title: Can we predict arterial lactate from venous lactate in the ED?
abstract: OBJECTIVE
  We aimed to generate equation to predict arterial lactate (a-Lac) using venous lactate (v-Lac) and other lab data.
  
  
  METHODS
  A prospective cross-sectional study was conducted on emergency patients in the emergency department for 6 months at a general hospital in Tokyo, Japan. We collected arterial and venous gas analysis data. Patients were eligible for entry into the study if an arterial blood gas analysis was required for appropriate diagnostic care by the treating physician. Univariate linear regression analysis was conducted to generate an equation to calculate a-Lac incorporating only v-Lac. A multivariate forward stepwise logistic regression model (p-value of 0.05 for entry, 0.1 for removal) was used to generate an equation including v-Lac and other potentially relevant variables. Bland-Altman plot was drawn and the two equations were compared for model fitting using R-squares.
  
  
  RESULTS
  Seventy-two arterial samples from 72 participants (61% male; mean age, 58.2 years) were included in the study. An initial regression equation was derived from univariate linear regression analysis:"(a-Lac) = -0.259 + (v-Lac) × 0.996". Subsequent multivariate forward stepwise logistic regression analysis, incorporating v-Lac and Po2, generated the following equation:"(a-Lac) = -0.469+(venous Po2) × 0.005 + (v-Lac) × 0.997". Calculated R-squares by single and multiple regression were 0.94 and 0.96, respectively.
  
  
  CONCLUSIONS
  v-Lac estimates showed a high correlation with arterial values and our data provide two clinically useful equations to calculate a-Lac from v-Lac data. Considering clinical flexibility, "Lac = -0.259 + v-Lac × 0.996" might be more useful while avoiding a time-consuming and invasive procedure.
authors.affiliation: St. Luke's International Hospital Emergency & Critical care medicine, Tokyo, Japan. mikaki@luke.or.jp
authors.forename: Akira
authors.initials: A
authors.lastname: Mikami
authors.forename: Sachiko
authors.initials: S
authors.lastname: Ohde
authors.forename: Gautam A
authors.initials: GA
authors.lastname: Deshpande
authors.forename: Toshiaki
authors.initials: T
authors.lastname: Mochizuki
authors.forename: Norio
authors.initials: N
authors.lastname: Otani
authors.forename: Shinichi
authors.initials: S
authors.lastname: Ishimatsu
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001158:Arteries; D015415:Biomarkers / Q000097:blood; D001784:Blood Gas Analysis / Q000379:methods*; D003430:Cross-Sectional Studies; D003661:Decision Support Techniques; D004636:Emergency Service, Hospital; D005260:Female; D006801:Humans; D019344:Lactic Acid / Q000097:blood*; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011446:Prospective Studies; D014680:Veins; D055815:Young Adult
chemical_list: D015415:Biomarkers; D019344:Lactic Acid