Veterinary Clinical Pathology ISSN 0275-6382
ORIGINAL RESEARCH
Clinical utility of serum biochemical variables for predicting acid–base balance in critically ill horses €mpfli1, Angelika Schoster1, Peter D. Constable2 Henry R. Sta 1
Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada; and 2Department of Veterinary Clinical Sciences, School of Veterinary Medicine, Purdue University, West Lafayette, IN, USA
Key Words Biochemistry profile, electroneutrality, pH, physicochemistry, strong ion difference Correspondence Henry R. St€ampfli, Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada N1G 2W1 E-mail: [email protected] DOI:10.1111/vcp.12200
Background: Profiles from serum biochemical analyzers include the concentration of strong electrolytes (including L-lactate), total carbon dioxide (tCO2), and total protein. These variables are associated with changes in acid–base balance. Application of physicochemical principles may allow predicting acid–base balance from serum biochemistry without measuring whole blood pH and pCO2. Objectives: The purpose of the study was to determine if the acid–base status of critically ill horses could be accurately predicted using variables included in standard serum biochemical profiles. Methods: Two jugular venous blood samples were prospectively obtained from critically ill horses and foals. Samples were analyzed using a whole blood gas and pH analyzer (BG) and a serum biochemistry multi analyzer system (AMAS). Linear regression, Deming regression, and Bland–Altman plots were used for method comparison and P < .05 was considered significant. Results: Values from 70 horses and foals for Na, K, Cl, and total protein concentrations, and consequently the calculated variables used for acid base interpretation, were different between the AMAS and BG analyzer. Using physicochemical principles, BG results accurately predicted pH, whereas the AMAS results did not when a fixed value for pCO2 was used. Conclusions: Measurement of pCO2 is required in critically ill horses for accurate prediction of whole blood pH. Differences in the measured values of Na and Cl concentration exist when measured in serum by the AMAS and in whole blood or plasma by BG, indicating that the accurate prediction of whole blood pH is analyzer-dependent. Application of physicochemical principles to plasma or serum provides a practical method to evaluate analyzer accuracy.
Introduction Whole blood pH and pCO2, calculated plasma bicarbonate concentration (cHCO3), and serum strong electrolyte analysis have been very helpful diagnostic tools for the assessment of metabolic and acid–base imbalances in critically ill patients.1 The bicarbonatecentered Henderson–Hasselbalch approach has been widely used for acid–base evaluation, utilizing the measured values for pH, pCO2, and sodium (Na), potassium (K), and chloride (Cl), as well as the cHCO3, calculated total CO2 concentration (ctCO2),
base excess (BE), and anion gap (AG).2–4 Increasingly, the application of a quantitative physicochemical approach based on the strong ion difference (SID) theory is used in the evaluation of complex acid–base disorders in domestic animals.1,5–12 The quantitative physicochemical approach emphasizes the importance of the plasma concentrations of strong electrolytes (such as Na, K, Cl, L-lactate), pCO2, and the plasma protein concentration in determining plasma pH and HCO3 concentrations.1,5,13 The major obstacle for the routine application of the Henderson–Hasselbalch and SID approaches to critically ill domestic animals is that
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they both require specialized equipment to measure whole blood pH and pCO2. Such equipment is expensive to maintain and operate, and may therefore not be readily available. A second obstacle to the widespread application of the SID approach is the difficulty in obtaining an accurate estimate for SID. Determination of SID requires identification and accurate measurement of all strong ions in plasma or serum, which can be an arduous and difficult task because of the presence of unidentified strong ions and cumulative measurement error.5 A clinically practical estimate of SID can be obtained by determining the plasma or serum concentration of at least 4 strong ions (Na, K, Cl, and Llactate), where SID = [Na] + [K] [Cl] [L-lactate].5 Statistically significant differences in the measured values for Na or Cl have been identified between analyzers.14–17 Such differences can lead to clinically and statistically significant differences in the calculated value for SID because the variabilities of [Na] and [Cl] has a major influence on the final value of SID.18 Therefore, identification of analyzers that provide accurate measurements of these 4 strong ions is required for an adequate application of the SID approach to complex acid–base disorders. For more than 3 decades, plasma or serum biochemical analysis has included measurements of the 3 quantitatively important nonmetabolizable electrolytes that are fully dissociated under physiologic conditions (Na, K, Cl), and the total concentration of nonvolatile buffers (such as the total protein or albumin concentration) that are important for quantitative acid–base interpretation and calculation.1,5,13 More recently, plasma or serum biochemical analysis has included measurements of ctCO2, a close approximation of cHCO3 in plasma and serum, as well as L-lactate concentration, one of the quantitatively most important metabolized strong anions in plasma and serum of horses.19 In a preliminary study utilizing jugular venous blood samples from horses, the results of serum biochemical analysis were shown to provide a clinically relevant insight into acid–base balance in individual horses.20 To further characterize the predictive value of serum biochemical analysis for acid–base status, we hypothesized that jugular venous blood samples taken at admission from critically ill horses and analyzed using AMAS would provide clinically similar interpretations of acid–base balance to those provided by automated whole blood BG. We also wanted to compare serum electrolyte concentrations measured by an AMAS in critically ill horses with plasma electrolyte levels determined in a BG in the presence of varying concentrations of plasma proteins.
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Material and Methods Horses admitted to the isolation unit of the Ontario Veterinary College Teaching Hospital for suspected infectious diseases were prospectively enrolled between January 2010 and March 2012. A pretreatment venous blood sample was collected from each horse’s jugular vein into a 10 mL partially evacuated glass tube (Vacutainer serum tube; Becton Dickinson, Franklin Lakes NJ, USA) and allowed to clot at room temperature for serum biochemical analysis. The serum sample was analyzed using a Cobas 6000 C501 automated multi-analyzer system (AMAS) (Roche Canada, Laval Quebec, Canada). A second pretreatment blood sample was collected at the same time anaerobically into a 3 mL polypropylene syringe containing lyophilized Lithium-heparin (Marquest-Gaslyte; Vital Signs Colorado Inc., Englewood, CO, USA) for determination of whole blood pH, pCO2, and strong cation (Na, K, Ca) and anion (Cl) concentrations in plasma. The heparinized blood sample was analyzed using a Radiometer 800 Flex blood gas analyzer (BG) (Radiometer Canada, 3020 Gore Road, London, ON N5V4T7 (Canada)). The concentration of the strong electrolytes Na, K, and Cl was measured on both analyzers using ionselective electrode (ISE) technology based on direct (BG) and indirect (AMAS) potentiometric measuring principles21, and using internal standards for each electrolyte. Direct potentiometry does not involve sample dilution and is commonly used in blood gas analyzers and point-of-care analyzers that use whole blood samples, whereas indirect potentiometry involves substantial sample dilution and is widely used in high-throughput central hospital analyzers such as the Cobas analyzer. The extensive sample dilution in indirect potentiometry means that changes in serum protein concentration from the assumed reference value of approximately 70 g/L result in underestimated Na, K, and Cl concentrations in hyperproteinemic samples, and overestimated Na, K, and Cl concentrations in hypoproteinemic samples.14,16,17,21 Ion-selective electrodes were also used for pH and pCO2 measurements on the BG analyzer using direct potentiometry for pH and indirect potentiometry for pCO2, both based on the Nernst equation.21 The BG also used electrochemical methodology (lactate oxidase and H2O2 and amperometric detection) to determine the plasma concentration of L-lactate. Total CO2 was measured indirectly on the AMAS using an enzymatic procedure with Phosphoenolpyruvate (PEP). Total serum protein and albumin concentrations were measured by the AMAS unit
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using standard colorimetric methods. Total protein concentration was estimated on the BG analyzer plasma sample using refractometry (Texas Medical Instruments TM) and reported as total protein (TP; g/ L). The following variables were calculated from values measured by the AMAS and BG analyzers: Plasma bicarbonate concentration ([HCO3] in mmol/L) was calculated using the Henderson–Hasselbalch equation: [HCO3] = S•pCO2•10(pHpK1) and experimentally determined values for S (0.0307 [mmol/L]/mmHg)22 and pK1 (6.095 at [NaCl] = 0.16 mmol/L) in plasma.4 Base excess was expressed as BEECF (also called standard base excess or in vivo base excess23), whereby: BEECF = 0.93 9 {[HCO3 24.4 + 14.83 9 (pH 7.40)}. For the AMAS, ctCO2 (mmol/L) was measured, whereas for the BG analyzer, ctCO2 was calculated using the Henderson–Hasselbalch equation according to the formula ctCO2 = S•pCO2•(10(pH pK1) + 1). Anion gap (AG) in mEq/L was calculated as follows: AG = (Na + K) (Cl + ctCO2) for the AMAS and BG.1 Strong ion gap (SIG) in mEq/L was calculated using a validated equation for horse plasma as follows: SIG = (0.224•[total proteinAMAS or total proteinBG, g/ L]/(1+10{pKapH})) AG, where pKa is the negative logarithm to the base 10 of the dissociation constant for nonvolatile buffers in equine serum or plasma, such that pKa = 6.65.5,19 A, which represents the net negative charge of protein in plasma, was calculated as: A = (0.224•[total proteinAMAS or total proteinBG, g/L]/(1+10{pKapH})).13
Acid-base interpretation in horses
Plasma or serum pH was calculated (pHcalc) using the simplified strong ion 6-factor equation from the results of plasma or serum biochemical measurements 0 whereby: pHcalc = log10 (2•SID/{K1 •S•pCO2 + Ka•Atot 0 Ka•SID + √{(K1 •S•pCO2 + Ka•SID + Ka•Atot)2 4•Ka2•SID•Atot}}). Strong ion difference was calculated from 4 (SID4) or 3 (SID3) factors as: SID4 = [Na+] + [K+] [Cl] [L-lactate]; SID3 = [Na+] + [K+] [Cl].5 The total nonvolatile buffer ion concentration in plasma or serum (Atot) was calculated in mmol/L from the measured total protein concentration using validated values for horse plasma, such that: Atot = 0.224• [total protein, g/L].5 For the AMAS, pH was calculated using an assumed value for pCO2 of 40 mmHg. For the BG analyzer, the actual measured value for pCO2 was used to calculate pH.
