Journal of Emergencies, Trauma and Shock (Jan 2017)

Lactate clearance as the predictor of outcome in pediatric septic shock

  • Richa Choudhary,
  • Sadasivan Sitaraman,
  • Anita Choudhary

DOI
https://doi.org/10.4103/JETS.JETS_103_16
Journal volume & issue
Vol. 10, no. 2
pp. 55 – 59

Abstract

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Context: Septic shock can rapidly evolve into multiple system organ failure and death. In the recent years, hyperlactatemia has been found to be a risk factor for mortality in critically ill adults. Aims: To evaluate the predictive value of lactate clearance and to determine the optimal cut-off value for predicting outcome in children with septic shock. Settings and Design: A prospective observational study was performed on children with septic shock admitted to pediatric Intensive Care Unit (PICU). Subjects and Methods: Serial lactate levels were measured at PICU admission, 24 and 48 h later. Lactate clearance, percent decrease in lactate level in 24 h, was calculated. The primary outcome measure was survival or nonsurvival at the end of hospital stay. We performed receiver operating characteristic analyses to calculate optimal cut-off values. Results: The mean lactate levels at admission were significantly higher in the nonsurvivors than survivors, 5.12 ± 3.51 versus 3.13 ± 1.71 mmol/L (P = 0.0001). The cut-off for lactate level at admission for the best prediction of mortality was determined as ≥4 mmol/L (odds ratio 5.4; 95% confidence interval [CI] =2.45–12.09). Mean lactate clearance was significantly higher in survivors than nonsurvivors (17.9 ± 39.9 vs. −23.2 ± 62.7; P < 0.0001). A lactate clearance rate of <10% at 24 h had a sensitivity and specificity of 78.7% and 72.2%, respectively and a positive predictive value of 83.1% for death. Failure to achieve a lactate clearance of more than 10% was associated with greater risk of mortality (likelihood ratio + 2.83; 95% CI = 1.82–4.41). Conclusions: Serial lactate levels can be used to predict outcome in pediatric septic shock. A 24 h lactate clearance cut-off of <10% is a predictor of in-hospital mortality in such patients.

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