This trial studies how well iohexol works in helping doctors calculate the dose of carboplatin given to patients with cancer. Drugs used in chemotherapy, such as carboplatin, work in different ways to stop the growth of tumor cells, either by killing the cells, by stopping them from dividing, or by stopping them from spreading. Understanding how to best calculate the dose of carboplatin given to patients with cancer may help doctors learn how to improve the use of carboplatin in the future.
PRIMARY OBJECTIVES: I. Evaluate the success of targeting a carboplatin area under the curve (AUC) with our current approach to dosing carboplatin. II. Assess the performance of Cockcroft-Gault (CG), four-variable Modification of Diet in Renal Disease (MDRD-4), and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) based on isotope dilution mass spectrometry (IDMS) calibrated serum creatinine in predicting measured glomerular filtration rate (mGFR) in patients with cancer. III. Define the relationship of mGFR and carboplatin clearance in patients with cancer. SECONDARY OBJECTIVES: I. Evaluate the divergence of estimated (e)GFR from mGFR based on patient demographic and other characteristics, thus identifying those most likely to benefit from determination of mGFR over use of eGFR. II. Determine the success rate of achieving the target carboplatin AUC in patients in whom the carboplatin dose is capped. III. Evaluate the relationship between carboplatin exposure and toxicity. IV. Assess the ability of markers other than creatinine in pre-treatment serum to better estimate kidney function in patients with cancer. OUTLINE: Patients receive iohexol intravenously (IV) over 30-60 seconds. Patients then receive standard of care carboplatin IV. Patients also undergo collection of 7-8 blood samples for analysis. After completion of study, patients are followed up for 3-4 weeks.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
TREATMENT
Masking
NONE
Enrollment
Undergo collection of blood samples
Given IV
Given IV
Accuracy of achieving the targeted carboplatin area under the curve (AUC)
Will be quantified by the median percentage error (PE), root-mean-squared error (RMSE), interquartile range (IQR) of the residuals, and median absolute percentage error (APE). In addition, the percentage of patients for which the observed carboplatin AUC is within 17% of target will be calculated. The actual AUC will be quantified using atomic absorption spectrophotometry.
Time frame: Up to 4 weeks
Precision of achieving the targeted carboplatin AUC
Will be quantified by the median PE, RMSE, IQR of the residuals, and median APE. In addition, the percentage of patients for which the observed carboplatin AUC is within 17% of target will be calculated. The actual AUC will be quantified using atomic absorption spectrophotometry.
Time frame: Up to 4 weeks
Bias of the formula for estimated glomerular filtration rate (eGFR) currently in existence in patients with cancer
Will be quantified by the median PE, RMSE, IQR of the residuals, and median APE will be used to assess the accuracy of each model's eGFR values for predicting measured (m)GFR. In addition, will calculate the percentage of patients for which eGFR is within 30%, 20%, and 10% of mGFR.
Time frame: Up to 4 weeks
Precision of the formula for eGFR currently in existence in patients with cancer
Will be quantified by the median PE, RMSE, IQR of the residuals, and median APE will be used to assess the accuracy of each model's eGFR values for predicting mGFR. In addition, will calculate the percentage of patients for which eGFR is within 30%, 20%, and 10% of mGFR.
Time frame: Up to 4 weeks
Correlation between carboplatin clearance (CL) and mGFR
Assessed by regression analysis. Carboplatin clearance will be derived by Empirical Bayes estimation using the POSTHOC option implemented in NONMEM. Will perform regression on the relationship between CL and mGFR. Initially this will follow a linear relationship analogous to the Calvert formula (CL = A + B\* mGFR), and will test if the observed values for A and B are significantly different from those defined by Calvert as A = 25 mL/min and B = 1 (unitless). Will also perform regression by other means, e.g. after log transformation of the data, and assess if this results in a formula that performs better than the Calvert formula or the initial linear model. In addition, the impact of covariates on this relationship will be explored.
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University of Arizona Cancer Center-Orange Grove Campus
Tucson, Arizona, United States
Banner University Medical Center - Tucson
Tucson, Arizona, United States
University of Arizona Cancer Center-North Campus
Tucson, Arizona, United States
Mercy Hospital Fort Smith
Fort Smith, Arkansas, United States
CARTI Cancer Center
Little Rock, Arkansas, United States
University of Arkansas for Medical Sciences
Little Rock, Arkansas, United States
UC San Diego Health System - Encinitas
Encinitas, California, United States
UC San Diego Moores Cancer Center
La Jolla, California, United States
University of California Davis Comprehensive Cancer Center
Sacramento, California, United States
UC San Diego Medical Center - Hillcrest
San Diego, California, United States
...and 179 more locations
Time frame: Up to 4 weeks
Divergence of eGFR from mGFR
The bias of eGFR versus mGFR will be modeled as a function of the patient's characteristics.
Time frame: Up to 4 weeks
Success rate of achieving the target carboplatin AUC in patients in whom the carboplatin dose is capped
Among patients with eGFR \> 125 mL/min, precision and bias will be estimated relative to the target carboplatin AUC.
Time frame: Up to 4 weeks
Relationship between carboplatin exposure and toxicity
Will be described by the regression parameters for the estimated relationship between carboplatin AUC and platelet count, neutrophil count, and non-hematologic grade 3 toxicities.
Time frame: Up to 4 weeks
Ability of markers in addition to creatinine in pre-treatment serum to better estimate kidney function in patients with cancer
These markers will include (but are not limited to) cystatin C, beta-2-microglobulin (B2M), and beta-trace-protein (BTP). Will use these markers and patient covariates (e.g. sex, race, weight etc.) as predictors for mGFR in regression efforts.
Time frame: Up to 4 weeks