Background: Acute kidney injury (AKI) is a prevalent and severe complication of sepsis contributing to high morbidity and mortality among critically ill patients. In this retrospective study, we develop a novel risk-predicted nomogram of sepsis associated-AKI (SA-AKI). Methods: A total of 2,871 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database were randomly assigned to primary (2,012 patients) and validation (859 patients) cohorts. A risk-predicted nomogram for SA-AKI was developed through multivariate logistic regression analysis in the primary cohort while the nomogram was evaluated in the validation cohort. Nomogram discrimination and calibration were assessed using C-index and calibration curves in the primary and external validation cohorts. The clinical utility of the final nomogram was evaluated using decision curve analysis. Results: Risk predictors included in the prediction nomogram included length of stay in intensive care unit (LOS in ICU), baseline serum creatinine (SCr), glucose, anemia, and vasoactive drugs. Nomogram revealed moderate discrimination and calibration in estimating the risk of SA-AKI, with an unadjusted C-index of 0.752, 95 %Cl (0.730–0.774), and a bootstrap-corrected C index of 0.749. Application of the nomogram in the validation cohort provided moderate discrimination (C-index, 0.757 [95 % CI, 0.724–0.790]) and good calibration. Besides, the decision curve analysis (DCA) confirmed the clinical usefulness of the nomogram. Conclusions: This study developed and validated an AKI risk prediction nomogram applied to critically ill patients with sepsis, which may help identify reasonable risk judgments and treatment strategies to a certain extent. Nevertheless, further verification using external data is essential to enhance its applicability in clinical practice.

Purpose: Differentiating between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) is difficult and inefficient. The aim of the present study was to create a model for the differential diagnosis of DN and NDRD in patients with type 2 diabetes mellitus (T2DM). Patients and methods: We consecutively screened 213 patients with T2DM complicated with chronic kidney disease, who underwent renal biopsy at The First Affiliated Hospital of Guangxi Medical University (Nanning, China) between 2011 and 2017. According to the pathological results derived from the renal biopsy, the patients were divided into three groups (74, 130, and nine in the DN, NDRD, and NDRD superimposed with DN group, respectively). Clinical and laboratory data were compared and a diagnostic model was developed based on the following logistic regression model: logit(P)=β₀+β₁X₁+β₂X₂+ … +β_mX_m. Results: We observed a high incidence of NDRD (61.0% of all patients), including various pathological types; the most common type was idiopathic membranous nephropathy. By comparing clinical variables, we identified a number of differences between DN and NDRD. Logistic regression analyses showed that the following variables were statistically significant: the absence of diabetic retinopathy (DR), proteinuria within the non-nephrotic range, the absence of anemia and an estimated glomerular filtration rate (eGFR) ≥90 mL/min/1.73 m². We subsequently constructed a diagnostic model for predicting NDRD, as follows: P_NDRD=1/[1+exp(−17.382–.339×DR−1.274×Proteinuria−2.217×Anemia-1.853×eGFR−0.993×DM+20.892Bp)]. P_NDRD refers to the probability of a diagnosis of NDRD (a P_NDRD≥0.5 predicts NDRD while a P_NDRD<0.5 predicts DN); while DM refers to the duration of diabetes. This model had a sensitivity of 95.4%, a specificity of 83.8%, and the area under the receiver operating characteristic curve was 0.925. Conclusion: Our diagnostic model may facilitate the clinical differentiation of DN and NDRD, and assist physicians in developing more effective and rational criteria for kidney biopsy in patients with T2DM complicated with chronic kidney disease.

