Abstract 1859P
Background
Renal and hepatic function are among the main determinants of chemotherapy drug exposure, meaning possible altered pharmacokinetic profiles in those with impaired organ function, necessitating dose adjustments. Practice is therefore to assess function at each treatment cycle; however, this can cause delays in patients receiving treatment. We aimed to determine if demographic and laboratory values from the first two treatment cycles could predict changes in subsequent renal and hepatic function, for subsequent treatment dosing.
Methods
Data from one large hospital was extracted for age, comorbidity, cancer type, treatment, and laboratory values for each treatment cycle received including creatinine, bilirubin, neutrophils and platelets. Using deep learning, we developed a Multilayer Perceptron regression model to predict creatinine and bilirubin values; these predictions are then used to classify patients into groupings using common-terminology-criteria-for-adverse-events. The F1 score was calculated to determine a balanced measure of accuracy of the model.
Results
Of 962 eligible patients, 334 were male (35%) and 626 female (65%), with a median age of 56 years. Tumour sites were breast cancer (n=323, 32.4%), colorectal cancer (n=362, 36.2%), and diffuse-large-b-cell lymphoma (n=277, 27.5%). All treatments were the first definitive treatment. 90 (9.4%) patients saw grade changes in creatinine, and 72 (7.5%) in bilirubin. The developed prediction models using laboratory values from baseline to cycle 2 showed good performance; on creatinine, we attained an F1 score of 0.8 and precision of 1, and on bilirubin an F1 of 0.85 and precision of 0.92. We estimate that around 70% of patients could be saved from having creatinine and bilirubin tests from cycle 3 onwards.
Conclusions
The developed models will be validated and used as an add on to e-prescribing systems, enabling accurate stratification of patients that need more intense renal and hepatic assessments. We estimate we will save 70% of assessments for patients, with the remaining 30% identified as needing further intensive monitoring. This would lead to fewer delays and improved patient experience.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
UCLH NHS Foundation Trust.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.