Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study.

BACKGROUND: Acute kidney disease (AKD) affects more than half of critically ill elderly patients with acute kidney injury (AKI), which leads to worse short-term outcomes. OBJECTIVE: We aimed to establish 2 machine learning models to predict the risk and prognosis of AKD in the elderly and to deploy the models as online apps. METHODS: Data on elderly patients with AKI (n=3542) and AKD (n=2661) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were used to develop 2 models for predicting the AKD risk and in-hospital mortality, respectively. Data collected from Xiangya Hospital of Central South University were for external validation. A bootstrap method was used for internal validation to obtain relatively stable results. We extracted the indicators within 24 hours of the first diagnosis of AKI and the fluctuation range of some indicators, namely delta (day 3 after AKI minus day 1), as features. Six machine learning algorithms were used for modeling; the area under the receiver operating characteristic curve (AUROC), decision curve analysis, and calibration curve for evaluating; Shapley additive explanation (SHAP) analysis for visually interpreting; and the Heroku platform for deploying the best-performing models as web-based apps. RESULTS: For the model of predicting the risk of AKD in elderly patients with AKI during hospitalization, the Light Gradient Boosting Machine (LightGBM) showed the best overall performance in the training (AUROC=0.844, 95% CI 0.831-0.857), internal validation (AUROC=0.853, 95% CI 0.841-0.865), and external (AUROC=0.755, 95% CI 0.699-0.811) cohorts. In addition, LightGBM performed well for the AKD prognostic prediction in the training (AUROC=0.861, 95% CI 0.843-0.878), internal validation (AUROC=0.868, 95% CI 0.851-0.885), and external (AUROC=0.746, 95% CI 0.673-0.820) cohorts. The models deployed as online prediction apps allowed users to predict and provide feedback to submit new data for model iteration. In the importance ranking and correlation visualization of the model's top 10 influencing factors conducted based on the SHAP value, partial dependence plots revealed the optimal cutoff of some interventionable indicators. The top 5 factors predicting the risk of AKD were creatinine on day 3, sepsis, delta blood urea nitrogen (BUN), diastolic blood pressure (DBP), and heart rate, while the top 5 factors determining in-hospital mortality were age, BUN on day 1, vasopressor use, BUN on day 3, and partial pressure of carbon dioxide (PaCO2). CONCLUSIONS: We developed and validated 2 online apps for predicting the risk of AKD and its prognostic mortality in elderly patients, respectively. The top 10 factors that influenced the AKD risk and mortality during hospitalization were identified and explained visually, which might provide useful applications for intelligent management and suggestions for future prospective research.

METTL3-mediated N6-methyladenosine modification stimulates mitochondrial damage and ferroptosis of kidney tubular epithelial cells following acute kidney injury by modulating the stabilization of MDM2-p53-LMNB1 axis.

N6-methyladenosine (m6A) and MELLT3 assume a role in the development of acute kidney injury (AKI). However, their mechanism in AKI remains under-explored. On this basis, this study explored the mechanism of MELLT3 in mitochondrial damage and ferroptosis of kidney tubular epithelial cells after AKI. HK-2 cells were induced by lipopolysaccharide (LPS) to simulate AKI, followed by gain and loss of function of genes, detection of mitochondrial damage and ferroptosis indicators, and analysis of gene interactions. An AKI mouse model was developed using the cecal ligation and puncture (CLP) method to investigate the effect of METTL3 knockdown on kidney injury. MDM2 and LMNB1 were upregulated and p53 was downregulated in LPS-treated HK-2 cells. Mechanistically, the E3 ubiquitin ligase MDM2 increased p53 ubiquitination to activate LMNB1. METTL3 knockdown decreased m6A methylation of MDM2, thus diminishing YTHDF1-mediated MDM2 mRNA stability and translation in LPS-treated HK-2 cells. Knockdown of LMNB1, MDM2, or METTL3 reduced NO, MDA, iron ion, and ROS levels as well as mitochondrial damage and raised SOD, GSH, XCT, GPX4, FPN1, and TFR1 levels in LPS-treated HK-2 cells. The in vivo results showed that METTL3 knockdown reduced renal injury and ferroptosis in CLP mice. METTL3 knockdown prevents mitochondrial damage and ferroptosis of kidney tubular epithelial cells after AKI via the MDM2-p53-LMNB1 axis.

