Osmometry: a new bedside laboratory aid for the management of surgical patients

Derivation And Validation Of A New Formula For Plasma Osmolality Estimation

BACKGROUND

Plasma osmolality is a physic and chemical property of interest in emergency medicine. This magnitude can be measured at the laboratory, but it is usually estimated with equations. A huge variety of formulas for calculating osmolality have been published, most of them relying on sodium, urea and glucose. The purpose of this study is to develop a new equation for plasma osmolality calculation. In addition we assess the new equation in a sample of healthy individuals.

METHODS

We used results of sodium, potassium, glucose, urea and osmolality recovered from our patient's database. Multivariate lineal regression was carried-out, considering sodium and potassium as separated variables and as unique variable. In a second phase the obtained equations were tested in a sample of healthy blood-donors. Osmolality was measured by freezing point depression.

RESULTS

In the first phase, 1362 plasma determinations for sodium, potassium, glucose, urea and osmolality were analyzed. All of included variables had a significant correlation with measured osmolality, being the highest correlation with sodium plus potassium and the lowest one was with potassium alone. The formulas obtained for the osmolality estimation were 1.86*Na+1.6*(Glucose/18)+1.12*(Urea/6)+21 (A) and 1.88*(Na+K)+1.59*(Glucose/18)+1.08*(Urea/6)+10.6 (B). Assess of the new equations in a sample of healthy individuals showed better results than equations previously published. The lowest difference versus measured osmolality was produced by formula B.

CONCLUSION

The equations produced in this study perform better in the estimation of plasma osmolality than previously published formulas. We recommend introducing formula B in the clinical chemistry routine.

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

BACKGROUND

Many studies have assessed the predictive accuracy of serum osmolality equations. Different approaches for selecting a usable equation were compared using thirty published equations and patient data from a regional hospital laboratory.

METHODS

Laboratory records were extracted with same-sample results for measured serum osmolality, sodium, potassium, urea and glucose analysed in a regional hospital laboratory between 1/1/2017-31/12/2018. Differences were analysed using Passing-Bablok and difference (Bland-Altman) analysis. Three approaches were compared: the shotgun approach, adjusting for bias, and deriving a novel equation using multivariate analysis. The criteria for success included bias ≤0.7%, a 230 - 400 mOsm/kg range, and osmolal gap (OG) 95% reference limits within ±10 mOsm/kg.

RESULTS

The majority of equations produced proportionally negative-biased results. The shotgun approach identified two equations (EQ19, EQ6) with bias ≤0.7% but unworkable OG reference limits. The bias adjustment approach produced several equations with bias ≤ 0.7% and OG reference limits within or equivalent to ±10 mOsm/kg. A novel equation generated by us (1.89Na⁺ + 1.71 K⁺ + 1.08 Urea + 1.08 Glucose + 13.7) improved with the adjustment of bias and was not superior to the adjusted published equations.

CONCLUSION

Few published equations are immediately usable. Adjustment of bias derives several usable equations of which the best had OG ranges <20 mOsm/kg. We conclude that adjustment of bias can generate equations of equal or superior performance to that of novel equations.

Importance of sample volume to the measurement and interpretation of plasma osmolality

Background

Small sample volumes may artificially elevate plasma osmolality (Posm) measured by freezing point depression. The purpose of this study was to compare two widely different sample volumes of measured Posm (mmol/kg) to each other, and to calculated osmolarity (mmol/L), across a physiological Posm range (~50 mmol/kg).

Methods

Posm was measured using freezing point depression and osmolarity calculated from measures of sodium, glucose, and blood urea nitrogen. The influence of sample volume was investigated by comparing 20 and 250 μL Posm samples (n = 126 pairs). Thirty‐two volunteers were tested multiple times while EUH (n = 115) or DEH (n = 11) by −4.0% body mass. Protinol™ (240, 280, and 320 mmol/kg) and Clinitrol™ (290 mmol/kg) reference solutions were compared similarly (n = 282 pairs).

