ICU: intensive care unit; AGc: corrected anion gap; BE: base excess; SBE: standard base excess; SID: strong ion difference; SIDe: effective strong ion difference; SIG: strong ion gap; SIGc: corrected strong ion gap; Mg: magnesium; Ca: calcium; Alb: albumin; Pi: inorganic phosphate; Lac: lactate; HCO 3 : bicarbonate; AUROC: area under receiver operating characteristic curve; ROC: receiver operating characteristic; N/A: not applicable; OR: odds ratio; CI: confidential interval; SD: standard deviation.
While 10 studies have shown the potential superiority of the Stewart approach, 6 , 27 – 29 , 40 , 44 , 46 , 48 – 50 four articles failed to show the superiority of the physicochemical approach over the traditional one, 33 , 41 – 43 and three articles even showed greater strength of the traditional method than the modern one. 24 , 45 , 47
Our literature search shows a discrepancy over the ability to detect acid–base disturbances on diagnostic performance of the two approaches. There are several possible explanations for the discordance. The first thing to be mentioned is the calculation of each variable in both approaches. Table 1 shows there are many differences in inclusive ions, especially lactate, phosphate, and magnesium ion, of each calculation of AGc and SIG among the studies. In addition, cumulative differences or errors in each variable should be considered. As each mathematical equation contains more measurement, there could be greater variability in the parameters, such as SIDa, SIDe, and SIG in Stewart approach, because the differences are exaggerated via complicated mathematical calculations. 51 As shown by Matousek et al., 12 there should be no difference between the approaches from a mathematical perspective. However, it is true only when the same ions are measured and taken into account and each measurement is accurate. Those differences of each calculation and potential cumulative errors could lead to the discordance about the usefulness as a diagnostic tool between the two approaches.
Another potential reason is technological differences or errors in measuring each variable. Morimatsu et al. 52 showed that chloride measurements, made with point-of-care blood gas and electrolyte analyzers, differed significantly from those made using central laboratory biochemistry analyzers, resulting in different SID values and assessments of the acid–base status. Nguyen et al. 51 compared two central laboratory analyzers for electrolyte measurement and reported that the biochemistry laboratory analyzers have large differences from each other. It should be noted that 12 of 17 articles in our review measured electrolytes using central laboratory analyzers, many of which are currently using diluted blood sample and indirect ion selective electrodes in order to measure the electrolytes, rather than blood gas analyzers ( Table 1 ). Measurements by this method are affected by hypoalbuminemia and could be inaccurate compared with the ones measured by blood gas analyzers. 53 Studies that used indirect ion selective electrodes could lead to wrong calculation and acid–base interpretation, which could make an implausible conclusion. Thus, interpretation of the results in papers comparing these approaches needs attention on the analyzer that each study used. We found a wide variety of machines and technologies used to measure pH, pCO 2 , and electrolytes in those articles, which could be one of the reasons for the inconsistent results on this topic.
Reference value of each parameter is another problem. The dependency on site recommends reference value should be determined in each institution. 46 However, our review showed that while only five studies collected healthy controls for the reference, 33 , 40 , 46 , 49 , 50 other studies used pre-determined numbers, 29 , 41 , 44 , 45 , 48 and the method for reference value selection in those studies was not specified. 6 , 24 , 27 , 28 , 42 , 43 These incongruences of reference value due to arbitrary choice may cause a variety of discordant results. As there is no consensuses on the normal range of each variable, especially in the Stewart approach, we recommend that future researchers collect healthy controls for reference in each research institute.
We also need to pay attention to the differences in the normal result range between the two approaches in these studies, since more than one parameter in each method aims to represent the same concept. For example, Boniatti et al. defined normal SBE as −5 to +5 and normal SIDe as 38–42 mEq/L. Since changes in BE represent changes in SIDe if A TOT is normal, 8 the large difference of normal range (10 vs 4) could mislead the interpretation. This sort of “unfair” comparison might be one reason of the inconsistent results.
Studied populations need another consideration. Several studies showed that patients with renal failure, 54 liver diseases, 55 sepsis and trauma 56 often have accumulations of unmeasured anions. However, Dubin et al. 33 and Ho et al. 47 reported patient demographics in their studies; the percentage of patients with shock, acute renal failure, and hepatic failure was only 13%, 13%, and 4%, respectively, in one study, and liver diseases were only 2% in the other. A study by Cusack et al. 41 included a high proportion of post-elective surgery patients, who generally have low severity of illness and low mortality. Hucker et al. 43 did not provide details about reasons for admission, patients’ illness severity, or the underlying medical conditions of patients in their accident and emergency department. For patients with severe illness, measuring more ions and involving them into variables such as AG and SIG could demonstrate their potential ability to detect unmeasured anions, revealing more detailed acid–base disturbances, no matter which approach is used. For populations with a small number of severe patients, measuring more ions would not be needed for detailed analysis of acid–base disorders. Thus, the combination of variety of the studied populations and the aforementioned differences of calculation in each variable among those studies could be one of the reasons of their inconsistent results.
Those factors as the potential reasons for inconsistent results on diagnostic performance could also yield a controversy about the prognostic performance of the two approaches. Some authors have investigated the predictive value of the traditional approach and the physicochemical Stewart approach, mainly, AGc versus SIG. One of their questions is “Is there any association between AGc or SIG and outcomes?” or “Can AGc or SIG levels be used as a marker of poor outcomes?” Here the difference of measured and involved ions in each calculation could again mislead the conclusion. Simple comparison of AGc with SIG does not always answer these particular questions. Although both parameters represent unmeasured ions, consideration of lactate for AGc and SIG depends on each individual study. A bulk of evidence has shown that the level of lactate is associated with poor prognosis. 57 , 58 If we would like to simply compare the prognostic abilities of the two approaches, the contribution of lactate should be removed from their equations. Only five of all 17 articles remove the effect of lactate from their calculations of AGc and SIG.
It is not only lactate but also other ions, such as magnesium, that need to be considered when comparing the two methods. The changes in magnesium concentration are usually so small that they may usually be neglected, but this simplification is not applicable if the changes are significant. Theoretically, an increased level of magnesium reduces the AG, increases SID, and does not change SIG. There are no studies so far that compare these approaches for patients with abnormal serum magnesium concentrations. Thus, radical question of the comparison should not be “Is there any association between AGc or SIG and outcomes?” but “Is there any association between unmeasured anions that we does not measure in clinical practice and outcomes?” In order to answer this question directly, we need to exclude the contribution of lactate and other measured ion.
