Comparison of various equations to calculate serum osmolality / Saraswathy Apparow
Background : Serum osmolality is often measured in laboratory by cryoscopic technique, which is the reference method. However, in clinical setting, routine measurement of serum osmolality is not always feasible at bedside. In normal subjects, sodium, potassium glucose, and urea are the primary circu...
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| Format: | Thesis |
| Published: |
2017
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| Online Access: | http://studentsrepo.um.edu.my/8919/4/saraswathy.pdf http://studentsrepo.um.edu.my/8919/ |
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| Summary: | Background : Serum osmolality is often measured in laboratory by cryoscopic technique, which is the reference method. However, in clinical setting, routine measurement of serum osmolality is not always feasible at bedside. In normal subjects, sodium, potassium glucose, and urea are the primary circulating solutes. If no other solutes are present in serum at high millimolar concentrations, then these solute concentrations can be used to predict in the measured osmolality. The osmolal gap (OG) is the difference between measured osmolality and the calculated osmolality. The major
use of OG is to screen for the possible presence of exogenous toxic substances and to screen for cases of alcohol intoxication. However, many equations for the calculation of have been proposed. The purpose of this study is to compare the calculated osmolality using various formulae with the measured osmolality in order to determine which calculated formula fit best with actual measured osmolality. Materials and Methods : Serum osmolality results which was done during the period of January 2015 to December 2015 were extracted from the laboratory information system (LIS). Serum osmolality that was performed simultaneously with renal and Iier function tests, serum electrolytes and plasma glucose were included for the study. Serum osmolality measured for patients with the history of drug abuse and poisoning were excluded from the study. 405 serum osmolality results were chosen for the study and were divided into two groups. Group 1 included 205 data with normal serum osmolality, renal, liver function tests and plasma glucose level less than 7.8 mmol/L. For the second group (n = 200), data with low serum osmolality (n=90) and high serum osmolality (n=80) and normal serum osmolality (n=30) were included. The first group data were to identify which equation correlated well with the measured osmolalit and the second group data to study the performance of equation that correlated well with the measured osmolality. Results Of the 19 formulae studied only four were identified as optimal by having the mean OG<2 mOsm/kg. The Smithline-Gardner formula (2Na+ Glu + BUN) showed the smallest osmolal gap with the mean bias of 0.3 mOsm/kg. This formula was observed to be the best fit betwe n measured and calculated smolality. The DorwartChalmers formula which has been incorporated in many autoanalysers for calculation of osmolality equation underestimates the osmolality compared to the measured osmolality and gave inferior results. Conclusion: Based on our results, we recommend Smithline-Gardner formula to be used by clinicians and laboratories for the calculation of osmolal gap, on the basis that (i) OG gap is close to zero, (ii) it is simple, easy to calculate at bedside for hospitalised patients and (iii) can be easily incorporated in the Laboratory Information System (LIS). Keywords: measured osmolality; calculated osmolality; osmolal gap; regression analysis. |
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