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A simplified quantitative acid–base approach for patients with acute respiratory diseases

  • Michalis AgrafiotisEmail author
  • Maria Papathanassiou
  • Christos Karachristos
  • Eleni Kerezidou
  • Stavros Tryfon
  • Evangelia Serasli
  • Diamantis Chloros
Original Research
  • 23 Downloads

Abstract

The Stewart-Figge acid–base model has been criticized for being mathematically complex. We aimed to develop simpler formalisms, which can be used at the bedside. The following simplifications were used: (1) [Ca2+] and [Mg2+] are replaced by their mid-reference concentrations (2) pH is set to 7.4. In the new model [SIDa] is replaced by its adjusted form, [SIDa, adj] = [Na+] + [K+] − [Cl] + 6.5 and [SIG] is replaced by “bicarbonate gap”, [BICgap] = [SIDa, adj] − (0.28⋅[Albumin]) − (1.82⋅[Phosphatei])- [HCO3̄]. The diagnostic performance of the model was tested in 210 patients with acute respiratory diseases and 17 healthy volunteers. [BICgap] was also compared to albumin-corrected anion gap ([AGc]). The concordant correlation coefficient between [SIDa, adj] and [SIDa] and between [BICgap] and [SIG] was 0.98 in both comparisons. The mean bias (limits of agreement) of [SIDa, adj] − [SIDa] and of [BICgap] − [SIG] were 0.53 meq/l (− 0.46 to 1.53) and 0.50 meq/l (− 0.70 to 1.70), respectively. A [SIDa, adj] < 50.4 meq/l had an accuracy of 0.995 (p < 0.001) for the diagnosis of strong ion (SI) acidosis, while a [SIDa, adj] > 52.5 meq/l had an accuracy of 0.997 (p < 0.001) for the diagnosis of SI alkalosis. A [BICgap] > 11.6 meq/l predicted unmeasured ion (UI) acidosis with an accuracy of 0.997 (p < 0.001), while an [AGc] > 19.88 meq/l predicted UI acidosis with an accuracy of 0.994 (p < 0.001). The “[BICgap] model” is a reliable tool for the assessment of acid–base disorders in patients with acute respiratory diseases. [BICgap] is not inferior to [AGc] in the diagnosis of UI acidosis.

Keywords

Anion gap Base excess Stewart-Figge acid–base model Metabolic acidosis Unmeasured ions 

Notes

Author contributions

MA designed the study, performed statistical analysis and co-authored the manuscript. MP, CK and EK collected and analyzed the data and co-authored the manuscript. ST and ES participated in data analysis and co-authored the manuscript. DC co-authored and reviewed the manuscript for important intellectual content. All authors read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflict of interest.

