1401例超长住院日患者分布与影响因素分析

目的分析超长住院患者分布及影响因素,探讨减少超长住院日的措施。方法从江苏省某三甲医院病案管理系统中调取2020年1月1日-2020年12月31日134016例出院患者的住院病案首页资料,对住院时间≥30天的1401例超长住院患者的分布特征进行统计描述,采用Logistic回归模型分析超长住院日的影响因素。结果2020年全院平均住院日为7.13天,其中超长住院患者平均住院日为41.85天。超长住院患者以60岁以上年龄组最多(39.61%);出院科室主要分布在血液科(42.18%)、普通外科(11.85%)、骨科(7.49%)等;疾病类别主要为肿瘤(47.32%)、影响健康状态和与保健机构接触的因素(10.56%)、循环系统疾病(7.07%)等;多因素Logistic回归结果显示,男性(OR=1.188)、离院方式为非医嘱离院或其他(OR=2.046)和死亡病例(OR=3.362)是超长住院的危险因素。结论控制超长住院日对平均住院日影响显著,医院应加强重点人群、重点科室和重点病种管理提高诊疗管理水平,缩短平均住院日。     

NICE’s recent guidelines on “the size of your waist” unfairly penalizes shorter people

The National Institute for Clinical Excellence (NICE) has just released its latest guidelines to assess and predict health risk, such as type 2 diabetes, hypertension or cardiovascular disease. Their latest advice is “Keep the size of your waist to less than half of your height”. We believe this advice is flawed and will seriously and unfairly penalize shorter people and lull taller people into a false sense of security. In this short communication, we provide this evidence. We consider this a serious oversight by NICE and feel strongly that this evidence needs to be made available in the public domain.     

435例原发性高血压患者脉图参数与心血管危险分层的相关性分析

目的 本研究旨在探讨原发性高血压患者脉图参数与心血管危险分层的相关性，为原发性高血压患者心血管风险事件预警提供中医特色的依据。方法 采集435例原发性高血压患者的症状体征、脉象信息、生化指标等，分为低中危组、高危组、很高危组，采用单因素方差分析、秩和检验、有序logistic回归分析等方法，探讨原发性高血压患者脉图参数与心血管危险分层的相关性。结果 ①高血压低中危组→高危组→很高危组患者病程逐渐延长，两两组间比较有统计学差异；②高血压低中危组→高危组→很高危组患者的脉图参数H3/H1、H4/H1逐渐上升；高血压患者很高危组H5/H1、T1/T低于低中危组；③有序logistic回归分析结果显示，脉图参数T1、T4、T5、T为影响因素。结论 病程、血脂、肾功能是高血压患者心血管危险分层的影响因素之一。心血管危险分层等级的升高与脉图参数H3/H1、H4/H1、T5、T的升高及H5/H1、T4、T1的降低相关，说明脉图能一定程度上反映原发性高血压患者血管壁增厚、血管内壁受损、血管硬化及左心室收缩功能的减退程度。     

A new analytical cut-off point for determining 18 years of age using MRI on medial clavicular epiphysis

Evaluation of the ossification of the medial clavicular epiphysis plays a key role in forensic age estimation. The purpose of the present study was to assess a new numerical cut-off at the age of 18 years, taking into consideration Magnetic Resonance (MR) images of the medial clavicular epiphysis. We analyzed 163 MR scans of Italian subjects aged between 14 and 25 years. Using the data obtained we calculated two ratios: REM-1 (ratio between the length of the whole epiphysis and the length of the metaphysis) and REM-2 (ratio between the length of epiphyseal-metaphyseal fusion and the length of the metaphysis). In 68 out of 163 cases it was not possible to measure REM-2. The reproducibility was demonstrated using the Intraclass Correlation Coefficient (ICC) (Cronbach’s alpha > 0.80). REM-1 and REM-2 were compared in each category of age (adult and minor) by the Wilcoxon signed-rank test. The cut-off points for measurements of REM-1 and REM-2 were determined by logistic regression. For REM-1, the cut-off scores were 0.83 for all individuals (accuracy = 94.77%) and males (accuracy = 96.05%), and 0.86 for females (accuracy = 92.30%). For REM-2, the cut-off values were 0.40 for all individuals and males (accuracy = 100.00%), and 0.41 for females (accuracy = 100.00%). Finally, receiver operating characteristic (ROC) curves for age classification based on REM-1 and REM-2 were constructed, showing that REM-2 had the highest discriminative power. Thus, a new cut-off model for predicting the age of majority has been introduced, conducting a quantitative analysis thanks to the use of a high-resolution imaging tool.     

EEG functional connectivity contributes to outcome prediction of postanoxic coma

ObjectiveTo investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.MethodsProspective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5).ResultsWe included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity.ConclusionFunctional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma.SignificanceFunctional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.     

青海地区获得性耐药结核病产生的危险因素及其预测模型的构建

目的探讨青海地区原发性与获得性耐药结核病的危险因素及临床发病风险预测模型构建。方法选取2018年7月-2019年7月青海地区132例耐药结核病患者为观察组,青海地区132例非耐药结核病患者为对照组,查阅患者的的临床资料,并自制调查问卷,以问卷调查的方式收集患者的相关信息,分析两组患者的临床指征,采用多因素Logistic回归分析完成风险模型建立,绘制ROC曲线分析风险模型的预测效能。结果单因素结果表明:青海地区原发性与获得性耐药结核病发生率与年龄、性别、与患者接触、3月底痰涂片结果无统计学意义(P>0.05);与居住地、婚姻状况、家庭收入、治疗末痰涂片结果、体重指数、抗结核药物性肝损害、结核病灶数、结核空洞、合并糖尿病及登记分类初治具有统计学意义(P<0.05);多因素logistic结果表明:青海地区原发性与获得性耐药结核病发生率与居住地、登记分类、体重指数、抗结核药物性肝损害、3月末肺结核病灶数及痰涂片结果,具有统计学意义(P<0.05);ROC曲线结果表明:构建获得性耐药结核病发病风险预测模型用于青海地区原发性与获得性耐药结核病患者中ACU值为0.847,预测敏感性为87.46%,特异性为90.29%。结论青海地区原发性与获得性耐药结核病危险因素较多,不同因素能相互作用、相互影响,构建获得性耐药结核病发病风险预测模型,能较好的预测临床发病,有助于指导临床诊疗。