A new prognostic model of esophageal squamous cell carcinoma based on Cloud-least squares support vector machine.

Background: In view of the low accuracy of the prognosis model of esophageal squamous cell carcinoma (ESCC), this study aimed to optimize the least squares support vector machine (LSSVM) algorithm to determine the uncertain prognostic factors using a Cloud model, and consequently, to establish a new high-precision prognosis model of ESCC. Methods: We studied 4,771 ESCC patients(training samples) from the Surveillance, Epidemiology, and End Results (SEER) database and 635 ESCC patients(validation samples) from the Henan Provincial Center for Disease Control and Prevention (HCDC) database, with the same exclusion criteria and inclusion criteria for both databases, and obtained permission to obtain a research data file in the SEER database from the National Cancer Institute. The independent risk factors were analyzed using the log-rank method, survival curves, univariate and multivariate Cox analysis. Finally, the independent prognostic factors were used to construct the nomogram, random forest and Cloud-LSSVM prognostic models were utilized for validation. Results: The overall median survival time of the SEER database was 14 months (HCDC samples was 46 months), the mean survival time was 26.5 months (HCDC samples was 36.8 months), and the 3-year survival rate was 65.8%. This is because most of the patients with Henan samples are early ESCC, and most of the Seer patients are T3 and T4 people. The multivariate Cox analysis showed that age at diagnosis (P<0.001), sex (P=0.001), race (P=0.002), differentiation grade (P<0.001), pathologic T category (P<0.001), and pathologic M category (P<0.001) were the factors affecting the prognosis of ESCC patients. The SEER data and HCDC database results showed that the accuracy of the Cloud-LSSVM (C-index =0.71, 0.689) model is higher than the differentiation grade (C-index =0.548, 0.506), random forest (C-index =0.649, 0.498), and nomogram (C-index =0.659, 0.563). This new model can realize the unity of the randomness and fuzziness of the Cloud model and utilize the powerful learning and non-linear mapping abilities of LSSVM. Conclusions: Due to the difference of clans between training samples and test samples, the accuracy of prediction is generally not high, but the accuracy of Cloud-LSSVM model is much higher than other models. The new model provides a clear prognostic superiority over the random forest, nomogram, and other models.

Apelin-13 alleviates contrast-induced acute kidney injury by inhibiting endoplasmic reticulum stress.

Contrast-induced acute kidney injury (CI-AKI) is a severe complication associated with significant morbidity and mortality, and effective therapeutic strategies are still lacking. Apelin is an endogenous physiological regulator with antioxidative, anti-inflammatory and antiapoptotic properties. However, the role of apelin-13 in CI-AKI remains unclear. In our study, we found that the protein expression levels of apelin were significantly downregulated in rat kidney tissues and HK-2 cells during contrast media treatment. Moreover, we explored the protective effect of apelin-13 on renal tubule damage using in vitro and in vivo models of CI-AKI. Exogenous apelin-13 ameliorated endoplasmic reticulum stress, reactive oxygen species and apoptosis protein expression in contrast media-treated cells and rat kidney tissues. Mechanistically, the downregulation of endoplasmic reticulum stress contributed critically to the antiapoptotic effect of apelin-13. Collectively, our findings reveal the inherent mechanisms by which apelin-13 regulates CI-AKI and provide a prospective target for the prevention of CI-AKI.

Association of underweight and obesity with adverse postoperative renal outcomes in infants and young children undergoing congenital heart surgery.