Results

The 20 μL samples of plasma showed a 7 mmol/kg positive bias compared to 250 μL samples and displayed a nearly constant proportional error across the range tested (slope = 0.929). Calculated osmolarity was lower than 20 μL Posm by the same negative bias (−6.9 mmol/kg) but not different from 250 μL Posm (0.1 mmol/kg). The differences between 20 and 250 μL samples of Protinol™ were significantly higher than Clinitrol™.

Conclusions

These results demonstrate that Posm measured by freezing point depression will be ~7 mmol/kg higher when using 20 μL vs 250 μL sample volumes. Approximately half of this effect may be due to plasma proteins. Posm sample volume should be carefully considered when calculating the osmole gap or assessing hydration status.

Prediction of neurologic deterioration based on support vector machine algorithms and serum osmolarity equations

OBJECTIVE

Dehydration on admission is correlated with neurological deterioration (ND). The primary objective of our study was to use support vector machine (SVM) algorithms to identify an ND prognostic model, based on dehydration equations.

METHODS

This study included a total of 382 patients hospitalized with acute ischemic stroke. The following parameters were recorded: age, sex, laboratory values (serum sodium, potassium, chlorinum, glucose, and urea), and vascular risk factor data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative performance of the BUN/Cr ratio as well as each of 38 equations for predicting ND. We used the Boruta algorithm for feature selection. After optimizing the SVM kernel parameters, we built an SVM model to predict ND and used the test set to obtain predictive values for assessing model accuracy.

RESULTS

In total, 102 of 382 patients (26.7%) with acute ischemic stroke developed ND. In all patients, the BUN/Cr ratio and each of 38 equations were significant predictors of ND. Equation 20 [1.86 × Na+ + glucose + urea + 9] yielded the maximum area under the ROC curve, and faired best in terms of prognostic performance (a cutoff value of 284.49 mM yielded a sensitivity of 94.12% and specificity of 61.43%). Equation 32 predicted ND poststroke across population groups, and worked well in older as well as young adults; (a cutoff value of 297.08 mM yielded a sensitivity of 93.14% and specificity of 60.00%). Feature selection by the Boruta algorithm was used to decrease the number of variables from 18 to 5 in the condition. The specificity of test samples for the SVM prediction model increased from 44.1% to 89.4%, and the AUC increased from 0.700 to 0.927.

CONCLUSIONS

SVM algorithms can be used to establish a prediction model for dehydration-associated ND, with good classification results.

Validation of equations used to predict plasma osmolality in a healthy adult cohort

BACKGROUND

Plasma osmometry and the osmol gap have long been used to provide clinicians with important diagnostic and prognostic patient information.

OBJECTIVE

We compared different equations used for predicting plasma osmolality when its direct measurement was not practical or an osmol gap was of interest and identified the best performers.

DESIGN

The osmolality of plasma was measured by using freezing point depression by microosmometer and osmolarity calculated from biosensor measures of select analytes according to the dictates of each formula tested. After a rigid analytic prescreen of 36 originally published equations, a bootstrap regression analysis was used to compare shrinkage and model agreement.

RESULTS

Sixty healthy volunteers provided 163 plasma samples for analysis. Of 36 equations considered, 11 equations met the prescreen variables for the bootstrap regression analysis. Of the 11 equations, 8 equations met shrinkage and apparent model error thresholds, and 5 equations were deemed optimal with an original model osmol gap <5 mmol.

CONCLUSIONS

The use of bootstrap regression provides a unique insight for osmolality prediction equation performance from a very large theoretical population of healthy people. Of the original 36 equations evaluated, 5 equations appeared optimal for the prediction of osmolality when its direct measurement was not practical or an osmol gap was of interest. Note that 4 of 5 optimal equations were derived from a nonhealthy population.

Accuracy of prediction equations for serum osmolarity in frail older people with and without diabetes

BACKGROUND

Serum osmolality is an accurate indicator of hydration status in older adults. Glucose, urea, and electrolyte concentrations are used to calculate serum osmolarity, which is an indirect estimate of serum osmolality, but which serum osmolarity equations best predict serum osmolality in the elderly is unclear.

OBJECTIVE

We assessed the agreement of measured serum osmolality with calculated serum osmolarity equations in older people.

