Nomogram to Predict Cycle-One Serious Drug-Related Toxicity in Phase I Oncology Trials
Author(s) -
David M. Hyman,
Anne Eaton,
Mrinal M. Gounder,
G. Smith,
Erika G. Pamer,
Martee L. Hensley,
David R. Spriggs,
Percy Ivy,
Alexia Iasonos
Publication year - 2014
Publication title -
journal of clinical oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.482
H-Index - 548
eISSN - 1527-7755
pISSN - 0732-183X
DOI - 10.1200/jco.2013.49.8808
Subject(s) - nomogram , medicine , oncology , clinical trial , concordance , logistic regression , creatinine
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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