Exosomal microRNA-342-5p secreted from adipose-derived mesenchymal stem cells mitigates acute kidney injury in sepsis mice by inhibiting TLR9.

BACKGROUND: Sepsis-related acute kidney injury (AKI) is an inflammatory disease associated with extremely high mortality and health burden. This study explored the possibility of exosomes secreted by adipose-derived mesenchymal stem cells (AMSCs) serving as a carrier for microRNA (miR)-342-5p to alleviate sepsis-related AKI and investigated the possible mechanism. METHODS: Serum was obtained from 30 patients with sepsis-associated AKI and 30 healthy volunteers for the measurement of miR-342-5p, blood urea nitrogen (BUN), and serum creatinine (SCr) levels. For in vitro experiments, AMSCs were transfected with LV-miR-342-5p or LV-miR-67 to acquire miR-342-5p-modified AMSCs and miR-67-modified AMSCs, from which the exosomes (AMSC-Exo-342 and AMSC-Exo-67) were isolated. The human renal proximal tubular epithelial cell line HK-2 was induced by lipopolysaccharide (LPS) to construct a cellular model of sepsis. The expression of Toll-like receptor 9 (TLR9) was also detected in AKI cells and mouse models. The interaction between miR-342-5p and TLR9 was predicted by dual luciferase reporter gene assay. RESULTS: Detection on clinical serum samples showed that BUN, SCr, and TLR9 were elevated and miR-342-5p level was suppressed in the serum of patients with sepsis-associated AKI. Transfection with LV-miR-342-5p reinforced miR-342-5p expression in AMSCs and AMSC-secreted exosomes. miR-342-5p negatively targeted TLR9. LPS treatment enhanced TLR9 expression, reduced miR-342-5p levels, suppressed autophagy, and increased inflammation in HK-2 cells, while the opposite trends were observed in LPS-induced HK-2 cells exposed to AMSC-Exo-342, Rapa, miR-342-5p mimic, or si-TLR9. Additionally, the effects of AMSC-Exo-342 on autophagy and inflammation in LPS-induced cells could be weakened by 3-MA or pcDNA3.1-TLR9 treatment. Injection of AMSC-Exo-342 enhanced autophagy, mitigated kidney injury, suppressed inflammation, and reduced BUN and SCr levels in sepsis-related AKI mouse models. CONCLUSION: miR-342-5p transferred by exosomes from miR-342-5p-modified AMSCs ameliorated AKI by inhibiting TLR9 to accelerate autophagy.

Development and deployment of interpretable machine-learning model for predicting in-hospital mortality in elderly patients with acute kidney disease.

BACKGROUND: Acute kidney injury (AKI) is more likely to develop in the elderly admitted to the intensive care unit (ICU). Acute kidney disease (AKD) affects ∼45% of patients with AKI and increases short-term mortality. However, there are no studies on the prognosis of AKD in the elderly. METHODS: Data from 2666 elderly patients with AKD in the Medical Information Mart for Intensive Care IV were used for model development and 535 in the eICU Collaborative Research Database for external validation. Based on 5 machine learning algorithms, 33 noninvasive parameters were extracted as features for modeling. RESULTS: In-hospital mortality of AKD in the elderly was 29.6% and 31.8% in development and validation cohorts, respectively. The comprehensive best-performing algorithm was the support vector machine (SVM), and a simplified online application included only 10 features employing SVM (AUC: 0.810 and 0.776 in the training and external validation cohorts, respectively) was deployed. Model interpretation by SHapley Additive exPlanation (SHAP) values revealed that the difference (AKD day - ICU day) in sequential organ failure assessment (delta SOFA), Glasgow coma scale (GCS), delta GCS, delta peripheral oxygen saturation (SpO2), and SOFA were the top five features associated with prognosis. The optimal target was determined by SHAP values from partial dependence plots. CONCLUSIONS: A web-based tool was externally validated and deployed to predict the early prognosis of AKD in the elderly based on readily available noninvasive parameters, assisting clinicians in intervening with precision and purpose to save lives to the greatest extent.

Establishment and external validation of an online dynamic nomogram for predicting in-hospital death risk in sepsis-associated acute kidney disease.

