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The early phase of critical illness is a progressive acidic state due to unmeasured anions

Published online by Cambridge University Press:  01 July 2008

B. Antonini*
Affiliation:
University of Brescia, Institute of Anesthesiology and Intensive Care, Spedali Civili, Brescia, Italy
S. Piva
Affiliation:
University of Brescia, Institute of Anesthesiology and Intensive Care, Spedali Civili, Brescia, Italy
M. Paltenghi
Affiliation:
University of Brescia, Institute of Anesthesiology and Intensive Care, Spedali Civili, Brescia, Italy
A. Candiani
Affiliation:
University of Brescia, Institute of Anesthesiology and Intensive Care, Spedali Civili, Brescia, Italy
N. Latronico
Affiliation:
University of Brescia, Institute of Anesthesiology and Intensive Care, Spedali Civili, Brescia, Italy
*
Institute of Anesthesiology and Intensive Care, University of Brescia, Spedali Civili di Brescia, Piazzale Spedali Civili, 1, 25123 Brescia, Italy. E-mail: [email protected]; Tel: +39 30 3995570; Fax: +39 30 3995841
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Summary

Background and objective

Stewart’s and Fencl’s methods have recently been proposed to interpret acid–base disorders where traditional theory has proven inadequate. Our objectives were to evaluate: (1) the occurrence of acid–base disturbances in critically ill patients and their trend over the first 3 intensive care unit days, (2) whether Stewart’s theory offers advantages over the traditional theory in the diagnosis of acid–base metabolic disturbances and (3) whether variables derived from Stewart’s and Fencl’s methods offer advantages over the traditional method to predict patient mortality.

Methods

A prospective cohort study in a general intensive care unit. Blood samples were analysed for arterial blood gases, electrolytes and proteins. PaCO2, pH, bicarbonate, base excess, standard base-excess, sodium, potassium, chloride, phosphorous, calcium, magnesium and lactate were measured. Anion gap, Stewart’s and Fencl’s variables were calculated.

Results

When using Stewart’s method, metabolic acidosis and metabolic alkalosis were found in 92.9% and 93.4% of samples, respectively. Corresponding figures obtained with the traditional method were 15% and 18.7%. In 245 (64.5%) samples, Stewart’s method revealed that metabolic acidosis and alkalosis were simultaneously present, whereas the traditional method revealed a normal acid–base status. Strong ion gap increased significantly over the first 3 intensive care unit days. Strong ion gap and lactate were independent predictors of 28-day mortality.

Conclusions

Metabolic acidosis by unmeasured anions is a clinically relevant phenomenon, which is correlated with mortality. Progressive metabolic acidosis may be ongoing in the early phase of critical illness despite the absence of acidaemia.

Type
Original Article
Copyright
Copyright © European Society of Anaesthesiology 2008

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References

1.Stewart, PA. Independent and dependent variables of acid–base control. Respir Physiol 1978; 33: 926.CrossRefGoogle ScholarPubMed
2.Stewart, PA. Modern quantitative acid–base chemistry. Can J Physiol Pharmacol 1983; 61: 14441461.CrossRefGoogle ScholarPubMed
3.Ronco, C, Bellomo, R. Crit care Nephrol. Dordrecht, Boston: Kluwer Academic, 1998.CrossRefGoogle Scholar
4.Kellum, JA, Kramer, DJ, Pinsky, MR. Strong ion gap: a methodology for exploring unexplained anions. J Crit Care 1995; 10: 5155.CrossRefGoogle Scholar
5.Alfaro, V, Peinado, VI, Palacios, L. Factors influencing acid–base status during acute severe hypothermia in unanesthetized rats. Respir Physiol 1995; 100: 139149.CrossRefGoogle ScholarPubMed
6.Russell, KE, Hansen, BD, Stevens, JB. Strong ion difference approach to acid–base imbalances with clinical applications to dogs and cats. Vet Clin North Am Small Anim Pract 1996; 26: 11851201.CrossRefGoogle ScholarPubMed
7.Constable, PD. A simplified strong ion model for acid–base equilibria: application to horse plasma. J Appl Physiol 1997; 83: 297311.CrossRefGoogle ScholarPubMed
8.Pesquero, J, Alfaro, V, Palacios, L. Acid–base analysis during experimental anemia in rats. Can J Physiol Pharmacol 2000; 78: 774780.CrossRefGoogle ScholarPubMed
9.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: 15771581.CrossRefGoogle ScholarPubMed
10.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: 22462251.CrossRefGoogle ScholarPubMed
11.Durward, A, Skellett, S, Mayer, A, Taylor, D, Tibby, SM, Murdoch, IA. The value of the chloride: sodium ratio in differentiating the aetiology of metabolic acidosis. Intensive Care Med 2001; 27: 828835.CrossRefGoogle ScholarPubMed
12.Cusack, RJ, Rhodes, A, Lochhead, P et al. The strong ion gap does not have prognostic value in critically ill patients in a mixed medical/surgical adult ICU. Intensive Care Med 2002; 28: 864869.CrossRefGoogle ScholarPubMed
13.Rocktaeschel, J, Morimatsu, H, Uchino, S, Bellomo, R. Unmeasured anions in critically ill patients: can they predict mortality? Crit Care Med 2003; 31: 21312136.CrossRefGoogle ScholarPubMed
14.Kaplan, LJ, Kellum, JA. Initial pH, base deficit, lactate, anion gap, strong ion difference, and strong ion gap predict outcome from major vascular injury. Crit Care Med 2004; 32: 11201124.CrossRefGoogle ScholarPubMed
15.Fencl, V, Leith, DE. Stewart’s quantitative acid–base chemistry: applications in biology and medicine. Respir Physiol 1993; 91: 116.CrossRefGoogle ScholarPubMed
16.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: 187197.CrossRefGoogle Scholar
17.Hosmer, DW, Lemeshow, S. Model-building strategies and methods for logistic regression. In: Hosmer, DW, Lemeshow, S, eds. Applied Logistic Regression. New York: John Wiley & Sons, Inc., 1989: 82134.Google Scholar
18.Bewick, V, Cheek, L, Ball, J. Statistics review 13: receiver operating characteristic curves. Crit Care 2004; 8: 508512.CrossRefGoogle ScholarPubMed
19.Levraut, J, Grimaud, D. Treatment of metabolic acidosis. Curr Opin Crit Care 2003; 9: 260265.CrossRefGoogle ScholarPubMed
20.De Backer, D. Lactic acidosis. Intensive Care Med 2003; 29: 699702.CrossRefGoogle ScholarPubMed
21.Fink, MP, Evans, TW. Mechanisms of organ dysfunction in critical illness: report from a Round Table Conference held in Brussels. Intensive Care Med 2002; 28: 369375.CrossRefGoogle ScholarPubMed
22.Shills, M. Modern Nutrition in Health and Disease. Philadelphia: Lea & Fabiger, 1988.Google Scholar
23.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: 680685.CrossRefGoogle ScholarPubMed
24.Waters, JH, Bernstein, CA. Dilutional acidosis following hetastarch or albumin in healthy volunteers. Anesthesiology 2000; 93: 11841187.CrossRefGoogle ScholarPubMed
25.Kellum, JA. Diagnosis and treatment of acid–base disorders. In: Shoemaker, WC, Ayers, SM, Grenvik, A, Holbrook, PR, eds. Textbook of Critical Care (TCC). Philadelphia: WB Saunders, 1999: 839853.Google Scholar