Statistical analyses Data were expressed as mean standard deviation (SD), and P < .05 was considered significant. Paired t-tests were used to compare measured and calculated AMAS and BG values when the difference between the 2 analyzers was normally distributed (ctCO2, anion gap, SID3, SID4), according to the Anderson–Darling test. When the difference in measured values between the 2 analyzers was not normally distributed (remaining variables), the Wilcoxon Signed Rank Test was used to compare measured and calculated AMAS and BG values. Agreement between the 2 analyzers was explored using Deming regression and Bland–Altman
Figure 1. Scatterplot of the relationship between the calculated and measured jugular venous blood pH, assuming pCO2 = 40 mmHg (left panel) or the measured value for pCO2 (right panel). Blood pH in the left panel was calculated using the simplified strong ion equation (see text for details) from the serum sodium (Na), potassium (K), chloride (Cl), and total protein concentration measured by a serum biochemical analyzer, and L-lactate concentration measured by a whole blood pH and gas analyzer. Blood pH in the right panel was calculated using the simplified strong ion equation (see text for details) from the plasma Na, K, Cl, and L-lactate concentrations measured by the blood pH and gas analyzer, and total plasma protein concentration measured by refractometry. Blood samples were obtained from 70 ill adult horses and foals upon admission to a hospital isolation unit. The solid line is the linear regression line, the dashed line is the 95% confidence interval for the linear regression line, and the thin solid line is the 95% confidence interval for prediction. The linear regression line is significantly different from the line of identity (slope < 1; intercept > 0) when pH was calculated using a fixed value for pCO2 = 40 mmHg, but not when the measured value for pCO2 was used.
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analysis (Analyse-it for Microsoft Excel version 2.20, Analyse-it Software, Ltd www.analyse-it.com). For Deming regression, the intercept value reflects constant bias and the slope reflects proportional bias. For Bland–Altman plots, the difference between AMAS and BG was plotted on the y-axis against the mean of the AMAS and BG value on the x-axis. The upper and lower limits of agreement were calculated from the bias 1.96 9 SD; the bias estimate from Bland– Altman plots reflects the mean bias over the range of measured values and therefore includes both the constant and proportional bias identified using Deming regression. The value for pHcalc was regressed against the measured pH value (pH) using least squares linear regression. The differences in Na+, K+, and Cl concentrations measured by indirect potentiometry
(AMAS) and direct (BG) potentiometry were regressed against the serum total protein concentration to determine whether indirect potentiometry overestimated Na+, K+, and Cl concentrations in hypoproteinemic serum or plasma. Calculated values for AG were regressed against the measured blood Llactate concentrations for both the AMAS and BG. Calculated values for SIG were regressed against the measured blood L-lactate concentrations for the BG. The accuracy of linear regression equations was evaluated using the R2 value for linear and polynomial equations and examination of normal and residual plots to ensure that the assumptions of regression analyses were met. A statistical software package (PROC UNIVARIATE, PROC REG; SAS 9.3, SAS Institute, Cary, NC, USA) was used for statistical analysis.
Table 1. Mean and standard deviation, minimum, and maximum values for selected whole blood, plasma, and serum clinicopathologic values for jugular venous blood samples obtained from 70 ill adult horses and foals upon admission to the hospital isolation unit. P-value is comparing the mean values for AMAS and BG using a paired t-test or Wilcoxon signed rank test. Serum Biochemical Analyzer (AMAS)
Measured variables Na (mEq/L) K (mEq/L) Cl (mEq/L) L-lactate (mmol/L) ctCO2 (mmol/L) Total protein (g/L) Albumin (g/L) pHmeas pCO2 (mmHg) Calculated variables SID3 (mEq/L)* SID4 (mEq/L)† HCO3 (mmol/L)‡ ctCO2-calc (mmHg)§ BEECF (mmol/L)¶ Atot (mmol/L)** AG (mEq/L)†† SIG (mEq/L)‡‡ pHcalc§§
Whole Blood Gas Analyzer (BG)
Mean SD
Min–Max
Mean SD
Min–Max
P-Value ND
128.6 7.4 3.5 0.8 93.2 8.9 ND 23.5 5.2 50.7 14.4 24.0 7.5 ND ND
105.0–142.0 1.6–7.1 65.0–118.0 ND 12.0–33.0 18.0–88.0 6.0–37.0 ND ND
129.5 8.2 3.4 0.7 97.2 8.0 2.5 2.3 ND 54.4 14.2 ND 7.37 0.08 40.6 5.7
104.0–145.0 1.9–6.3 72.0–121.0 0.7–13.5 ND 20.0–80.0 ND 7.03–7.48 28.6–53.4
.0011 .0044 < .0001 ND ND < .0001 ND ND ND
27.6–51.2 25.8–43.7 ND ND ND 4.0–19.4 7.1–34.1 ND 7.19–7.57
35.7 5.5 33.3 5.3 23.1 4.7 25.0 4.9 1.5 5.5 12.0 3.1 10.8 4.3 1.8 3.5 7.36 0.09
21.3–48.1 20.5–45.4 11.1–31.4 12.4–33.8 19.1–+6.2 4.4–17.6 3.9–25.4 16.4–+3.5 7.05–7.52
< .0001 < .0001 ND ND ND < .0001 < .0001 ND < .0001
38.9 36.5 ND ND ND 11.1 15.4 ND 7.43
5.4 4.5
3.2 5.9 0.08
*SID3 = [Na + K Cl ], strong ion difference. SID4 = [Na+ + K+ Cl-Lactate], strong ion difference. 0 ‡ [HCO3] = S•pCO2•10(pHpK1 ). § ctCO2-calc = HCO3 + S•pCO2, calculated total CO2. ¶ BEECF = 0.93 9 {[HCO3] 24.4 + 14.83 9 (pH 7.40)}, base excess. **Atot = 0.224•[total protein, g/L], total nonvolatile buffer concentration. †† AGAMAS = [Na+ + K+ Cl ctCO2] and AGBG = [Na++K+Cl ctCO2-calc], anion gap. ‡‡ SIG = ((0.224•[Total proteinBG, g/L]/(1 + 10{6.65pH})) AGBG, strong ion gap. §§ pHcalc = log10 (2•SID4/{K10 •S•pCO2 + Ka•AtotKa•SID4 + √{(K10 •S•pCO2 + Ka•SID4 + Ka•Atot)24•Ka2•SID4•Atot}}), calculated pH. pCO2 = 40 mmHg (AMAS) or measured value (BG). ND indicates not determined. +
+
†
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Results In total, 70 horses (45 adult and 25 foals) were included in the study. Values for pHcalc calculated using AMAS measured values for serum Na, K, Cl, and total protein concentration, the value for Llactate measured by the whole blood pH and gas analyzer, and an assumed fixed value for pCO2 of 40 mmHg, were significantly different from the measured pH and different from the line of identity (Figure 1, Tables 1 and 2). In contrast, values for pHcalc calculated using BG values for plasma Na, K, Cl, and L-lactate concentrations, total protein concentration determined by refractometry, and the measured value for pCO2 were similar to the measured pH, as indicated by a linear regression line that was not significantly different from the line of identity. Statistically significant differences were present between the AMAS and BG units, especially evidenced by measured and calculated values for Na and Cl concentrations (Figure 2). In addition, K and total protein concentrations were different as well, and consequently these differences also contributed to the observed statistically significant differences in the calculated values for SID3, SID4, Atot, and A (Table 1). The numerically most relevant difference was between
plasma and serum Cl concentration, where plasma Cl measured by the BG was on average 4.0 mEq/L higher than the serum Cl concentration measured by the AMAS. The 4.0 mEq/L difference in chloride concentration accounted for most of the difference in values for SID3 and SID4 between the AMAS and BG analyzers. The results of Deming regression and Bland–Altman plots indicated significant methodologic differences in measuring Na and Cl concentration (Figure 2), while Deming regression did not identify methodologic differences in the measurement of K and total protein concentration between the 2 analyzers (Table 2). Specifically, the difference in Na concentration (AMAS BG) was linearly and negatively dependent on the protein concentration (y = 4.3 0.103x; P < .001; R2 = 0.39; Figure 3), whereas the difference in Cl concentration (AMAS BG) was weakly and positively associated with the protein concentration (y = 7.1 + 0.060x, P = 0.043; R2 = 0.06). The difference in K concentration was independent of total protein concentration (data not shown). As a result of methodologic differences in measuring Na and Cl concentrations, the results of Deming regression and Bland–Altman plots indicated significant methodologic differences in the calculated value for SID3 (Table 2).
