The predict value of serum/urocystatin C on acute kidney injury in elderly patients with sepsis

Albumin corrected anion gap and clinical outcomes in elderly patients with acute kidney injury caused or accompanied by sepsis: a MIMIC-IV retrospective study

BACKGROUND

Elderly acute kidney injury (AKI) occurring in the intensive care unit (ICU), particularly when caused or accompanied by sepsis, is linked to extended hospital stays, increased mortality rates, heightened prevalence of chronic diseases, and diminished quality of life. This study primarily utilizes a comprehensive critical care database to examine the correlation of albumin corrected anion gap (ACAG) levels with short-term prognosis in elderly patients with AKI caused or accompanied by sepsis, thus assisting physicians in early identification of high-risk patients.

METHODS

This study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v2.0) database. The patient population was divided into death and survival groups based on a 14-day prognosis. Subsequently, the entire population was further categorized into a normal ACAG group (12-20 mmol/L) and a high ACAG group (> 20 mmol/L) based on ACAG levels. The LASSO regression cross-validation method was employed to identify significant risk factors for inclusion in multivariate Cox regression analyses. A restricted cubic spline (RCS) was then employed to visually represent the correlation between ACAG levels and the risk of mortality in patients. Kaplan-Meier curves were utilized to plot the cumulative survival rates at 14 and 30 days for both patient groups. The robustness of the findings was subsequently evaluated through subgroup analyses.

RESULTS

Our study identified a total of 3741 eligible subjects, revealing higher all-cause mortality rates at both 14-day and 30-day intervals in the high ACAG group compared to the normal ACAG group (χ2 = 87.023, P < 0.001; χ2 = 90.508, P < 0.001). Cox regression analysis further demonstrated that an elevated ACAG on ICU admission independently posed a risk factor for both 14- and 30-day prognosis within this population. In addition, the analysis conducted using RCS revealed a non-linear association between the levels of ACAG and the risk of mortality at both 14 and 30 days in the patient cohort (χ2 = 18.220, P < 0.001; χ2 = 18.360, P < 0.001). The application of Kaplan-Meier analysis demonstrated a statistically significant decrease in cumulative survival rates among individuals with high ACAG levels (P < 0.001). Subgroup analyses indicated that ACAG levels interacted with cerebrovascular disease and acute pancreatitis on 14-day mortality (P < 0.05 for interaction).

CONCLUSION

Elevated ACAG levels at ICU admission are an independent risk factor for poor short-term prognosis, correlating with increased all-cause mortality at 14 and 30 days in elderly patients with AKI caused or accompanied by sepsis. This highlights the importance of monitoring ACAG in critically ill patients to identify those at higher risk of adverse outcomes early.

Renal arterial resistive index versus novel biomarkers for the early prediction of sepsis-associated acute kidney injury

Acute kidney injury (AKI) is a critical complication of sepsis. There is a continuous need to identify and validate biomarkers for early detection. Serum and urinary biomarkers have been investigated, such as neutrophil gelatinase associated lipocalin (NGAL) and cystatin C (Cys C), but their reliability in the intensive care unit (ICU) remains unknown. Renal hemodynamics can be investigated by measuring the renal resistive index (RRI). This study aimed to compare the performance of RRI, serum NGAL (sNGAL), urinary NGAL (uNGAL), and serum Cys C levels as early predictors of the diagnosis and persistence of sepsis-associated AKI. A total of 166 adult patients with sepsis syndrome were enrolled immediately after ICU admission. Biomarkers were measured directly (T1) and on day 3 (T3). RRI was measured directly (T1) and 24 h later (T2). Patients were categorized (according to the occurrence and persistence of AKI within the first 7 days) into three groups: no AKI, transient AKI, and persistent AKI. The incidence rate of sepsis-associated AKI was 60.2%. Sixty-six patients were categorized as in the no AKI group, while another 61 were in transient AKI and only 39 were in persistent AKI. The RRI value (T1 ≥ 0.72) was the best tool for predicting AKI diagnosis (area under the receiver operating characteristic curve, AUROC = 0.905). Cys C (T1 ≥ 15.1 mg/l) was the best tool to predict the persistence of AKI (AUROC = 0.977). RRI (T1) was the best predictive tool for sepsis-associated AKI, while Cys C was the best predictor of its persistence and 28-day mortality.