Finally, we cannot forget the effect of fluids used for resuscitation, which lead to iatrogenic acidosis. Hayhoe et al. 59 found 40% of acidosis were attributed to the use of polygeline, which acts as an acid resulting in increased unmeasured circulating anions. Similarly, gelatin-derived colloids have also been found to iatrogenically increase the SIG due to increased unmeasured anions. 59 None of the studies included in our review provided detailed information about the type and volume of administered resuscitation fluids. This iatrogenically fluid-induced increment of SIG and metabolic acidosis in less critical patients is not expected to have many adverse outcomes, and therefore, the prognostic value of these indices of the Stewart approach could be wrongly affected.
Although the traditional approach and the Stewart approach are seen as complementary giving the same information about the acid–base phenomena despite their different concepts, our literature search shows inconsistent results on the comparison between the traditional approach and the physicochemical approach for their diagnostic and prognostic performance. Many studies to date have crucial limitations in comparing these approaches. Those limitations are considered the reasons for the discrepancy in clinical researches.
Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
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Anion Gap and Stewart's Strong Ion Difference
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In metabolic acidosis, for every molecule of acid produced, a molecule of bicarbonate is replaced by another anion. However, the law of electroneutrality dictates that the sum of positive charges is exactly balanced by the negative charges. The anion gap quantifies this apparent difference between total cation concentration and total anion concentration. Stewart's hypothesis clarifies the role of the lungs, kidneys, liver and gut in acid–base control. According to Stewart's hypothesis, the alkalosis is not caused by loss of hydrogen ions because there is an inexhaustible supply of H+ ions from the dissociation of water. Measurement of chloride concentration is important: if the chloride is low or normal and there is metabolic acidosis, unmeasured ions such as lactic acid and ketoacids may be present in blood and is indicated by the strong ion gap. The classification of acid–base disturbances according to derangements of independent variables provides a better understanding of the primary clinical problem and helps direct treatment.
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Acknowledgements.
D. A. Story, H. Morimatsu, R. Bellomo, Strong ions, weak acids and base excess: a simplified Fencl–Stewart approach to clinical acid–base disorders † , BJA: British Journal of Anaesthesia , Volume 92, Issue 1, January 2004, Pages 54–60, https://doi.org/10.1093/bja/aeh018
Background. The Fencl–Stewart approach to acid–base disorders uses five equations of varying complexity to estimate the base excess effects of the important components: the strong ion difference (sodium and chloride), the total weak acid concentration (albumin) and unmeasured ions. Although this approach is straightforward, most people would need a calculator to use the equations. We proposed four simpler equations that require only mental arithmetic and tested the hypothesis that these simpler equations would have good agreement with more complex Fencl–Stewart equations.
Methods. We reduced two complex equations for the sodium–chloride effect on base excess to one simple equation: sodium–chloride effect (meq litre –1 )=[Na + ]–[Cl – ]–38. We simplified the equation of the albumin effect on base excess to an equation with two constants: albumin effect (meq litre –1 )=0.25×(42–[albumin]g litre –1 ). Using 300 blood samples from critically ill patients, we examined the agreement between the more complex Fencl–Stewart equations and our simplified versions with Bland–Altman analyses.
Results. The estimates of the sodium–chloride effect on base excess agreed well, with no bias and limits of agreement of –0.5 to 0.5 meq litre –1 . The albumin effect estimates required log transformation. The simplified estimate was, on average, 90% of the Fencl–Stewart estimate. The limits of agreement for this percentage were 82–98%.
Conclusions. The simplified equations agree well with the previous, more complex equations. Our findings suggest a useful, simple way to use the Fencl–Stewart approach to analyse acid–base disorders in clinical practice.
Br J Anaesth 2004; 92 : 54–60
Accepted for publication: July 18, 2003
A challenge in clinical acid–base assessment is to analyse the size of the acid–base change and the underlying physiological mechanisms. 1 2 Base excess is a single variable used to quantify the metabolic (non‐respiratory) component of a patient’s acid–base status. Several research groups 3 – 5 have combined the base excess approach with the Stewart approach to acid–base physiology. 6 To combine these approaches, 5 these groups examined the base excess effects of two of Stewart’s independent variables: the strong ion difference and the total weak acid concentration. Balasubramanyan and colleagues 5 called this approach to base excess the Fencl–Stewart approach.
Gilfix and colleagues 4 used the work of Figge and colleagues 7 and Fencl’s unpublished work 8 to derive five equations to estimate the base excess effects of the strong ion difference and the total weak acid concentration. In plasma, sodium and chloride are the principal components of the extracellular strong ion difference, 6 and albumin is the principal extracellular weak acid. 9 While this approach is reasonably simple, most people would need a calculator to use the five equations.
We believe that these equations, used to estimate the sodium–chloride effect on base excess, can be simplified. Balasubramanyan and colleagues 5 simplified the Fencl–Stewart albumin equation. Work by Figge’s group 9 has further modified the Fencl–Stewart equation for the base excess effect of albumin. 8 This equation can also be simplified in the same way that Balasubramanyan simplified the older equation. 5 We proposed four simpler equations that require only simple mental arithmetic for clinical use.
We tested the hypothesis that the simplified estimates of the base excess effects of the plasma sodium–chloride concentration and the plasma albumin concentration have sufficiently strong agreement with the Fencl–Stewart estimates 3 4 to be used clinically. We used blood samples from critically ill adults to test this hypothesis.
Data were collected from intensive care unit records at the Austin and Repatriation Medical Centre, a tertiary referral hospital affiliated with the University of Melbourne. All samples were taken from arterial cannulae in patients requiring intensive care. No additional sampling was required. The Austin and Repatriation Medical Centre Human Research Ethics Committee waived the need for informed consent.
Arterial blood samples were collected in heparinized blood‐gas syringes (Rapidlyte; Chiron Diagnostics, East Walpole, MA, USA) and analysed in the intensive care unit blood‐gas analyser (Ciba Corning 865; Ciba Corning Diagnostics, Medfield, MA, USA). The analyser made measurements at 37°C. Nursing staff from the intensive care unit who had been taught to use the machine by support staff performed the analysis. Samples were not stored on ice. We collected data on the pH, partial pressure of carbon dioxide and the standard base excess.
