Critical evaluation of equations for serum osmolality: Proposals for effective clinical utility

The accuracy of serum osmolarity calculation in small children

Background

Serum osmolality can be measured (Omeas) or calculated (Ocal). Many formulas for Ocal have been already published, but data regarding the most accurate equation in small babies is not available. Thus, we aim to compare Omeas and Ocal obtained by different formulas in newborns and small children.

Methods

The study included 280 serum samples taken from children, from the first day of life to 2 years (mean age 8.2 ± 7.6 months) treated in the University Children's Hospital in Krakow. The serum osmolality was measured by osmometer and calculated by 8 common formulas.

Results

The mean value of Omeas (2 8 5 .8 ± 5 .1 mOsm/kgH2O) was significantly different as compared to the mean values of Ocal (p< 0.01) for all formulas, except Ocal obtained by the formula: 1.86*(N a + K) +1.15*Glu + Urea + 14. According to Bland-Altman analysis, this formula showed the best performance for estimating osmolality. In children under 3 months of life Passing-Bablok regression indicated both systematic and proportional error for results obtained by each formula compared to the measured values.

Conclusions

To calculate osmolarity in children aged between 3 months and 2 years old the following equation: 1 .86*(N a + K) + 1.1 5*G lu+ U rea+ 14 might be used, whereas serum osmolality in children up to 3 month of life should be measured.

Association Between Serum Osmolality and Acute Kidney Injury in Critically Ill Patients: A Retrospective Cohort Study

Purposes: Acute kidney injury (AKI) is a common complication in critically ill patients and is usually associated with poor outcomes. Serum osmolality has been validated in predicting critically ill patient mortality. However, data about the association between serum osmolality and AKI is still lacking in ICU. Therefore, the purpose of the present study was to investigate the association between early serum osmolality and the development of AKI in critically ill patients.

Methods: The present study was a retrospective cohort analysis based on the medical information mart for intensive care III (MIMIC-III) database. 20,160 patients were involved in this study and divided into six subgroups according to causes for ICU admission. The primary outcome was the incidence of AKI after ICU admission. The association between early serum osmolality and AKI was explored using univariate and multivariate logistic regression analyses.

Results: The normal range of serum osmolality was 285–300 mmol/L. High serum osmolality was defined as serum osmolality >300 mmol/L and low serum osmolality was defined as serum osmolality <285 mmol/L. Multivariate logistic regression indicated that high serum osmolality was independently associated with increased development of AKI with OR = 1.198 (95% CL = 1.199–1.479, P < 0.001) and low serum osmolality was also independently associated with increased development of AKI with OR = 1.332 (95% CL = 1.199–1.479, P < 0.001), compared with normal serum osmolality, respectively.

Conclusions: In critically ill patients, early high serum osmolality and low serum osmolality were both independently associated with an increased risk of development of AKI.

Comparability of 11 different equations for estimating LDL cholesterol on different analysers

OBJECTIVES

Low-density lipoprotein cholesterol (LDL-C) estimation is critical for risk classification, prevention and treatment of atherosclerotic cardiovascular disease (ASCVD). Predictive equations and direct LDL-C are used. We investigated the comparability between the Martin/Hopkins, Sampson, Friedewald and eight other predictive equations on two analysers, to determine whether the equation or analyser influences predicted LDL-C result.

METHODS

In two unpaired datasets, 9,995 lipid profiles were analysed by the Abbott Architect and 4,782 by the Roche Cobas analysers. Non-parametric statistics and Bland Altman plots were used to compare LDL-C.

RESULTS

On the Abbott analyser; the Martin/Hopkins, Sampson and Friedewald LDL-C were comparable (median bias ≤1.8%) over a range of 1-4.9 mmol/L. On the Roche platform, Martin/Hopkins LDL-C was comparable to Friedewald (median bias 0.3%) but not to Sampson LDL-C (median bias 25%). In patients with LDL-C <1.8 mmol/L and triglycerides (TG) ≤1.7 mmol/L, predicted LDL-C using Abbott reagents was similar between Martin/Hopkins, Sampson and Friedewald equations but not comparable using Roche reagents. Abbott reagents classified 10-20% of patients in the 1.0-1.8 mmol/L range (Martin/Hopkins 13.4%; Sampson 14.5%; Friedewald 16%; direct LDL-C 13.2%). Roche reagents classified 11-30% in the 1.0-1.8 mmol/L range (Martin/Hopkins 23%; Sampson 11%; Friedewald 25%; direct LDL-C 17%).

CONCLUSIONS

Performance of predictive equations is influenced by the choice of analyser for total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and TG. Replacement of the Friedewald equation with Martin/Hopkins estimation to improve quality of LDL-C results can be safely implemented across analysers, whereas caution is advised regarding the Sampson equation.