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

Abstract

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.

Keywords

• chemotherapy
• hepatic
• machine learning
• renal
• treatment‐dose