OBJECTIVES: Approximately one-third of patients with sepsis-associated acute kidney injury (AKI) progress to acute kidney disease (AKD) with higher short-term mortality. We aimed to identify the clinical characteristics that influence in-hospital death in sepsis-associated AKD and develop a nomogram to facilitate early warning. METHODS: Logical regression was applied to screen variables based on clinical data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. A nomogram was established to predict in-hospital death risk in patients with sepsis-associated AKD. The eICU Collaborative Research Database (eICU-CRD) was used for external validation. The receiver operating characteristic and calibration curves were used to determine the model's performance. RESULTS: A total of 1,779 patients with sepsis-associated AKD were included from the MIMIC-IV and 344 from the eICU-CRD. Age, Glasgow coma scale score, systolic blood pressure, peripheral oxygen saturation, platelet count, white blood cell count, and bicarbonate levels were significantly correlated with death. The nomogram demonstrated high discrimination in the training (C-index, 0.829; 95% confidence interval [CI] [0.807-0.852]) and testing sets (C-index: 0.760; 95% CI [0.706-0.814]). At the optimal cut-off value of 0.270, the model's sensitivity in the training and validation datasets was 72.8% (95% CI [68.3-76.9%]) and 64.5% (95% CI [54.9-73.4%]), while the specificity was 79.2% (95% CI [76.9-81.4%]) and 74.8% (95% CI [68.7-80.2%]), respectively. CONCLUSION: We identified seven predictors of in-hospital death in patients with sepsis-associated AKD. In addition, we developed an online dynamic nomogram to accurately and conveniently predict short-term outcomes, which performed well in the external dataset.

Nomogram to predict the risk of septic acute kidney injury in the first 24 h of admission: an analysis of intensive care unit data.

Background: Acute kidney injury (AKI) is a significant cause of morbidity and mortality, especially in sepsis patients. Early prediction of AKI can help physicians determine the appropriate intervention, and thus, improve the outcome. This study aimed to develop a nomogram to predict the risk of AKI in sepsis patients (S-AKI) in the initial 24 h following admission.Methods: Sepsis patients with AKI who met the Sepsis 3.0 criteria and Kidney Disease: Improving Global Outcomes criteria in the Massachusetts Institute of Technology critical care database, Medical Information Mart for Intensive Care (MIMIC-III), were identified for analysis. Data were analyzed using multiple logistic regression, and the performance of the proposed nomogram was evaluated based on Harrell's concordance index (C-index) and the area under the receiver operating characteristic curve.Results: We included 2917 patients in the analysis; 1167 of 2042 patients (57.14%) and 469 of 875 patients (53.6%) had AKI in the training and validation cohorts, respectively. The predictive factors identified by multivariate logistic regression were blood urea nitrogen level, infusion volume, lactate level, weight, blood chloride level, body temperature, and age. With the incorporation of these factors, our model had well-fitted calibration curves and achieved good C-indexes of 0.80 [95% confidence interval (CI): 0.78-0.82] and 0.79 (95% CI: 0.76-0.82) in predicting S-AKI in the training and validation cohorts, respectively.Conclusion: The proposed nomogram effectively predicted AKI risk in sepsis patients admitted to the intensive care unit in the first 24 h.

Optimization of extraction technology of poly-mannuronic acid to a green delivery system for the water-insoluble pesticide, λ-Cyhalothrin.

Poly-mannuronic acids (PMs) have been considered as great biodegradable polymers as a green carrier for the potential pesticide deliver. In this work, the response surface design and microwave-assisted degradation were employed to obtain the optimum extraction conditions (i.e., 81 °C, 4.1 h, acid concentration 17.65 g/L). Meanwhile, the Ugi multi-component reaction makes the PM to be amphiphilic, called Ugi-PM, which induces the aggregation in aqueous solution at the concentration of 0.0895 g/L. The corresponding chemical structure and thermal stability of PM and Ugi-PM were determined by the FTIR, 1H NMR and thermogravimetric analysis (TG). Furthermore, the construction of novel emulsion-based delivery system using synthetic Ugi-PM was explored to prepare the pesticide of λ-Cyhalothrin. Interestingly, with the Ugi-PM concentration at 0.5 wt%, the stability of the Ugi-PM emulsion and the sustainable release of λ-Cyhalothrin are better than other concentrations and our previous system without degradation (Ugi-Alg emulsion). It is possible that electrostatic repulsion and steric hindrance derived from the hydrophobic Ugi-PM can promote the stability and flexible structure may be the reason for excellent sustained release of Ugi-PM emulsions in the pesticide deliver. The above-mentioned preparation progress is an efficient way to provide a valuable pesticide formulation.