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Lactate normalization within 6 hours of bundle therapy and 24 hours of delayed achievement were associated with 28-day mortality in septic shock patients.

Seung Mok RyooRyeok AhnTae Gun ShinYou Hwan JoSung Phil ChungJin Ho BeomSung-Hyuk ChoiYoung-Hoon YoonByuk Sung KoHui Jai LeeGil Joon SuhWon Young Kimnull null
Published in: PloS one (2019)
This study evaluated the prognostic ability of lactate normalization achieved within 6 and 24 h from septic shock recognition. Data from a septic shock registry from October 2015 to February 2017 were reviewed. The study included 2,102 eligible septic shock patients to analyze the prognostic ability of lactate normalization, defined as a follow-up lactate level <2 mmol/L within six hours of bundle therapy and within 24 hours of delayed normalization. The primary outcome was 28-day mortality. The overall 28-day mortality rate was 21.4%. The rates of lactate normalization within 6 and 24 h were significantly higher in the survivor groups than in the non-survivor group (42.4% vs. 23.4% and 60.2% vs. 31.2%; P<0.001, respectively). Multivariate logistic regression analysis showed that both 6- and 24-h lactate normalization were independent predictors (odds ratio [OR] 0.58, 95% confidence interval [CI] 0.45-0.75, p<0.001 and OR 0.42, 95% CI 0.33-0.54, p<0.001, respectively). When we could not achieve the lactate normalization, the sensitivity, specificity, positive, and negative predictive value to predict mortality were 76.6%, 42.4%, 26.5% and 87.0% respectively for 6-h normalization, and 68.8%, 60.2%, 32.0% and 87.7% respectively for 24-h normalization. Besides 6-h lactate normalization, 24-h delayed lactate normalization was associated with decreasing mortality in septic shock patients. Lactate normalization may have a role in early risk stratification and as a therapeutic target.
Keyphrases
  • septic shock
  • end stage renal disease
  • newly diagnosed
  • chronic kidney disease
  • cardiovascular events
  • prognostic factors
  • stem cells
  • type diabetes
  • coronary artery disease
  • bone marrow
  • big data