Objective: Sepsis is a serious complication of acute cholangitis. We aimed to establish a nomogram for predicting the probability of sepsis in patients with acute cholangitis. Methods: Subjects were patients with acute cholangitis in the Medical Information Mart for Intensive Care database. Extraneous variables were excluded based on stepwise regression. The nomogram was established using logistic regression.Results: The predictive model comprised five variables: age (odds ratio [OR]: 1.03, 95% confidence interval [CI]: 1.01-1.04), ventilator-support time (OR: 1.004, 95% CI: 1.001-1.008), diabetes (OR: 10.74, 95% CI: 2.80-70.57), coagulopathy (OR: 2.92, 95% CI: 1.83-4.73) and systolic blood pressure (OR: 0.62, 95% CI: 0.41-0.93). The areas under the receiver operating characteristic curve of the nomogram for the training and validation sets were 0.700 and 0.647, respectively. The Hosmer-Lemeshow goodness-of-fit test revealed high concordance between the predicted and observed probabilities for both the training and validation sets. The calibration plot also demonstrated good agreement between the predicted and observed outcomes for both the training and validation sets. Conclusions: We developed and validated a risk-prediction model for sepsis in patients with acute cholangitis. Our results will be helpful for preventing sepsis in patients with acute cholangitis.

Objective: Clinical trials are the source of evidence. ClinicalTrials.gov is valuable for analyzing current conditions. Until now, the state of drug interventions for heart infections is unknown. The purpose of this study was to comprehensively assess the characteristics of trials on cardiac-related infections and the status of drug interventions. Methods: The website ClinicalTrials.gov was used to obtain all registered clinical trials on drug interventions for cardiac-related infections as of February 16, 2019. All registration studies were collected, regardless of their recruitment status, research results, and research type. Registration information, results, and weblink-publications of those trials were analyzed. Results: A total of 45 eligible trials were evaluated and 86.7% of them began from or after 2008 while 91.1% of them adopted interventional study design. Of all trials, 35.6% were completed and 15.6% terminated. Besides, 62.2% of interventional clinical trials recruited more than 100 subjects. Meanwhile, 86.7% of the eligible trials included adult subjects only. Of intervention trials, 65.8% were in the third or fourth phase; 78.1% adopted randomized parallel assignment, containing two groups; 53.6% were masking, and 61.0% described treatment. Moreover, 41.5% of the trials were conducted in North America while 29.3% in Europe. Sponsors for 40.0% of the studies were from the industry. Furthermore, 48.9% of the trials mentioned information on monitoring committees, 24.4% have been published online, and 13.3% have uploaded their results. Drugs for treatments mainly contained antibiotics, among which glycopeptides, β-lactams, and lipopeptides were the most commonly studied ones in experimental group, with the former ones more common. Additionally, 16.2% of the trials evaluated new antimicrobials. Conclusions: Most clinical trials on cardiac-related infections registered at ClinicalTrials.gov were interventional randomized controlled trials (RCTs) for treatment. Most drugs focused in trials were old antibiotics, and few trials reported valid results. It is necessary to strengthen supervision over improvements in results, and to combine antibacterial activity with drug delivery regimens to achieve optimal clinical outcomes.

Background: Among the problems caused by hypertension, the early renal damage is often ignored. It can not be diagnosed until the condition is serious and irreversible damage occurs. So we decide to screen and explore related risk factors for hypertensive patients with early renal damage, and establish early-warning model of renal damage based on the data-mining method to achieve early diagnosis for hypertensive patients with renal damage. Methods: With the aid of electronic information management system for hypertensive specialist out-patient, we collected 513 cases of original untreated hypertensive patients, and recorded their demographic data, ambulatory blood pressure parameters, blood routine index and blood biochemical index to establish the clinical database, then we screen risk factors for early renal damage through feature engineering, and use Random Forest, Extra-Trees and XGBoost to build a early-warning model respectively. Finally, we build a new model by model fusion based on Stacking strategy. We use cross validation to evaluate the stability and reliability of each mode to determine the best risk assessment model. Results: According to the degree of importance, the descending order of features selected by feature engineering is the drop rate of systolic blood pressure at night, the red blood cell distribution width, blood pressure circadian rhythm, the average diastolic blood pressure at daytime, body surface area, smoking, age and HDL. Among the early-warning models of renal damage without model fusion, XGBoost has the best effect, the average accuracy of 5-fold cross validation is 0.90457. And the average accuracy of the two-dimensional fusion model based on Stacking strategy is 0.91428, which is greatly improved. Conclusions: Through feature engineering and risk factor analysis, we select the drop rate of systolic blood pressure at night, the red blood cell distribution width, blood pressure circadian rhythm and the average diastolic blood pressure at daytime as early-warning factors of early renal damage in patients with hypertension. On this basis, the two-dimensional fusion model based on Stacking strategy has a better effect than the single model, which can be used for risk assessment of early renal damage in hypertensive patients.