For each data set, a further sample was drawn at the same time from the same arterial cannula using a vacuum technique with lithium heparin tubes or clot‐activating tubes (Vacuette; Greiner Labortechnik, Kremsmunster, Austria). These samples were sent to the hospital core laboratory in the Division of Laboratory Medicine. Plasma and serum underwent multicomponent analysis (Hitachi 747; Roche Diagnostics, Sydney, Australia). Scientific staff from the hospital clinical chemistry department analysed the samples. Samples were not stored on ice. We collected data on the plasma or serum concentrations of sodium, chloride and albumin.
Fencl divided the effect of strong ion difference on base excess into sodium and chloride effects. This group calculated the base excess effects of changes in free water on the sodium concentration and changes in the chloride concentration: 4 5 8
sodium effect (meq litre –1 )=0.3×([Na + ]–140)(1)
chloride effect (meq litre –1 )=102–([Cl – ]×140/[Na + ]).(2)
Sodium and chloride are the principal contributors to the strong ion difference. 6 The sum of the sodium and chloride effects will give the Fencl–Stewart estimate of the strong ion difference effect on base excess:
sodium–chloride effect (meq litre –1 )=0.3×([Na + ]–140)+102–([Cl – ]×140/[Na + ]).(3)
Separately estimating the base excess effects of changes in free water and changes in chloride provides useful information. These separate effects, however, do not need to be quantified initially to determine the effect of the sodium–chloride component of the effect of strong ion difference on base excess. Changes in the difference in sodium and chloride can be used to calculate directly the major changes in the strong ion difference. As the strong ion difference is decreased the blood becomes more acidic. 6
We proposed that the calculation of the strong ion difference effect on base excess could be simplified. From the reference range in our laboratory, the median value for sodium is 140 mmol litre –1 and that for chloride is 102 mmol litre –1 . Therefore the median difference is 38 mmol litre –1 . The measured sodium–chloride difference minus 38 mmol litre –1 will be an estimate of the change in the strong ion difference. For sodium and chloride, 1 millimole equals 1 milliequivalent.
A change in the sodium–chloride component of the strong ion difference will change the base excess directly. Therefore our simplified version of the equation for the sodium–chloride effect on base excess is:
sodium–chloride effect (meq litre –1 )=[Na + ]–[Cl – ]–38.(4)
Albumin is the principal contributor to the plasma total weak acid concentration. The effect of albumin on the base excess is due to the anionic effect of albumin. Figge and colleagues 9 developed a pH‐dependent formula for the anionic effect of albumin:
albumin anionic effect (meq litre –1 )=(0.123×pH–0.631)×albumin (g litre –1 ).(5)
Changes in the concentration of albumin will cause changes in the anionic effect of albumin. Changes in the anionic effect of albumin will change the base excess. As the albumin concentration is decreased the blood becomes more alkaline. We calculated the Fencl–Stewart estimates for the base excess effects of albumin. We used the most recent estimates of the effects of albumin ionization: 8
albumin effect (meq litre –1 )=(0.123×pH–0.631)×[42–albumin (g litre –1 )].(6)
We simplified this equation by using a single pH of 7.40:
albumin effect (meq litre –1 )=0.28×[42–albumin (g litre –1 )].(7)
To facilitate calculation at the bedside we further simplified the equation by using the constant of 0.25. This allows the simple mathematics of dividing the difference in albumin concentrations by 4. Therefore the simplified equation became:
albumin effect (meq litre –1 )=0.25×[42–albumin (g litre –1 )].(8)
Data were collected from patient charts and the hospital computer system. Data were stored on a computer spreadsheet (Excel, Microsoft, Seattle, WA, USA). All statistical calculations were done with Statview software (Abacus Concepts, Berkeley, CA, USA).
We used the limits of agreement method of Bland and Altman 10 11 to determine the agreement between the Fencl–Stewart and simplified estimates of the albumin and strong ion difference effects on base excess. We proposed that a bias of ±1 meq litre –1 and limits of agreement of bias ±2 meq litre –1 were acceptable for clinical use of the simplified equations. That is, the greatest difference between two estimates would be 3 meq litre –1 . Data were analysed for the overall group and three subgroups: an acidaemic group (pH <7.35), a reference range group (pH 7.35–7.45) and an alkalaemic group (pH >7.45). We used these groups to examine the possibility that different acid–base states may affect the agreement.
Where the difference between the estimates varied with the average of the two estimates (heterodasticity), the relationship was analysed with correlation statistics. If the correlation were statistically significant, at a P value of <0.05, the data were log‐transformed. The limits of agreement statistics were reported as proportions because a log minus a log is the ratio of the antilogs. 10
We analysed the relative risk of death where the standard base excess, sodium–chloride effect or unmeasured ion effect was less than –5 meq litre –1 . The effect of albumin was almost always alkalinizing; therefore we calculated the relative risk of death of an albumin effect on base excess greater than 5 meq litre –1 . We assumed the increase in mortality risk was statistically significant if the 95% confidence interval for the risk ratio did not include 1. We used Confidence Interval Analysis software (BMJ Books).
Three hundred pairs of data were collected from 300 adult patients. The median age of the patients was 65 yr (range 12–94 yr). The median Simplified Acute Physiology Score (SAPS II) score 12 was 17 (range 2–40). The median risk of death calculated from the SAPS II was 26% (range 0–96%).
The agreement between the Fencl–Stewart and simplified estimates was analysed for the entire set of 300 samples (Figs 1 and 2 ) and for the three subgroups: pH <7.35 (acidaemic), pH 7.35–7.45 (reference range) and pH >7.45 (alkalaemic) (Tables 1 , 2 and 3 ).
There was strong agreement between the Fencl–Stewart and simplified estimates of the sodium–chloride effect. There was no bias and the limits of agreement were from –0.5 to 0.5 meq litre –1 (Fig. 1 ). There was an apparent pattern in the data points (Fig. 1 ); however, the cause and importance of this pattern are unclear. The agreement was similar in the three subgroups classified according to pH (Tables 2 and 3 ).