References

  1. 1.
    Stewart PA. Modern quantitative acid-base chemistry. Can J Physiol Pharmacol. 1983;61(12):1444–61.CrossRefGoogle Scholar
  2. 2.
    Figge J, Rossing TH, Fencl V. The role of serum proteins in acid-base equilibria. J Lab Clin Med. 1991;117(6):453–67.Google Scholar
  3. 3.
    Figge J. Role of non-volatile weak acids (albumin, phosphate and citrate). In Kellum J, Elbers PWJ, editors. Stewart’s textbook of acid base. Oxford: Lulu Enterprises; 2009. p. 217–22.Google Scholar
  4. 4.
    Figge J, Bellomo R, Egi M. Quantitative relationships among plasma lactate, inorganic phosphorus, albumin, unmeasured anions and the anion gap in lactic acidosis. J Crit Care. 2018;44:101–10.CrossRefGoogle Scholar
  5. 5.
    Kellum JA, Kramer DJ, Pinsky MR. Strong ion gap: a methodology for exploring unexplained anions. J Crit Care. 1995;10(2):51–5.CrossRefGoogle Scholar
  6. 6.
    Gunnerson KJ, Saul M, He S, Kellum JA. Lactate versus non-lactate metabolic acidosis: a retrospective outcome evaluation of critically ill patients. Crit Care. 2006;10(1):R22.CrossRefGoogle Scholar
  7. 7.
    Boniatti MM, Cardoso PRC, Castilho RK, Vieira SRR. Acid-base disorders evaluation in critically ill patients: we can improve our diagnostic ability. Intensive Care Med. 2009;35(8):1377–82.CrossRefGoogle Scholar
  8. 8.
    Antonogiannaki E-M, Mitrouska I, Amargianitakis V, Georgopoulos D. Evaluation of acid-base status in patients admitted to ED-physicochemical vs traditional approaches. Am J Emerg Med. 2015;33(3):378–82.CrossRefGoogle Scholar
  9. 9.
    Masevicius FD, Dubin A. Has Stewart approach improved our ability to diagnose acid-base disorders in critically ill patients? World J Crit Care Med. 2015;4(1):62–70.CrossRefGoogle Scholar
  10. 10.
    Nguyen B-V, Vincent J-L, Hamm JB, Abalain J-H, Carre J-L, Nowak E, et al. The reproducibility of Stewart parameters for acid-base diagnosis using two central laboratory analyzers. Anesth Analg. 2009;109(5):1517–23.CrossRefGoogle Scholar
  11. 11.
    Dubin A, Menises MM, Masevicius FD, Moseinco MC, Kutscherauer DO, Ventrice E, et al. Comparison of three different methods of evaluation of metabolic acid-base disorders. Crit Care Med. 2007;35(5):1264–70.CrossRefGoogle Scholar
  12. 12.
    Agrafiotis M, Mpliamplias D, Papathanassiou M, Ampatzidou F, Drossos G. Comparison of a new simplified acid-base tool to the original Stewart-Figge approach: a study on cardiac surgical patients. J Anesth. 2018;32(4):499–505.CrossRefGoogle Scholar
  13. 13.
    Roberts WL, McMillin GA, Burtis CA, Bruns DE. Reference information for the clinical laboratory. In: Burtis CA, Ashwood ER, Bruns DE, editors. Tietz’s fundamentals of clinical chemistry. 6th ed. St Louis: Saunders; 2008. pp. 836–87.Google Scholar
  14. 14.
    Altman DG. Practical statistics for medical research. London: Chapman and Hall; 1991.Google Scholar
  15. 15.
    Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45(1):255–68.CrossRefGoogle Scholar
  16. 16.
    Mcbride GB. Proposal for strength-of-agreement criteria for Lin’s concordant correlation coefficient. Hamilton: NIWA Client Report: Ham 2005-062; 2005. p. 6. https://www.medcalc.org/download/pdf/McBride2005.pdf.
  17. 17.
    Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20:37–46.CrossRefGoogle Scholar
  18. 18.
    Youden W. An index for rating diagnostic tests. Cancer. 1950;3:32–5.CrossRefGoogle Scholar
  19. 19.
    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(6):2246–51. 20.CrossRefGoogle Scholar
  20. 20.
    Kimura S, Shabsigh M, Morimatsu H. Traditional approach versus Stewart approach for acid-base disorders: Inconsistent evidence. SAGE Open Med. 2018;6:2050312118801255.CrossRefGoogle Scholar
  21. 21.
    Morgan TJ, Anstey CM, Wolf MB. A head to head evaluation of 8 biochemical scanning tools for unmeasured ions. J Clin Monit Comput. 2017;31(2):449–57.CrossRefGoogle Scholar
  22. 22.
    Nagaoka D, Nassar Junior AP, Maciel AT, Taniguchi LU, Noritomi DT, Azevedo LCP, et al. The use of sodium-chloride difference and chloride-sodium ratio as strong ion difference surrogates in the evaluation of metabolic acidosis in critically ill patients. J Crit Care. 2010;25(3):525–31.CrossRefGoogle Scholar
  23. 23.
    Mallat J, Barrailler S, Lemyze M, Pepy F, Gasan G, Tronchon L, et al. Use of sodium-chloride difference and corrected anion gap as surrogates of Stewart variables in critically ill patients. PLoS ONE. 2013;8(2):e56635.CrossRefGoogle Scholar
  24. 24.
    Weaving G, Batstone GF, Jones RG. Age and sex variation in serum albumin concentration: an observational study. Ann Clin Biochem. 2016;53(Pt 1):106–11.CrossRefGoogle Scholar
  25. 25.
    Horowitz GL, Altaie S, Boyd JC, Ceriotti F, Garg U, Horn P, et al. Defining. establishing, and verifying reference intervals in the clinical laboratory; Approved Guideline—Third Edition. Document C28-A3, Vol. 28 No.30. CLSI; 2010.Google Scholar
  26. 26.
    Adrogué HJ, Madias NE. Secondary responses to altered acid-base status: the rules of engagement. J Am Soc Nephrol. 2010;21(6):920–3.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Pulmonary Medicine“Georgios Papanikolaou” General Hospital of ThessalonikiExohiGreece

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