Postoperative acute kidney injury (AKI) is a prevalent condition and associated with increased morbidity and mortality following cardiac surgery. This study aimed to investigate the association of underweight and obesity with adverse postoperative renal outcomes in infants and young children undergoing congenital heart surgery. This retrospective cohort study included patients aged from 1 month to 5 years who underwent congenital heart surgery with cardiopulmonary bypass at the Second Xiangya Hospital of Central South University from January 2016 to March 2022. On the basis of the percentile of body mass index (BMI) for age and sex, eligible participants were divided into three nutritional groups: normal bodyweight, underweight (BMI P5), and obesity (BMI P95). Primary outcomes included postoperative AKI and major adverse kidney events within 30 days (MAKE30). Multivariable logistic regression was performed to determine the association of underweight and obesity with postoperative outcomes. The same analyses were reproduced for classifying patients using weight-for-height instead of BMI. A total of 2,079 eligible patients were included in the analysis, including 1,341 (65%) patients in the normal bodyweight group, 683 (33%) patients in the underweight group, and 55 (2.6%) patients in the obesity group. Postoperative AKI (16% vs. 26% vs. 38%; P < 0.001) and MAKE30 (2.5% vs. 6.4% vs. 9.1%; P < 0.001) were more likely to occur in the underweight and obesity groups. After adjusting for potential confounders, underweight (OR1.39; 95% CI 1.08-1.79; P = 0.008) and obesity (OR 3.85; 95% CI 1.97-7.50; P < 0.001) were found to be associated with an increased risk of postoperative AKI. In addition, both underweight (OR 1.89; 95% CI 1.14-3.14; P = 0.014) and obesity (OR 3.14; 95% CI 1.08-9.09; P = 0.035) were independently associated with MAKE30. Similar results were also found when weight-for-height was used instead of BMI.    Conclusion: In infants and young children undergoing congenital heart surgery, underweight and obesity are independently associated with postoperative AKI and MAKE30. These results may help assess prognosis in underweight and obese patients, and will guide future quality improvement efforts. What is Known: • Postoperative acute kidney injury (AKI) is prevalent and associated with increased morbidity and mortality following pediatric cardiac surgery. • Major adverse kidney events within 30 days (MAKE30) have been recommended as a patient-centered endpoint for evaluating AKI clinical trajectories. A growing concern arises for underweight and obesity in children with congenital heart disease. What is New: • Prevalence of underweight and obesity among infants and young children undergoing congenital heart surgery was 33% and 2.6%, respectively. • Both underweight and obesity were independently associated with postoperative AKI and MAKE30 following congenital heart surgery.

Risk factors, prognostic predictors, and nomograms for pancreatic cancer patients with initially diagnosed synchronous liver metastasis.

BACKGROUND: Liver metastasis (LM) remains a major cause of cancer-related death in patients with pancreatic cancer (PC) and is associated with a poor prognosis. Therefore, identifying the risk and prognostic factors in PC patients with LM (PCLM) is essential as it may aid in providing timely medical interventions to improve the prognosis of these patients. However, there are limited data on risk and prognostic factors in PCLM patients. AIM: To investigate the risk and prognostic factors of PCLM and develop corresponding diagnostic and prognostic nomograms. METHODS: Patients with primary PC diagnosed between 2010 and 2015 were reviewed from the Surveillance, Epidemiology, and Results Database. Risk factors were identified using multivariate logistic regression analysis to develop the diagnostic mode. The least absolute shrinkage and selection operator Cox regression model was used to determine the prognostic factors needed to develop the prognostic model. The performance of the two nomogram models was evaluated using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and risk subgroup classification. The Kaplan-Meier method with a log-rank test was used for survival analysis. RESULTS: We enrolled 33459 patients with PC in this study. Of them, 11458 (34.2%) patients had LM at initial diagnosis. Age at diagnosis, primary site, lymph node metastasis, pathological type, tumor size, and pathological grade were identified as independent risk factors for LM in patients with PC. Age > 70 years, adenocarcinoma, poor or anaplastic differentiation, lung metastases, no surgery, and no chemotherapy were the independently associated risk factors for poor prognosis in patients with PCLM. The C- index of diagnostic and prognostic nomograms were 0.731 and 0.753, respectively. The two nomograms could accurately predict the occurrence and prognosis of patients with PCLM based on the observed analysis results of ROC curves, calibration plots, and DCA curves. The prognostic nomogram could stratify patients into prognostic groups and perform well in internal validation. CONCLUSION: Our study identified the risk and prognostic factors in patients with PCLM and developed corresponding diagnostic and prognostic nomograms to help clinicians in subsequent clinical evaluation and intervention. External validation is required to confirm these results.

Machine Learning-Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study.

