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 ofepoetin 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 200x10(9) 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 > or = 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.
RCT Entities:
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 200x10(9) 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 > or = 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.
Authors: Mark Vincent; George Dranitsaris; Sunil Verma; Cathy Lau; Pere Gascon; Simon Van Belle; Heinz Ludwig Journal: Support Care Cancer Date: 2006-11-21 Impact factor: 3.603
Authors: Lorenzo Gianni; Bernard F Cole; Ilaria Panzini; Raymond Snyder; Stig B Holmberg; Michael Byrne; Diana Crivellari; Marco Colleoni; Stefan Aebi; Edda Simoncini; Olivia Pagani; Monica Castiglione-Gertsch; Karen N Price; Aron Goldhirsch; Alan S Coates; Alberto Ravaioli Journal: Support Care Cancer Date: 2007-07-13 Impact factor: 3.603
Authors: Annelot G J van Rossum; Marleen Kok; Danielle McCool; Mark Opdam; Nienke C Miltenburg; Ingrid A M Mandjes; Elise van Leeuwen-Stok; Alex L T Imholz; Johanneke E A Portielje; Monique M E M Bos; Aart van Bochove; Erik van Werkhoven; Marjanka K Schmidt; Hendrika M Oosterkamp; Sabine C Linn Journal: Oncotarget Date: 2017-11-27