[Acute kidney injury following acute pancreatitis (AP-AKI): Definition, Pathophysiology, Diagnosis and Therapy]

Establishment and Diagnostic Value of an Early Prediction Model for Acute Pancreatitis Complicated With Acute Kidney Injury

OBJECTIVES

To establish an early prediction model for acute pancreatitis (AP) complicated with acute kidney injury (AKI) and evaluate its diagnostic value.

METHOD

AP patients were recruited from the Emergency Department at Peking University People's Hospital in 2021 and stratified into AKI and control (no AKI) groups. Their clinical data were analyzed. The risk for AKI development was determined using logistic analyses to establish a risk prediction model, whose diagnostic value was analyzed using a receiver operating characteristic curve.

RESULTS

There was no significant difference in the basic renal function between the AKI (n = 79) and control (n = 179) groups. The increased triglyceride glucose index (odds ratio [OR], 2.613; 95% confidence interval [CI], 1.324-5.158; P = 0.006), age (OR, 1.076; 95% CI, 1.016-1.140; P = 0.013), and procalcitonin (OR, 1.377; 95% CI, 1.096-1.730, P = 0.006) were associated with AKI development. A model was established for prediction of AKI (sensitivity 79.75%, specificity 96.65%). The area under the receiver operating characteristic curve was 0.856 which was superior to the Ranson, Bedside Index for Severity in AP, and Acute Physiology and Chronic Health Evaluation II scores (0.856 vs 0.691 vs 0.745 vs 0.705).

CONCLUSIONS

The prediction model based on age, triglyceride glucose, and procalcitonin is valuable for the prediction of AP-related AKI.

Oxidative stress and acute pancreatitis (Review)

Acute pancreatitis (AP) is a common inflammatory disorder of the exocrine pancreas that causes severe morbidity and mortality. Although the pathophysiology of AP is poorly understood, a substantial body of evidence suggests some critical events for this disease, such as dysregulation of digestive enzyme production, cytoplasmic vacuolization, acinar cell death, edema formation, and inflammatory cell infiltration into the pancreas. Oxidative stress plays a role in the acute inflammatory response. The present review clarified the role of oxidative stress in the occurrence and development of AP by introducing oxidative stress to disrupt cellular Ca2+ balance and stimulating transcription factor activation and excessive release of inflammatory mediators for the application of antioxidant adjuvant therapy in the treatment of AP.

Development and validation of a prediction model for the early occurrence of acute kidney injury in patients with acute pancreatitis

BACKGROUND

Acute pancreatitis (AP) is associated with a high incidence of acute kidney injury (AKI). This study aimed to develop a nomogram for predicting the early onset of AKI in AP patients admitted to the intensive care unit.

METHOD

Clinical data for 799 patients diagnosed with AP were extracted from the Medical Information Mart for Intensive Care IV database. Eligible AP patients were randomly divided into training and validation cohorts. The independent prognostic factors for the early development of AKI in AP patients were determined using the all-subsets regression method and multivariate logistic regression. A nomogram was constructed for predicting the early occurrence of AKI in AP patients. The performance of the nomogram was evaluated based on the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCA).

RESULTS

Seven independent prognostic factors were identified as predictive factors for early onset AKI in AP patients. The AUC of the nomogram in the training and validation cohorts were 0.795 (95% CI, 0.758-0.832) and 0.772 (95% CI, 0.711-0.832), respectively. The AUC of the nomogram was higher compared with that of the BISAP, Ranson, APACHE II scores. Further, the calibration curve revealed that the predicted outcome was in agreement with the actual observations. Finally, the DCA curves showed that the nomogram had a good clinical applicability value.

CONCLUSION

The constructed nomogram showed a good predictive ability for the early occurrence of AKI in AP patients.

Machine learning for acute kidney injury: Changing the traditional disease prediction mode

Acute kidney injury (AKI) is a serious clinical comorbidity with clear short-term and long-term prognostic implications for inpatients. The diversity of risk factors for AKI has been recognized in previous studies, and a series of predictive models have been developed using traditional statistical methods in conjunction with its preventability, but they have failed to meet the expectations in limited clinical applications, the rapid spread of electronic health records and artificial intelligence machine learning technology has brought new hope for the construction of AKI prediction models. In this article, we systematically review the definition and classification of machine learning methods, modeling ideas and evaluation methods, and the characteristics and current status of modeling studies. According to the modeling objectives, we subdivided them into critical care medical setting models, all medical environment models, special surgery models, special disease models, and special nephrotoxin exposure models. As the first review article to comprehensively summarize and analyze machine learning prediction models for AKI, we aim to objectively describe the advantages and disadvantages of machine learning approaches to modeling, and help other researchers more quickly and intuitively understand the current status of modeling research, inspire ideas and learn from experience, so as to guide and stimulate more research and more in-depth exploration in the future, which will ultimately provide greater help to improve the overall status of AKI diagnosis and treatment.

Machine Learning-Assisted Ensemble Analysis for the Prediction of Acute Pancreatitis with Acute Kidney Injury

Purpose

Acute kidney injury (AKI) is a frequent complication of severe acute pancreatitis (AP) and carries a very poor prognosis. The present study aimed to construct a model capable of accurately identifying those patients at high risk of harboring occult acute kidney injury (AKI) characteristics.

