In ICU-resuscitated patients, targeting only ScvO(2) may not be sufficient to guide therapy. When the 70% ScvO(2) goal-value is reached, the presence of a P(cv-a)CO(2) larger than 6 mmHg might be a useful tool to identify patients who still remain inadequately resuscitated.

BackgroundThis study was design to investigate the prognostic value for death at day-28 of lactate course and lactate clearance during the first 24 hours in Intensive Care Unit (ICU), after initial resuscitation.MethodsProspective, observational study in one surgical ICU in a university hospital. Ninety-four patients hospitalized in the ICU for severe sepsis or septic shock were included. In this septic cohort, we measured blood lactate concentration at ICU admission (H0) and at H6, H12, and H24. Lactate clearance was calculated as followed: [(lactateinitial - lactatedelayed)/ lactateinitial] x 100%].ResultsThe mean time between severe sepsis diagnosis and H0 (ICU admission) was 8.0 ± 4.5 hours. Forty-two (45%) patients died at day 28. Lactate clearance was higher in survivors than in nonsurvivors patients for H0-H6 period (13 ± 38% and −13 ± 7% respectively, p = 0.021) and for the H0-H24 period (42 ± 33% and −17 ± 76% respectively, p < 0.001). The best predictor of death at day 28 was lactate clearance for the H0-H24 period (AUC = 0.791; 95% CI 0.6-0.85). Logistic regression found that H0-H24 lactate clearance was independently correlated to a survival status with a p = 0.047 [odds ratio = 0.35 (95% CI 0.01-0.76)].ConclusionsDuring the first 24 hr in the ICU, lactate clearance was the best parameter associated with 28-day mortality rate in septic patients. Protocol of lactate clearance-directed therapy should be considered in septic patients, even after the golden hours.

This study demonstrated for the first time to our knowledge a significantly better performance of TUS than LUS in the diagnosis of ARF. The value of the TUS approach was particularly important to disambiguate cases of hemodynamic pulmonary edema and pneumonia. We suggest that the bedside use of artificial intelligence methods in this setting could pave the way for the development of new clinically relevant integrative diagnostic models.

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