DESIGN

Serum osmolality was measured by using freezing point depression in a cross-sectional study. Plasma glucose, urea, and electrolytes were analyzed and entered into 38 serum osmolarity-prediction equations. The Bland-Altman method was used to evaluate the agreement and differential bias between measured osmolality and calculated osmolarity. The sensitivity and specificity of the most-promising equations were examined against serum osmolality (reference standard).

RESULTS

A total of 186 people living in UK residential care took part in the Dehydration Recognition In our Elders study (66% women; mean ± SD age: 85.8 ± 7.9 y; with a range of cognitive and physical impairments) and were included in analyses. Forty-six percent of participants had impending or current dehydration (serum osmolality ≥295 mmol/kg). Participants with diabetes (n = 33; 18%) had higher glucose (P < 0.001) and serum osmolality (P < 0.01). Of 38 predictive equations used to calculate osmolarity, 4 equations showed reasonable agreement with measured osmolality. One [calculated osmolarity = 1.86 × (Na⁺ + K⁺) + 1.15 × glucose + urea +14; all in mmol/L] was characterized by narrower limits of agreement and the capacity to predict serum osmolality within 2% in >80% of participants, regardless of diabetes or hydration status. The equation's sensitivity (79%) and specificity (89%) for impending dehydration (≥295 mmol/kg) and current dehydration (>300 mmol/kg) (69% and 93%, respectively) were reasonable.

CONCLUSIONS

The assessment of a panel of equations for the prediction of serum osmolarity led to identification of one formula with a greater diagnostic performance. This equation may be used to predict hydration status in frail older people (as a first-stage screening) or to estimate hydration status in population studies. This trial was registered at the Research Register for Social Care (http://www.researchregister.org.uk) as 122273.

A comparison of whole blood and plasma osmolality and osmolarity

BACKGROUND

Substituting whole blood osmolality for plasma osmolality could expedite treatments otherwise delayed by the time required to separate erythrocytes from plasma. The purpose of this study was to compare the measured osmolality (mmol/kg) and calculated osmolarity (mmol/l) of whole blood and plasma.

METHODS

The osmolality of whole blood and plasma was measured using freezing point depression by micro-osmometer and osmolarity calculated from biosensor measures of sodium, glucose, and blood urea nitrogen. The influence of sample volume was also investigated post hoc by comparing measured osmolality at 20 and 250 μl.

RESULTS

Sixty-two volunteers provided 168 paired whole blood and plasma samples for analysis. The mean difference (whole blood - plasma; ±standard deviation) in osmolality was 10 ± 3 mmol/kg. Whole blood was greater than plasma in 168 of 168 cases (100%) and data distributions overlapped by 27%. The mean difference in osmolarity was 0 ± 2 mmol/l. Whole blood was greater than plasma in 90 of 168 cases (56%) and data distributions overlapped by 90%. The osmol gap (osmolality - osmolarity) was 16 ± 6 mmol for whole blood and 7 ± 5 mmol for plasma. Ten volunteers were tested on one occasion post hoc to investigate the potential effects of sample volume. The difference between whole blood and plasma was reduced to 3 ± 2 mmol/kg with a larger (250 μl vs. 20 μl) sample volume.

CONCLUSIONS

This investigation provides strong evidence that whole blood and plasma osmolality are not interchangeable measurements when a 20 μl sample is used.

Evaluation of 36 formulas for calculating plasma osmolality

PURPOSE

Measuring or calculating plasma osmolality is of interest in critical care medicine. Moreover, the osmolal gap (i.e. the difference between the measured and calculated osmolality) helps in the differentiation of metabolic acidosis. A variety of formulas for calculating osmolality have been published, most of them relying on sodium, urea and glucose. A novel formula developed by Zander has recently been published, which also takes into account the effects of potassium, chloride, lactate and bicarbonate on osmolality. We evaluate the previously published formulas including the novel formula by comparing calculated and measured osmolality.

METHODS

Arterial or venous blood samples from 41 outpatients and 195 acutely ill inpatients (total 236 subjects) were used to compare measured osmolality with calculated osmolality as obtained from 36 published formulas including the new formula. The performance of the formulas was statistically evaluated using the method of Bland and Altman.