Table 2. Method comparison estimates from Deming regression (intercept, slope) and Bland–Altman plots (bias, 95% limits of agreement) for selected whole blood, plasma, and serum variables in jugular venous blood samples obtained from 70 ill adult horses and foals upon admission to a hospital isolation unit. Values were measured using a serum biochemical analyzer (AMAS) or a whole blood pH, pCO2, and electrolyte analyzer (BG). The 95% confidence interval for the intercept and slope estimate obtained using Deming regression is in parentheses. The P-value for Deming regression tests the probability that the intercept = 0 and the slope = 1. Factor Measured variables Na(AMAS) vs Na(BG) (mEq/L) K(AMAS) vs K(BG) (mEq/L) Cl(AMAS) vs Cl(BG) (mEq/L) SID3(AMAS) vs SID3(BG) (mEq/L) Total protein(BG) vs total protein(AMAS) (g/L) Calculated variables tCO2(BG) vs tCO2(AMAS) (mmol/L) pH(calc-BG) vs pH pH(calc-AMAS) vs pH
Intercept
Deming regression Slope
12.6 (3.7 to 21.6) P = .0062 0.33 (1.01 to +0.34) P = .33 16.0 (24.9 to 7.1) P = .0006 11.8 (6.6 to 17.0) P < .0001 4.6 (1.1 to 10.3) P = .14
0.90 (0.83 to 0.96) P = .0030 1.12 (0.92 to 1.33) P = .24 1.12 (1.03 to 1.21) P = .0079 0.76 (0.61 to 0.92) P = .0037 0.98 (0.86 to 1.11) P = .79
2.8 (0.6 to 4.9) P = .014 1.0 (3.6 to 1.5) P = .43 1.6 (1.7 to 5.0) P = .33
0.95 (0.85 to 1.04) P = .26 1.14 (0.79 to 1.48) P = .43 0.78 (0.33 to 1.24) P = .34
Bias
Bland–Altman plot 95% Limits of agreement
0.9
5.6 to +3.8
+0.09
0.57 to +0.75
4.0
10.9 to +2.9
+3.9
3.6 to +11.5
3.7
7.6 to +15.0
+1.5
2.7 to +5.6
0.010
0.12 to +0.10
+0.054
0.09 to +0.20
Na indicates sodium; K, potassium; Cl, chloride; SID, strong ion difference; tCO2, total CO2; pH calc, calculated pH.
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Figure 2. Scatterplots (top panels) and difference (Bland–Altman) plots (bottom panels) for the comparison of plasma and serum sodium (Na) and chloride (Cl) concentrations as measured using a serum biochemical analyzer (AMAS) or a whole blood pH, pCO2, and electrolyte analyzer (BG) in 70 ill adult horses and foals upon admission to a hospital isolation unit. Top left panel: the dashed line is the line of identity, and the solid line is the mean Deming linear regression line for Na concentration (y = 0.90x + 12.6), which was significantly different from the line of identity (P < .05). Top right panel: the dashed line is the line of identity, and the solid line is the mean Deming linear regression line for Cl concentration (y = 1.12x 16.0), which was significantly different from the line of identity (P < .05). Bottom panels reflect differences plotted against the mean value. The horizontal solid line identifies the mean bias between the 2 analyzers (Na, 0.9 mEq/L; Cl, 4.0 mEq/L), and the horizontal dashed lines reflect the limits of agreement (mean bias 1.96 9 SD = 5.6 to 3.8 mEq/L for Na; 10.9 to 2.9 for Cl), which is equivalent to the range of differences that contains 95% of future measurements.
Figure 3. Scatterplots of the relationship between the difference in measured sodium (Na) (left panel) and chloride (Cl) concentration (right panel), and measured total protein concentration as measured using a serum biochemical analyzer (AMAS) or a whole blood pH, pCO2, and electrolyte analyzer (BG) in 70 ill adult horses and foals upon admission to a hospital isolation unit. The difference is the concentration measured by the AMAS (indirect potentiometry on serum) minus the concentration measured by the BG (direct potentiometry on plasma). The thick solid line is the linear regression line, the dashed lines represent the 95% confidence interval for the linear regression line, and the thin solid lines represent the 95% confidence interval for prediction. The difference in Na concentration (AMAS BG) was linearly and negatively dependent on the serum protein concentration (y = 4.3 0.103x; P < .001; R2 = 0.39; left panel). The difference in Cl concentration (AMAS BG) was weakly and positively associated with the serum protein concentration (y = 7.1 + 0.060x, P = .043; R2 = 0.06; right panel).
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Values for ctCO2 calculated by the blood pH and gas analyzer from measured values for pH and pCO2, and the ctCO2 values measured by the serum biochemical analyzer were significantly different (Table 2, Figure 4). The results of Deming regression indicated that the bias was significantly greater than 0. The Bland– Altman plot indicated that the mean bias between the 2 analyzers was 1.5 mmol/L.
Acid-base interpretation in horses
Anion gap calculated using AMAS measured values for serum Na, K, Cl, and tCO2 was linearly associated with the plasma L-lactate concentration (R2 = 0.53; Figure 5); the slope (1.85) was significantly greater than 1 (P = .0002), and the estimate for the intercept value was 10.9 mEq/L. The anion gap calculated from values measured by BG analyzer for serum Na, K, and Cl, and calculated values for tCO2 from measured values for pH and pCO2 was linearly associated with the plasma L-lactate concentration (R2 = 0.29; Figure 5); the slope (1.02) was not significantly different from 1 (P = .93), and the estimate for the intercept value was 8.3 mEq/L. Strong ion gap calculated using the BG analyzer measured values for serum concentrations of Na, K, Cl, and refractometry values for total protein concentration were linearly and negatively associated with the plasma L-lactate concentration (R2 = 0.38; Figure 6); the slope (-0.92) was not significantly different than 1 (P = 0.61) and the estimate for the intercept value was 0.4 mEq/L and not significantly different from 0 (P = .38).
Discussion
Figure 4. Scatterplot (top panel) and difference (Bland–Altman) plot (bottom panel) for the comparison of tCO2 concentrations as measured or calculated by using a serum biochemical analyzer (AMAS) or a whole blood pH, pCO2, and electrolyte analyzer (BG) in 70 ill adult horses and foals upon admission to a hospital isolation unit. Top panel: the dashed line is the line of identity, and the solid line is the mean Deming linear regression line for tCO2 concentration (y = 0.95x + 2.8); the slope was not significantly different from 1, but the intercept value was significantly greater than 0 (P < .05). The bottom panel shows differences plotted against the mean value. The horizontal solid line identifies the mean bias between the 2 analyzers (1.5 mmol/L), and the horizontal dashed lines reflect the limits of agreement (mean bias 1.96 9 SD = 2.7 to 5.6 mmol/L, which is equivalent to the range of differences that contains 95% of future measurements.
The results of this study demonstrate that measurement of pCO2 is required in critically ill horses for accurate prediction of blood pH. Additionally, the results indicate that differences exist between some values measured with the BG analyzer on whole blood and plasma measured with the automated serum biochemistry analyzer. This was true for the measured sodium and chloride concentrations, and the calculated value for ctCO2. These results indicate that the ability to accurately predict blood pH is dependent on the methodology used to quantitatively measure important strong cations (Na) and anions (Cl, L-lactate, D-lactate, uremic anions). The results of this study further show that application of physicochemical principles to plasma, such as electroneutrality, provides a practical method for evaluating the overall accuracy of a biochemical analyzer when applied to plasma or serum. The Cobas 6000 employs indirect potentiometry to measure Na, K, and Cl concentrations, whereas the Radiometer 800 employs direct potentiometry. Direct ISE technologies are widely used in point-of-care devices and measure the electrolyte activity in the water phase of plasma. When direct ISE is applied to whole blood, the measured electrolyte activity is not impacted by the plasma protein content or packed cell volume. In contrast, indirect ISE technologies are widely used in clinical pathology laboratories because they require a lower sample volume and have a larger
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Figure 5. Scatterplots of the relationship between anion gap (AG) and measured whole blood L-lactate concentration as measured by using a serum biochemical analyzer (AMAS) or a whole blood pH, pCO2, and electrolyte analyzer (BG) in 70 ill adult horses and foals upon admission to a hospital isolation unit. AG was calculated as: AG = (Na + K) (Cl + ctCO2). The thick solid line is the linear regression line, the dashed lines represent the 95% confidence interval for the linear regression line, and the thin solid lines represent the 95% confidence interval for prediction. The slope (1.02) was not significantly different from 1 (P = .93) and the estimate for the intercept value was 8.3 mEq/L for AG calculated from the results of the whole blood gas analyzer (BG, left panel). The slope (1.85) was significantly greater than 1 (P = .0002) and the estimate for the intercept value was 10.9 mEq/L for AG calculated from the results of automated multi analyzer system (AMAS, right panel).