Patients and Methods

We retrospectively recruited a total of 424 consecutive patients at the Gezhouba central hospital of Sinopharm and Xianning central hospital between January 1, 2016, and October 30, 2021. ML-assisted models were developed from candidate clinical features using two-step estimation methods. The receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were performed to evaluate the robustness and clinical practicability of each model.

Results

Finally, a total of 30 candidate variables were included, and the AKI prediction model was established by an ML-based algorithm. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine (SVM), eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and decision tree (DT) ranged from 0.725 (95% CI 0.223–1.227) to 0.902 (95% CI 0.400–1.403). Among them, RFC obtained the optimal prediction efficiency via adding inflammatory factors, which are serum creatinine (Scr), C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-albumin ratio (NAR), and CysC, respectively.

Conclusion

We successfully developed ML-based prediction models for AKI, particularly the RFC, which can improve the prediction of AKI in patients with AP. The practicality of prediction and early detection may be greatly beneficial to risk stratification and management decisions.

Blood Urea Nitrogen as a Prognostic Marker in Severe Acute Pancreatitis

Objectives

To explore independent risk factors with good and early predictive power for SAP severity and prognosis.

Methods

Patients with SAP were enrolled at Central South University Xiangya Hospital between April 2017 and May 2021 and used as the training cohort. From June 2021 to February 2022, all patients with SAP were defined as external patients for validation. Patients were grouped by survival status at a 30-day posthospital admission and then compared in terms of basic information and laboratory tests to screen the independent risk factors.

Results

A total of 249 patients with SAP were enrolled in the training cohort. The all-cause mortality rate at a 30-day postadmission was 25.8% (51/198). Blood urea nitrogen (BUN) levels were significantly higher in the mortality group (20.45 [interquartile range (IQR), 19.7] mmol/L) than in the survival group (6.685 [IQR, 6.3] mmol/L; P < 0.001). After propensity score matching (PSM), the BUN level was still higher in the mortality group than in the survival group (18.415 [IQR, 19.555] mmol/L vs. 10.63 [IQR, 6.03] mmol/L; P = 0.005). The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of BUN was 0.820 (95% confidence interval, 0.721–0.870; P < 0.001). The optimal BUN level cut-off for predicting a 30-day all-cause mortality was 10.745 mmol/L. Moreover, patients with SAP were grouped according to BUN levels and stratified according to optimal cut-off value. Patients with high BNU levels were associated with significantly higher rates of invasive mechanical ventilation (before PSM: 61.8% vs. 20.6%, P < 0.001; after PSM: 71.1% vs. 32%, P = 0.048) and a 30-day all-cause mortality (before PSM: 44.9% vs. 6.9%, P < 0.001; after PSM: 60% vs. 34.5%, P = 0.032) than those with low BNU levels before or after PSM. The effectiveness of BUN as a prognostic marker was further validated using ROC curves for the external validation set (n = 49). The AUC of BUN was 0.803 (95% CI, 0.655–0.950; P = 0.011). It showed a good ability to predict a 30-day all-cause mortality in patients with SAP. We also observed similar results regarding disease severity, including the Acute Physiology and Chronic Health Evaluation II score (before PSM: 16 [IQR, 8] vs. 8 [IQR, 6], P < 0.001; after PSM: 18 [IQR, 10] vs. 12 [IQR, 7], P < 0.001), SOFA score (before PSM: 7 [IQR, 5] vs. 3 [IQR, 3], P < 0.001; after PSM: 8 [IQR, 5] vs. 5 [IQR, 3.5], P < 0.001), and mMarshall score (before PSM: 4 [IQR, 3] vs. 3 [IQR, 1], P < 0.001; after PSM: 5 [IQR, 2.5] vs. 3 [IQR, 1], P < 0.001). There was significant increase in intensive care unit occupancy in the high BUN level group before PSM (93.3% vs. 73.1%, P < 0.001), but not after PSM (97.8% vs. 86.2%, P = 0.074).

Conclusions

Our results showed that BUN levels within 24 h after hospital admission were independent risk factors for a 30-day all-cause death in patients with SAP.

A Novel Risk-Prediction Scoring System for Sepsis among Patients with Acute Pancreatitis: A Retrospective Analysis of a Large Clinical Database

Background

The prognosis is poor when acute pancreatitis (AP) progresses to sepsis; therefore, it is necessary to accurately predict the probability of sepsis and develop a personalized treatment plan to reduce the disease burden of AP patients.

Methods

A total of 1295 patients with AP and 43 variables were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV database. The included patients were randomly assigned to the training set and to the validation set at a ratio of 7 : 3. The chi-square test or Fisher's exact test was used to test the distribution of categorical variables, and Student's t-test was used for continuous variables. Multivariate logistic regression was used to establish a prognostic model for predicting the occurrence of sepsis in AP patients. The indicators to verify the overall performance of the model included the area under the receiver operating characteristic curve (AUC), calibration curves, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and a decision curve analysis (DCA).