The agreement between the Fencl–Stewart and the simplified estimates of the albumin effect varied with the average effect. This correlation had an R 2 of 0.47 and a P value of <0.001. The data were log‐transformed and analysed again. The log transformation removed the correlation between the difference of the estimates and the average value (Fig. 2 ). After log transformation there was good agreement between the Fencl and simplified estimates of the albumin effect. The simplified estimate was, on average, 90% of the Fencl estimate. The limits of agreement for this percentage were 82–98%. The results were similar in the three pH subgroups, with the best agreement in the acidaemic group (Tables 2 and 3 ).
The relative risk of death was greater when either the standard base excess or the unmeasured ion effect was less than –5 meq litre –1 . A sodium–chloride effect on base excess less than –5 meq litre –1 or an albumin effect greater than 5 meq litre –1 was not associated with an increased risk of death (Table 4 ).
We studied 300 blood samples from 300 critically ill adults. When analysing the acid–base status of these samples we used sodium–chloride as the principal component of the plasma strong ion difference 6 and albumin as the principal component of the plasma total weak acid concentration. 9 We found that the simplified equations to estimate the base excess effects of plasma sodium–chloride concentration and plasma albumin concentration agreed well with more complex equations used in other studies. 4 5 8 Furthermore, we found good agreement in the three subgroups classified according to pH.
Balasubramanyan and colleagues 5 simplified an earlier version of the equation for the albumin effect. 4 These researchers, however, did not examine the agreement of the simplified equation with the more complex Fencl–Stewart version. 4 One strength of our study is that we used the most recent versions of the Fencl equations. 8 Furthermore, we used a large number of samples from different patients with a wide range of acid–base disorders, including some with increased plasma lactate (another strong ion) 5 or increased plasma phosphate (another important weak acid). 9 Another strength is that we avoided overestimating the strength of agreement attributable to mathematical linking. 13 We avoided this problem by using the limits of agreement approach of Bland and Altman. 10 11
In unpublished work, Fencl 8 proposed a method of combining base excess and the Stewart approach 6 to acid–base physiology and disease. This approach was designed to facilitate clinical application of the Stewart approach. We suggest the following simplified version of the Fencl method. 4 5
Four variables are determined (standard base excess and the base excess effects of sodium–chloride, albumin and unmeasured ions) using the following four equations:
standard base excess (mmol litre –1 =meq litre –1 ) from a blood gas machine;
sodium–chloride effect (meq litre –1 )=[Na + ]–[Cl – ]–38;(4)
albumin effect (meq litre –1 )=0.25×[42–albumin (g litre –1 )];(8)
unmeasured ion effect (meq litre –1 )=standard baseexcess–sodium–chloride effect–albumin effect.(9)
These four variables, with the partial pressure of carbon dioxide, allow physicians to examine the base excess effects of the principal components of Stewart’s independent factors: carbon dioxide, strong ion difference (sodium–chloride) and total weak acid concentration (albumin). The strong ion difference effect can be further analysed with the separate Fencl–Stewart equations for sodium and chloride. 4 The unmeasured ions may be strong ions, such as sulphate and acetate, 14 or weak acids, such as phosphate and polygeline. 15
These equations require four input variables: the base excess and the plasma concentrations of sodium, chloride and albumin. By using the plasma sodium and chloride concentrations and the simplified sodium–chloride equation we can estimate the base excess effects of electrolyte changes from i.v. fluid therapies. 16 17 For example, Scheingraber and colleagues 16 studied acid–base changes during major gynaecological surgery. Patients received 0.9% saline or lactated Ringer’s solution. The saline group had a greater metabolic acidosis, as shown by a more negative base excess. One cause of this acidosis was a decreased strong ion difference. The Scheingraber group showed that these changes in base excess and strong ion difference occurred in parallel but they did not quantify the effect. The method described in our study allows easy quantification of the effects of changes in plasma sodium and chloride (strong ion difference) on base excess (Table 5 ).
Analysis of the fourth variable, plasma albumin concentration, is useful in the intensive care unit and in the perioperative setting. In addition to the acidifying effects of saline, Scheingraber and colleagues also found an intraoperative decrease in plasma albumin concentration in both their groups. 16 They speculated that this decrease in albumin would affect the base excess, but did not quantify the effect. Decreased plasma albumin leads to a decreased total weak acid concentration that produces a metabolic alkalosis. 3 Using a different method, Figge and colleagues 18 developed the same constant (0.25) to quantify the effect of changes in plasma albumin on the anion gap, as we did for the effects on base excess. Our work supports this finding because the physiology is the same: changes in the anionic effect of albumin will alter both the base excess and the anion gap. 19 Decreased plasma albumin concentration is common in critically ill patients. 20 The method described in our study allows easy quantification of the effects of changes in plasma albumin (total weak acid concentration) on base excess (Table 5 ).
Using an approach similar to ours, Balasubramanyan’s group 5 studied critically ill children. In a subgroup of 66 children, they found that a base excess effect of unmeasured ions more negative than –5 meq litre –1 was an important predictor of mortality. Our approach simplifies estimation of the unmeasured ion effect on base excess by simplifying the calculations for the effects of the strong ion difference and the total weak acid concentration. Among 300 patients, we found that an unmeasured ion effect on base excess less than –5 meq litre –1 increased the risk of death by 50%. The risk of death with a standard base excess less than –5 meq litre –1 was increased by 100%. There was, however, considerable overlap in the 95% confidence intervals for the relative risk of death for the unmeasured ion effect and the standard base excess. Furthermore, similar changes in the base excess effects of sodium–chloride and albumin did not increase the relative risk of death. These findings suggest that it is the unmeasured ion component of the base excess that is the important clinical marker for mortality.
We have reduced five Fencl–Stewart equations to four simpler equations. We have maintained good agreement with the previous, more complex equations. These simple equations may allow easy, direct application of Stewart’s independent factors to clinical work both inside and outside the operating room. We propose these equations as bedside clinical tools rather than as tools for detailed physiological research. Future studies should examine the importance of each of the base excess effects on patient outcome.
Funding was provided by the Research Fund, Department of Anaesthesia, Austin and the Repatriation Medical Centre, Heidelberg, Victoria, Australia.
Fig 1 Bland–Altman plot, for 300 samples, of the differences in sodium–chloride effect on base excess between the Fencl–Stewart (FS) and simplified methods ( y axis), and the average of the two methods: (Fencl–Stewart+simplified)/2. The full lines are the limits of agreement and the dashed line is the bias. The y axis represents our proposed upper limits of agreement of bias ±2 meq litre –1 .