Figure 6. Scatterplot of the relationship between strong ion gap (SIG) and measured whole blood L-lactate concentration as measured by a whole blood gas analyzer (BG) in 70 ill adult horses and foals upon admission to a hospital isolation unit. Strong ion gap was calculated as: SIG = (0.224•[total protein, g/L]/(1+10{6.65pH})) (Na + K) + (Cl + ctCO2). The thick solid line is the linear regression line, the dashed lines represent the 95% confidence interval for the linear regression line, and the thin solid lines represent the 95% confidence interval for prediction. The slope (0.92) was not significantly different from 1 (P = .61) and the estimate for the intercept value (0.4 mEq/L) was not significantly different from 0 (P = .38).
analytical range, meaning that they can be used to measure electrolyte activity in urine or fluids other than plasma or serum.24,25 Indirect ISE methods
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measure a diluted sample of plasma or serum that is assumed to have a normal plasma protein concentration of 70 g/L. As a consequence, the values for strong electrolyte concentrations measured by indirect ISE methods differs from that measured by direct ISE methods whenever the plasma protein concentration is higher or lower than 70 g/L. This is particularly true for the measurement of Na concentration, as in the study reported here. In general, hypoproteinemia leads to pseudohypernatremia, and hyperproteinemia leads to pseudohyponatremia, when electrolytes are measured using indirect ISE technology.24,25 As a consequence, direct ISE methodologies are preferred whenever accurate estimates of plasma Na, K, and Cl concentrations and SID are required.24,25 The mean ctCO2 calculated from the whole blood pH and pCO2 determination was 1.5 mmol/L higher than the mean value measured by serum biochemical analysis. The serum biochemistry analyzer used in this study measured ctCO2 using a coupled enzymatic method that involved sequential alkalinization, addition of NADH, phosphoenolpyruvate (PEP) carboxylase and malate dehydrogenase, and spectrophotometric measurement of unreacted NADH. As such, the PEP carboxylase method measures the sum of plasma HCO3 and dissolved CO2, but not carbon dioxide from acid-labile compounds such as carbamate groups (R-NHCOOH) on plasma proteins26, and therefore provides a slightly lower value than that calculated using the Henderson–Hasselbalch equation, and assumed values for S and pK1 from measured values for pH and pCO2. Even though plasma
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pH and pCO2 can be measured accurately, and the value for S can be considered constant in the range of plasma protein concentration in critically ill animals22, the value for pK1 can differ by more than 1% of the mean value of 6.095 in healthy people27–29, and is therefore likely to vary by more than 1% in critically ill horses. Part of this variability in pK1 has been attributed to changes in plasma Na concentration and therefore ionic strength5, and plasma Na concentration ranged from 104 to 145 mmol/L in the study reported here. Another possible reason for the difference between the measured and calculated ctCO2 values is that the measured value for ctCO2 is the concentration per liter of plasma, whereas the calculated value for ctCO2 is the concentration per liter of plasma water, which averaged 94.3 mL/100 g of plasma in this study which reported a mean measured ctCO2 of 23.5 mmol/L. Adjustment of this mean ctCO2 value by the mean plasma water value provided a measured ctCO2 of 24.9 mmol/L of plasma water, which represents an increase of 1.4 mmol/L and approximates the mean observed difference of 1.5 mmol/L. Additionally, correction of the AMASmeasured ctCO2 by the total protein (TP, g/L) concentrations for all 70 samples, whereby (corrected ctCO2) = (measured ctCO2)/(1 {TP/1000}), provided a nonsignificant difference (0.2 mmol/L, P = .46) between the mean value for ctCO2 calculated from the results of blood pH and pCO2 determination and the corrected ctCO2. Application of electroneutrality principles in the present study indicated that the whole blood BG analyzer, but not the AMAS, provided accurate measurements. This finding supports that of a previous study30 that electroneutrality calculation may provide a useful physicochemical parameter to assess the accuracy of serum and plasma biochemical analyzers. Values measured by the AMAS were not useful in predicting blood pH using physicochemical principles. This result appeared to be due to the unavailability of accurate pCO2 measurements (a fixed value of 40 mmHg was assumed) and apparent discrepancy in the measurement of the quantitatively most important strong cation (Na) and strong anion (Cl) in serum. In summary, the application of physicochemical principles to plasma or serum variables provides a practical method for critically evaluating the overall accuracy of biochemical analyzers, such as the 2 instruments studied here. The metabolic component of acid–base imbalance can be assessed using AMAS (ctCO2 and strong ion concentrations), but the results from the AMAS are not sufficiently accurate to predict blood pH and serum SIG in critically ill horses.
Acid-base interpretation in horses
Disclosure: The authors have indicated that they have no affiliations or financial involvement with any organization or entity with a financial interest in, or in financial competition with, the subject matter or materials discussed in this article.
References 1. Constable PD. Clinical assessment of acid-base status: comparison of the Henderson-Hasselbalch and strong ion approaches. Vet Clin Pathol. 2000;29:115–128. 2. Constable PD. Hyperchloremic acidosis: the classic example of strong ion acidosis. Anesth Analg. 2003;96:919–922. 3. Kurtz I, Kraut J, Ornekian V, Nguyen MK. Acid-base analysis: a critique of the Stewart and bicarbonate-centered approaches. Am J Physiol Renal Physiol. 2008;294: F1009–F1031. 4. Clinical and Laboratory Standards Institute. Blood Gas and pH Analysis and Related Measurements; Approved Guidelines. 2nd ed. Wayne, PA: CLSI; 2009. 5. Constable PD. A simplified strong ion model for acidbase equilibria: application to horse plasma. J Appl Physiol. 1997;83:297–311. 6. Navarro M, Monreal L, Segura D, Armengou L, A~ nor SA. Comparison of traditional and quantitative analysis of acid-base and electrolyte imbalances in horses with gastrointestinal disorders. J Vet Intern Med. 2005;19:871–877. 7. Constable PD, St€ ampfli HR, Navetat H, Berchtold J, Schelcher F. Use of a quantitative strong ion approach to determine the mechanism for acid-base abnormalities in sick calves with or without diarrhea. J Vet Intern Med. 2005;19:581–589. 8. Siegling-Vlitakis C, Kohn B, Kellermeier C, Schmitz R, Hartmann H. Qualification of the Stewart variables for the assessment of the acid-base status in healthy dogs and dogs with different diseases. Berl Munch Tierarztl Wochenschr. 2007;120:148–155. 9. Reinhold P, Hartmann H, Constable PD. Characterisation of acid-base abnormalities in pigs experimentally infected with Chlamydia suis. Vet J. 2010;184:212–218. 10. Sławuta P, Gli nska-Suchocka K. Comparison of the utility of the classic model (the Henderson-Hasselbach equation) and the Stewart model (Strong Ion Approach) for the diagnostics of acid-base balance disorders in dogs with right sided heart failure. Polish J Vet Sci. 2012;15:119–124. 11. M€ uller KR, Gentile A, Klee W, Constable PD. Importance of the effective strong ion difference of an intravenous solution in the treatment of diarrheic calves with
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naturally-acquired acidemia and strong ion (metabolic) acidosis. J Vet Intern Med. 2012;26:674–683. 12. van Galen G, Cerri S, Porter S, et al. Traditional and quantitative assessment of acid-base and shock variables in horses with atypical myopathy. J Vet Intern Med. 2013;27:186–193. 13. Stewart PA. How to Understand Acid-Base. New York: Elsevier, 1981. Available at: http://www.acidbase.org/ index.php?show=sb 14. Morimatsu H, Rocktaschel J, Bellomo R, Uchino S, Goldsmith D, Gutteridge G. Comparison of point-ofcare versus central laboratory measurement of electrolyte concentrations on calculations of the anion gap and the strong ion difference. Anesthesiology. 2003;98:1077– 1084. 15. van Gammeren AJ, van Gool N, de Groot MJM, Cobbaert CM. Analytical performance evaluation of the Cobas 6000 analyzer – special emphasis on trueness verification. Clin Chem Lab Med. 2008;46:863–871. 16. Jain A, Subhan I, Joshi M. Comparison of the point-ofcare blood gas analyzer versus the laboratory auto-analyzer for the measurement of electrolytes. Int J Emerg Med. 2009;2:117–120. 17. Nguyen BV, Vincent JL, Hamm JB, et al. The reproducibility of Stewart parameters for acid-base diagnosis using two central laboratory analyzers. Anesth Analg. 2009;109:1517–1523. 18. Mallat J, Barrailler S, Lemyze M, et al. Use of sodiumchloride difference and corrected anion gap as surrogates of Stewart variables in critically ill patients. PLoS ONE. 2013;8:e56635. 19. Constable PD, Hinchcliff KW, Muir WW 3rd. Comparison of anion gap and strong ion gap as predictors of unmeasured strong ion concentration in plasma and serum from horses. Am J Vet Res. 1998;59:881–887. 20. St€ampfli HR, Carlson GP. How to use routine serum biochemical profile to understand and interpret acid-base disorders in the horse. AAEP Proc. 2001;47:257–261.
