David M Hyman1, Anne A Eaton, Mrinal M Gounder, Gary L Smith, Erika G Pamer, Martee L Hensley, David R Spriggs, Percy Ivy, Alexia Iasonos. 1. David M. Hyman, Anne A. Eaton, Mrinal M. Gounder, Erika G. Pamer, Martee L. Hensley, David R. Spriggs, and Alexia Iasonos, Memorial Sloan-Kettering Cancer Center; David M. Hyman, Mrinal M. Gounder, Martee L. Hensley, David R. Spriggs, and Alexia Iasonos, Weill Cornell Medical College, New York, NY; and Gary L. Smith and Percy Ivy, National Cancer Institute, Bethesda, MD.
Abstract
PURPOSE: All patients in phase I trials do not have equivalent susceptibility to serious drug-related toxicity (SDRT). Our goal was to develop a nomogram to predict the risk of cycle-one SDRT to better select appropriate patients for phase I trials. PATIENTS AND METHODS: The prospectively maintained database of patients with solid tumor enrolled onto Cancer Therapeutics Evaluation Program-sponsored phase I trials activated between 2000 and 2010 was used. SDRT was defined as a grade ≥ 4 hematologic or grade ≥ 3 nonhematologic toxicity attributed, at least possibly, to study drug(s). Logistic regression was used to test the association of candidate factors to cycle-one SDRT. A final model, or nomogram, was chosen based on both clinical and statistical significance and validated internally using a bootstrapping technique and externally in an independent data set. RESULTS: Data from 3,104 patients enrolled onto 127 trials were analyzed to build the nomogram. In a model with multiple covariates, Eastern Cooperative Oncology Group performance status, WBC count, creatinine clearance, albumin, AST, number of study drugs, biologic study drug (yes v no), and dose (relative to maximum administered) were significant predictors of cycle-one SDRT. All significant factors except dose were included in the final nomogram. The model was validated both internally (bootstrap-adjusted concordance index, 0.60) and externally (concordance index, 0.64). CONCLUSION: This nomogram can be used to accurately predict a patient's risk for SDRT at the time of enrollment. Excluding patients at high risk for SDRT should improve the safety and efficiency of phase I trials.
PURPOSE: All patients in phase I trials do not have equivalent susceptibility to serious drug-related toxicity (SDRT). Our goal was to develop a nomogram to predict the risk of cycle-one SDRT to better select appropriate patients for phase I trials. PATIENTS AND METHODS: The prospectively maintained database of patients with solid tumor enrolled onto Cancer Therapeutics Evaluation Program-sponsored phase I trials activated between 2000 and 2010 was used. SDRT was defined as a grade ≥ 4 hematologic or grade ≥ 3 nonhematologic toxicity attributed, at least possibly, to study drug(s). Logistic regression was used to test the association of candidate factors to cycle-one SDRT. A final model, or nomogram, was chosen based on both clinical and statistical significance and validated internally using a bootstrapping technique and externally in an independent data set. RESULTS: Data from 3,104 patients enrolled onto 127 trials were analyzed to build the nomogram. In a model with multiple covariates, Eastern Cooperative Oncology Group performance status, WBC count, creatinine clearance, albumin, AST, number of study drugs, biologic study drug (yes v no), and dose (relative to maximum administered) were significant predictors of cycle-one SDRT. All significant factors except dose were included in the final nomogram. The model was validated both internally (bootstrap-adjusted concordance index, 0.60) and externally (concordance index, 0.64). CONCLUSION: This nomogram can be used to accurately predict a patient's risk for SDRT at the time of enrollment. Excluding patients at high risk for SDRT should improve the safety and efficiency of phase I trials.
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