Fig 2 Bland–Altman plot, for 300 samples after log transformation, of the differences in estimates of the albumin effect on base excess between the Fencl–Stewart (FS) and simplified methods ( y axis), and the average of the two methods: (Fencl–Stewart+simplified)/2. The full lines are the limits of agreement and the dashed line is the bias.
Plasma acid–base variables for three subgroups. Median (range)
Number | 105 | 136 | 59 |
pH | 7.29 (6.93 to 7.34) | 7.40 (7.35 to 7.45) | 7.49 (7.46 to 7.61) |
Carbon dioxide (kPa) | 6.4 (1.5 to 12.9) | 5.6 (3.2 to 9.3) | 4.7 (2.7 to 7.3) |
Base excess (meq litre ) | –4.8 (–24.8 to 19.0) | 0.1 (–10.8 to 12.5) | 4.1 (–9.4 to 23.4) |
Bicarbonate (mmol litre ) | 22.1 (4.7 to 44.8) | 24.9 (14.2 to 37.8) | 27.0 (14.2 to 46.0) |
Lactate (mmol litre ) | 2.6 (0.3 to 18.8) | 1.6 (0.1 to 8.6) | 1.7 (0.38 to 11.9) |
Phosphate (mmol litre ) | 1.53 (0.24 to 3.60) | 1.14 (0.10 to 2.58) | 1.10 (0.28 to 3.45) |
Number | 105 | 136 | 59 |
pH | 7.29 (6.93 to 7.34) | 7.40 (7.35 to 7.45) | 7.49 (7.46 to 7.61) |
Carbon dioxide (kPa) | 6.4 (1.5 to 12.9) | 5.6 (3.2 to 9.3) | 4.7 (2.7 to 7.3) |
Base excess (meq litre ) | –4.8 (–24.8 to 19.0) | 0.1 (–10.8 to 12.5) | 4.1 (–9.4 to 23.4) |
Bicarbonate (mmol litre ) | 22.1 (4.7 to 44.8) | 24.9 (14.2 to 37.8) | 27.0 (14.2 to 46.0) |
Lactate (mmol litre ) | 2.6 (0.3 to 18.8) | 1.6 (0.1 to 8.6) | 1.7 (0.38 to 11.9) |
Phosphate (mmol litre ) | 1.53 (0.24 to 3.60) | 1.14 (0.10 to 2.58) | 1.10 (0.28 to 3.45) |
Subgroup analysis for sodium–chloride and albumin effects on base excess. Median (range)
Sodium (meq litre ) | 141 (113 to 161) | 140 (119 to 162) | 140 (121 to 152) |
Chloride (meq litre ) | 103 (78 to 128) | 103 (81 to 119) | 100 (86 to 113) |
Fencl–Stewart sodium–chloride effect on base excess (meq litre ) | –1.1 (–9.8 to 13.8) | –0.9 (–11.5 to 14.5) | 2.0 (–7.9 to 13.5) |
Simplified sodium–chloride effect on base excess (meq litre ) | –1.0 (–10 to 14) | –1.0 (–12 to 15) | 2.0 (–7 to 14) |
Albumin (g litre ) | 25 (9 to 42) | 27 (12 to 50) | 26 (13 to 44) |
Fencl–Stewart albumin effect on base excess (meq litre ) | 4.3 (0 to 8.5) | 4.2 (–2.2 to 8.4) | 4.8 (–0.6 to 8.3) |
Simplified albumin effect on base excess (meq litre ) | 4.3 (0 to 8.3) | 3.8 (–2.0 to 7.5) | 4.1 (–0.5 to 7.3) |
Sodium (meq litre ) | 141 (113 to 161) | 140 (119 to 162) | 140 (121 to 152) |
Chloride (meq litre ) | 103 (78 to 128) | 103 (81 to 119) | 100 (86 to 113) |
Fencl–Stewart sodium–chloride effect on base excess (meq litre ) | –1.1 (–9.8 to 13.8) | –0.9 (–11.5 to 14.5) | 2.0 (–7.9 to 13.5) |
Simplified sodium–chloride effect on base excess (meq litre ) | –1.0 (–10 to 14) | –1.0 (–12 to 15) | 2.0 (–7 to 14) |
Albumin (g litre ) | 25 (9 to 42) | 27 (12 to 50) | 26 (13 to 44) |
Fencl–Stewart albumin effect on base excess (meq litre ) | 4.3 (0 to 8.5) | 4.2 (–2.2 to 8.4) | 4.8 (–0.6 to 8.3) |
Simplified albumin effect on base excess (meq litre ) | 4.3 (0 to 8.3) | 3.8 (–2.0 to 7.5) | 4.1 (–0.5 to 7.3) |
Limits of agreement between the Fencl–Stewart and simplified estimates for three subgroups
Sodium–chloride effect (Fencl–Stewart estimate compared with simplified estimate) | |||
Bias (meq litre ) | 0.1 | 0.0 | 0.0 |
Limits of agreement (meq litre ) | –0.5 to 0.7 | –0.4 to 0.4 | –0.4 to 0.4 |
Albumin effect (simplified estimate as % of Fencl–Stewart estimate) | |||
Average (%) | 94 | 90 | 86 |
Limits of agreement (%) | 88 to 101 | 87 to 92 | 84 to 89 |
Sodium–chloride effect (Fencl–Stewart estimate compared with simplified estimate) | |||
Bias (meq litre ) | 0.1 | 0.0 | 0.0 |
Limits of agreement (meq litre ) | –0.5 to 0.7 | –0.4 to 0.4 | –0.4 to 0.4 |
Albumin effect (simplified estimate as % of Fencl–Stewart estimate) | |||
Average (%) | 94 | 90 | 86 |
Limits of agreement (%) | 88 to 101 | 87 to 92 | 84 to 89 |
Relative risk of death. *Compared with patients with a base excess or base excess effect equal to or greater than –5 meq litre –1 ; **compared with patients with an albumin base excess effect of equal to or less than 5 meq litre –1
Standard base excess less than –5 meq litre * | 1.97 | 1.38 to 2.80 |
Sodium–chloride effect less than –5 meq litre * | 0.86 | 0.41 to 1.81 |
Albumin effect greater than 5 meq litre ** | 1.26 | 0.84 to 1.81 |
Unmeasured ion effect less than –5 meq litre * | 1.50 | 1.05 to 2.16 |
Standard base excess less than –5 meq litre * | 1.97 | 1.38 to 2.80 |
Sodium–chloride effect less than –5 meq litre * | 0.86 | 0.41 to 1.81 |
Albumin effect greater than 5 meq litre ** | 1.26 | 0.84 to 1.81 |
Unmeasured ion effect less than –5 meq litre * | 1.50 | 1.05 to 2.16 |
