Purpose: The goal of this study is to construct a mortality prediction model using the XGBoot (eXtreme Gradient Boosting) decision tree model for AKI (acute kidney injury) patients in the ICU (intensive care unit), and to compare its performance with that of three other machine learning models. Methods: We used the eICU Collaborative Research Database (eICU-CRD) for model development and performance comparison. The prediction performance of the XGBoot model was compared with the other three machine learning models. These models included LR (logistic regression), SVM (support vector machines), and RF (random forest). In the model comparison, the AUROC (area under receiver operating curve), accuracy, precision, recall, and F1 score were used to evaluate the predictive performance of each model. Results: A total of 7548 AKI patients were analyzed in this study. The overall in-hospital mortality of AKI patients was 16.35%. The best performing algorithm in this study was XGBoost with the highest AUROC (0.796, p < 0.01), F1(0.922, p < 0.01) and accuracy (0.860). The precision (0.860) and recall (0.994) of the XGBoost model rank second among the four models. Conclusion: XGBoot model had obvious advantages of performance compared to the other machine learning models. This will be helpful for risk identification and early intervention for AKI patients at risk of death.

Aim: This meta‐analysis explored cerebral microstructural changes in individuals born preterm using fractional anisotropy from diffusion tensor imaging.Method: We used the activation likelihood estimate (ALE) method for the meta‐analysis to locate anatomical regions with white matter abnormalities in a group of individuals born preterm and in term‐born comparison participants. A statistical analysis of fractional anisotropy was conducted to quantitatively explore the extent of fractional anisotropy changes in the three subregions of the corpus callosum in the preterm group.Results: ALE analysis identified 11 regions of decreased fractional anisotropy and four regions of increased fractional anisotropy. Analysis of the corpus callosum revealed the largest decrease in fractional anisotropy in the splenium (standardized mean difference [SMD]=−0.75, 95% confidence interval [CI] −0.93 to −0.57), followed by the body (SMD=−0.73, 95% CI −1.13 to −0.32) and the genu (SMD=−0.65, 95% CI −0.97 to −0.33).Interpretation: Significant changes in fractional anisotropy in individuals born preterm reflect white matter abnormalities from childhood to young adulthood, and the mechanism of fractional anisotropy alterations in preterm infants may vary during different stages of white matter development. Furthermore, the variability of fractional anisotropy between studies can primarily be attributed to the age of the individuals at scanning and to the field strength of magnetic resonance scanners.

We hypothesized that a meta-analysis of existing studies may help to reveal significant changes on diffusion tensor imaging (DTI) in patients with glaucoma. Therefore, a meta-analysis was utilized to investigate the possibility that DTI can detect white matter damage in patients with glaucoma. The study design and report adhered to the PRISMA Statement guidelines. DTI studies that compared glaucoma patients and controls were surveyed using PubMed, Web of Science and EMBASE (January 2008 to September 2013). Stata was used to analyze the decrease in fractional anisotropy (FA) and increase in mean diffusivity (MD) in the optic nerve and optic radiation in patients with glaucoma. Eleven DTI studies were identified through a comprehensive literature search, and 10 independent DTI studies of glaucoma patients were eligible for the meta-analysis. A random effects model revealed a significant FA reduction in the optic nerve and optic radiation, as well as a significant MD increase in the tracts. A heterogeneity analysis suggested that FA may be related to glaucoma severity. Our findings revealed that the optic nerve and optic radiation were vulnerable regions in patients with glaucoma and that FA may be correlated with glaucoma severity and age. Furthermore, this study suggests that magnetic resonance imaging in patients with glaucoma may help to provide objective evidence to aid in the diagnosis and management of glaucoma.

To deeply analyze the basic information and disease information of adult patients in the MIMIC-III (Medical Information Mart for Intensive Care III) database, and provide data reference for clinicians and researchers. Tableau2019.1.0 and Navicat12.0.29 were used for data analysis and extraction of disease distribution of adult patients in the MIMIC-III database. A total of 38,163 adult patients were included in the MIMIC-III database. Only 38,156 patients with the first diagnosis were selected. Among them, 21,598 were males accounting for 56.6% the median age was 66 years (Q1-Q3: 53–78), the median length of a hospital stay was 7 days (Q1-Q3: 4–12), and the median length of an ICU stay was 2.1 days (Q1-Q3: 1.2–4.1). Septicemia was the disease with the highest mortality rate among patients and the total mortality rate was 48.9%. The disease with the largest number of patients at the last time was other forms of chronic ischemic heart disease. By analyzing the patients’ basic information, the admission spectrum and the disease morbidity and mortality can help more researchers understand the MIMIC-III database and facilitate further research.

Objective: This study sought to investigate the association between metabolic syndrome (MetS) and Composite Dietary Antioxidant Index (CDAI) in females, with the goal of informing evidence-based prevention and clinical management strategies for MetS.Methods: The 2011–2016 National Health and Nutrition Examination Survey (NHANES) recruited a total of 2,790 female participants and screened 1,562 participants for estrogen non-deficiency. The diagnosis of MetS was based on criteria set by the National Cholesterol Education Program-Adult Treatment Panel III. The CDAI was calculated according to the intake of 10 dietary antioxidants. Multivariable logistic regression was performed to investigate the relationship between the CDAI and MetS in females. We also performed restricted cubic splines, two-piecewise linear regression, and subgroup analysis in further analysis.Results: Our analyses demonstrated a significant inverse association between the Composite Dietary Antioxidant Index (CDAI) and metabolic syndrome (MetS) prevalence in females. Restricted cubic spline analysis indicated a linear dose–response relationship (p for linearity = 0.029), with two-piecewise linear regression analysis revealed an inflection point at 1.99. Below 1.99, each unit increase in the CDAI was associated with a 2% reduction in the risk of MetS in females; above 1.99, the risk reduction was 1%. Participants without MetS were significantly younger than those with MetS (43.49 ± 16.04 vs. 54.77 ± 15.52 years, p < 0.001). Notably, estrogen levels also were negatively correlated with the prevalence of MetS. Subgroup analysis revealed that the relationship between the CDAI and MetS remained consistent across all subgroups.Conclusion: In the female population, CDAI levels exhibited an inverse relationship with the prevalence of metabolic syndrome, and estrogen levels demonstrated a negative correlation with its incidence.