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21. Rundle CC. A Beginners Guide to Ion-Selective Electrode Measurements, 2000. Available at: http:// www.nico2000.net/Book/Guide1.html 22. Austin WH, Lacombe E, Rand PW, Chatterjee M. Solubility of carbon dioxide in serum from 15 to 38 C. J Appl Physiol. 1963;18:301–304. 23. Siggaard-Andersen O, Wimberley PD, Fogh-Andersen N, Gøthgen IH. Measured and derived quantities with modern pH and blood gas equipment: calculation algorithms with 54 equations. Scand J Clin Lab Invest. 1988;48(S189):7–15. 24. Dimeski G, Barnett RJ. Effects of total plasma protein concentration on plasma sodium, potassium and chloride measurements by an indirect ion selective electrode measuring system. Crit Care Resusc. 2005;7:12–15. 25. Dimeski G, Morgan TJ, Presneill JJ, Venkatesh B. Disagreement between ion selective electrode direct and indirect sodium measurements: estimation of the problem in a tertiary referral hospital. J Crit Care. 2012;27:326.e9–326. e16. 26. Jarrett M, Hibbert B, Osborne R, Young EB. Alternative instrumentation for the analysis of total carbon dioxide (TCO2) in equine plasma. Anal Bioanal Chem. 2010;397:717–722. 27. Tibi L, Bhattacharya SS, Flear CTG. Variability in pK’1 of human plasma. Clin Chim Acta. 1982;121:15–31. 28. Rosan RC, Enlander D, Ellis J. Unpredictable error in calculated bicarbonate homeostasis during pediatric intensive care: the delusion of fixed pK’. Clin Chem. 1983;29:69–73. 29. Flear CTG, Roberts SW, Hayes S, Stoddart JC, Covington AK. pK1’and bicarbonate concentration in plasma. Clin Chem. 1987;33:13–20. 30. St€ ampfli HR, Stevenson AJ, Brooks L, Weber MP. The use of electroneutrality equation applied to plasma/ serum biochemical profile reports as an additional quality control system of measured strong electrolytes, weak acids and total CO2. J Vet Intern Med. 2004;18:457.
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ORIGINAL RESEARCH
Clinical utility of serum biochemical variables for predicting acid–base balance in critically ill horses €mpfli1, Angelika Schoster1, Peter D. Constable2 Henry R. Sta 1
Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada; and 2Department of Veterinary Clinical Sciences, School of Veterinary Medicine, Purdue University, West Lafayette, IN, USA
Key Words Biochemistry profile, electroneutrality, pH, physicochemistry, strong ion difference Correspondence Henry R. St€ampfli, Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada N1G 2W1 E-mail: [email protected] DOI:10.1111/vcp.12200
Background: Profiles from serum biochemical analyzers include the concentration of strong electrolytes (including L-lactate), total carbon dioxide (tCO2), and total protein. These variables are associated with changes in acid–base balance. Application of physicochemical principles may allow predicting acid–base balance from serum biochemistry without measuring whole blood pH and pCO2. Objectives: The purpose of the study was to determine if the acid–base status of critically ill horses could be accurately predicted using variables included in standard serum biochemical profiles. Methods: Two jugular venous blood samples were prospectively obtained from critically ill horses and foals. Samples were analyzed using a whole blood gas and pH analyzer (BG) and a serum biochemistry multi analyzer system (AMAS). Linear regression, Deming regression, and Bland–Altman plots were used for method comparison and P < .05 was considered significant. Results: Values from 70 horses and foals for Na, K, Cl, and total protein concentrations, and consequently the calculated variables used for acid base interpretation, were different between the AMAS and BG analyzer. Using physicochemical principles, BG results accurately predicted pH, whereas the AMAS results did not when a fixed value for pCO2 was used. Conclusions: Measurement of pCO2 is required in critically ill horses for accurate prediction of whole blood pH. Differences in the measured values of Na and Cl concentration exist when measured in serum by the AMAS and in whole blood or plasma by BG, indicating that the accurate prediction of whole blood pH is analyzer-dependent. Application of physicochemical principles to plasma or serum provides a practical method to evaluate analyzer accuracy.
Introduction Whole blood pH and pCO2, calculated plasma bicarbonate concentration (cHCO3), and serum strong electrolyte analysis have been very helpful diagnostic tools for the assessment of metabolic and acid–base imbalances in critically ill patients.1 The bicarbonatecentered Henderson–Hasselbalch approach has been widely used for acid–base evaluation, utilizing the measured values for pH, pCO2, and sodium (Na), potassium (K), and chloride (Cl), as well as the cHCO3, calculated total CO2 concentration (ctCO2),
base excess (BE), and anion gap (AG).2–4 Increasingly, the application of a quantitative physicochemical approach based on the strong ion difference (SID) theory is used in the evaluation of complex acid–base disorders in domestic animals.1,5–12 The quantitative physicochemical approach emphasizes the importance of the plasma concentrations of strong electrolytes (such as Na, K, Cl, L-lactate), pCO2, and the plasma protein concentration in determining plasma pH and HCO3 concentrations.1,5,13 The major obstacle for the routine application of the Henderson–Hasselbalch and SID approaches to critically ill domestic animals is that
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they both require specialized equipment to measure whole blood pH and pCO2. Such equipment is expensive to maintain and operate, and may therefore not be readily available. A second obstacle to the widespread application of the SID approach is the difficulty in obtaining an accurate estimate for SID. Determination of SID requires identification and accurate measurement of all strong ions in plasma or serum, which can be an arduous and difficult task because of the presence of unidentified strong ions and cumulative measurement error.5 A clinically practical estimate of SID can be obtained by determining the plasma or serum concentration of at least 4 strong ions (Na, K, Cl, and Llactate), where SID = [Na] + [K] [Cl] [L-lactate].5 Statistically significant differences in the measured values for Na or Cl have been identified between analyzers.14–17 Such differences can lead to clinically and statistically significant differences in the calculated value for SID because the variabilities of [Na] and [Cl] has a major influence on the final value of SID.18 Therefore, identification of analyzers that provide accurate measurements of these 4 strong ions is required for an adequate application of the SID approach to complex acid–base disorders. For more than 3 decades, plasma or serum biochemical analysis has included measurements of the 3 quantitatively important nonmetabolizable electrolytes that are fully dissociated under physiologic conditions (Na, K, Cl), and the total concentration of nonvolatile buffers (such as the total protein or albumin concentration) that are important for quantitative acid–base interpretation and calculation.1,5,13 More recently, plasma or serum biochemical analysis has included measurements of ctCO2, a close approximation of cHCO3 in plasma and serum, as well as L-lactate concentration, one of the quantitatively most important metabolized strong anions in plasma and serum of horses.19 In a preliminary study utilizing jugular venous blood samples from horses, the results of serum biochemical analysis were shown to provide a clinically relevant insight into acid–base balance in individual horses.20 To further characterize the predictive value of serum biochemical analysis for acid–base status, we hypothesized that jugular venous blood samples taken at admission from critically ill horses and analyzed using AMAS would provide clinically similar interpretations of acid–base balance to those provided by automated whole blood BG. We also wanted to compare serum electrolyte concentrations measured by an AMAS in critically ill horses with plasma electrolyte levels determined in a BG in the presence of varying concentrations of plasma proteins.
