, S., Steventon, L., Harmsworth King, J., Duncan, N., & al Moubayed, N. (2023). Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function. Cancer Medicine, 12(17), 17856-17865. https://doi.org/10.1002/cam4.6418
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 26, 2023 |
Online Publication Date | Aug 23, 2023 |
Publication Date | 2023-09 |
Deposit Date | Aug 29, 2023 |
Publicly Available Date | Aug 30, 2023 |
Journal | Cancer Medicine |
Electronic ISSN | 2045-7634 |
Publisher | Wiley Open Access |
Peer Reviewed | Peer Reviewed |
Volume | 12 |
Issue | 17 |
Pages | 17856-17865 |
DOI | https://doi.org/10.1002/cam4.6418 |
Public URL | https://durham-repository.worktribe.com/output/1726311 |
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http://creativecommons.org/licenses/by/4.0/
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Published Journal Article
(1.1 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
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