RESULTS

Mean differences up to 35 mosmol/kg H(2)O were observed between measured and calculated osmolality using the previously published formulas. In contrast, the novel formula had a negligible mean difference of 0.5 mosmol/kg H(2)O. The novel formula also had the closest 95 % limits of agreement ranging from -6.5 to 7.5 mosmol/kg H(2)O.

CONCLUSION

Only 4 out of the 36 evaluated formulas gave mean differences between measured and calculated osmolality of less than 1 mosmol/kg H(2)O. Zander's novel formula showed excellent concordance with measured osmolality and facilitates a more precise diagnosis based on blood gas analysers. The new equation has the potential to replace separate measurements of osmolality in many cases.

Comparison of methods for calculating serum osmolality: multivariate linear regression analysis

Background: There are several methods for calculating serum osmolality, and their accordance with measured osmolality is the subject of controversy.

Methods: The concentrations of sodium, potassium, glucose, blood urea nitrogen (BUN) and osmolalities of 210 serum samples were measured. Two empirical equations were deduced for the calculation of serum osmolality by regression analysis of the data. To choose the best equation, chemical concentrations were also used to calculate osmolalities according to our formulas and 16 different equations were taken from the literature and compared with the measured osmolalities. Correlation and linear regression analyses were performed using Excel and SPSS software.

Results: Multiple linear regression analysis showed that serum concentrations of sodium (β=0.778, p≤0.000), BUN (β=0.315, p≤0.000), glucose (β=0.0.089, p≤0.007) and potassium (β=0.109, p≤0.008) are strong predictors of serum osmolality. The data were also analyzed by manual linear regression to yield the equations: osmolality=1.897[Na

Conclusions: Our data suggest use of the Worthley et al. formula Osm=2[Na

Osmole gap in neurologic-neurosurgical intensive care unit: Its normal value, calculation, and relationship with mannitol serum concentrations

OBJECTIVE

To determine a) if the admission osmole gap, the difference between osmolality and osmolarity, is the same in the neurologic-neurosurgical intensive care unit (NNICU) population as in healthy controls; b) which of 11 osmole gap formulas, or osmolality, correlates best with mannitol serum concentrations; c) whether osmole gap correction for plasma water content improves this correlation; and d) whether the osmole gap can predict mannitol serum concentrations.

DESIGN

Prospectively collected data.

SETTINGS

NNICU of a tertiary teaching hospital.

SUBJECTS

Ten NNICU patients on mannitol and eight not on mannitol, and 95 healthy controls.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We compared the admission osmole gap between all 18 NNICU patients and healthy controls and the correlation between osmole gap or osmolality and mannitol serum concentrations in ten NNICU patients while receiving mannitol. The osmole gap was calculated using 11 osmolarity formulas (six corrected for plasma water content). Student's t-test was used to compare the mean osmole gap between control and patient groups.We found that the mean osmole gap in healthy subjects and NNICU patients was not different. There were no statistically significant differences between any of the 11 osmole gap formulas and the correlation of osmole gap with serum mannitol concentrations; the highest R =.80, with formula 4, 1.86 (sodium + potassium) + (blood urea nitrogen/2.8) + (glucose/18) + 10, requires the least laboratory measurements. Osmolality had the lowest correlation with mannitol concentration (R =.60), significantly lower than any of the osmole gap calculations. Plasma water content correction did not improve this correlation. The osmole gap-mannitol serum concentrations relationship is 1 to 0.81, not accurate enough to predict specific mannitol serum concentrations.

CONCLUSIONS

The osmole gap correlates better with mannitol serum concentrations than osmolality, and although it cannot predict a specific mannitol serum concentration, a normal osmole gap concentration, as we find at trough times, indicates sufficient clearance for a new mannitol dose.

