Prediction of Postoperative Sepsis Based on Changes in Presepsin Levels of Critically Ill Patients with Acute Kidney Injury after Abdominal Surgery

Advances of presepsin in sepsis-associated ARDS

This article reviews the correlation between presepsin and sepsis and the resulting acute respiratory distress syndrome (ARDS). ARDS is a severe complication of sepsis. Despite the successful application of protective mechanical ventilation, restrictive fluid therapy, and neuromuscular blockade, which have effectively reduced the morbidity and mortality associated with ARDS, the mortality rate among patients with sepsis-associated ARDS remains notably high. The challenge lies in the prediction of ARDS onset and the timely implementation of intervention strategies. Recent studies have demonstrated significant variations in presepsin (PSEP) levels between patients with sepsis and those without, particularly in the context of ARDS. Moreover, these studies have revealed substantially elevated PSEP levels in patients with sepsis-associated ARDS compared to those with nonsepsis-associated ARDS. Consequently, PSEP emerges as a valuable biomarker for identifying patients with an increased risk of sepsis-associated ARDS and to predict in-hospital mortality.

Indirect Method for Estimation of Reference Intervals of Inflammatory Markers

Background

The direct method for reference interval (RI) estimating is limited due to the requirement of resources, difficulties in defining a non-diseased population, or ethical problems in obtaining samples. We estimated the RI for inflammatory biomarkers using an indirect method (RII).

Methods

C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and presepsin (PSEP) data of patients visiting a single hospital were retrieved from April 2009 to April 2021. Right-skewed data were transformed using the Box-Cox transformation method. A mixed population of non-diseased and diseased distributions was assumed, followed by latent profile analysis for the two classes. The intersection point of the distribution curve was estimated as the RI. The influence of measurement size was evaluated as the ratio of abnormal values and adjustment (n×bandwidth) of the distribution curve.

Results

The RIs estimated by the proposed RII method (existing method) were as follows: CRP, 0-4.1 (0-4.7) mg/L; ESR, 0-10.2 (0-15) mm/hr and PSEP, 0-411 (0-300) pg/mL. Measurement sizes ≥2,500 showed stable results. An abnormal-to-normal value ratio of 0.5 showed the most accurate result for CRP. Adjustment values ≤5 or >5 were applicable for a measurement size <25,000 or ≥25,000, respectively.

Conclusions

The proposed RII method could provide additional information for RI verification or estimation with some limitations.

Molecular mechanisms underlying the renal protective effects of coenzyme Q10 in acute kidney injury

Coenzyme Q10 (CoQ10), an endogenous antioxidant, has been reported frequently to exert an outstanding protective effect on multiple organ injury, including acute kidney injury (AKI). In this study, we aim to summarize all the current evidence of the protective action of CoQ10 against AKI as there are presently no relevant reviews in the literature. After a systematic search, 20 eligible studies, either clinical trials or experimental studies, were included and further reviewed. CoQ10 treatment exhibited a potent renal protective effect on various types of AKI, such as AKI induced by drugs (e.g., ochratoxin A, cisplatin, gentamicin, L-NAME, and nonsteroidal anti-inflammatory drug), extracorporeal shock wave lithotripsy (ESWL), sepsis, contrast media, and ischemia–reperfusion injury. The renal protective role of CoQ10 against AKI might be mediated by the antiperoxidative, anti-apoptotic, and anti-inflammatory potential of CoQ10. The molecular mechanisms for the protective effects of CoQ10 might be attributed to the regulation of multiple essential genes (e.g., caspase-3, p53, and PON1) and signaling cascades (e.g., Nrf2/HO-1 pathway). This review highlights that CoQ10 may be a potential strategy in the treatment of AKI.

Presepsin predicts 1-year all-cause mortality better than N-terminal pro-B-type natriuretic peptide in patients undergoing transcatheter aortic valve implantation

Aim: Presepsin is a sensitive biomarker for the diagnosis and estimation of prognosis in septic patients. The prognostic role of presepsin in patients undergoing transcatheter aortic valve implantation (TAVI) has never been investigated. Patients, materials & methods: In 343 patients, presepsin and N-terminal pro-B-type natriuretic peptide were measured before TAVI. One-year all-cause mortality was used as outcome measure. Results: Patients with high presepsin levels were more likely to succumb than patients with low presepsin values (16.9% vs 12.3%; p = 0.015). Elevated presepsin remained a significant predictor of 1-year all-cause mortality (odds ratio: 2.2 [95% CI: 1.12-4.29]; p = 0.022) after adjustment. N-terminal pro-B-type natriuretic peptide did not predict 1-year all-cause mortality. Conclusion: Elevated baseline presepsin levels are an independent predictor of 1-year mortality in TAVI patients.

Impact of Different KDIGO Criteria on Clinical Outcomes for Early Identification of Acute Kidney Injury after Non-Cardiac Surgery

The Kidney Disease Improving Global Outcomes (KDIGO) guidelines are currently used in acute kidney injury (AKI) diagnosis and include both serum creatinine (SCR) and urine output (UO) criteria. Currently, many AKI-related studies have inconsistently defined AKI, which possibly affects the comparison of their results. Therefore, we hypothesized that the different criteria in the KDIGO guidelines vary in measuring the incidence of AKI and its association with clinical outcomes. We retrospectively analyzed that data of patients admitted to the intensive care unit after non-cardiac surgery in 2019. Three different criteria used to define AKI were included: UOmean, mean UO < 0.5 mL/kg/h over time; UOcont, hourly UO < 0.5 mL/kg/h over time; or SCR, KDIGO guidelines SCR criteria. A total of 777 patients were included, and the incidence of UOmean-AKI was 33.1%, the incidence of UOcont-AKI was 7.9%, and the incidence of SCR-AKI was 2.0%. There were differences in the length of ICU stay and hospital stay between AKI and non-AKI patients under different criteria. We found differences in the incidence and clinical outcomes of AKI after non-cardiac surgery when using different KDIGO criteria.