PURPOSE: A tool that quantifies the risk of treatment-related toxicity based on individual patient characteristics can augment the informed consent process and safety monitoring in the setting of phase II cancer treatment trials of molecularly targeted agents (MTAs). METHODS: A regression model was constructed to predict the risk of a serious adverse event (SAE) with an MTA and presented as a nomogram. Estimation of risk can be performed by integrating risk estimates from the nomogram and from a reference or average patient. Internal validation was performed using bootstrapping techniques. RESULTS: A total of 578 patients were treated with one of 14 MTAs given alone or in combination on one of 27 clinical trials performed by the Princess Margaret Hospital Drug Development Program between 2001 and 2006. Approximately 50% and 24% of patients experienced an SAE and an attributable SAE (SAEatt) during cycle 1, respectively. Albumin, lactate dehydrogenase (LDH), number of target lesions, prior radiotherapy, Charlson score, age, and performance status were included in the optimal model as predictors of a cycle 1 SAE, whereas the number of prior chemotherapy regimens, baseline creatinine, LDH, prior radiotherapy, Charlson score, body-surface area, and performance status were included as predictors of an SAEatt. Moderate-good internal validity was demonstrated, with area under the curve estimates ranging from 56.7% to 86.1% for all SAEs and 63.0% to 89.7% for SAEatts. CONCLUSION: A regression model was constructed that predicts the SAE and SAEatt risk for an individual patient during cycle 1 of phase II trial treatment with moderate to good internal validity. External validation is still required.
PURPOSE: A tool that quantifies the risk of treatment-related toxicity based on individual patient characteristics can augment the informed consent process and safety monitoring in the setting of phase II cancer treatment trials of molecularly targeted agents (MTAs). METHODS: A regression model was constructed to predict the risk of a serious adverse event (SAE) with an MTA and presented as a nomogram. Estimation of risk can be performed by integrating risk estimates from the nomogram and from a reference or average patient. Internal validation was performed using bootstrapping techniques. RESULTS: A total of 578 patients were treated with one of 14 MTAs given alone or in combination on one of 27 clinical trials performed by the Princess Margaret Hospital Drug Development Program between 2001 and 2006. Approximately 50% and 24% of patients experienced an SAE and an attributable SAE (SAEatt) during cycle 1, respectively. Albumin, lactate dehydrogenase (LDH), number of target lesions, prior radiotherapy, Charlson score, age, and performance status were included in the optimal model as predictors of a cycle 1 SAE, whereas the number of prior chemotherapy regimens, baseline creatinine, LDH, prior radiotherapy, Charlson score, body-surface area, and performance status were included as predictors of an SAEatt. Moderate-good internal validity was demonstrated, with area under the curve estimates ranging from 56.7% to 86.1% for all SAEs and 63.0% to 89.7% for SAEatts. CONCLUSION: A regression model was constructed that predicts the SAE and SAEatt risk for an individual patient during cycle 1 of phase II trial treatment with moderate to good internal validity. External validation is still required.
Authors: B Basu; J Vitfell-Pedersen; V Moreno Garcia; M Puglisi; A Tjokrowidjaja; K Shah; S Malvankar; B Anghan; J S de Bono; S B Kaye; L R Molife; U Banerji Journal: Oncology Date: 2012-08-07 Impact factor: 2.935