Article

# Serum anion gap: its uses and limitations in clinical medicine.

Medical and Research Services VHAGLA Healthcare System, UCLA Membrane Biology Laboratory, and Division of Nephrology VHAGLA Healthcare System and David Geffen School of Medicine, Los Angeles, California 90073, USA.

Clinical Journal of the American Society of Nephrology (Impact Factor: 5.07). 02/2007; 2(1):162-74. DOI: 10.2215/CJN.03020906 Source: PubMed

- Indian Journal of Critical Care Medicine 01/2014; 18(1):1-2.
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**ABSTRACT:**The Guide to the Expression of Uncertainty in Measurement (usually referred to as the GUM) provides the basic framework for evaluating uncertainty in measurement. The GUM however does not always provide clearly identifiable procedures suitable for medical laboratory applications, particularly when internal quality control (IQC) is used to derive most of the uncertainty estimates. The GUM modelling approach requires advanced mathematical skills for many of its procedures, but Monte Carlo simulation (MCS) can be used as an alternative for many medical laboratory applications. In particular, calculations for determining how uncertainties in the input quantities to a functional relationship propagate through to the output can be accomplished using a readily available spreadsheet such as Microsoft Excel. The MCS procedure uses algorithmically generated pseudo-random numbers which are then forced to follow a prescribed probability distribution. When IQC data provide the uncertainty estimates the normal (Gaussian) distribution is generally considered appropriate, but MCS is by no means restricted to this particular case. With input variations simulated by random numbers, the functional relationship then provides the corresponding variations in the output in a manner which also provides its probability distribution. The MCS procedure thus provides output uncertainty estimates without the need for the differential equations associated with GUM modelling. The aim of this article is to demonstrate the ease with which Microsoft Excel (or a similar spreadsheet) can be used to provide an uncertainty estimate for measurands derived through a functional relationship. In addition, we also consider the relatively common situation where an empirically derived formula includes one or more 'constants', each of which has an empirically derived numerical value. Such empirically derived 'constants' must also have associated uncertainties which propagate through the functional relationship and contribute to the combined standard uncertainty of the measurand.The Clinical biochemist. Reviews / Australian Association of Clinical Biochemists 02/2014; 35(1):37-61. - [Show abstract] [Hide abstract]

**ABSTRACT:**Purpose. To determine the effect of each of independent acid base variables on the anion gap (AG) value in cardiac surgical patients. Methods. This retrospective study involved 128 cardiac surgical patients admitted for postoperative care. The variation of AG (AGvar) between the day of admission and the first postoperative day was correlated via a multiple linear regression model with the respective variations of the independent acid base variables, that is, apparent strong ion difference (SIDa), strong ion gap (SIG), carbon dioxide (PCO2), and albumin and phosphate concentrations. Results. The variations of all the above variables contributed significantly to the prediction of AGvar (adjusted R (2) = 0.9999, F = 201890.24, and P < 0.001). According to the standardized coefficients ( β ), SIGvar ( β = 0.948, P < 0.001), [Albumin]var ( β = 0.260, P < 0.001), and [Phosphate]var ( β = 0.191, P < 0.001) were the major determinants of AGvar with lesser contributions from SIDa, var ( β = 0.071, P < 0.001) and PCO2, var ( β = -0.067, P < 0.001). Conclusions. All the independent acid base variables contribute to the prediction of the AG value. However, albumin and phosphate and SIG variations seem to be the most important predictors, while AG appears to be rather stable with changes in PCO2 and SIDa.The Scientific World Journal 01/2014; 2014:907521. · 1.73 Impact Factor

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