Clinical example of the simplified Fencl–Stewart approach. An acid–base assessment of a patient after anaesthetic induction and after 2 h of major gynaecological surgery. Normal saline was used as the intraoperative fluid. Data are the average values from Scheingrabber et al . 16 In this patient, after 2 h of surgery most of the metabolic acidosis can be explained by a decrease in the strong ion difference secondary to an increase in plasma chloride. This is partly offset by a decrease in the total weak acid concentration (albumin). Unmeasured ions are unimportant in this acidaemia. These changes follow the infusion of about 6 litres of 0.9% sodium chloride. *Sodium–chloride effect on base excess (meq litre –1 )=[Na + ]–[Cl – ]–38; **albumin effect on base excess (meq litre –1 )=0.25×(42–[albumin] g litre –1 ); ***unmeasured ion effect (meq litre –1 )=standard base excess–(sodium–chloride effect)–albumin effect
pH | 7.41 | 7.28 |
Carbon dioxide (kPa) | 5.3 | 5.3 |
Sodium (meq litre ) | 140 | 142 |
Chloride (meq litre ) | 104 | 115 |
Albumin (g litre ) | 40 | 28 |
Base excess (meq litre ) | –0.4 | –6.7 |
Sodium–chloride effect (meq litre )* | –2 | –11 |
Albumin effect (meq litre )** | 0.5 | 3.5 |
Unmeasured ion effect (meq litre )*** | 1.1 | 0.8 |
pH | 7.41 | 7.28 |
Carbon dioxide (kPa) | 5.3 | 5.3 |
Sodium (meq litre ) | 140 | 142 |
Chloride (meq litre ) | 104 | 115 |
Albumin (g litre ) | 40 | 28 |
Base excess (meq litre ) | –0.4 | –6.7 |
Sodium–chloride effect (meq litre )* | –2 | –11 |
Albumin effect (meq litre )** | 0.5 | 3.5 |
Unmeasured ion effect (meq litre )*** | 1.1 | 0.8 |
Shangraw R. Acid–base balance. In: Miller R, ed. Anesthesia , 5th edn. Philadelphia: Churchill‐Livingstone, 2000 ; 1390 –413
Sirker AA, Rhodes A, Grounds RM, Bennett ED. Acid–base physiology: the ‘traditional’ and the ‘modern’ approaches. Anaesthesia 2002 ; 57 : 348 –56
Fencl V, Jabor A, Kazda A, Figge J. Diagnosis of metabolic acid–base disturbances in critically ill patients. Am J Respir Crit Care Med 2000 ; 162 : 2246 –51
Gilfix BM, Bique M, Magder S. A physical chemical approach to the analysis of acid–base balance in the clinical setting. J Crit Care 1993 ; 8 : 187 –97
Balasubramanyan N, Havens PL, Hoffman GM. Unmeasured anions identified by the Fencl–Stewart method predict mortality better than base excess, anion gap, and lactate in patients in the pediatric intensive care unit. Crit Care Med 1999 ; 27 : 1577 –81
Stewart PA. Modern quantitative acid–base chemistry. Can J Physiol Pharmacol 1983 ; 61 : 1444 –61
Figge J, Rossing T, Fencl V. Serum proteins and acid–base equilibria. J Lab Clin Med 1991 ; 117 : 453 –67
Magder S. Clinical approach to acid–base balance. In: Gullo A, ed. Anaesthesia, Pain, Intensive Care and Emergency Medicine—APICE 16. Critical Care Medicine . Milan: Springer, 2001 ; 617 –30
Figge J, Mydosh T, Fencl V. Serum proteins and acid–base equilibria: a follow‐up. J Lab Clin Med 1992 ; 120 : 713 –9
Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986 ; 1 : 307 –10
Bland J, Altman D. Measuring methods in medical research. Stat Methods Med Res 1999 ; 8 : 135 –60
Le Gall J‐R, Lemeshow S, Saulnier F. A new simplified acute physiological score (SAPS II) based on a European/North American multicenter study. JAMA 1993 ; 270 : 2957 –63
Walsh T, Lee A. Mathematical coupling in medical research: lessons from studies of oxygen kinetics. Br J Anaesth 1998 ; 81 : 118 –20
Liskaser FJ, Bellomo R, Hayhoe M, et al . Role of pump prime in the etiology and pathogenesis of cardiopulmonary bypass‐associated acidosis. Anesthesiology 2000 ; 93 : 1170 –3.
Hayhoe M, Bellomo R, Liu G, McNicol L, Buxton B. The aetiology and pathogenesis of cardiopulmonary bypass‐associated metabolic acidosis using polygeline pump prime. Intensive Care Med 1999 ; 25 : 680 –5
Scheingraber S, Rehm M, Sehmisch C, Finsterer U. Rapid saline infusion produces hyperchloremic acidosis in patients undergoing gynecologic surgery. Anesthesiology 1999 ; 90 : 1265 –70
O’Connor M, Roizen M. Lactate versus chloride. Anesth Analg 2001 ; 93 : 809 –10
Figge J, Jabor A, Kazda A, Fencl V. Anion gap and hypoalbuminemia. Crit Care Med 1998 ; 26 : 1807 –10
Story D, Poustie S, Bellomo R. Estimating unmeasured anions in critically ill patients: anion‐gap, base‐deficit, and strong‐ion‐gap. Anaesthesia 2002 ; 57 : 1109 –14
Story D, Poustie S, Bellomo R. Quantitative physical chemistry analysis of acid–base disorders in critically ill patients. Anaesthesia 2001 ; 56 : 530 –3
1Department of Anaesthesia, and 2Department of Intensive Care, Austin and Repatriation Medical Centre, Heidelberg, Victoria 3084, Australia
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Introduction: Base deficit (BD) is a validated surrogate for lactate in injured patients and correlates with trauma severity. Stewart proposed a more comprehensive measure of acidosis based on the strong ion difference (SID) (SID = Na + K + Mg + Ca - CL - lactate [mEq/L]). We compared operating characteristics of BD, anion gap (AG), and SID in identifying major injury in emergency department (ED) trauma patients.
