Adult ; Age Factors ; Aged ; Alanine Transaminase ; blood ; Alkaline Phosphatase ; blood ; Aspartate Aminotransferases ; blood ; China ; Female ; Hepatitis B ; complications ; Hepatitis C ; complications ; Hospitalization ; statistics & numerical data ; Humans ; Liver Cirrhosis, Alcoholic ; complications ; mortality ; pathology ; Male ; Middle Aged ; Survival Rate ; gamma-Glutamyltransferase ; blood

ObjectiveTo investigate the risk factors for new-onset diabetes after incipient acute pancreatitis (AP). MethodsA retrospective analysis was performed for 95 patients with post-acute pancreatitis diabetes mellitus (PPDM-A) after incipient AP who were admitted to The Affiliated Hospital of Southwest Medical University from June 2013 to January 2020 (PPDM-A group), and 190 patients without diabetes after incipient AP during the same period of time were selected at a ratio of 2∶1 and were enrolled as non-PPDM-A group. Baseline data and clinical data were collected. The t-test or the U test was used for comparison of continuous data, and the chi-square test or the Fisher’s exact test was used for comparison of categorical data; a logistic regression analysis was used for multivariate analysis. Results There were significant differences between the two groups in body mass index (BMI), body weight, and proportion of patients with a drinking history, hyperuricemia, or fatty liver disease (all P＜0.05), while there were no significant differences between the two groups in age, male sex, and proportion of patients with a smoking history, a family history of diabetes, or hypertension (all P＞0.05). There were also significant differences in etiologies (biliary, hyperlipidemic, and alcoholic AP) between the two groups (P＜0.05). Compared with the non-PPDM-A group, the PPDM-A group had significantly higher triglyceride, blood glucose, white blood cell count (WBC), C-reactive protein, and proportion of patients with blood glucose ＞11.1 mmol/L on admission (all P＜0.05), while there were no significant differences in Ca2+, blood amylase, and blood lipase between the two groups (all P＞0.05). Compared with the non-PPDM-A group, the PPDM-A group had significantly higher incidence rates of acute peripancreatic necrotic collections and acute peripancreatic fluid collections, proportion of patients with multiple onset of AP, and proportion of patients with CTSI score ＞4 (all P＜0.05), while there were no significant differences in the proportion of patients with systemic inflammatory response syndrome and disease severity between the two groups (both P＞0.05). The multivariate analysis showed that the outcome of PPDM-A in alcoholic AP patients was 5.868 times that in biliary AP patients (95% confidence interval ［CI］: 1.607-21.418, P=0.007), and the outcome of PPDM-A in hyperlipidemic AP patients was 3.312 time that in biliary AP patients (95%CI: 1.593-6.887, P=0.001). The outcome of PPDM-A in overweight patients was 3.694 times that in patients with normal BMI (95%CI: 1.575-8.667, P=0.003), and the outcome of PPDM-A in obese patients was 5.964 times that in patients with normal BMI (95%CI: 2.516-14.139, P＜0.001). Multiple onset of AP (OR=4.522,95%CI: 2.298-8.900, P＜0.001), blood glucose on admission ＞11.1 mmol/L (OR=6.749,95% CI: 3.381-13.469, P＜0.001), CTSI score ＞4 (OR=1.176,95%CI: 1008-1.371, P=0.039), and WBC (OR=1.082,95%CI: 1.009-1.160, P=0.026) were independent risk factors for PPDM-A. ConclusionMultiple onset of AP, alcoholic AP, hyperlipidemic AP, blood glucose on admission ＞11.1 mmol/L, overweight or obesity, CTSI score ＞4, and WBC are independent risk factors for PPDM-A, which can provide a reference for formulating strategies to prevent or reduce the onset of PPDM-A.

Objective To explore the establishment of a prognostic model based on machine learning algorithm to predict primary graft dysfunction(PGD)in patients with idiopathic pulmonary fibrosis(IPF)after lung transplantation.Methods Clinical data of 226 IPF patients who underwent lung transplantation were retrospectively analyzed.All patients were randomly divided into the training and test sets at a ratio of 7∶3.Using regularized logistic regression,random forest,support vector machine and artificial neural network,the prognostic model was established through variable screening,model establishment and model optimization.The performance of this prognostic model was assessed by the area under the receiver operating characteristic curve(AUC),positive predictive value,negative predictive value and accuracy.Results Sixteen key features were selected for model establishment.The AUC of the four prognostic models all exceeded 0.7.DeLong and McNemar tests found no significant difference in the performance among different models(both P＞0.05).Conclusions Based on four machine learning algorithms,the prognostic model for grade 3 PGD after lung transplantation is preliminarily established.The overall prediction performance of each model is similar,which may predict the risk of grade 3 PGD in IPF patients after lung transplantation.

Lung transplantation is the only effective treatment for end-stage lung disease. Acute kidney injury is a common complication after lung transplantation, which is related to the occurrence of chronic kidney disease and increased postoperative fatality. The factors and mechanisms affecting the occurrence of acute kidney injury are very complex. Clinically, it has been found that various risk factors during the perioperative period of lung transplantation may lead to the occurrence of acute kidney injury, including preoperative, intraoperative and postoperative factors. Early diagnosis of acute kidney injury after lung transplantation and timely intervention are of great significance to improving patient prognosis. Therefore, this article reviews the definition of acute kidney injury, non-invasive assessment, risk factors, prognosis, and clinical management of acute kidney injury after lung transplantation, aiming to provide a reference for the diagnosis and treatment of acute kidney injury after lung transplantation in clinical practice and to improve the survival rate of lung transplant recipients.