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Material and Methods Horses admitted to the isolation unit of the Ontario Veterinary College Teaching Hospital for suspected infectious diseases were prospectively enrolled between January 2010 and March 2012. A pretreatment venous blood sample was collected from each horse’s jugular vein into a 10 mL partially evacuated glass tube (Vacutainer serum tube; Becton Dickinson, Franklin Lakes NJ, USA) and allowed to clot at room temperature for serum biochemical analysis. The serum sample was analyzed using a Cobas 6000 C501 automated multi-analyzer system (AMAS) (Roche Canada, Laval Quebec, Canada). A second pretreatment blood sample was collected at the same time anaerobically into a 3 mL polypropylene syringe containing lyophilized Lithium-heparin (Marquest-Gaslyte; Vital Signs Colorado Inc., Englewood, CO, USA) for determination of whole blood pH, pCO2, and strong cation (Na, K, Ca) and anion (Cl) concentrations in plasma. The heparinized blood sample was analyzed using a Radiometer 800 Flex blood gas analyzer (BG) (Radiometer Canada, 3020 Gore Road, London, ON N5V4T7 (Canada)). The concentration of the strong electrolytes Na, K, and Cl was measured on both analyzers using ionselective electrode (ISE) technology based on direct (BG) and indirect (AMAS) potentiometric measuring principles21, and using internal standards for each electrolyte. Direct potentiometry does not involve sample dilution and is commonly used in blood gas analyzers and point-of-care analyzers that use whole blood samples, whereas indirect potentiometry involves substantial sample dilution and is widely used in high-throughput central hospital analyzers such as the Cobas analyzer. The extensive sample dilution in indirect potentiometry means that changes in serum protein concentration from the assumed reference value of approximately 70 g/L result in underestimated Na, K, and Cl concentrations in hyperproteinemic samples, and overestimated Na, K, and Cl concentrations in hypoproteinemic samples.14,16,17,21 Ion-selective electrodes were also used for pH and pCO2 measurements on the BG analyzer using direct potentiometry for pH and indirect potentiometry for pCO2, both based on the Nernst equation.21 The BG also used electrochemical methodology (lactate oxidase and H2O2 and amperometric detection) to determine the plasma concentration of L-lactate. Total CO2 was measured indirectly on the AMAS using an enzymatic procedure with Phosphoenolpyruvate (PEP). Total serum protein and albumin concentrations were measured by the AMAS unit
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using standard colorimetric methods. Total protein concentration was estimated on the BG analyzer plasma sample using refractometry (Texas Medical Instruments TM) and reported as total protein (TP; g/ L). The following variables were calculated from values measured by the AMAS and BG analyzers: Plasma bicarbonate concentration ([HCO3] in mmol/L) was calculated using the Henderson–Hasselbalch equation: [HCO3] = S•pCO2•10(pHpK1) and experimentally determined values for S (0.0307 [mmol/L]/mmHg)22 and pK1 (6.095 at [NaCl] = 0.16 mmol/L) in plasma.4 Base excess was expressed as BEECF (also called standard base excess or in vivo base excess23), whereby: BEECF = 0.93 9 {[HCO3 24.4 + 14.83 9 (pH 7.40)}. For the AMAS, ctCO2 (mmol/L) was measured, whereas for the BG analyzer, ctCO2 was calculated using the Henderson–Hasselbalch equation according to the formula ctCO2 = S•pCO2•(10(pH pK1) + 1). Anion gap (AG) in mEq/L was calculated as follows: AG = (Na + K) (Cl + ctCO2) for the AMAS and BG.1 Strong ion gap (SIG) in mEq/L was calculated using a validated equation for horse plasma as follows: SIG = (0.224•[total proteinAMAS or total proteinBG, g/ L]/(1+10{pKapH})) AG, where pKa is the negative logarithm to the base 10 of the dissociation constant for nonvolatile buffers in equine serum or plasma, such that pKa = 6.65.5,19 A, which represents the net negative charge of protein in plasma, was calculated as: A = (0.224•[total proteinAMAS or total proteinBG, g/L]/(1+10{pKapH})).13
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Plasma or serum pH was calculated (pHcalc) using the simplified strong ion 6-factor equation from the results of plasma or serum biochemical measurements 0 whereby: pHcalc = log10 (2•SID/{K1 •S•pCO2 + Ka•Atot 0 Ka•SID + √{(K1 •S•pCO2 + Ka•SID + Ka•Atot)2 4•Ka2•SID•Atot}}). Strong ion difference was calculated from 4 (SID4) or 3 (SID3) factors as: SID4 = [Na+] + [K+] [Cl] [L-lactate]; SID3 = [Na+] + [K+] [Cl].5 The total nonvolatile buffer ion concentration in plasma or serum (Atot) was calculated in mmol/L from the measured total protein concentration using validated values for horse plasma, such that: Atot = 0.224• [total protein, g/L].5 For the AMAS, pH was calculated using an assumed value for pCO2 of 40 mmHg. For the BG analyzer, the actual measured value for pCO2 was used to calculate pH.
Statistical analyses Data were expressed as mean standard deviation (SD), and P < .05 was considered significant. Paired t-tests were used to compare measured and calculated AMAS and BG values when the difference between the 2 analyzers was normally distributed (ctCO2, anion gap, SID3, SID4), according to the Anderson–Darling test. When the difference in measured values between the 2 analyzers was not normally distributed (remaining variables), the Wilcoxon Signed Rank Test was used to compare measured and calculated AMAS and BG values. Agreement between the 2 analyzers was explored using Deming regression and Bland–Altman
Figure 1. Scatterplot of the relationship between the calculated and measured jugular venous blood pH, assuming pCO2 = 40 mmHg (left panel) or the measured value for pCO2 (right panel). Blood pH in the left panel was calculated using the simplified strong ion equation (see text for details) from the serum sodium (Na), potassium (K), chloride (Cl), and total protein concentration measured by a serum biochemical analyzer, and L-lactate concentration measured by a whole blood pH and gas analyzer. Blood pH in the right panel was calculated using the simplified strong ion equation (see text for details) from the plasma Na, K, Cl, and L-lactate concentrations measured by the blood pH and gas analyzer, and total plasma protein concentration measured by refractometry. Blood samples were obtained from 70 ill adult horses and foals upon admission to a hospital isolation unit. The solid line is the linear regression line, the dashed line is the 95% confidence interval for the linear regression line, and the thin solid line is the 95% confidence interval for prediction. The linear regression line is significantly different from the line of identity (slope < 1; intercept > 0) when pH was calculated using a fixed value for pCO2 = 40 mmHg, but not when the measured value for pCO2 was used.
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analysis (Analyse-it for Microsoft Excel version 2.20, Analyse-it Software, Ltd www.analyse-it.com). For Deming regression, the intercept value reflects constant bias and the slope reflects proportional bias. For Bland–Altman plots, the difference between AMAS and BG was plotted on the y-axis against the mean of the AMAS and BG value on the x-axis. The upper and lower limits of agreement were calculated from the bias 1.96 9 SD; the bias estimate from Bland– Altman plots reflects the mean bias over the range of measured values and therefore includes both the constant and proportional bias identified using Deming regression. The value for pHcalc was regressed against the measured pH value (pH) using least squares linear regression. The differences in Na+, K+, and Cl concentrations measured by indirect potentiometry
(AMAS) and direct (BG) potentiometry were regressed against the serum total protein concentration to determine whether indirect potentiometry overestimated Na+, K+, and Cl concentrations in hypoproteinemic serum or plasma. Calculated values for AG were regressed against the measured blood Llactate concentrations for both the AMAS and BG. Calculated values for SIG were regressed against the measured blood L-lactate concentrations for the BG. The accuracy of linear regression equations was evaluated using the R2 value for linear and polynomial equations and examination of normal and residual plots to ensure that the assumptions of regression analyses were met. A statistical software package (PROC UNIVARIATE, PROC REG; SAS 9.3, SAS Institute, Cary, NC, USA) was used for statistical analysis.
