Improving population health encompasses not just prolonging life or increasing life expectancy at birth, but also addressing the presence of diseases and the level of functioning [1]. Significant reductions in mortality rates have been achieved in most regions of the world over the past six decades, attributed to advancements in medicine and public health, improved […]

Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599–0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients.

Objectives: This study aims to retrospectively evaluate the survival rate of pulpectomy performed under dental general anesthesia (DGA) through long-term follow-up and to explore the risk factors associated with treatment failure. Methods: The medical records of the children who were diagnosed with S-ECC and received pulpectomy treatment under general anesthesia (GA) from 1 August 2014 to 1 December 2019, in the Stomatological Hospital of Xi’an Jiaotong University, were collected. Two dentistry postgraduates extracted the necessary information and filled in a predesigned excel form. Survival analysis was performed using the Kaplan-Meier method. The shared frailty model was used to explore possible factors affecting the success rate of pulpectomy in primary teeth. Results: A total of 381 children (mean age 3.49 ± 0.90) with S-ECC and 1220 teeth were included in the study, including 590 primary anterior teeth and 630 primary molars. The overall 35-month survival rate was 38.5%, which was 52.9% for anterior teeth and 31.1% for molars. The overall median survival time was 31 months, in which anterior teeth were 35 months and molars were 26 months. The older the children were, the greater the risk of treatment failure (HR 1.56, 95% CI 1.09, 2.24). The risk of pulpectomy failure of primary molars was 1.9 times that of primary anterior teeth (95% CI 1.36, 2.65) and the teeth with abnormal radiological findings before treatment was 1.41 times higher than that of teeth without imaging abnormalities (95% CI 1.74, 3.36). Conclusion: The survival rate of primary tooth pulpectomy is acceptable but decreased gradually with time. The failure rate of pulpectomy in primary molars is higher than that of primary anterior teeth. When the primary caries has extended to the pulp and resulted in a nonvital lesion, pulpectomy could be an option for maximum retention of the primary tooth.

Colorectal cancer is among the leading causes of cancer worldwide. Processed meat was known to be positively associated with a higher risk of gastrointestinal cancer. This study focused on the long-time trends of colorectal cancer mortality attributable to high processed meat intake in China from 1990 to 2019 and the projection for the next decade based on data obtained from the Global Burden of Disease 2019 study. We used an age-period-cohort model to fit the long-time trend. The joinpoint model was conducted to estimate the average and annual change of the attributable mortality. The Bayesian age-period-cohort model was used to project the crude attributable mortality from 2020 to 2030. An upward trend in colorectal cancer mortality attributable to high processed meat intake was observed for both sexes in China from 1990 to 2019, with an overall net drift of 4.009% for males and 2.491% for females per year. Projection analysis suggested that the burden of colorectal cancer incidence and mortality would still be high. Our findings suggested that colorectal cancer death attributable to high processed meat intake is still high in China, and elderly males were at higher risk. Gradually decreasing the intake of processed meat could be an effective way to reduce colorectal cancer mortality.

Background: The impact of aortic arch (AA) morphology on the management of the procedural details and the clinical outcomes of the transfemoral artery (TF)-transcatheter aortic valve replacement (TAVR) has not been evaluated. The goal of this study was to evaluate the AA morphology of patients who had TF-TAVR using an artificial intelligence algorithm and then to evaluate its predictive value for clinical outcomes. Materials and methods: A total of 1480 consecutive patients undergoing TF-TAVR using a new-generation transcatheter heart valve at 12 institutes were included in this retrospective study. The AA measurements were evaluated by deep learning, and then the approach index (I_A) was determined. The machine learning algorithm was used to construct the predictive model and was validated externally. Results: The area under the curve of the I_A model using random forest and logistic regression was 0.675 [95% confidence interval (CI): 0.586–0.764] and 0.757 (95% CI: 0.665–0.849), respectively. The I_A model was validated externally, and consistent distinctions were obtained. After we used a generalized propensity score matching method for continuous exposure, the I_A was the strongest correlation factor for major procedural events (odds ratio: 3.87; 95% CI: 2.13–7.59, P < 0.001). When leaflet morphology or transcatheter heart valve type was an interactive item with I_A, neither of them was statistically significant in terms of clinical outcomes. Conclusion: I_A may be used to identify the impact of AA morphology on procedural and clinical outcomes in patients having TF-TAVR and to help to predict the procedural complications.