Sepsis is a serious complication of liver cirrhosis. This study aimed to develop a risk prediction model for sepsis among patients with liver cirrhosis. A total of 3130 patients with liver cirrhosis were enrolled from the Medical Information Mart for Intensive Care IV database, and randomly assigned into training and validation cohorts in a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression was used to filter variables and select predictor variables. Multivariate logistic regression was used to establish the prediction model. Based on LASSO and multivariate logistic regression, gender, base excess, bicarbonate, white blood cells, potassium, fibrinogen, systolic blood pressure, mechanical ventilation, and vasopressor use were identified as independent risk variables, and then a nomogram was constructed and validated. The consistency index (C‐index), receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram. As a result of the nomogram, good discrimination was achieved, with C‐indexes of 0.814 and 0.828 for the training and validation cohorts, respectively, and an area under the curve of 0.849 in the training cohort and 0.821 in the validation cohort. The calibration curves demonstrated good agreement between the predictions and observations. The DCA curves showed the nomogram had significant clinical value. We developed and validated a risk‐prediction model for sepsis in patients with liver cirrhosis. This model can assist clinicians in the early detection and prevention of sepsis in patients with liver cirrhosis.

Background:Pancreatic ductal adenocarcinoma (PDAC) accounts for 85% of pancreatic carcinoma cases. Patients with PDAC have a poor prognosis. The lack of reliable prognostic biomarkers makes treatment challenging for patients with PDAC. Using a bioinformatics database, we sought to identify prognostic biomarkers for PDAC. Material/Methods:Using proteomic analysis of the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database, we were able to identify core differential proteins between early and advanced pancreatic ductal adenocarcinoma tissue, and then we used survival analysis, Cox regression analysis, and area under the ROC curves to screen for more significant differential proteins. Additionally, the Kaplan-Meier plotter database was utilized to determine the relationship between prognosis and immune infiltration in PDAC. Results:We identified 378 differential proteins in early (n=78) and advanced stages (n=47) of PDAC (P<0.05). PLG, COPS5, FYN, ITGB3, IRF3, and SPTA1 served as independent prognostic factors of patients with PDAC. Patients with higher COPS5 expression had shorter overall survival (OS) and recurrence-free survival, and those with higher PLG, ITGB3, and SPTA1, and lower FYN and IRF3 expression had shorter OS. More importantly, COPS5, IRF3 were negatively associated with macrophages and NK cells, but PLG, FYN, ITGB3, and SPTA1 were positively related to the expression of CD8+ T cells and B cells. COPS5 affected the prognosis of PDAC patients by acting on B cells, CD8+ T cells, macrophages, and NK cells immune infiltration, while PLG, FYN, ITGB3, IRF3, and SPTA1 affected PDAC patient prognosis through some immune cells. Conclusions:PLG, COPS5, FYN, IRF3, ITGB3 and SPTA1 could be potential immunotherapeutic targets and valuable prognostic biomarkers of PDAC.

It is unclear whether activated partial thromboplastin time (APTT) is predictive of survival in patients with acute pancreatitis (AP). Our study aimed to investigate the relationship between APTT and short-term prognosis in AP. From the Medical Information Mart for Intensive Care (MIMIC)-IV database, a total of 844 patients with AP were randomly divided into the training cohort (n = 591) and the validation cohort (n = 253) at a ratio of 7:3. Based on their APTT values, the patients were divided into the normal and high groups. The primary outcome of this study was 30-and 60-day survival. Kaplan-Meier survival analysis and Cox regression models were used to analyze associations between groups and outcomes. The training and validation cohort matched well on all parameters (p > 0.05). In terms of 30-and 60-day survival, Kaplan-Meier survival curves from both training and validation cohorts demonstrated a lower survival probability for patients in the high APTT group than the normal group (log-rank p < 0.05). In the training cohort, patients in the high APTT group had a statistically significantly higher risk of death than those in the normal group after controlling for possible confounders in Cox regression (p < 0.05). For the high APTT group, the hazard ratios (95% confidence interval [CI]) were 1.63 (95% CI 1.10, 2.61, p = 0.035) and 1.49 (95% CI 1.01, 2.38, p = 0.041), respectively. APTT performed as well as BISAP, Ranson, and APACHE II models in predicting 30-and 60-day survival in patients with AP. The results above have been verified in the validation cohort.Prolonged APTT in patients with AP may increase the risk of short-term death. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?The lack of biomarkers for predicting the short-term prognosis of acute pancreatitis (AP) is a significant problem. Patients with AP are often suffering from coagulation disorders. Activated partial thromboplastin time (APTT) is a biomarker that represents the endogenous coagulation pathway. This study was designed How to cite this article: Yang Y, Du S, Yuan W, Kou Y, Nie B. Prolonged activated partial thromboplastin time predicts poor short-term prognosis in patients with acute pancreatitis: A retrospective cohort study.