The serum osmole gap

Estimation and measurement of serum osmolality can be of value in the clinical management of certain forms of critical illness. Osmolality is a measure of the concentration of osmotically active particles, or solutes, in a solution. Only low-formula weight ions and uncharged molecules that are present in relatively high concentrations contribute significantly to serum osmolality. Serum osmolality can be easily estimated from the three major osmotic constituents of normal serum (sodium, urea, and glucose) by a simple formula. An understanding of serum osmolality, its laboratory measurement, its bedside estimation, and the concept of the osmole gap, is crucial in making a preliminary diagnosis of methanol and ethylene glycol intoxication, as well as a few other related compounds. There are important caveats to this use of the osmole gap, because under certain circumstances both false-positive and false-negative interpretations may occur. The osmole gap may also be helpful for confirming pseudohyponatremia, as a gauge for dosing mannitol and glycerol when used to treat intracranial hypertension, and as a prognostic indicator in circulatory shock.

Validity of whole blood osmolality measurement in sick neonates

A comparison of heparinized whole blood with plasma osmolality measurements was performed on 100 sick newborns using a vapor pressure osometer. Aliquots of blood samples which had been collected into 1-ml syringes containing sodium heparin for blood gas analyses were used for osmolality determinations. There was excellent correlation between the two methods (r = 0.993). A total of 85 samples agreed within 2.0 units. In 38 samples the results obtained by the two methods were identical. The maximum observed discrepancy, present in only 2 samples, was 4 mosmol. Thus, the measurement of whole blood or plasma osmolality provides comparable information. The simplicity, the small volume of the blood sample required and the rapidity of the method make the determination of whole blood osmolality particularly useful in newborns requiring intensive care.

The concept of osmolality: its use in the evaluation of "dehydration" in the horse

Osmolality is an indication of the osmotic pressure of plasma and depends on the amount of solute and solvent (water) present. The mean (+sd) plasma osmolality of 100 clinically normal animals was 282 (+6) mOsm/kg using lithium heparin as anticoagulant. The equation, osmolality=1.86 (sodium + potassium) +glucose +blood urea nitrogen + 9, was found to predict only crudely plasma osmolality. The plasma sodium: osmolality ratio was 0.49. Water and electrolyte disorders are classified into 3 types based on the measurement of electrolytes and osmolality: (1) Hypertonic dehydration (true dehydration desiccation), osmolality greater than 300 mOsm/kg, associated with water deprivation, some gastrointestinal emergencies and some types of diarrhoea; (2) hypotonic dehydration (acute desalting water loss), osmolalities less than 260 mOsm/kg, associated with acute diarrhoea, particularly salmonellosis; (3) isotonic dehydration (normal electrolyte and osmolality levels), in horses losing electrolytes and water in almost equal proportions. The importance of these observations and their significance in rational clinical management are discussed.

The significane of measurement of osmolality in critically ill patients

The plasma and urine osmolality and their ratios were measured in 774 patients in critical care. We found that changes in osmolality were related to the alterations of ratios of sodium ion, glucose, blood urea nitrogen and unknown metabolites, and this knowledge may be of therapeutic and prognostic value.

Osmometry. 1. Terminology and principles of measurement

The measurement of the osmolality of body fluids is used increasingly in clinical practice. This paper discusses the terminology and describes the methods available for its measurement.

Osmometry. 3. Clinical applications

Measurement of plasma and urine osmolality is quick, easy and accurate. The recognition of the interdependence of urine volume and osmolality on the excretion of the daily obligatory solute load assists in the diagnosis and management of fluid balance and renal excretory problems in the acutely ill. In addition, syndromes of osmotic disequilibrium present a challenge in metabolic care.

Osmolality measurements in heart disease

A study of the variations of plasma and urinary osmolality in patients with acute myocardial infarction and heart failure of different origin was made. It was shown that the plasma osmolality may be related to the clinical evolution of heart disease. The effectiveness of monitoring the osmolality in establishing the alterations of water-electrolyte balance is also reported.

Biochemical changes in extracorporeal circulation in patients

The effect of priming extracorporeal perfusion pumps with 50% and 80% diluted homologous blood on the serum electrolytes, acid-base status and plasma osmolality has been investigated in 103 patients undergoing open-heart surgery for congenital and acquired heart disease. The value in prognosis of plasma osmolality is discussed.