Chambers, Pinkie;
Watson, Matthew;
Bridgewater, John;
Forster, Martin;
Roylance, Rebecca;
Burgoyne, Rebecca;
Masento, Sebastian;
... Al-Moubayed, Noura; + view all
(2023)
Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function.
Cancer Medicine
, 12
(17)
pp. 17856-17865.
10.1002/cam4.6418.
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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.
| Type: | Article |
|---|---|
| Title: | Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/cam4.6418 |
| Publisher version: | https://doi.org/10.1002/cam4.6418 |
| Language: | English |
| Additional information: | © 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. 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. |
| Keywords: | Chemotherapy, hepatic, machine learning, renal, treatment-dose |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10175117 |
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