BACKGROUND: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication following pediatric cardiac surgery, which is associated with increased morbidity and mortality. The early prediction of CSA-AKI before and immediately after surgery could significantly improve the implementation of preventive and therapeutic strategies during the perioperative periods. However, there is limited clinical information on how to identify pediatric patients at high risk of CSA-AKI. OBJECTIVE: The study aims to develop and validate machine learning models to predict the development of CSA-AKI in the pediatric population. METHODS: This retrospective cohort study enrolled patients aged 1 month to 18 years who underwent cardiac surgery with cardiopulmonary bypass at 3 medical centers of Central South University in China. CSA-AKI was defined according to the 2012 Kidney Disease: Improving Global Outcomes criteria. Feature selection was applied separately to 2 data sets: the preoperative data set and the combined preoperative and intraoperative data set. Multiple machine learning algorithms were tested, including K-nearest neighbor, naive Bayes, support vector machines, random forest, extreme gradient boosting (XGBoost), and neural networks. The best performing model was identified in cross-validation by using the area under the receiver operating characteristic curve (AUROC). Model interpretations were generated using the Shapley additive explanations (SHAP) method. RESULTS: A total of 3278 patients from one of the centers were used for model derivation, while 585 patients from another 2 centers served as the external validation cohort. CSA-AKI occurred in 564 (17.2%) patients in the derivation cohort and 51 (8.7%) patients in the external validation cohort. Among the considered machine learning models, the XGBoost models achieved the best predictive performance in cross-validation. The AUROC of the XGBoost model using only the preoperative variables was 0.890 (95% CI 0.876-0.906) in the derivation cohort and 0.857 (95% CI 0.800-0.903) in the external validation cohort. When the intraoperative variables were included, the AUROC increased to 0.912 (95% CI 0.899-0.924) and 0.889 (95% CI 0.844-0.920) in the 2 cohorts, respectively. The SHAP method revealed that baseline serum creatinine level, perfusion time, body length, operation time, and intraoperative blood loss were the top 5 predictors of CSA-AKI. CONCLUSIONS: The interpretable XGBoost models provide practical tools for the early prediction of CSA-AKI, which are valuable for risk stratification and perioperative management of pediatric patients undergoing cardiac surgery.

Effects of mesenchymal stem cells in renovascular disease of preclinical and clinical studies: a systematic review and meta-analysis.

Renal artery stenosis (RAS) causes severe renovascular hypertension, worsening kidney function, and increased cardiovascular morbidity. According to recent studies, mesenchymal stem cells (MSCs) administration is a promising therapy for the improvement of RAS outcomes. The meta-analysis aims to evaluate the therapeutic effects of MSC therapy on RAS. We performed a search in MEDLINE, Web of Science, Embase, and Cochrane Library from inception to 5, October 2022. We included 16 preclinical and 3 clinical studies in this meta-analysis. In preclinical studies, the pooled results indicated that animals treated with MSCs had lower levels of systolic blood pressure (SBP) (SMD = - 1.019, 95% CI - 1.434 to - 0.604, I2 = 37.2%, P = 0.000), serum creatinine (Scr) (SMD = - 1.112, 95% CI - 1.932 to - 0.293, I2 = 72.0%, P = 0.008), and plasma renin activity (PRA) (SMD = - 0.477, 95% CI - 0.913 to 0.042, I2 = 43.4%, P = 0.032). The studies also revealed increased levels of renal blood flow (RBF) in stenotic kidney (STK) (SMD = 0.774, 95% CI - 0.351 to 1.197, I2 = 0%, P = 0.000) and the glomerular filtration rate (GFR) of STK (SMD = 1.825, 95% CI 0.963 to 2.688, I2 = 72.6%, P = 0.000). In clinical studies, the cortical perfusion and fractional hypoxia of the contralateral kidney (CLK) were alleviated by MSC therapy. Taken together, this meta-analysis revealed that MSCs therapy might be a promising treatment for RAS. However, due to the discrepancy between preclinical studies and early clinical trials outcomes, MSC therapy couldn't be recommended in clinical care for the moment, more high-quality randomized controlled clinical trials are needed to validate our conclusions and standardize MSCs protocols.

Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury.

