• Neurocritical care · Apr 2013

    Comparative Study

    Outcome of poor-grade subarachnoid hemorrhage as determined by biomarkers of glucose cerebral metabolism.

    • Gleicy K Barcelos, Yannick Tholance, Sebastien Grousson, Bernard Renaud, Armand Perret-Liaudet, Frederic Dailler, and Luc Zimmer.
    • BioRaN Team, Lyon Neuroscience Research Center, CNRS UMR 5292, INSERM U1028, University Lyon 1, Lyon, France. gleicy.barcelos@hcuge.ch
    • Neurocrit Care. 2013 Apr 1;18(2):234-44.

    PurposeThe aim of this study was to determine if the measurement of blood biomarkers of glucose cerebral metabolism, performed with retrograde jugular catheter, could predict the outcome of poor-grade aneurysmal subarachnoid hemorrhage (aSAH) patients.MethodsThis study was conducted in 68 poor-grade aSAH patients. A total of 4,024 blood samples obtained from jugular and radial catheters were analyzed for glucose, lactate, and oxygen content every 8 h for 10 ± 0.5 days. Metabolic ratio (MR) and lactate-oxygen index (LOI) were obtained by ratios using arterio-jugular differences. Functional outcome was evaluated at 12 months with the Glasgow Outcome Scale.ResultsOutcome was unfavorable in 40 patients. In this group of patients, the MR was significantly lower (p < 0.0001) and the LOI was significantly higher (p = 0.0001) than in the group with favorable outcome. The MR cutoff value, below which the patients are likely to have an unfavorable outcome, was determined to be 3.35. More interestingly, the data obtained in this study demonstrated that the patients achieving an unfavorable outcome were distinguished from those with a favorable outcome by having at least three events of MR inferior to 3.35 (sensitivity = 90 %, specificity = 82.1 %). Moreover, in patients who developed cerebral vasospasm, we observed a significant decrease in the MR.ConclusionOur data provide additional support to the view that the MR is a reliable marker for predicting the outcome of poor-grade aSAH patients. Prospective studies are needed to confirm its value in multimodal monitoring.

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