Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Biomarkers in sepsis for severity, prediction of outcome or reversibility of organ dysfunction are warranted. Measurements of plasma DAMP levels at admission can reflect the severity of cellular damage in septic shock, which might predict the prognosis and reduce the risk of overtreating patients with costly therapies. We measured plasma levels of two DAMPs, S100A8/S100A9 and S100A12 during the first 24 h of admission of septic shock patients. Forty-nine septic shock patients with a similar SOFA scores were selected from our sepsis database to compare a similar proportion of survivors and non-survivors. Plasma levels of S100A8/S100A9 and S100A12 were compared with healthy volunteers using in-house ELISA. Plasma levels of S100A8/S100A9 and S100A12 (5.71 [2.60-13.63] µg/mL and 0.48 [0.22-1.05] µg/mL) were higher in septic shock patients than in healthy volunteers (1.18 [0.74-1.93] µg/mL and 0.09 [0.02-0.39] µg/mL) (P < 0.0001 and P = 0.0030). Levels of S100A8/S100A9 and S100A12 in non-survivors at day 28 (11.70 [2.85-24.36] µg/mL and 0.62 [0.30-1.64] µg/mL) were significantly higher than in survivors (4.59 [2.16-7.47] µg/mL and 0.30 [0.20-0.49] µg/mL) (P = 0.0420 and P = 0.0248) and correlated well (Spearman r = 0.879, P < 0.0001). The high level of plasma calgranulins at admission in septic shock, were higher in non-survivors compared to survivors. These markers could indicate a higher risk of death when SOFA scores are similar and help the stratification of patients for improved care and therapy selection.
Pubmed ID: 31666644
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Statistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.
View all literature mentions