Acute kidney injury (AKI) is commonly present in critically ill patients with sepsis. Early prediction of short-term reversibility of AKI is beneficial to risk stratification and clinical treatment decision. The study sought to use machine learning methods to discriminate between transient and persistent sepsis-associated AKI. Septic patients who developed AKI within the first 48 h after ICU admission were identified from the Medical Information Mart for Intensive Care III database. AKI was classified as transient or persistent according to the Acute Disease Quality Initiative workgroup consensus. Five prediction models using logistic regression, random forest, support vector machine, artificial neural network and extreme gradient boosting were constructed, and their performance was evaluated by out-of-sample testing. A simplified risk prediction model was also derived based on logistic regression and features selected by machine learning algorithms. A total of 5984 septic patients with AKI were included, 3805 (63.6%) of whom developed persistent AKI. The artificial neural network and logistic regression models achieved the highest area under the receiver operating characteristic curve (AUC) among the five machine learning models (0.76, 95% confidence interval [CI] 0.74–0.78). The simplified 14-variable model showed adequate discrimination, with the AUC being 0.76 (95% CI 0.73–0.78). At the optimal cutoff of 0.63, the sensitivity and specificity of the simplified model were 63% and 76% respectively. In conclusion, a machine learning-based simplified prediction model including routine clinical variables could be used to differentiate between transient and persistent AKI in critically ill septic patients. An easy-to-use risk calculator can promote its widespread application in daily clinical practice.

ObjectiveThe epidemiology and outcomes of acute kidney disease (AKD) after acute kidney injury (AKI) in hospitalized children are poorly described. The aim of this study is to investigate the prevalence, predictive factors, and clinical outcomes of AKD in hospitalized children with AKI.MethodsChildren (1 month–18 years) with AKI during hospitalization in the Second Xiangya Hospital from January 2015 to December 2020 were identified. AKD was defined based on the consensus report of the Acute Disease Quality Initiative 16 workgroup. The endpoints include adverse outcomes in 30 and 90 days. Multivariable logistic regression analyses were used to estimate the odds ratio of 30- and 90-day adverse outcomes associated with AKD and identify the risk factors of AKD.ResultsAKD was developed in 42.3% (419/990) of the study patients, with 186 in AKD stage 1, 107 in AKD stage 2, and 126 in AKD stage 3. Pediatric patients with AKD stages 2–3 had significantly higher rates of developing 30- and 90-day adverse outcomes than those with AKD stage 0 and 1. The adjusted odds ratio of AKD stage 2–3 was 12.18 (95% confidence interval (CI), 7.38 - 20.09) for 30-day adverse outcomes and decreased to 2.49 (95% CI, 1.26 - 4.91) for 90-day adverse outcomes. AKI stages 2 and 3, as well as glomerulonephritis, were the only predictive factors for AKD stage 2–3.ConclusionAKD is frequent among hospitalized pediatric AKI patients. AKD stage 2–3 represents a high-risk subpopulation among pediatric AKI survivors and is independently associated with 30- and 90-day adverse outcomes. Awareness of the potential risks associated with AKD stage 2–3 and its risk factors may help improve outcomes through careful monitoring and timely intervention.

Acute kidney disease (AKD) is a state between acute kidney injury (AKI) and chronic kidney disease (CKD), but the prognosis of AKD is unclear and there are no risk-prediction tools to identify high-risk patients. 2,556 AKI patients were selected from 277,898 inpatients of three affiliated hospitals of Central South University from January 2015 to December 2015. The primary point was whether AKI patients developed AKD. The endpoint was death or end stage renal disease (ESRD) 90 days after AKI diagnosis. Multivariable Cox regression was used for 90-day mortality and two prediction models were established by using multivariable logistic regression. Our study found that the incidence of AKD was 53.17% (1,359/2,556), while the mortality rate and incidence of ESRD in AKD cohort was 19.13% (260/1,359) and 3.02% (41/1,359), respectively. Furthermore, adjusted hazard ratio of mortality for AKD versus no AKD was 1.980 (95% CI 1.427–2.747). In scoring model 1, age, gender, hepatorenal syndromes, organic kidney diseases, oliguria or anuria, respiratory failure, blood urea nitrogen (BUN) and acute kidney injury stage were independently associated with AKI progression into AKD. In addition, oliguria or anuria, respiratory failure, shock, central nervous system failure, malignancy, RDW-CV ≥ 13.7% were independent risk factors for death or ESRD in AKD patients in scoring model 2 (goodness-of fit, P1 = 0.930, P2 = 0.105; AUROC1 = 0.879 (95% CI 0.862–0.896), AUROC2 = 0.845 (95% CI 0.813–0.877), respectively). Thus, our study demonstrated AKD was independently associated with increased 90-day mortality in hospitalized AKI patients. A new prediction model system was able to predict AKD following AKI and 90-day prognosis of AKD patients to identify high-risk patients.

Passage of apolipoprotein B-containing lipoproteins (apoB-LPs), i.e., triglyceride-rich lipoproteins (TRLs), intermediate-density lipoproteins (IDLs), and low-density lipoproteins (LDLs), through the endothelial monolayer occurs in normal and atherosclerotic arteries. Among these lipoproteins, TRLs and IDLs are apoE-rich apoB-LPs (E/B-LPs). Recycling of TRL-associated apoE has been shown to form apoE-carrying high-density lipoprotein (HDL)-like (HDLE) particles in many types of cells. The current report studied the formation of HDLE particles by transcytosis of apoB-LPs through mouse aortic endothelial cells (MAECs). Our data indicated that passage of radiolabeled apoB-LPs, rich or poor in apoE, through the MAEC monolayer is inhibited by filipin and unlabeled competitor lipoproteins, suggesting that MAECs transport apoB-LPs via a caveolae-mediated pathway. The cholesterol and apoE in the cell-untreated E/B-LPs, TRLs, IDLs, and LDLs distributed primarily in the low-density (LD) fractions (d ≤ 1.063). A substantial portion of the cholesterol and apoE that passed through the MAEC monolayer was allotted into the high-density (HD) (d > 1.063) fractions. In contrast, apoB was detectable only in the LD fractions before or after apoB-LPs were incubated with the MAEC monolayer, suggesting that apoB-LPs pass through the MAEC monolayer in the forms of apoB-containing LD particles and apoE-containing HD particles.