First-cycle blood counts and subsequent neutropenia, dose reduction, or delay in early-stage breast cancer therapy.

Abstract

PURPOSE If patients could be ranked according to their projected need for supportive care therapy, then more efficient and less costly treatment algorithms might be developed. This work reports on the construction of a model of neutropenia, dose reduction, or delay that rank-orders patients according to their need for costly supportive care such as granulocyte growth factors. PATIENTS AND METHODS A case series and consecutive sample of patients treated for breast cancer were studied. Patients had received standard-dose adjuvant chemotherapy for early-stage nonmetastatic breast cancer and were treated by four medical oncologists. Using 95 patients and validated with 80 additional patients, development models were constructed to predict one or more of the following events: neutropenia (absolute neutrophil count [ANC] < or = 250/microL), dose reduction > or = 15% of that scheduled, or treatment delay > or = 7 days. Two approaches to modeling were attempted. The pretreatment approach used only pretreatment predictors such as chemotherapy regimen and radiation history; the conditional approach included, in addition, blood count information obtained in the first cycle of treatment. RESULTS The pretreatment model was unsuccessful at predicting neutropenia, dose reduction, or delay (c-statistic = 0.63). Conditional models were good predictors of subsequent events after cycle 1 (c-statistic = 0.87 and 0.78 for development and validation samples, respectively). The depth of the first-cycle ANC was an excellent predictor of events in subsequent cycles (P = .0001 to .004). Chemotherapy plus radiation also increased the risk of subsequent events (P = .0011 to .0901). Decline in hemoglobin (HGB) level during the first cycle of therapy was a significant predictor of events in the development study (P = .0074 and .0015), and although the trend was similar in the validation study, HGB decline failed to reach statistical significance. CONCLUSION It is possible to rank patients according to their need of supportive care based on blood counts observed in the first cycle of therapy. Such rankings may aid in the choice of appropriate supportive care for patients with early-stage breast cancer.