Hypernatremia is one of the commonly syndromes in critically ill patients. Severe hypernatremia has a low incidence (0.6%–1.0%) but with a very high mortality (58%–87%). Conventional treatments include the limitation of sodium intake and the supplement of sodium free liquid according to the assessed water lost. The reduction rates of conventional treatments are commonly not effective enough to decrease the serum sodium concentration in severe euvolemic or hypervolemic hypernatremia patients. Continuous renal replacement therapy (CRRT) has been reported to be effective on the reduction of sodium level in severe hypernatremia patients. However, the evidences on the use of CRRT for hypernatremia are limited. Our present review summarizes the current evidences on the prevalence of hypernatremia, the outcome of hypernatremia patients, the conventional treatment of hypernatremia, and the advantages and indications of CRRT for the management of hypernatremia. Additionally, we introduce our experiences on the management of hypernatremia using CRRT as well.
Acute kidney injury (AKI) is characterized by a sudden and rapid decline of renal function and associated with high morbidity and mortality. AKI can be caused by various factors, and ischemia-reperfusion injury (IRI) is one of the most common causes of AKI. An increasing number of studies found out that exosomes of mesenchymal stem cells (MSCs) could alleviate IRI-AKI by the adjustment of the immune response, the suppression of oxidative stress, the reduction of cell apoptosis, and the promotion of tissue regeneration. This article summarizes the effect and mechanism of MSC-derived exosomes in the treatment of renal ischemia-reperfusion injury, in order to provide useful information for the researches on this field.
Continuous renal replacement therapy (CRRT) is one of the important therapeutic techniques for critically ill patients. In recent years, the field of artificial intelligence has developed rapidly and has been widely applied in manufacturing, automotive, and even daily life. The development and application of artificial intelligence in the medical field are also advancing rapidly, and artificial intelligence radiographic imaging result judgment, pathological result judgment, patient prognosis prediction are gradually being used in clinical practice. The development of artificial intelligence in the field of CRRT has also made rapid progress. Therefore, this article will elaborate on the current application status of artificial intelligence in CRRT, as well as its future prospects in CRRT, so as to provide a reference for understanding the application of artificial intelligence in CRRT.
Objective To identify risk factors for death in patients with rhabdomyolysis-induced acute kidney injury (RI-AKI) treated with continuous renal replacement therapy (CRRT), then to develop and validate the efficacy of prediction models based on these risk factors. Methods Clinical data and prognostic information of patients with RI-AKI requiring CRRT from 2008 to 2019 were extracted from the MIMIC-IV 2.2 database. The enrolled patients were divided into a training set and a test set at a ratio of 7∶3. LASSO regression, random forest (RF) and extreme gradient boosting (XGBoost) were used to identify the risk factors affecting patients’ 28-day survival in the training set, then to develop logistic model, RF model, support vector machine (SVM) model and XGBoost model. The accuracy of above prediction models and the area under the receiver operating characteristic curve (AUC) were calculated in the test set. Results A total of 175 patients were included. Lactic acid, age, Acute Physiology Score Ⅲ, hemoglobin, mean arterial pressure and body mass index measured at intensive care unit admission were identified as the six risk factors affecting 28-day survival of enrolled patients by LASSO regression, RF and XGBoost. The accuracy of the logistic model, RF model, SVM model and XGBoost model in the test set was 0.75, 0.79, 0.79 and 0.81, with the AUC of 0.82, 0.85, 0.87 and 0.87, respectively. Conclusion The XGBoost model, incorporating six risk factors including lactic acid, age, Acute Physiology Score Ⅲ, hemoglobin, mean arterial pressure, and body mass index assessed at the time of admission to the intensive care unit, demonstrates superior clinical predictive performance, thereby enhancing the clinical decision-making process for healthcare professionals.