title: Evaluation of metabolic acidosis in patients with a kidney graft: comparison of the bicarbonate-based and strong ion-based methods.
abstract: BACKGROUND
  According to the traditional bicarbonate-based approach, metabolic acidosis is highly prevalent in kidney transplant recipients. However, the bicarbonate-based approach has been questioned by intensivists using strong ion difference-based methods.
  
  
  METHODS
  We compared the results obtained by the strong ion-based with the traditional approach based on bicarbonate among a cohort of 83 kidney transplant recipients.
  
  
  RESULTS
  Fifty-five percent of the patients were acidotic based on venous bicarbonate (<23 mmol/L) and 49% by the use of the effective strong ion difference (SID(effective)) (<37 mmol/L). Bicarbonate and SID(effective) were linearly correlated (r=0.94; P<.0001), with a slope close to 1. A greater percentage of patients presented with an increase in unexplained anions by the strong ion gap (SIG) than by the anion gap corrected (AG(corrected)) method (42 vs 32%, respectively). AG(corrected) and SIG were directly related (r=0.919; P<.0001), but the best fit of the relationship was polynomial with a progressively greater effect on SIG with increased AG(corrected), suggesting that as anions progressively accumulate, their detection by SIG increases. By multiple regression analysis, plasma chloride, potassium, uric acid, and phosphate predicted blood bicarbonate. Analogously, chloride, potassium and uric acid predicted SID(effective). Age was a predictor of changes in AG(corrected), whereas age and plasma urea predicted SIG.
  
  
  CONCLUSIONS
  The use of the SID yielded results that were similar to the traditional bicarbonate-based approach. Conversely, SIG appeared to be more sensitive than AG for detection of anion accumulation among patients with a kidney graft.
authors.affiliation: Department of Internal Medicine, University of Genoa, Genoa, Italy.
authors.forename: F
authors.initials: F
authors.lastname: Abdulraof Menesi
authors.forename: D
authors.initials: D
authors.lastname: Verzola
authors.forename: B
authors.initials: B
authors.lastname: Villaggio
authors.forename: R
authors.initials: R
authors.lastname: Russo
authors.forename: A
authors.initials: A
authors.lastname: Sofia
authors.forename: I
authors.initials: I
authors.lastname: Fontana
authors.forename: A
authors.initials: A
authors.lastname: Gallina
authors.forename: I
authors.initials: I
authors.lastname: Mannucci
authors.forename: M
authors.initials: M
authors.lastname: Mussap
authors.forename: G
authors.initials: G
authors.lastname: Garibotto
mesh_terms: D000136:Acid-Base Equilibrium; D000138:Acidosis / Q000097:blood / Q000175:diagnosis* / Q000209:etiology; D000328:Adult; D000367:Age Factors; D000704:Analysis of Variance; D001639:Bicarbonates / Q000097:blood*; D015415:Biomarkers / Q000097:blood; D002712:Chlorides / Q000097:blood; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D006863:Hydrogen-Ion Concentration; D007558:Italy; D016030:Kidney Transplantation / Q000009:adverse effects*; D008297:Male; D008875:Middle Aged; D010710:Phosphates / Q000097:blood; D011188:Potassium / Q000097:blood; D011237:Predictive Value of Tests; D016896:Treatment Outcome; D014527:Uric Acid / Q000097:blood
chemical_list: D001639:Bicarbonates; D015415:Biomarkers; D002712:Chlorides; D010710:Phosphates; D014527:Uric Acid; D011188:Potassium