Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function.

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.