The Optimisation of Pre-Chemotherapy Blood Assessments through Prognostic Modelling

Background: Evidence guiding pre-chemotherapy blood assessments would enable accurate patient-planning and support the growing numbers of patients treated with chemotherapy. The aim of this PhD was to guide chemotherapy providers on the appropriate timing of pre-treatment blood assessments and develop a prognostic model to predict dose delays, mitigating the need for multiple assessments. Methods and analysis: A literature review guided retrospective data collection of risk factors for cancer patients receiving chemotherapy from four hospitals in England. Descriptive analysis was used to demonstrate changes in laboratory values of pre-chemotherapy blood tests, specifically neutrophils, when taken at different times. Using multivariable logistic regression, the relationship between potential risk factors and the outcome of a chemotherapy dose-administration delay was determined. Results: The study included 4,604 patients (2,022 breast cancer patients, 1,904 colorectal cancer patients and 678 diffuse large B-cell lymphoma patients) between 1 January 2013 and 1 January 2018. Of these, 616 patients had two neutrophil values within 7 days of treatment. 23% of neutrophils assessed 4-6 days prior to treatment did not meet the required threshold; these were repeated nearer to the treatment time. Among all patients, 628 (14%) experienced a second cycle treatment delay of 7 days or more. Significant variability was noted in the rate of delays at different hospitals ranging from 8% for hospital 4 to 22% for hospital 1 (P<0.005). Fourteen risk factors were pre-selected for the development of the prognostic model and fair predictive performance (concordance index 0.67) with good calibration was found. A net benefit analysis demonstrated the model was most beneficial in predicting patients receiving treatment for colorectal cancer; here the model would have value in 50% of all patients. Conclusions: The use of prognostic modelling offers an alternative to understanding a patient’s likeliness to encounter a dose delay, aiding service providers to plan accordingly and negating the need for inappropriate blood tests

Neutropenia Prediction Based on First-Cycle Blood Counts Using a FOS-3NN Classifier

Background. Delivery of full doses of adjuvant chemotherapy on schedule is key to optimal breast cancer outcomes. Neutropenia is a serious complication of chemotherapy and a common barrier to this goal, leading to dose reductions or delays in treatment. While past research has observed correlations between complete blood count data and neutropenic events, a reliable method of classifying breast cancer patients into low- and high-risk groups remains elusive. Patients and Methods. Thirty-five patients receiving adjuvant chemotherapy for early-stage breast cancer under the care of a single oncologist are examined in this study. FOS-3NN stratifies patient risk based on complete blood count data after the first cycle of treatment. All classifications are independent of breast cancer subtype and clinical markers, with risk level determined by the kinetics of patient blood count response to the first cycle of treatment. Results. In an independent test set of patients unseen by FOS-3NN, 19 out of 21 patients were correctly classified (Fisher’s exact test probability P<0.00023 [2 tailed], Matthews’ correlation coefficient +0.83). Conclusions. We have developed a model that accurately predicts neutropenic events in a population treated with adjuvant chemotherapy in the first cycle of a 6-cycle treatment