Acute Kidney Injury Prediction Model Using Cystatin-C, Beta-2 Microglobulin, and Neutrophil Gelatinase-Associated Lipocalin Biomarker in Sepsis Patients

Introduction

AKI is a frequent complication in sepsis patients and is estimated to occur in almost half of patients with severe sepsis. However, there is currently no effective therapy for AKI in sepsis. Therefore, the therapeutic approach is focused on prevention. Based on this, there is an opportunity to examine a panel of biomarker models for predicting AKI.

Material and Methods

This prospective cohort study analysed the differences in Cystatin C, Beta-2 Microglobulin, and NGAL levels in sepsis patients with AKI and sepsis patients without AKI. The biomarker modelling of AKI prediction was done using machine learning, namely Orange Data Mining. In this study, 130 samples were analysed by machine learning. The parameters used to obtain the biomarker panel were 23 laboratory examination parameters.

Results

This study used SVM and the Naïve Bayes model of machine learning. The SVM model's sensitivity, specificity, NPV, and PPV were 50%, 94.4%, 71.4%, and 87.5%, respectively. For the Naïve Bayes model, the sensitivity, specificity, NPV, and PPV were 83.3%, 77.8%, 87.5%, and 71.4%, respectively.

Discussion

This study's SVM machine learning model has higher AUC and specificity but lower sensitivity. The Naïve Bayes model had better sensitivity; it can be used to predict AKI in sepsis patients.

Conclusion

The Naïve Bayes machine learning model in this study is useful for predicting AKI in sepsis patients.

An early warning model to predict acute kidney injury in sepsis patients with prior hypertension

Background

In the context of sepsis patients, hypertension has a significant impact on the likelihood of developing sepsis-associated acute kidney injury (S-AKI), leading to a considerable burden. Moreover, sepsis is responsible for over 50 % of cases of acute kidney injuries (AKI) and is linked to an increased likelihood of death during hospitalization. The objective of this research is to develop a dependable and strong nomogram framework, utilizing the variables accessible within the first 24 h of admission, for the anticipation of S-AKI in sepsis patients who have hypertension.

Methods

In this study that looked back, a total of 462 patients with sepsis and high blood pressure were identified from Nanfang Hospital. These patients were then split into a training set (consisting of 347 patients) and a validation set (consisting of 115 patients). A multivariate logistic regression analysis and a univariate logistic regression analysis were performed to identify the factors that independently predict S-AKI. Based on these independent predictors, the model was constructed. To evaluate the efficacy of the designed nomogram, several analyses were conducted, including calibration curves, receiver operating characteristics curves, and decision curve analysis.

Results

The findings of this research indicated that diabetes, prothrombin time activity (PTA), thrombin time (TT), cystatin C, creatinine (Cr), and procalcitonin (PCT) were autonomous prognosticators for S-AKI in sepsis individuals with hypertension. The nomogram model, built using these predictors, demonstrated satisfactory discrimination in both the training (AUC = 0.823) and validation (AUC = 0.929) groups. The S-AKI nomogram demonstrated superior predictive ability in assessing S-AKI within the hypertension grade I (AUC = 0.901) set, surpassing the hypertension grade II (AUC = 0.816) and III (AUC = 0.810) sets. The nomogram exhibited satisfactory calibration and clinical utility based on the calibration curve and decision curve analysis.

Conclusion

In patients with sepsis and high blood pressure, the nomogram that was created offers a dependable and strong evaluation for predicting S-AKI. This evaluation provides valuable insights to enhance individualized treatment, ultimately resulting in improved clinical outcomes.

Bone Marrow Mesenchymal Stem Cells (BMSC) from Exosome with High miR-184 Level Ameliorates Sepsis

This study assesses whether BMSC from exosome with high miR-184 level ameliorates sepsis. BMSC with high miR-184 expression established. RAW264.7 cells were cultivated in vitro and divided into control set, model set, BMSC set and BMSC with high miR-184 level set. The model was established through infection of RAW264.7 cells with LPS followed by analysis of cell proliferation and apoptosis, activity of ROS and SOD, secretion of IL-1β, IL-6 and TNF-α as well as the expression of NF-κB and TRAIL. BMSC set showed significantly upregulated miR-184 expression, increased cell proliferation and SOD activity, reduced ROS activity, decreased secretion of IL-1β, IL-6 and TNF-α as well as the expression of NF-κB and TRAIL. The above changes were more significant in the set of BMSC with overexpression of miR-184. In conclusion, cell proliferation, apoptosis and inflammation in RAW264.7 cells induced with LPS is regulated by BMSC from exosome with high expression of miR-184, which is possibly through restraining the NF-κB and TRAIL and oxidative stress.

