Background Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis. Methods Data of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model. Results A total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models. Conclusion The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.

Background. Acute kidney injury (AKI) is an important complication in critically ill patients, especially in sepsis and septic shock patients. Early prediction of AKI in septic shock can provide clinicians with sufficient information for timely intervention so that improve the patients’ survival rate and quality of life. The aim of this study was to establish a nomogram that predicts the risk of AKI in patients with septic shock in the intensive care unit (ICU). Methods. The data were collected from the Medical Information Mart for Intensive Care III (MIMIC-III) database between 2001 and 2012. The primary outcome was AKI in the 48 h following ICU admission. Univariate and multivariate logistic regression analyses were used to screen the independent risk factors of AKI. The performance of the nomogram was evaluated according to the calibration curve, receiver operating characteristic (ROC) curve, decision curve analysis, and clinical impact curve. Results. A total of 2415 patients with septic shock were included in this study. In the training and validation cohort, 1091 (64.48%) of 1690 patients and 475 (65.52%) of 725 patients developed AKI, respectively. The predictive factors for nomogram construction were gender, ethnicity, congestive heart failure, diabetes, obesity, Simplified Acute Physiology Score II (SAPS II), angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARBs), bilirubin, creatinine, blood urea nitrogen (BUN), and mechanical ventilation. The model had a good discrimination with the area under the ROC curve of 0.756 and 0.760 in the training and validation cohorts, respectively. The calibration curve for probability of AKI in septic shock showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analysis indicated that the nomogram conferred high clinical net benefit. Conclusion. The proposed nomogram can quickly and effectively predict the risk of AKI at an early stage in patients with septic shock in ICU, which can provide information for timely and efficient intervention in patients with septic shock in the ICU setting.

ObjectivesTo provide a comprehensive assessment of the estimated burden and trend of urolithiasis at the global, regional, and national levels.MethodsThe age-standardized rates (ASRs) of the incidence and disability-adjusted life years (DALYs) of urolithiasis from 1990 to 2019 were obtained from the Global Burden of Disease Study 2019 database. Estimated annual percentage changes (EAPCs) were calculated to quantify the temporal trends in urolithiasis burden.ResultsIn 2019, the ASRs of the incidence and DALYs were 1,394.03/100,000 and 7.35/100,000, respectively. The ASRs of the incidence and DALYs of urolithiasis decreased from 1990 to 2019 with EAPCs of −0.83 and −1.77, respectively. Males had a higher burden of urolithiasis than females. In 2019, the highest burden of urolithiasis was observed in regions with high–middle sociodemographic index (SDI), particularly in Eastern Europe, Central Asia, and Southeast Asia. The burden of urolithiasis increased in most countries or territories. The burden of urolithiasis and SDI had a non-linear relationship, and the estimated value of urolithiasis burden was the highest when the SDI value was ~0.7.ConclusionGlobally, the ASRs of the incidence and DALYs of urolithiasis decreased from 1990 to 2019, but an increasing trend was observed among many countries. More effective and appropriate medical and health policies are needed to prevent and early intervene in urolithiasis.

Background. As a novel inflammatory index, the ratio of red cell distribution width (RDW) to platelet count (RPR) may have prognostic value in some critical illnesses. However, studies on the prognostic influence of RPR in patients with sepsis are few. This study is aimed at investigating the association between RPR levels and 28-day mortality in patients with sepsis. Methods. Data of patients with sepsis were obtained from the Medical Information Mart for Intensive Care III database. The best cut-off value was calculated by establishing the receiver operating characteristic curve (ROC), and the predictive ability of different indicators was compared through the area under the curve (AUC). The association between RPR levels and 28-day mortality was assessed using the Cox proportional hazards model. Restrictive cubic spline analysis was applied to the multivariable Cox model to investigate the nonlinear relationship between RPR and 28-day mortality. Results. A total of 3367 patients with sepsis were included in the study. A nonlinear relationship was observed between RPR and 28-day mortality, showing a trend of a first rapid increase and a gradual increase. For the prediction of mortality, the best cut-off value for RPR was 0.109, with an AUC of 0.728 (95% confidence interval [CI]: 0.709–0.747). The predictive capability of RPR was superior to those of RDW, platelet, SOFA score, and SAPS II score. After adjusting for various confounding factors, high RPR was significantly associated with increased mortality with adjusted hazard ratios of 1.210 (95% CI: 1.045–1.400) for categorical variables and 2.826 (95% CI: 2.025–3.944) for continuous variables. Conclusion. Elevated RPR level is significantly correlated with a high risk of 28-day mortality in patients with sepsis and can be a new predictor of patient prognosis.