Table 1. Mean and standard deviation, minimum, and maximum values for selected whole blood, plasma, and serum clinicopathologic values for jugular venous blood samples obtained from 70 ill adult horses and foals upon admission to the hospital isolation unit. P-value is comparing the mean values for AMAS and BG using a paired t-test or Wilcoxon signed rank test. Serum Biochemical Analyzer (AMAS)
Measured variables Na (mEq/L) K (mEq/L) Cl (mEq/L) L-lactate (mmol/L) ctCO2 (mmol/L) Total protein (g/L) Albumin (g/L) pHmeas pCO2 (mmHg) Calculated variables SID3 (mEq/L)* SID4 (mEq/L)† HCO3 (mmol/L)‡ ctCO2-calc (mmHg)§ BEECF (mmol/L)¶ Atot (mmol/L)** AG (mEq/L)†† SIG (mEq/L)‡‡ pHcalc§§
Whole Blood Gas Analyzer (BG)
Mean SD
Min–Max
Mean SD
Min–Max
P-Value ND
128.6 7.4 3.5 0.8 93.2 8.9 ND 23.5 5.2 50.7 14.4 24.0 7.5 ND ND
105.0–142.0 1.6–7.1 65.0–118.0 ND 12.0–33.0 18.0–88.0 6.0–37.0 ND ND
129.5 8.2 3.4 0.7 97.2 8.0 2.5 2.3 ND 54.4 14.2 ND 7.37 0.08 40.6 5.7
104.0–145.0 1.9–6.3 72.0–121.0 0.7–13.5 ND 20.0–80.0 ND 7.03–7.48 28.6–53.4
.0011 .0044 < .0001 ND ND < .0001 ND ND ND
27.6–51.2 25.8–43.7 ND ND ND 4.0–19.4 7.1–34.1 ND 7.19–7.57
35.7 5.5 33.3 5.3 23.1 4.7 25.0 4.9 1.5 5.5 12.0 3.1 10.8 4.3 1.8 3.5 7.36 0.09
21.3–48.1 20.5–45.4 11.1–31.4 12.4–33.8 19.1–+6.2 4.4–17.6 3.9–25.4 16.4–+3.5 7.05–7.52
< .0001 < .0001 ND ND ND < .0001 < .0001 ND < .0001
38.9 36.5 ND ND ND 11.1 15.4 ND 7.43
5.4 4.5
3.2 5.9 0.08
*SID3 = [Na + K Cl ], strong ion difference. SID4 = [Na+ + K+ Cl-Lactate], strong ion difference. 0 ‡ [HCO3] = S•pCO2•10(pHpK1 ). § ctCO2-calc = HCO3 + S•pCO2, calculated total CO2. ¶ BEECF = 0.93 9 {[HCO3] 24.4 + 14.83 9 (pH 7.40)}, base excess. **Atot = 0.224•[total protein, g/L], total nonvolatile buffer concentration. †† AGAMAS = [Na+ + K+ Cl ctCO2] and AGBG = [Na++K+Cl ctCO2-calc], anion gap. ‡‡ SIG = ((0.224•[Total proteinBG, g/L]/(1 + 10{6.65pH})) AGBG, strong ion gap. §§ pHcalc = log10 (2•SID4/{K10 •S•pCO2 + Ka•AtotKa•SID4 + √{(K10 •S•pCO2 + Ka•SID4 + Ka•Atot)24•Ka2•SID4•Atot}}), calculated pH. pCO2 = 40 mmHg (AMAS) or measured value (BG). ND indicates not determined. +
+
†
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Results In total, 70 horses (45 adult and 25 foals) were included in the study. Values for pHcalc calculated using AMAS measured values for serum Na, K, Cl, and total protein concentration, the value for Llactate measured by the whole blood pH and gas analyzer, and an assumed fixed value for pCO2 of 40 mmHg, were significantly different from the measured pH and different from the line of identity (Figure 1, Tables 1 and 2). In contrast, values for pHcalc calculated using BG values for plasma Na, K, Cl, and L-lactate concentrations, total protein concentration determined by refractometry, and the measured value for pCO2 were similar to the measured pH, as indicated by a linear regression line that was not significantly different from the line of identity. Statistically significant differences were present between the AMAS and BG units, especially evidenced by measured and calculated values for Na and Cl concentrations (Figure 2). In addition, K and total protein concentrations were different as well, and consequently these differences also contributed to the observed statistically significant differences in the calculated values for SID3, SID4, Atot, and A (Table 1). The numerically most relevant difference was between
plasma and serum Cl concentration, where plasma Cl measured by the BG was on average 4.0 mEq/L higher than the serum Cl concentration measured by the AMAS. The 4.0 mEq/L difference in chloride concentration accounted for most of the difference in values for SID3 and SID4 between the AMAS and BG analyzers. The results of Deming regression and Bland–Altman plots indicated significant methodologic differences in measuring Na and Cl concentration (Figure 2), while Deming regression did not identify methodologic differences in the measurement of K and total protein concentration between the 2 analyzers (Table 2). Specifically, the difference in Na concentration (AMAS BG) was linearly and negatively dependent on the protein concentration (y = 4.3 0.103x; P < .001; R2 = 0.39; Figure 3), whereas the difference in Cl concentration (AMAS BG) was weakly and positively associated with the protein concentration (y = 7.1 + 0.060x, P = 0.043; R2 = 0.06). The difference in K concentration was independent of total protein concentration (data not shown). As a result of methodologic differences in measuring Na and Cl concentrations, the results of Deming regression and Bland–Altman plots indicated significant methodologic differences in the calculated value for SID3 (Table 2).
Table 2. Method comparison estimates from Deming regression (intercept, slope) and Bland–Altman plots (bias, 95% limits of agreement) for selected whole blood, plasma, and serum variables in jugular venous blood samples obtained from 70 ill adult horses and foals upon admission to a hospital isolation unit. Values were measured using a serum biochemical analyzer (AMAS) or a whole blood pH, pCO2, and electrolyte analyzer (BG). The 95% confidence interval for the intercept and slope estimate obtained using Deming regression is in parentheses. The P-value for Deming regression tests the probability that the intercept = 0 and the slope = 1. Factor Measured variables Na(AMAS) vs Na(BG) (mEq/L) K(AMAS) vs K(BG) (mEq/L) Cl(AMAS) vs Cl(BG) (mEq/L) SID3(AMAS) vs SID3(BG) (mEq/L) Total protein(BG) vs total protein(AMAS) (g/L) Calculated variables tCO2(BG) vs tCO2(AMAS) (mmol/L) pH(calc-BG) vs pH pH(calc-AMAS) vs pH
Intercept
Deming regression Slope
12.6 (3.7 to 21.6) P = .0062 0.33 (1.01 to +0.34) P = .33 16.0 (24.9 to 7.1) P = .0006 11.8 (6.6 to 17.0) P < .0001 4.6 (1.1 to 10.3) P = .14
0.90 (0.83 to 0.96) P = .0030 1.12 (0.92 to 1.33) P = .24 1.12 (1.03 to 1.21) P = .0079 0.76 (0.61 to 0.92) P = .0037 0.98 (0.86 to 1.11) P = .79
2.8 (0.6 to 4.9) P = .014 1.0 (3.6 to 1.5) P = .43 1.6 (1.7 to 5.0) P = .33
0.95 (0.85 to 1.04) P = .26 1.14 (0.79 to 1.48) P = .43 0.78 (0.33 to 1.24) P = .34
Bias
Bland–Altman plot 95% Limits of agreement
0.9
5.6 to +3.8
+0.09
0.57 to +0.75
4.0
10.9 to +2.9
+3.9
3.6 to +11.5
3.7
7.6 to +15.0
+1.5
2.7 to +5.6
0.010
0.12 to +0.10
+0.054
0.09 to +0.20
Na indicates sodium; K, potassium; Cl, chloride; SID, strong ion difference; tCO2, total CO2; pH calc, calculated pH.
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Figure 2. Scatterplots (top panels) and difference (Bland–Altman) plots (bottom panels) for the comparison of plasma and serum sodium (Na) and chloride (Cl) concentrations as measured using a serum biochemical analyzer (AMAS) or a whole blood pH, pCO2, and electrolyte analyzer (BG) in 70 ill adult horses and foals upon admission to a hospital isolation unit. Top left panel: the dashed line is the line of identity, and the solid line is the mean Deming linear regression line for Na concentration (y = 0.90x + 12.6), which was significantly different from the line of identity (P < .05). Top right panel: the dashed line is the line of identity, and the solid line is the mean Deming linear regression line for Cl concentration (y = 1.12x 16.0), which was significantly different from the line of identity (P < .05). Bottom panels reflect differences plotted against the mean value. The horizontal solid line identifies the mean bias between the 2 analyzers (Na, 0.9 mEq/L; Cl, 4.0 mEq/L), and the horizontal dashed lines reflect the limits of agreement (mean bias 1.96 9 SD = 5.6 to 3.8 mEq/L for Na; 10.9 to 2.9 for Cl), which is equivalent to the range of differences that contains 95% of future measurements.
Figure 3. Scatterplots of the relationship between the difference in measured sodium (Na) (left panel) and chloride (Cl) concentration (right panel), and measured total protein concentration as measured using a serum biochemical analyzer (AMAS) or a whole blood pH, pCO2, and electrolyte analyzer (BG) in 70 ill adult horses and foals upon admission to a hospital isolation unit. The difference is the concentration measured by the AMAS (indirect potentiometry on serum) minus the concentration measured by the BG (direct potentiometry on plasma). The thick solid line is the linear regression line, the dashed lines represent the 95% confidence interval for the linear regression line, and the thin solid lines represent the 95% confidence interval for prediction. The difference in Na concentration (AMAS BG) was linearly and negatively dependent on the serum protein concentration (y = 4.3 0.103x; P < .001; R2 = 0.39; left panel). The difference in Cl concentration (AMAS BG) was weakly and positively associated with the serum protein concentration (y = 7.1 + 0.060x, P = .043; R2 = 0.06; right panel).
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Values for ctCO2 calculated by the blood pH and gas analyzer from measured values for pH and pCO2, and the ctCO2 values measured by the serum biochemical analyzer were significantly different (Table 2, Figure 4). The results of Deming regression indicated that the bias was significantly greater than 0. The Bland– Altman plot indicated that the mean bias between the 2 analyzers was 1.5 mmol/L.