Methods: This was a retrospective review. Major injury was defined as Injury Severity Score > or =15, blood transfusions, or significant drop in hematocrit. Receiver operating characteristic curves compared BD, AG, and SID in differentiating major from minor injuries.
Results: The study included 1181 patients. Both BD and SID were significantly (P = .0001) different after major vs minor injury (mean difference, 3.40; 95% confidence interval, 2.70-4.00 and mean difference, 2.50; 95% confidence interval, 1.90-3.10, respectively). Receiver operating characteristic curves were minimally different from one another (P = .0035).
Conclusion: Stewart's SID can identify major injury in the ED.
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Research output : Contribution to journal › Article › Research › peer-review
Background. The Fencl-Stewart approach to acid-base disorders uses five equations of varying complexity to estimate the base excess effects of the important components: the strong ion difference (sodium and chloride), the total weak acid concentration (albumin) and unmeasured ions. Although this approach is straightforward, most people would need a calculator to use the equations. We proposed four simpler equations that require only mental arithmetic and tested the hypothesis that these simpler equations would have good agreement with more complex Fencl-Stewart equations. Methods. We reduced two complex equations for the sodium-chloride effect on base excess to one simple equation: sodium-chloride effect (meq litre -1 )=[Na + ]-[CI]-38. We simplified the equation of the albumin effect on base excess to an equation with two constants: albumin effect (meq litre -- )=0.25×(42-[albumin]g litre -1 ). Using 300 blood samples from critically ill patients, we examined the agreement between the more complex Fencl-Stewart equations and our simplified versions with Bland-Altman analyses. Results. The estimates of the sodium-chloride effect on base excess agreed well, with no bias and limits of agreement of -0.5 to 0.5 meq litre -1 . The albumin effect estimates required log transformation. The simplified estimate was, on average, 90% of the Fencl-Stewart estimate. The limits of agreement for this percentage were 82-98%. Conclusions. The simplified equations agree well with the previous, more complex equations. Our findings suggest a useful, simple way to use the Fencl-Stewart approach to analyse acid-base disorders in clinical practice.
Original language | English |
---|---|
Pages (from-to) | 54-60 |
Number of pages | 7 |
Journal | |
Volume | 92 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Jan 2004 |
Externally published | Yes |
T1 - Strong ions, weak acids and base excess
T2 - A simplified Fencl-Stewart approach to clinical acid-base disorders
AU - Story, David A.
AU - Morimatsu, H.
AU - Bellomo, R.
PY - 2004/1/1
Y1 - 2004/1/1
N2 - Background. The Fencl-Stewart approach to acid-base disorders uses five equations of varying complexity to estimate the base excess effects of the important components: the strong ion difference (sodium and chloride), the total weak acid concentration (albumin) and unmeasured ions. Although this approach is straightforward, most people would need a calculator to use the equations. We proposed four simpler equations that require only mental arithmetic and tested the hypothesis that these simpler equations would have good agreement with more complex Fencl-Stewart equations. Methods. We reduced two complex equations for the sodium-chloride effect on base excess to one simple equation: sodium-chloride effect (meq litre-1)=[Na+]-[CI]-38. We simplified the equation of the albumin effect on base excess to an equation with two constants: albumin effect (meq litre--)=0.25×(42-[albumin]g litre-1). Using 300 blood samples from critically ill patients, we examined the agreement between the more complex Fencl-Stewart equations and our simplified versions with Bland-Altman analyses. Results. The estimates of the sodium-chloride effect on base excess agreed well, with no bias and limits of agreement of -0.5 to 0.5 meq litre-1. The albumin effect estimates required log transformation. The simplified estimate was, on average, 90% of the Fencl-Stewart estimate. The limits of agreement for this percentage were 82-98%. Conclusions. The simplified equations agree well with the previous, more complex equations. Our findings suggest a useful, simple way to use the Fencl-Stewart approach to analyse acid-base disorders in clinical practice.
AB - Background. The Fencl-Stewart approach to acid-base disorders uses five equations of varying complexity to estimate the base excess effects of the important components: the strong ion difference (sodium and chloride), the total weak acid concentration (albumin) and unmeasured ions. Although this approach is straightforward, most people would need a calculator to use the equations. We proposed four simpler equations that require only mental arithmetic and tested the hypothesis that these simpler equations would have good agreement with more complex Fencl-Stewart equations. Methods. We reduced two complex equations for the sodium-chloride effect on base excess to one simple equation: sodium-chloride effect (meq litre-1)=[Na+]-[CI]-38. We simplified the equation of the albumin effect on base excess to an equation with two constants: albumin effect (meq litre--)=0.25×(42-[albumin]g litre-1). Using 300 blood samples from critically ill patients, we examined the agreement between the more complex Fencl-Stewart equations and our simplified versions with Bland-Altman analyses. Results. The estimates of the sodium-chloride effect on base excess agreed well, with no bias and limits of agreement of -0.5 to 0.5 meq litre-1. The albumin effect estimates required log transformation. The simplified estimate was, on average, 90% of the Fencl-Stewart estimate. The limits of agreement for this percentage were 82-98%. Conclusions. The simplified equations agree well with the previous, more complex equations. Our findings suggest a useful, simple way to use the Fencl-Stewart approach to analyse acid-base disorders in clinical practice.
KW - Chemistry, analytical
KW - Complications, acid-base disorders
KW - Intensive care
UR - http://www.scopus.com/inward/record.url?scp=0346336778&partnerID=8YFLogxK
U2 - 10.1093/bja/aeh018
DO - 10.1093/bja/aeh018
M3 - Article
C2 - 14665553
AN - SCOPUS:0346336778
SN - 0007-0912
JO - British Journal of Anaesthesia
JF - British Journal of Anaesthesia
IMAGES
VIDEO
COMMENTS
In blood at pH 7.4: Strong Ion Difference (SID) is the difference between the concentrations of strong cations and strong anions. In normal human plasma the SID is 42 mEq/L (which suits fans of the Hitchhiker's Guide to the Galaxy) The SID can be changed by two methods: (1) Concentration change. (2) Strong Ion changes.