Results

The multifactor analysis results showed that temperature, phosphate, calcium, lactate, the mean blood pressure (MBP), urinary output, Glasgow Coma Scale (GCS), Charlson Comorbidity Index (CCI), sodium, platelet count, and albumin were independent risk factors. All of the indicators proved that the prediction performance and clinical profitability of the newly established nomogram were better than those of other common indicators (including SIRS, BISAP, SOFA, and qSOFA).

Conclusions

The new risk-prediction system that was established in this research can accurately predict the probability of sepsis in patients with acute pancreatitis, and this helps clinicians formulate personalized treatment plans for patients. The new model can reduce the disease burden of patients and can contribute to the reasonable allocation of medical resources, which is significant for tertiary prevention.

Targeting ferroptosis in acute kidney injury

Acute kidney injury (AKI) is a major public health problem with high incidence and mortality. As a form of programmed cell death (PCD), ferroptosis could be considered as a process of iron accumulation and enhanced lipid peroxidation. Recently, the fundamental roles of ferroptosis in AKI have attracted much attention. The network mechanism of ferroptosis in AKI and its roles in the AKI to chronic kidney disease (CKD) transition is complicated and multifactorial. Strategies targeting ferroptosis show great potential. Here, we review the research progress on ferroptosis and its participation in AKI. We hope that this work will provide clues for further studies of ferroptosis in AKI.

The Impact of Normal Saline or Balanced Crystalloid on Plasma Chloride Concentration and Acute Kidney Injury in Patients With Predicted Severe Acute Pancreatitis: Protocol of a Phase II, Multicenter, Stepped-Wedge, Cluster-Randomized, Controlled Trial

Introduction/aim: The supraphysiologic chloride concentration of normal saline may contribute to acute kidney injury (AKI). Balanced crystalloids can decrease chloride concentration and AKI in critically ill patients. We aim to test the hypothesis that, in patients with predicted severe acute pancreatitis (pSAP), compared with saline, fluid therapy with balanced crystalloids will decrease plasma chloride concentration. Methods/Design: This is a multicenter, stepped-wedge, cluster-randomized, controlled trial. All eligible patients presenting to the 11 participating sites across China during the study period will be recruited. All sites will use saline for the first month and sequentially change to balanced crystalloids at the pre-determined and randomly allocated time point. The primary endpoint is the plasma chloride concentration on day 3 of enrollment. Secondary endpoints will include major adverse kidney events on hospital discharge or day 30 (MAKE 30) and free and alive days to day 30 for intensive care admission, invasive ventilation, vasopressors, and renal replacement therapy. Additional endpoints include daily serum chloride and sequential organ failure assessment (SOFA) score over the first seven days of enrollment. Discussion: This study will provide data to define the impact of normal saline vs. balanced crystalloids on plasma chloride concentration and clinical outcomes in pSAP patients. It will also provide the necessary data to power future large-scale randomized trials relating to fluid therapy. Ethics and Dissemination: This study was approved by the ethics committee of Jinling Hospital, Nanjing University (2020NZKY-015-01) and all the participating sites. The results of this trial will be disseminated in peer-reviewed journals and at scientific conferences. Trial registration: The trial has been registered at the Chinese Clinical Trials Registry (ChiCTR2100044432).

Overexpression of SP1 restores autophagy to alleviate acute renal injury induced by ischemia-reperfusion through the miR-205/PTEN/Akt pathway

Background

Acute kidney injury (AKI) is a major kidney disease with poor clinical outcome. SP1, a well-known transcription factor, plays a critical role in AKI and subsequent kidney repair through the regulation of various cell biologic processes. However, the underlying mechanism of SP1 in these pathological processes remain largely unknown.

Methods

An in vitro HK-2 cells with anoxia-reoxygenation injury model (In vitro simulated ischemic injury disease) and an in vivo rat renal ischemia-reperfusion injury model were used in this study. The expression levels of SP1, miR-205 and PTEN were detected by RT-qPCR, and the protein expression levels of SP1, p62, PTEN, AKT, p-AKT, LC3II, LC3I and Beclin-1 were assayed by western blot. Cell proliferation was assessed by MTT assay, and the cell apoptosis was detected by flow cytometry. The secretions of IL-6 and TNF-α were detected by ELISA. The targeted relationship between miR-205 and PTEN was confirmed by dual luciferase report assay. The expression and positioning of LC-3 were observed by immunofluorescence staining. TUNEL staining was used to detect cell apoptosis and immunohistochemical analysis was used to evaluate the expression of SP1 in renal tissue after ischemia-reperfusion injury in rats.

Results

The expression of PTEN was upregulated while SP1 and miR-205 were downregulated in renal ischemia-reperfusion injury. Overexpression of SP1 protected renal tubule cell against injury induced by ischemia-reperfusion via miR-205/PTEN/Akt pathway mediated autophagy. Overexpression of SP1 attenuated renal ischemia-reperfusion injury in rats.

Conclusions

SP1 overexpression restored autophagy to alleviate acute renal injury induced by ischemia-reperfusion through the miR-205/PTEN/Akt pathway.