A Predictive Model Based on Inflammatory and Coagulation Indicators for Sepsis-Induced Acute Kidney Injury

Background

Sepsis-induced acute kidney injury (S-AKI) is associated with systemic inflammatory responses and coagulation system dysfunction, and it is associated with an increased risk of mortality. However, there was no study to explore the predictive value of inflammatory and coagulation indicators for S-AKI.

Methods

In this retrospective study, 1051 sepsis patients were identified and divided into a training cohort (75%, n = 787) and a validation cohort (25%, n = 264) in chronological order according to the date they were admitted. Univariate analyses and multivariate logistic regression analyses were performed to identify the independent predictors of S-AKI. The logistic regression analyses (enter methods) were used to conducted the prediction models. The ROC curves were used to determine the predictive value of the constructed models on S-AKI. To test whether the increase in the AUC is significant, we used a two-sided test for ROC curves available online (http://vassarstats.net/roc_comp.html). The secondary outcome was different AKI stages and major adverse kidney events within 30 days (MAKE30). Stage 3B of S-AKI was defined as both meeting the stage 3 criteria [increase of Cr level by > 300% (≥ 4.0 mg/dL with an acute increase of ≥ 0.5 mg/dL) and/or UO < 0.3 mL/kg/h for > 24 h or anuria for > 12 h and/or acute kidney replacement therapy] and having cystatin C positive. MAKE30 were a composite of death, new renal replacement therapy (RRT), or persistent renal dysfunction (PRD).

Results

We discovered that cardiovascular disease, white blood cell (WBC), mean arterial pressure (MAP), platelet (PLT), serum procalcitonin (PCT), prothrombin time activity (PTA), and thrombin time (TT) were independent predictors for S-AKI. The predictive value (AUC = 0.855) of the simplest model 3 (constructed with PLT, PCT, and PTA), with a sensitivity of 77.6% and a specificity of 82.4%, had a similar predictive value comparing with the model 1 (AUC = 0.872) and the model 2 (AUC = 0.864) in the training cohort (P > 0.05). Compared with the model 1 (AUC = 0.888) and the model 2 (AUC = 0.887), the model 3 (AUC = 0.887) had a similar predictive value in the validation cohort. Moreover, model 3 had the best predictive power for predicting S-AKI in the stage 3 (AUC = 0.777), especially in stage 3B (AUC = 0.771). Finally, the model 3 (AUC = 0.843) had perfect predictive power for predicting MAKE30 in sepsis patients.

Conclusion

Within 24 hours after admission, the simplest model 3 (constructed with PLT, PCT, and PTA) might be a robust predictor of the S-AKI in sepsis patients, providing information for timely and efficient intervention.

Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus

BACKGROUND

In sepsis patients, Type 2 Diabetes Mellitus (T2DM) was associated with an increased risk of kidney injury. Furthermore, kidney damage is among the dangerous complications, with a high mortality rate in sepsis patients. However, the underlying predictive model on the prediction of major adverse kidney events within 30 days (MAKE30) in sepsis patients with T2DM has not been reported by any study.

METHODS

A total of 406 sepsis patients with T2DM were retrospectively enrolled and divided into a non-MAKE30 group (261 cases) and a MAKE30 group (145 cases). In sepsis patients with T2DM, univariate and multivariate logistic regression analyses were conducted to identify independent predictors of MAKE30. Based on the findings of multivariate logistic regression analysis, the corresponding nomogram was constructed. The nomogram was evaluated using the calibration curve, Receiver Operating Characteristic (ROC) curve, and decision curve analysis. A composite of death, new Renal Replacement Therapy (RRT), or Persistent Renal Dysfunction (PRD) comprised MAKE30. Finally, subgroup analyses of the nomogram for 30-day mortality, new RRT, and PRD were performed.

RESULTS

In sepsis patients with T2DM, Mean Arterial Pressure (MAP), Platelet (PLT), cystatin C, High-Density Lipoprotein (HDL), and apolipoprotein E (apoE) were independent predictors for MAKE30. According to the ROC curve, calibration curve, and decision curve analysis, the nomogram model based on those predictors had satisfactory discrimination (AUC = 0.916), good calibration, and clinical application. Additionally, in sepsis patients with T2DM, the nomogram model exhibited a high ability to predict the occurrence of 30-day mortality (AUC = 0.822), new RRT (AUC = 0.874), and PRD (AUC = 0.801).

CONCLUSION

The nomogram model, which is available within 24 hours after admission, had a robust and accurate assessment for the MAKE30 occurrence, and it provided information to better manage sepsis patients with T2DM.