IS THE PLASMA STRONG-ION DIFFERENCE In the Stewart approach, the 3 independent controllers of acid-base status in body fluids are the partial pressure of CO 2, the strong-ion difference (SID), and the total amount of weak acids. Strong ions are those that are completely dissociated in a solution, in this case plasma. The mea-
The strong ion difference [SID] Accounting for the law of mass conservation, electroneutrality and equilibrium constants for all incompletely dissociated species in biological solution, Stewart derived a fourth- order polynomial equation expressing [H+] as directly related to PₐCO₂ and ATOT and inversely to SID (Sirker et al., 2002 ...
By comparison, many surgeons, critical care specialists, and anesthesiologists have embraced an approach introduced by Peter Stewart in 1981 . This alternative approach, termed "strong ion difference" (SID) or the Stewart approach, will be reviewed briefly in this topic.
simplified stewart approach acid-base 1. base excess (be) = measure of metabolic acid-base status 2. key metabolic factor = plasma strong-ion difference 3. weak acids are also important for metabolic acid-base changes 4. change in b.e. = changes in sid and the amount of weak acid 6. the difference between na+ and cl- ion conc = predominant sid 7.
The Strong Ion Calculator - a Laboratory Application of the Stewart Approach. The Strong Ion Calculator is a software program linked to the Laboratory Information System of Hawke's Bay Regional Hospital, Hastings, New Zealand. 83, 84 It was developed by the late Peter Lloyd to counter criticism that the Stewart approach lacks bedside utility ...
The Fencl-Stewart approach to acid-base disorders uses five equations of varying complexity to estimate the base excess effects of the important components: the strong ion difference (sodium and chloride), the total weak acid concentration (albumin) and unmeasured ions. Although this approach is straightforward, most people would need a calculator to use the equations.
Peter Stewart proposed a radically different approach to acid-base physiology based upon physicochemical principles. ... magnesium, calcium, chloride and lactate. Strong ion difference is the amount by which strong cations exceed strong anions, measured in milliequivalents per litre. ... According to Stewart's hypothesis the alkalosis is not ...
Introduction. In 1981 Stewart published his book 1, 2 where he tried to establish the quantitative relationships between hydrogen ion concentration and all the other variables in a solution. About 10 years later Figge et al. 3, 4 defined more precisely the quantitative role of plasmatic nonbicarbonate buffers, including serum proteins and inorganic phosphate in acid-base equilibria, leading ...
Acid-base derangements are commonly encountered in the critical care unit [], and there is renewed interest in the precise description of these disorders in critically ill patients [2-5].This new interest has led to a renovation of the quantitative assessment of physiological acid-base balance, with increasing use of the Stewart model (strong ion difference [SID] theory) to calculate ...
In blood plasma in vivo these variables are: (a) the PCO 2; (b) the "strong ion difference" (SID), i.e., the difference between the sums of all the strong (fully dissociated, chemically nonreacting) cations (Na +, K +, Ca 2+, Mg 2+) and all the strong anions (Cl-plus other strong anions such as ketones and lactate); (c) the concentrations ...
Stewart 3 suggests that Strong Ion Difference (SID) difference, together with the total concentration of non-volatile, weak acids ([A TOT]) and PCO 2 are three independent variables that determine the acid-base status of the organism. Stewart also defined the way these variables interact, which obeys three fundamental physicochemical principles: (a) the principle of mass conservation, (b) the ...
SID - the strong ion difference; A TOT - the total weak acid concentration; PaCO 2; Thus, changes in any of the independent variables can cause a change in pH and HCO 3-, i.e. acidosis and alkalosis. All the independent variables must be known to calculate the dependent variables; Thus, acid-base disorders can be classified as:
In his theory, there are three responsible variables to independently determine the dissociation of water, and consequently the hydrogen ion concentration, in order to maintain electrical neutrality: (1) strong ion difference (SID), (2) total concentration of weak acids (A TOT), and (3) partial pCO 2 of the solution. 8,30 Thus, in the Stewart ...
in the strong ion difference. As the strong ion difference is decreased the blood becomes more acidic.6 We proposed that the calculation of the strong ion difference effect on base excess could be simplified. From the reference range in our laboratory, the median value for sodium is 140 mmol litre-1 and that for chloride is 102 mmol litre ...
The anion gap quantifies this apparent difference between total cation concentration and total anion concentration. Stewart's hypothesis clarifies the role of the lungs, kidneys, liver and gut in acid-base control. According to Stewart's hypothesis, the alkalosis is not caused by loss of hydrogen ions because there is an inexhaustible supply ...
Strong ion difference: strong ions are those that largely exist in a dissociated or charged state in plasma. In humans, the difference between measurable strong cations (Na +, ... In the Stewart hypothesis, plasma SID is increased because chloride (a strong ion) is lost without a corresponding strong cation. ...
The strong ion difference effect can be further analysed with the separate Fencl-Stewart equations for sodium and chloride. 4 The unmeasured ions may be strong ions, such as sulphate and acetate, 14 or weak acids, such as phosphate and polygeline. 15
According to the Stewart concept, plasma pH results from the degree of plasma water dissociation which is determined by 3 independent variables: 1) strong ion difference (SID) which is the difference between all the strong plasma cations and anions; 2) quantity of plasma weak acids; 3) PaCO2. Thus, metabolic acid-base disorders are always ...
Background: The Fencl-Stewart approach to acid-base disorders uses five equations of varying complexity to estimate the base excess effects of the important components: the strong ion difference (sodium and chloride), the total weak acid concentration (albumin) and unmeasured ions. Although this approach is straightforward, most people would need a calculator to use the equations.
Stewart proposed a more comprehensive measure of acidosis based on the strong ion difference (SID) (SID = Na + K + Mg + Ca - CL - lactate [mEq/L]). We compared operating characteristics of BD, anion gap (AG), and SID in identifying major injury in emergency department (ED) trauma patients. Methods: This was a retrospective review.
A follow‐up to a recent discussion of urinary acidity focused on Stewarts's theory of 'strong ion difference' (SID). The comments of Vasileiadis et al. warrant a reevaluation of Stewart's approach. Here, it will be discussed (i) that the SID is unlikely to be an independent variable as claimed by Stewart, (ii) that the calculation of ...
N2 - Background. The Fencl-Stewart approach to acid-base disorders uses five equations of varying complexity to estimate the base excess effects of the important components: the strong ion difference (sodium and chloride), the total weak acid concentration (albumin) and unmeasured ions.