ObjectivePresepsin is a novel biomarker to diagnose sepsis but its prognostic value has not been comprehensively reviewed. We conducted this meta-analysis to evaluate the mortality prediction value of presepsin in sepsis.MethodsWe searched comprehensive electronic databases from PubMed, EMBASE, and Cochrane Library through September 2017 using the key words of (‘presepsin’ or ‘sCD14-ST’ or ‘soluble CD14 subtype’) and (‘sepsis’ or ‘septic shock’) and (‘prognosis’ or ‘prognostic value’ or ‘prognostic biomarker’ or ‘mortality’). We extracted the presepsin levels in survivors and non-survivors from each individual study and evaluated the standardized mean difference (SMD) using a web-based meta-analysis with the R statistical analysis program.ResultsA total of 10 studies and 1617 patients were included. Presepsin levels in the first sampling (within 24 hours) were significantly lower among survivors as compared with non-survivors: the pooled SMD between survivors and non-survivors was 0.92 (95% CI: 0.62–1.22) in the random effects model (I2 = 79%, P< 0.01). In subgroups, divided by the sepsis severity or study site, pooled SMD was consistently noting higher presepsin levels in non-survivals (P< 0.05).ConclusionThis meta-analysis demonstrates some mortality prediction value in presepsin in patients with sepsis. Further studies are needed to define the optimal cut-off point to predict mortality in sepsis.

Background and Objectives The 30‐min rule has been used to maintain a core temperature (CT) of red‐blood‐cell (RBC) units below 10°C during transportation. We evaluated the utility of temperature‐sensitive indicators (TIs) to monitor the surface temperature (ST) of RBC units and to explore whether TIs can help with compliance with the 30‐min rule by extrapolating or correlating temperature change with time. Materials and Methods Two US FDA‐approved TIs, Safe‐T‐Vue 10 (STV10; Temptime Corporation, Morris Plains, NJ, USA) and Timestrip Blood Temp 10 (BT10; Timestrip UK Ltd, Cambridge, UK), were attached to 50 RBC units. After issue, their colour change indicating 10°C was monitored, and temperature excursions were measured by standard reading. In additional 18 RBC units, both ST and CT were monitored simultaneously. Results In 50 RBC units, 94% of STV10 and 100% of BT10 showed colour change indicating 10°C within 30 min; 4% of STV10 and 18% of BT10 showed it during transportation. The time for colour change indicating 10°C differed significantly between STV10 and BT10 (19·0 vs. 5·6 min, P < 0·001). In additional 18 RBC units, 83·3% of STV10, 100% of BT10 and 88·9% of CT reached 10°C within 30 min, and the time for colour change indicating 10°C was 24·4 min in STV10, 14·6 min in BT 10 and 24·2 min in CT (P < 0·001). Conclusion In two TIs, the time for colour change indicating 10°C varied considerably. To enhance the utility of TIs, further improvement and standardization would be needed.

Background Neutrophil gelatinase-associated lipocalin (NGAL) is a useful biomarker for acute kidney injury (AKI) prediction. However, studies on whether using both plasma NGAL (PNGAL) and urine NGAL (UNGAL) can improve AKI prediction are limited. We investigated the best approach to predict AKI in high-risk patients when using PNGAL and UNGAL together. Methods We enrolled 151 AKI suspected patients with one or more AKI risk factors. We assessed the diagnostic performance of PNGAL and UNGAL for predicting AKI according to chronic kidney disease (CKD) status by determining the areas under the receiver operating curve (AuROC). Independent predictors of AKI were assessed using univariate and multivariate logistic regression analyses. Results In the multivariate logistic regression analysis for all patients (N=151), Model 2 and 3, including PNGAL ( P =0.012) with initial serum creatinine (S-Cr), showed a better AKI prediction power (R 2 =0.435, both) than Model 0, including S-Cr only (R 2 =0.390). In the non-CKD group (N=135), the AuROC of PNGAL for AKI prediction was larger than that of UNGAL (0.79 vs 0.66, P =0.010), whereas in the CKD group (N=16), the opposite was true (0.94 vs 0.76, P =0.049). Conclusions PNGAL may serve as a useful biomarker for AKI prediction in high-risk patients. However, UNGAL predicted AKI better than PNGAL in CKD patients. Our findings provide guidance for selecting appropriate specimens for NGAL testing according to the presence of CKD in AKI high-risk patients.

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BackgroundThe prognostic utility of cardiac biomarkers, high-sensitivity cardiac troponin I (hs-cTnI) and soluble suppression of tumorigenicity-2 (sST2), in non-cardiac surgery is not well-defined. We evaluated hs-cTnI and sST2 as predictors of 30-day major adverse cardiac events (MACE) in patients admitted to the surgical intensive care unit (SICU) following major non-cardiac surgery.Methodshs-cTnI and sST2 concentrations were measured in 175 SICU patients immediately following surgery and for three days postoperatively. The results were analyzed in relation to 30-day MACE and were compared with the revised Goldman cardiac risk index (RCRI) score.ResultsOverall, 30-day MACE was observed in 16 (9.1%) patients. hs-cTnI and sST2 concentrations differed significantly between the two groups with and without 30-day MACE (P<0.05). The maximum concentration of sST2 was an independent predictor of 30-day MACE (odds ratio=1.016, P=0.008). The optimal cut-off values of hs-cTnI and sST2 for predicting 30-day MACE were 53.0 ng/L and 182.5 ng/mL, respectively. A combination of hs-cTnI and sST2 predicted 30-day MACE better than the RCRI score. Moreover, 30-day MACE was observed more frequently with increasing numbers of above-optimal cut-off hs-cTnI and sST2 values (P<0.0001). Reclassification analyses indicated that the addition of biomarkers to RCRI scores improved the prediction of 30-day MACE.ConclusionsThis study demonstrates the utility of hs-cTnI and sST2 in predicting 30-day MACE following non-cardiac surgery. Cardiac biomarkers would provide enhanced risk stratification in addition to clinical RCRI scores for patients undergoing major non-cardiac surgery.