This study aimed to construct and assess a machine-learning algorithm designed to forecast survival rates and risk stratification for patients with gastric neuroendocrine neoplasms (gNENs) after diagnosis. Data on patients with gNENs were extracted and randomly divided into training and validation sets using the Surveillance, Epidemiology, and End Results database. We developed a prediction model using 10 machine learning algorithms across 101 combinations to forecast cancer-related mortality in patients with gNENs, selecting the best model using the highest mean over a sequence of time-dependent area under the receiver operating characteristic (ROC) curve (AUC). The performance of the final model was assessed through time-dependent ROC curves for discrimination and calibration curves for calibration. The maximum selection rank method was used to determine the best prognostic risk score threshold for classifying patients into high- and low-risk groups. Afterward, Kaplan–Meier analysis and log-rank test were used to compare survival rates among these groups. Our study examined 775 patients with gNENs, dividing them into training and validation sets. A training set comprised 543 patients, with a median follow-up of 42 months and cumulative mortality rates of 40.0% at 1 year, 48.6% at 3 years, and 54.0% at 5 years. A validation set comprised 232 patients, with cumulative mortality rates of 29.1% at 1 year, 43.5% at 3 years, and 53.2% at 5 years. The optimal random survival forest (RSF) model (mtry = 4, node size = 5) achieved an AUC of 0.839 for survival prediction in the training set. Comprising 11 variables such as demographics, treatment details, tumor characteristics, T staging, N staging, and M staging, the RSF model revealed high predictive accuracy with AUCs of 0.92, 0.96, and 0.96 for 1-, 3-, and 5-year survival, respectively, which was consistently reflected in the validation set with AUCs of 0.88, 0.92, and 0.89, respectively. Moreover, patients were risk-stratified. Although our RSF model effectively stratified patients into different prognostic groups, it needs external validation to confirm its utility for noninvasive prognostic prediction and risk stratification in gNENs. Further research is required to verify its broader clinical applicability.

Background: Acute kidney injury (AKI) is a common and serious complication in patients with acute non-variceal upper gastrointestinal bleeding (NVUGIB). Early prediction and intervention are crucial for improving patient outcomes.Methods: Data for patients presenting with acute NVUGIB in this retrospective study were sourced from the MIMC-IV database. Patients were randomly allocated into training and validation cohorts for further analysis. Independent predictors for AKI were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses in the training cohort. Based on the logistic regression results, a nomogram was developed to predict early AKI onset in acute NVUGIB patients, and implemented as a web-based calculator for clinical application. The nomogram’s performance was evaluated through discrimination, using the C-index, calibration curves, and decision curve analysis (DCA) to assess its clinical value.Results: The study involved 1082 acute NVUGIB patients, with 406 developing AKI. A multivariable logistic regression identified five key AKI predictors: CKD, use of human albumin, chronic liver disease, glucose, and blood urea nitrogen. The nomogram was constructed based on independent predictors. The nomogram exhibited robust accuracy, evidenced by a C-index of 0.73 in the training cohort and 0.72 in the validation cohort. Calibration curves demonstrated satisfactory concordance between predicted and observed AKI occurrences. DCA revealed that the nomogram offered considerable clinical benefit within a threshold probability range of 7% to 54%.Conclusion: Our nomogram is a valuable tool for predicting AKI risk in patients with acute NVUGIB, offering potential for early intervention and improved clinical outcomes.

Purpose: Digestive carcinomas remain a major health burden worldwide and are closely related to type 2 diabetes. The aim of this study was to develop and validate a digestive carcinoma risk prediction model to identify high-risk individuals among those with type 2 diabetes. Patients and Methods: The prediction model was developed in a primary cohort that consisted of 655 patients with type 2 diabetes. Data were collected from November 2013 to December 2018. Clinical parameters and demographic characteristics were analyzed by logistic regression to develop a model to predict the risk of digestive carcinomas; then, a nomogram was constructed. The performance of the nomogram was assessed with respect to calibration, discrimination, and clinical usefulness. The results were internally validated by a bootstrapping procedure. The independent validation cohort consisted of 275 patients from January 2019 to December 2019. Results: Predictors in the prediction nomogram included sex, age, insulin use, and body mass index. The model showed good discrimination (C-index 0.747 [95% CI, 0.718– 0.791]) and calibration (Hosmer–Lemeshow test P=0.541). The nomogram showed similar discrimination in the validation cohort (C-index 0.706 [95% CI, 0.682– 0.755]) and good calibration (Hosmer–Lemeshow test P=0.418). Decision curve analysis demonstrated that the nomogram would be clinically useful. Conclusion: We developed a low-cost and low-risk model based on clinical and demographic parameters to help identify patients with type 2 diabetes who might benefit from digestive cancer screening.