一种预测子宫内膜病变患者内膜病理类型的临床模型＊

目的 开发并评估一种用于预测子宫内膜病变患者内膜病理类型的临床模型；方法 选取2019年11月至2021年11月因妇科B超发现子宫内膜病变并于山东中医药大学附属医院行宫腔镜下内膜活检的患者，结合其病史与最小绝对收缩和选择算子法（Least absolute shrinkage and selection operator，LASSO）筛选影响内膜病变的独立危险因素，列线图（nomogram）函数建立列线图模型，采用ROC曲线下方的面积大小（Area Under Curve， AUC）、C指数（C-index）、拟合优度（Hosmer-Lemeshow）检验、自举法（bootstrap）评估及验证模型，依据列线图对纳入患者进行风险赋分，绘制ROC曲线获取风险评分的截断值，从而划分高低风险的人群；结果 阴道流血、绝经、无流产史、并发高血压、B超内膜厚度增厚、内膜回声不正常，中医证型为虚实夹杂证是发生子宫内膜癌及癌前病变的独立危险因素，评估模型所得AUC值与C-index均>0.9，Hosmer-Lemeshow检验显示P>0.05，bootstrap内部验证法所得C-index亦高于0.9，均说明模型准确度、区分度及可信度良好，当患者的风险评分总和≥177分时，属于高风险人群；结论 本模型可以预测B超提示子宫内膜病变患者的内膜病理类型为癌前或恶变型的概率并识别高风险人群。     

Numerical Investigation of Analytical Models of Drug Flux Through Microporated Skin

In this study, drug flux through microporated skin was modeled using detailed numerical solution of the diffusion equation. The results of the modeling were compared to previously published simplified and easy to use analytical equations. Limitations and accuracy of these equations were investigated. Appropriate modifications of the equations were identified to expand them to wider practical applications when pore shape is not circular. Numerical simulations have shown a good accuracy of the new simple equations when these are used within their limits of application.     

The validity of using profile predictions for class III patients planned for bimaxillary orthognathic surgery

This study assessed whether preoperative class III patients could recreate their facial difference based on a profile photograph. Twenty class III pre-surgery bimaxillary orthognathic patients used CASSOS (SoftEnable Technology Ltd.) to manipulate a distorted soft tissue image of them until they felt it resembled their current soft tissue profile. Patients were able to move their upper lip and lower chin backward and forwards, as well as the lower chin up and down. Differences in the mean absolute distance between the patient-perceived position of the upper lip (Labrale superious) and chin (Pogonion) and the actual position of their upper lip and chin were measured on two occasions. Intra-patient reproducibility was found to be excellent (ICC 0.93 to 0.98). All differences were statistically significantly greater than 3mm, and would be clinically significant. Patients were better at re-creating their AP chin position rather than their AP upper lip and vertical chin positions. Approximately half of patients undergoing surgical correction of their class III skeletal pattern were unable to correctly identify their pre-surgical facial profile. Given the lack of awareness of their profile, this questions the validity of using profile planning for informed consent.     

Patient Management Assisted by a Neural Network Reduces Mortality in an Intermediate Care Unit

IntroductionMortality risk prediction for Intermediate Respiratory Care Unit's (IRCU) patients can facilitate optimal treatment in high-risk patients. While Intensive Care Units (ICUs) have a long term experience in using algorithms for this purpose, due to the special features of the IRCUs, the same strategics are not applicable. The aim of this study is to develop an IRCU specific mortality predictor tool using machine learning methods.MethodsVital signs of patients were recorded from 1966 patients admitted from 2007 to 2017 in the Jiménez Díaz Foundation University Hospital's IRCU. A neural network was used to select the variables that better predict mortality status. Multivariate logistic regression provided us cut-off points that best discriminated the mortality status for each of the parameters. A new guideline for risk assessment was applied and mortality was recorded during one year.ResultsOur algorithm shows that thrombocytopenia, metabolic acidosis, anemia, tachypnea, age, sodium levels, hypoxemia, leukocytopenia and hyperkalemia are the most relevant parameters associated with mortality. First year with this decision scene showed a decrease in failure rate of a 50%.ConclusionsWe have generated a neural network model capable of identifying and classifying mortality predictors in the IRCU of a general hospital. Combined with multivariate regression analysis, it has provided us with an useful tool for the real-time monitoring of patients to detect specific mortality risks. The overall algorithm can be scaled to any type of unit offering personalized results and will increase accuracy over time when more patients are included to the cohorts.     

三种智慧放疗计划预测模型的性能评价

目的比较三种智慧放疗计划预测模型的精度与泛化鲁棒性,为模型选择提供依据。方法收集45例前列腺癌和25例鼻咽癌临床放疗计划,运用Z、L、S模型预测前列腺癌中膀胱和直肠、鼻咽癌中左右腮腺的剂量体积直方图(DVH)。应用预测DVH与临床DVH曲线下面积的差别(|DVH预测-DVH临床|)评价预测误差,误差越小则预测精度越高。在单个危及器官(OAR)上比较3种预测模型的精度,并在不同OAR中计算各模型预测精度的标准差以评价和比较模型的泛化鲁棒性。结果对于膀胱和直肠,L模型的预测误差(0.114和0.163)显著大于Z和S模型(≤0.071,P<0.05);对于左腮腺,S模型的预测误差(0.033)与Z和L模型相近(≤0.025,P>0.05);对于右腮腺,S模型的预测误差(0.033)显著大于Z和L模型(≤0.028,P<0.05)。在不同OAR上,S模型的预测精度标准差比Z、L模型小(分别为0.016、0.018和0.060)。结论在前列腺癌膀胱和直肠的DVH预测中Z和S模型的精度较高,而在鼻咽癌左右腮腺中Z和L模型较高,在不同OAR上S模型的泛化鲁棒性相对较好。