Chemotherapy-induced anaemia during adjuvant treatment for breast cancer: development of a prediction model

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• 作者：George Dranitsaris ; Mark Clemons ; Sunil Verma ; Cathy Lau and Mark Vincent
• 刊名：Lancet Oncology
• 出版年：2005
• 期刊代码：184_14702045
• 类别：med
• 出版时间：2005
• 卷：6
• 期：11
• 页码：856-863
• 文件大小：243 K

摘要

Summary

Background

At present, oncologists prescribe chemotherapy according to standard dose schedules, and as a result many patients develop serious, dose-limiting toxic effects such as anaemia. We aimed to develop a prediction model for anaemia in patients with breast cancer who were receiving adjuvant chemotherapy.

Methods

We reviewed medical records of 331 patients who had received adjuvant chemotherapy for breast cancer. Patients were divided randomly into a derivation sample (n=221) and internal-validation sample (n=110). An external sample of 119 patients enrolled onto the control group of a randomised trial of epoetin alfa was used to validate the model further. Multivariable logistic regression was applied to develop the initial model. We then developed a risk-scoring system, ranging from 0 (low risk) to 50 (high risk), based on the final regression variables. A receiver operating characteristic (ROC) curve analysis was done to measure the accuracy of the scoring system when applied to both validation samples.

Findings

The risk of anaemia increased as the pretreatment haemoglobin concentration decreased and was reduced with successive chemotherapy cycles. Risk was also predicted by a platelet count of 200×10⁹ cells/L or less before chemotherapy, age 65 years or older, type of adjuvant chemotherapy, and use of prophylactic antibiotics. ROC analysis had acceptable areas under the curve of 0·88 for the internal-validation sample and 0·84 for the external validation sample. A risk score of 24 to <25 before chemotherapy was identified as the optimum cut-off for maximum sensitivity (83·5%) and specificity (92·3%) of the prediction model.

Interpretation

The application and continued refinement of this prediction model will help oncologists to identify patients at risk of developing anaemia during chemotherapy for breast cancer, and might enhance patient-centred care by the application of anaemia treatment in a proactive and appropriate way.