Rhabdomyolysis-induced acute kidney injury (RIAKI) is a serious clinical disease in intensive care unit, characterized by high mortality and low cure rate. Continuous renal replacement therapy (CRRT) is a common form of treatment for RIAKI. There are currently no guidelines to guide the application of CRRT in RIAKI. To solve this problem, this article reviews the advantages and limitations of CRRT in the treatment of RIAKI, as well as new viewpoints and research progress in the selection of treatment timing, treatment mode, treatment dose and filtration membrane, with the aim of providing theoretical guidance for the treatment of CRRT in RIAKI patients.
Objective To systematically evaluate the research quality and efficacy of prediction models for acute kidney injury (AKI) after heart valve surgery, screen key predictive factors, and provide evidence-based basis for clinical risk assessment. Methods Computer search was carried out in PubMed, Web of Science, EMBASE, Cochrane Library, Medline, China Biology Medicine Database, China National Knowledge Infrastructure, Wanfang Database, and VIP Database to collect studies on AKI prediction models after heart valve surgery published from January 2015 to July 2025. The PROBAST tool was used to evaluate the bias risk and applicability of the models, and the TRIPOD was used to assess the reporting quality. Meta-analysis was performed to integrate the effect sizes of high-frequency (≥3 times) predictive factors. Results A total of 24 studies (39 models) were included. Area under the curve (AUC) of the receiver operational characteristic curve was between 0.551 and 0.928, and the combined AUC was 0.77 (95%CI 0.72-0.82). The overall bias risk of the models was relatively high (100% of the studies had a high bias risk), only 2 studies conducted external validation, and the models in 10 studies were not validated. In terms of TRIPOD reporting quality, the overall reporting quality of 24 studies was low, with a compliance percentage (number of items) ranging from 36.36% to 77.27%. Meta-analysis showed that age (OR=1.041, P=0.006), diabetes (OR=1.64, P=0.001), hypertension (OR=2.529, P <0.001), blood transfusion (OR=1.49, P=0.001), cystatin C (OR=2.408, P=0.018), history of cardiac surgery (OR=2.585, P <0.001), atrial fibrillation (OR=1.33, P <0.001), and vascular complications (OR=1.22, P=0.008) were independent risk factors for postoperative AKI. Conclusion The clinical applicability of existing prediction models is limited, with high bias risk and low reporting quality, and the methodology needs to be optimized. Eight factors such as age and hypertension can be used as core indicators for postoperative AKI risk assessment. In the future, multicenter prospective studies should be carried out to develop more reliable prediction tools.
Acute kidney injury (AKI) is a common critical illness in clinical practice, with complex etiologies, acute onset, and rapid progression. It not only significantly increases the mortality rate of patients, but also may progress to chronic kidney disease. Currently, its incidence remains high, and improving early diagnosis rate and treatment efficacy is a major clinical challenge. Artificial intelligence (AI), with its powerful data processing and analysis capabilities, is developing rapidly in medical field, providing new ideas for disease diagnosis and treatment, and showing great potential in revolutionizing the early diagnosis, condition assessment, and treatment decision-making models in the AKI field. This article will review the application progress of AI in AKI prediction, condition assessment, and treatment decision-making, so as to provide references for clinicians and promote the further application and development of AI in the AKI field.
Most patients with coronavirus disease 2019 (COVID-19) have a good prognosis, but a certain proportion of the elderly and people with underlying diseases are still prone to develop into severe and critical COVID-19. Kidney is one of the common target organs of COVID-19. Acute kidney injury (AKI) is a common complication of severe COVID-19 patients, especially critical COVID-19 patients admitted to intensive care units. AKI associated with COVID-19 is also an independent risk factor for poor prognosis in patients. This article mainly focuses on the epidemiological data, possible pathogenesis, diagnostic criteria, and prevention and treatment based on the 5R principle of AKI associated with COVID-19. It summarizes the existing evidence to explore standardized management strategies for AKI associated with COVID-19.
Objective To assess the relationship between the change in fluid overload at 48 h after initiation of continuous renal replacement therapy (CRRT) and 28-day mortality in critically ill patients with acute kidney injury (AKI). Methods A retrospective cohort study was performed using data from the MIMIC-IV database from 2008 to 2019. Patients who received CRRT for AKI for more than 24 h within 14 d of admission to the intensive care unit were included. The exposure variable was the proportion of change of fluid overload (ΔFO%, defined as the difference between body weight normalized fluid input and output) at 48 h after CRRT initiation, and the endpoint was 28-day mortality. Generalized additive linear regression models and logistic regression models were used to determine the relationship between the exposure and endpoint. Results A total of 911 patients were included in the study, with a median (lower quartile, upper quartile) ΔFO% of −3.27% (−6.03%, 0.01%) and a 28-day mortality of 40.1%. Generalized additive linear regression model showed that the ΔFO% at 48 h after CRRT initiation was associated with a J-shaped curve with 28-day mortality. After adjusting for other variables, as compared with the second quartile of ΔFO% group, the first quartile group [odds ratio (OR)=1.23, 95% confidence interval (CI) (0.81, 1.87), P=0.338] was not associated with higher risk of 28-day mortality, while the third quartile group [OR=1.54, 95%CI (1.01, 2.35), P=0.046] and the fourth quartile group [OR=2.05, 95%CI (1.32, 3.18), P=0.001] were significantly associated with higher risk of 28-day mortality. There was no significant relationship between ΔFO% groups and 28-day mortality in the first 24-hour after CRRT initiation (P>0.05), but there was a linear relationship between ΔFO% and 28-day mortality in the second 24-hour after CRRT initiation, the larger the ΔFO%, the higher the mortality rate [OR=1.10, 95%CI (1.04 1.16), P<0.001 for per 1% increase]. ConclusionIn critically ill patients with AKI, the ΔFO% greater than −3.27% within 48 h after CRRT initiation is independently associated with an increased risk of 28-day mortality, and the goals of CRRT fluid management may be dynamical.