Background: Sepsis-associated acute kidney injury (SA-AKI) is common in critically ill patients, which is associated with significantly increased mortality. Existing mortality prediction tools showed insufficient predictive power or failed to reflect patients' dynamic clinical evolution. Therefore, the study aimed to develop and validate machine learning-based models for real-time mortality prediction in critically ill patients with SA-AKI. Methods: The multi-center retrospective study included patients from two distinct databases. A total of 12,132 SA-AKI patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) were randomly allocated to the training, validation, and internal test sets. An additional 3,741 patients from the eICU Collaborative Research Database (eICU-CRD) served as an external test set. For every 12 h during the ICU stays, the state-of-the-art eXtreme Gradient Boosting (XGBoost) algorithm was used to predict the risk of in-hospital death in the following 48, 72, and 120 h and in the first 28 days after ICU admission. Area under the receiver operating characteristic curves (AUCs) were calculated to evaluate the models' performance. Results: The XGBoost models, based on routine clinical variables updated every 12 h, showed better performance in mortality prediction than the SOFA score and SAPS-II. The AUCs of the XGBoost models for mortality over different time periods ranged from 0.848 to 0.804 in the internal test set and from 0.818 to 0.748 in the external test set. The shapley additive explanation method provided interpretability for the XGBoost models, which improved the understanding of the association between the predictor variables and future mortality. Conclusions: The interpretable machine learning XGBoost models showed promising performance in real-time mortality prediction in critically ill patients with SA-AKI, which are useful tools for early identification of high-risk patients and timely clinical interventions.

Is intravenous iodinated contrast medium administration really harmful in hospitalized acute kidney injury patients: a propensity score-matched study.

OBJECTIVES: To evaluate the effects of intravenous iodinated contrast medium (ICM) administration on the deterioration of renal function (DRF), new renal replacement therapy (RRT) induction and mortality of hospitalized acute kidney injury (AKI) patients. METHODS: Adult hospitalized patients undergoing a contrast-enhanced or unenhanced CT scan within 7 days after AKI diagnosis from January 2015 to December 2019 were identified in this retrospective study. Propensity score matching was performed. Outcomes in 7 and 30 days after CT scan were compared between the contrast and non-contrast groups. Additional analyses were also performed in patients stratified by SCr levels at AKI diagnosis, times and time of CT scan, and in patients without chronic kidney disease or RRT requirement prior to CT scan. RESULTS: In total, 1172 pairs were generated after 1:1 propensity score matching from 1336 cases exposed to ICM and 2724 unexposed. No significant differences were found in the outcomes between the two groups: DRF, 7.8% vs 9.0% (odds ratio (OR) 0.83, 95% confidence interval (CI) 0.62-1.11) in 7 days, 5.1% vs 5.4% (OR 0.93, 95%CI 0.64-1.34) in 30 days; new RRT induction, 2.3% vs 3.3% (OR 0.72,95%CI 0.43-1.18) in 7 days, 4.2% vs 4.5% (OR 0.95,95%CI 0.64-1.41) in 30 days; and mortality, 3.9% vs 4.8% (OR 0.83,95%CI 0.56-1.22) in 7 days, 9.0% vs 10.2% (OR 0.88,95%CI 0.68-1.15) in 30 days. Subset analyses showed similar results. CONCLUSION: Intravenous ICM administration during AKI duration did not increase the risks of DRF, new RRT induction, and mortality in 7 and 30 days after CT scan. KEY POINTS: • Intravenous ICM administration in hospitalized AKI patients does not increase the risks of deterioration of renal function, RRT induction, and mortality in 7 and 30 days after CT scan. • The effects of intravenous ICM on adverse outcomes are minimal even in AKI patients with high level of SCr values or multiple CT scans.

Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis.

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.

Development of mortality prediction model in the elderly hospitalized AKI patients.

Acute kidney injury (AKI) correlates with increased health-care costs and poor outcomes in older adults. However, there is no good scoring system to predict mortality within 30-day, 1-year after AKI in older adults. We performed a retrospective analysis screening data of 53,944 hospitalized elderly patients (age > 65 years) from multi-centers in China. 944 patients with AKI (acute kidney disease) were included and followed up for 1 year. Multivariable regression analysis was used for developing scoring models in the test group (a randomly 70% of all the patients). The established models have been verified in the validation group (a randomly 30% of all the patients). Model 1 that consisted of the risk factors for death within 30 days after AKI had accurate discrimination (The area under the receiver operating characteristic curves, AUROC: 0.90 (95% CI 0.875-0.932)) in the test group, and performed well in the validation groups (AUROC: 0.907 (95% CI 0.865-0.949)). The scoring formula of all-cause death within 1 year (model 2) is a seven-variable model including AKI type, solid tumor, renal replacement therapy, acute myocardial infarction, mechanical ventilation, the number of organ failures, and proteinuria. The area under the receiver operating characteristic (AUROC) curves of model 2 was > 0.80 both in the test and validation groups. Our newly established risk models can well predict the risk of all-cause death in older hospitalized AKI patients within 30 days or 1 year.