Acid-base interpretation in horses
Anion gap calculated using AMAS measured values for serum Na, K, Cl, and tCO2 was linearly associated with the plasma L-lactate concentration (R2 = 0.53; Figure 5); the slope (1.85) was significantly greater than 1 (P = .0002), and the estimate for the intercept value was 10.9 mEq/L. The anion gap calculated from values measured by BG analyzer for serum Na, K, and Cl, and calculated values for tCO2 from measured values for pH and pCO2 was linearly associated with the plasma L-lactate concentration (R2 = 0.29; Figure 5); the slope (1.02) was not significantly different from 1 (P = .93), and the estimate for the intercept value was 8.3 mEq/L. Strong ion gap calculated using the BG analyzer measured values for serum concentrations of Na, K, Cl, and refractometry values for total protein concentration were linearly and negatively associated with the plasma L-lactate concentration (R2 = 0.38; Figure 6); the slope (-0.92) was not significantly different than 1 (P = 0.61) and the estimate for the intercept value was 0.4 mEq/L and not significantly different from 0 (P = .38).
Discussion
Figure 4. Scatterplot (top panel) and difference (Bland–Altman) plot (bottom panel) for the comparison of tCO2 concentrations as measured or calculated by using a serum biochemical analyzer (AMAS) or a whole blood pH, pCO2, and electrolyte analyzer (BG) in 70 ill adult horses and foals upon admission to a hospital isolation unit. Top panel: the dashed line is the line of identity, and the solid line is the mean Deming linear regression line for tCO2 concentration (y = 0.95x + 2.8); the slope was not significantly different from 1, but the intercept value was significantly greater than 0 (P < .05). The bottom panel shows differences plotted against the mean value. The horizontal solid line identifies the mean bias between the 2 analyzers (1.5 mmol/L), and the horizontal dashed lines reflect the limits of agreement (mean bias 1.96 9 SD = 2.7 to 5.6 mmol/L, which is equivalent to the range of differences that contains 95% of future measurements.
The results of this study demonstrate that measurement of pCO2 is required in critically ill horses for accurate prediction of blood pH. Additionally, the results indicate that differences exist between some values measured with the BG analyzer on whole blood and plasma measured with the automated serum biochemistry analyzer. This was true for the measured sodium and chloride concentrations, and the calculated value for ctCO2. These results indicate that the ability to accurately predict blood pH is dependent on the methodology used to quantitatively measure important strong cations (Na) and anions (Cl, L-lactate, D-lactate, uremic anions). The results of this study further show that application of physicochemical principles to plasma, such as electroneutrality, provides a practical method for evaluating the overall accuracy of a biochemical analyzer when applied to plasma or serum. The Cobas 6000 employs indirect potentiometry to measure Na, K, and Cl concentrations, whereas the Radiometer 800 employs direct potentiometry. Direct ISE technologies are widely used in point-of-care devices and measure the electrolyte activity in the water phase of plasma. When direct ISE is applied to whole blood, the measured electrolyte activity is not impacted by the plasma protein content or packed cell volume. In contrast, indirect ISE technologies are widely used in clinical pathology laboratories because they require a lower sample volume and have a larger
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Figure 5. Scatterplots of the relationship between anion gap (AG) and measured whole blood L-lactate concentration as measured by using a serum biochemical analyzer (AMAS) or a whole blood pH, pCO2, and electrolyte analyzer (BG) in 70 ill adult horses and foals upon admission to a hospital isolation unit. AG was calculated as: AG = (Na + K) (Cl + ctCO2). The thick solid line is the linear regression line, the dashed lines represent the 95% confidence interval for the linear regression line, and the thin solid lines represent the 95% confidence interval for prediction. The slope (1.02) was not significantly different from 1 (P = .93) and the estimate for the intercept value was 8.3 mEq/L for AG calculated from the results of the whole blood gas analyzer (BG, left panel). The slope (1.85) was significantly greater than 1 (P = .0002) and the estimate for the intercept value was 10.9 mEq/L for AG calculated from the results of automated multi analyzer system (AMAS, right panel).
Figure 6. Scatterplot of the relationship between strong ion gap (SIG) and measured whole blood L-lactate concentration as measured by a whole blood gas analyzer (BG) in 70 ill adult horses and foals upon admission to a hospital isolation unit. Strong ion gap was calculated as: SIG = (0.224•[total protein, g/L]/(1+10{6.65pH})) (Na + K) + (Cl + ctCO2). The thick solid line is the linear regression line, the dashed lines represent the 95% confidence interval for the linear regression line, and the thin solid lines represent the 95% confidence interval for prediction. The slope (0.92) was not significantly different from 1 (P = .61) and the estimate for the intercept value (0.4 mEq/L) was not significantly different from 0 (P = .38).
analytical range, meaning that they can be used to measure electrolyte activity in urine or fluids other than plasma or serum.24,25 Indirect ISE methods
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measure a diluted sample of plasma or serum that is assumed to have a normal plasma protein concentration of 70 g/L. As a consequence, the values for strong electrolyte concentrations measured by indirect ISE methods differs from that measured by direct ISE methods whenever the plasma protein concentration is higher or lower than 70 g/L. This is particularly true for the measurement of Na concentration, as in the study reported here. In general, hypoproteinemia leads to pseudohypernatremia, and hyperproteinemia leads to pseudohyponatremia, when electrolytes are measured using indirect ISE technology.24,25 As a consequence, direct ISE methodologies are preferred whenever accurate estimates of plasma Na, K, and Cl concentrations and SID are required.24,25 The mean ctCO2 calculated from the whole blood pH and pCO2 determination was 1.5 mmol/L higher than the mean value measured by serum biochemical analysis. The serum biochemistry analyzer used in this study measured ctCO2 using a coupled enzymatic method that involved sequential alkalinization, addition of NADH, phosphoenolpyruvate (PEP) carboxylase and malate dehydrogenase, and spectrophotometric measurement of unreacted NADH. As such, the PEP carboxylase method measures the sum of plasma HCO3 and dissolved CO2, but not carbon dioxide from acid-labile compounds such as carbamate groups (R-NHCOOH) on plasma proteins26, and therefore provides a slightly lower value than that calculated using the Henderson–Hasselbalch equation, and assumed values for S and pK1 from measured values for pH and pCO2. Even though plasma
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pH and pCO2 can be measured accurately, and the value for S can be considered constant in the range of plasma protein concentration in critically ill animals22, the value for pK1 can differ by more than 1% of the mean value of 6.095 in healthy people27–29, and is therefore likely to vary by more than 1% in critically ill horses. Part of this variability in pK1 has been attributed to changes in plasma Na concentration and therefore ionic strength5, and plasma Na concentration ranged from 104 to 145 mmol/L in the study reported here. Another possible reason for the difference between the measured and calculated ctCO2 values is that the measured value for ctCO2 is the concentration per liter of plasma, whereas the calculated value for ctCO2 is the concentration per liter of plasma water, which averaged 94.3 mL/100 g of plasma in this study which reported a mean measured ctCO2 of 23.5 mmol/L. Adjustment of this mean ctCO2 value by the mean plasma water value provided a measured ctCO2 of 24.9 mmol/L of plasma water, which represents an increase of 1.4 mmol/L and approximates the mean observed difference of 1.5 mmol/L. Additionally, correction of the AMASmeasured ctCO2 by the total protein (TP, g/L) concentrations for all 70 samples, whereby (corrected ctCO2) = (measured ctCO2)/(1 {TP/1000}), provided a nonsignificant difference (0.2 mmol/L, P = .46) between the mean value for ctCO2 calculated from the results of blood pH and pCO2 determination and the corrected ctCO2. Application of electroneutrality principles in the present study indicated that the whole blood BG analyzer, but not the AMAS, provided accurate measurements. This finding supports that of a previous study30 that electroneutrality calculation may provide a useful physicochemical parameter to assess the accuracy of serum and plasma biochemical analyzers. Values measured by the AMAS were not useful in predicting blood pH using physicochemical principles. This result appeared to be due to the unavailability of accurate pCO2 measurements (a fixed value of 40 mmHg was assumed) and apparent discrepancy in the measurement of the quantitatively most important strong cation (Na) and strong anion (Cl) in serum. In summary, the application of physicochemical principles to plasma or serum variables provides a practical method for critically evaluating the overall accuracy of biochemical analyzers, such as the 2 instruments studied here. The metabolic component of acid–base imbalance can be assessed using AMAS (ctCO2 and strong ion concentrations), but the results from the AMAS are not sufficiently accurate to predict blood pH and serum SIG in critically ill horses.
Acid-base interpretation in horses
Disclosure: The authors have indicated that they have no affiliations or financial involvement with any organization or entity with a financial interest in, or in financial competition with, the subject matter or materials discussed in this article.
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