Objective To retrospectively analyze the clinical characteristics of different lactate trajectories in sepsis patients receiving mechanical ventilation (MV) and to investigate their associations with acute kidney injury (AKI) and in-hospital death risk, aiming to provide references for early renal protection in critically ill sepsis patients. Methods Data from sepsis patients receiving MV were extracted from the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database. The daily mean lactate values over the first 10 days were calculated. The latent class trajectory model (LCTM) was used to identify lactate trajectories over time and group the patients accordingly. AKI was the primary outcome measure, while in-hospital death was the secondary outcome measure. Logistic regression and Cox regression analyses were used to explore the associations between different lactate trajectories and these outcomes. Kaplan-Meier curves were drawn to compare in-hospital death risks among different lactate trajectory groups. Results A total of 2 062 MV-treated sepsis patients were included. After LCTM analysis, 1 396 patients were classified into the low lactate trajectory group, 451 into the moderate lactate trajectory group, and 215 into the high lactate trajectory group. After adjusting for confounding factors, the high lactate trajectory group was associated with an increased risk of AKI and in-hospital death (P<0.05). Conclusions In sepsis patients receiving MV, those with high lactate trajectories have a higher risk of AKI. Lactate trajectory changes can serve as an early assessment indicator for AKI and mortality risk in critically ill sepsis patients.
Objective To explore the risk factors affecting the prognosis of patients with acute kidney injury (AKI) after extracorporeal circulation surgery who receive continuous renal replacement therapy (CRRT). Methods Patients who developed AKI and underwent CRRT treatment after extracorporeal circulation surgery at the First Affiliated Hospital of Chongqing Medical University between May 2019 and May 2024 were retrospectively selected. According to the prognosis, patients were divided into the good prognosis group and the poor prognosis group. Basic information, duration of extracorporeal circulation during surgery, aortic occlusion time, timing and duration of CRRT initiation therapy, relevant laboratory indicators before surgery, during CRRT intervention, and upon discharge or death were collected. The risk factors affecting the prognosis of such patients were analyzed. Results A total of 45 patients were included. Among them, there were 20 cases in the good prognosis group and 25 cases in the poor prognosis group. There was no statistically significant difference in the basic information between the two groups (P>0.05). Compared with the poor prognosis group, the good prognosis group had decreased preoperative urea nitrogen and increased hemoglobin levels, reduced levels of alanine aminotransferase and aspartate aminotransferase during the initiation of CRRT treatment, and reduced levels of white blood cell count, neutrophil percentage, alanine aminotransferase and aspartate aminotransferase and elevated platelet count before discharge or death (P<0.05). The results of multivariate logistic regression analysis showed that the total duration of CRRT treatment [odds ratio (OR)=1.007, 95% confidence interval (CI) (1.000, 1.015), P=0.046], white blood cell count before discharge or death [OR=1.541, 95%CI (1.011, 2.349), P=0.044], and platelet count before discharge or death [OR=0.964, 95%CI (0.937, 0.991), P=0.010] could affect patient prognosis. Conclusions In patients with AKI after extracorporeal circulation surgery, if combined with renal dysfuction and anemia before surgery, liver function damage and secondary infection during CRRT initiation therapy may be related to poor patient prognosis. The longer the duration of CRRT treatment, the higher the white blood cells before discharge or death, and the lower the platelet count are independent risk factors for poor prognosis in patients.
Objective To investigate the effect of monocyte count to high density lipoprotein ratio (MHR) on early complications after off-pump coronary artery bypass grafting and to explore the predictive factors for early complications in patients after off-pump coronary artery bypass grafting. Methods The clinical data of patients who underwent simple off-pump coronary artery bypass grafting from October 2021 to September 2023 in our hospital were retrospectively analyzed. The patients were divided into a low value group and a high value group according to the median MHR value. The clinical data of the two groups were compared, and binary logistic regression analysis was used to explore the and predictors of atrial fibrillation (AF) and acute kidney injury (AKI) after coronary artery bypass grafting. Results A total of 220 patients were included, with a median MHR of 0.48. There were 108 patients in the low value group (MHR<0.48), including 71 males and 37 females, with an average age of 65.28±7.85 years. There were 112 patients in the high-value group (MHR≥0.48), including 84 males and 28 females, with an average age of 64.57±8.75 years. There was no statistical difference between the two groups in terms of general basic data such as gender or age (P>0.05). The incidence of postoperative AF and AKI in the high-value group was significantly higher than that in the low-value group (P<0.05), and no statistical difference in terms of other postoperative complications was observed. Binary logistic regression analysis showed that MHR was a risk factor for postoperative AKI and postoperative AF (P<0.05). Conclusion The study shows that MHR is a risk factor for new-onset AF and AKI after coronary artery bypass grafting.
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.