Bone mineral density changes after parathyroidectomy are dependent on biochemical profile

Background

Bone mineral density (BMD) has been found to improve after parathyroidectomy (PTX) in patients with primary hyperparathyroidism. There are few data on the effect of PTX on BMD in normocalcemic and normohormonal primary hyperparathyroidism.

1.5T 磁共振加速器X线束剂量学特性测试

目的 由于磁场会改变次级电子运动轨迹,继而影响剂量场分布,磁共振加速器(MR-Linac) X线束剂量学特性与常规加速器有差别。本项目旨在测量和分析1.5T MR-Linac的X线束剂量学特性。方法 中国医学科学院肿瘤医院于2019年5月安装1台瑞典医科达公司Unity型1.5T MR-Linac,使用磁场兼容工具对其进行测量,测量项目包括表面剂量、最大剂量点深度、射线质、离轴比曲线(OAR)中心位置、对称性、半影宽度、不同机架角度的输出量变化。结果 不同射野面积的平均表面剂量为40.48%,平均最大剂量点深度为1.25cm。10cm×10cm射野面积下,x轴方向的OAR中心位置往x₂侧偏移1.47mm,对称性为101.33%,两侧半影宽度分别为6.86mm和7.14mm;y轴方向的OAR中心位置偏移0.3mm,对称性为100.85%,两侧半影宽度分别为5.92mm和5.95mm。不同机架角度下输出量最大偏差达1.50%。结论 与常规加速器不同,MR-Linac不同射野面积表面剂量数值趋于一致,最大剂量点深度上升。x轴方向的OAR中心位置往x₂侧偏移,造成对称性变差和半影不对称。不同机架角度下的输出量变化明显,需要修正。     

Defining the relative contribution of health care environmental components to patient outcomes in the model of 30-day readmission after coronary artery bypass graft (CABG)

BackgroundPatient outcomes following health care interventions may be dependent on a variety of factors: patient, surgeon, hospital, information technology, and temporal, cultural, and socioeconomic factors, among others. In this study, we characterize the relative contribution of each of these factors using a model of 30-day readmission following coronary artery bypass graft.MethodsThe Healthcare Cost and Utilization Project, the American Hospital Association Annual Health Survey Databases, the Healthcare Information and Management Systems Society, and the Distressed Communities Index from 2010 to 2013 were linked for Florida, Iowa, Massachusetts, Maryland, New York, and Washington. Logistic regression, random forest, decision tree, gradient boosting, k-nearest-neighbors classification, and XGBoost tree models were implemented. Modeling results were compared on the basis of predictive accuracy, sensitivity, specificity, and area under the curve. Decision tree performed best and was selected for further analysis. A gradient-boosted model was used to quantify factor contribution.ResultsThe model had 45,352 patients, 54,096 admissions, and a 16.2% 30-day readmission rate after coronary artery bypass graft. The top 10 predictors were disposition at discharge, number of chronic conditions, total procedures, median household income, adults without high school diplomas, primary payer method, Agency for Healthcare Research and Quality comorbidity: renal failure, patient location (urban-rural), admission type, and age categories. The top 3 socioeconomic predictors were estimated state median household income, adults without high school diplomas, and patient location (urban versus rural designation). The relative contribution of patient/temporal, socioeconomic, hospital information technology, and hospital factors to readmission is 83.45%, 5.71%, 6.34%, and 4.31%, respectively.ConclusionIn this model, the contribution of socioeconomic factors is substantive but lags significantly behind patient/temporal factors. With ever increasing availability of data, identification of contributors to patient outcomes within the overall health care macroenvironment will allow prioritization of interventions.