Abstract
Alcohol researchers/clinicians have two ways to collect subject /patient field data, standard-drink self-report and the breath analyzer, neither of which is passive or accurate because active subject participation is required. Transdermal alcohol sensors have been developed to measure transdermal alcohol concentration (TAC), but they are used primarily as abstinence monitors because converting TAC into more meaningful blood/breath alcohol concentration (BAC/BrAC) is difficult. In this paper, BAC/BrAC is estimated from TAC by first calibrating forward distributed parameter-based convolution models for ethanol transport from the blood through the skin using patient-collected drinking data for a single drinking episode and a nonlinear pharmacokinetic metabolic absorption/elimination model to estimate BAC. TAC and estimated BAC are then used to fit the forward convolution filter. Nonlinear least squares with adjoint-based gradient computation are used to fit both models. Calibration results are compared with those obtained using BAC/BrAC from alcohol challenges and from standard, linear, metabolic absorption, and zero order kinetics-based elimination models, by considering peak BAC, time of peak, and area under the BAC curve. Our models (with population parameters) could be included in a smart phone app that makes it convenient for the subject/patient to enter drinking data for a single episode in the field.
Highlights
Alcohol researchers and clinicians have only two ways to collect qualitative field data from research subjects and patients: the standard drink self-report and the breath analyzer
Our approach is based on replacing the alcohol challenge data with patient collected drinking diary data for the first drinking episode while the transdermal alcohol concentration (TAC) sensor is worn. This can be done either in the clinic or lab or it can be done in the field by having the subject record their drinks for the first, or for that matter, any drinking episode
In order to capture both the zeroand first-order metabolism/elimination kinetics, we model the absorption of ethanol through the gut using a linear term and the metabolism/elimination using a Michaelis-Menten [13] term that will capture the kinetics switching phenomenon
Summary
Alcohol researchers and clinicians have only two ways to collect qualitative field data from research subjects and patients: the standard drink self-report and the breath analyzer. This can be done either in the clinic or lab (by giving the subject a measured dose of alcohol when they are fitted with the TAC sensor) or it can be done in the field by having the subject record their drinks for the first, or for that matter, any drinking episode We use this drinking data together with a nonlinear pharmacokinetic (Michaelis-Menten) based model for the absorption, metabolism, and elimination of alcohol from the bloodstream to obtain an estimate of BAC/BrAC for this drinking episode. The population parameters, , M, and α would be determined by minimizing the least squares performance index where ui,j(K,M, α) denotes the model generated BAC/BrAC for subject i at time j with the parameters K,Mand α This optimization problem can be solved using a constrained (to maintain the non-negativity of the parameters) gradient projection method.
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Published Version
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