The global epidemic of obesity has led to an increased prevalence of chronic diseases and need for pharmacological intervention. However, little is known about the influence of obesity on the drug exposure profile, resulting in few clear dosing guidelines for the obese. Here we present a semi-mechanistic model for lean body weight (LBW) that we believe is sufficiently robust to quantify the influence of body composition on drug clearance, and is therefore an ideal metric for adjusting chronic dosing in the obese.

Background: Intravenous (IV) fluid administration is an integral component of clinical care. Errors in administration can cause detrimental patient outcomes and increase healthcare costs, although little is known about medication administration errors associated with continuous IV infusions. Objectives: (1) To ascertain the prevalence of medication administration errors for continuous IV infusions and identify the variables that caused them. (2) To quantify the probability of errors by fitting a logistic regression model to the data. Methods: A prospective study was conducted on three surgical wards at a teaching hospital in Australia. All study participants received continuous infusions of IV fluids. Parenteral nutrition and non-electrolyte containing intermittent drug infusions (such as antibiotics) were excluded. Medication administration errors and contributing variables were documented using a direct observational approach. Results: Six hundred and eighty seven observations were made, with 124 (18.0%) having at least one medication administration error. The most common error observed was wrong administration rate. The median deviation from the prescribed rate was 247 ml/h (interquartile range 275 to +33.8 ml/h). Errors were more likely to occur if an IV infusion control device was not used and as the duration of the infusion increased. Conclusions: Administration errors involving continuous IV infusions occur frequently. They could be reduced by more common use of IV infusion control devices and regular checking of administration rates.

This study explored how study design influences the probability of selecting a 'true' covariate from two competing covariate models. The probability of selecting the 'True Model' (lean body weight on clearance) over the 'False Model' (total body weight (WT) on clearance) was compared for designs where WT was either lognormally distributed (i.e. non-stratified), or stratified into 3 equal strata. The probability of selecting the 'True Model' increased as the WT inclusion criterion widened, and was always greater under the stratified design. Incorporating stratification into study designs, in combination with a wide covariate range, can aid identification of true parameter-covariate relationships. This has particular importance if the model is to be extrapolated beyond the studied population (e.g. dosing in obesity).

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