早产影响因素的多分类Logistic回归分析

目的探讨早产儿发生的危险因素.方法采用病例对照研究法对227名早产儿(孕周＜33周73人,33～36周154人)和750名对照组进行相关因素调查,用多分类Logistic回归分析法进行危险因素分析.结果与分娩孕周＜33周早产儿有关的因素是:母亲孕前体质指数≥28者、有人工流产史、孕早期发热、母亲文化水平为小学及以下者,首次产前检查血红蛋白低于100g/L者、尿蛋白和尿糖阳性等因素是分娩孕周＜33周早产儿的危险因素.与分娩孕周在33～36周早产儿有关的因素有:母亲分娩年龄大于35岁、母亲孕期吸烟及饮酒、年家庭收入低及父亲吸烟等因素.父亲职业为农民、多胎妊娠、孕期精神刺激、产前检查次数少、前置胎盘和妊高症等因素是二者的共同危险因素.结论早产是多种因素联合作用的结果,应采取综合性措施降低早产儿的发生率.     

Evolving classification of intensive care patients from event data

ObjectiveThis work aims at predicting the patient discharge outcome on each hospitalization day by introducing a new paradigm—evolving classification of event data streams. Most classification algorithms implicitly assume the values of all predictive features to be available at the time of making the prediction. This assumption does not necessarily hold in the evolving classification setting (such as intensive care patient monitoring), where we may be interested in classifying the monitored entities as early as possible, based on the attributes initially available to the classifier, and then keep refining our classification model at each time step (e.g., on daily basis) with the arrival of additional attributes.Materials and methodsAn oblivious read-once decision-tree algorithm, called information network (IN), is extended to deal with evolving classification. The new algorithm, named incremental information network (IIN), restricts the order of selected features by the temporal order of feature arrival. The IIN algorithm is compared to six other evolving classification approaches on an 8-year dataset of adult patients admitted to two Intensive Care Units (ICUs) in the United Kingdom.ResultsRetrospective study of 3452 episodes of adult patients (≥ 16 years of age) admitted to the ICUs of Guy’s and St. Thomas’ hospitals in London between 2002 and 2009. Random partition (66:34) into a development (training) set n = 2287 and validation set n = 1165. Episode-related time steps: Day 0—time of ICU admission, Day x—end of the x-th day at ICU. The most accurate decision-tree models, based on the area under curve (AUC): Day 0: IN (AUC = 0.652), Day 1: IIN (AUC = 0.660), Day 2: J48 decision-tree algorithm (AUC = 0.678), Days 3–7: regenerative IN (AUC = 0.717–0.772). Logistic regression AUC: 0.582 (Day 0)—0.827 (Day 7).ConclusionsOur experimental results have not identified a single optimal approach for evolving classification of ICU episodes. On Days 0 and 1, the IIN algorithm has produced the simplest and the most accurate models, which incorporate the temporal order of feature arrival. However, starting with Day 2, regenerative approaches have reached better performance in terms of predictive accuracy.     

留置PICC影响因素的Logistic回归分析与研究

目的:探讨影响PICC留置的影响因素,为正常留置采取干预措施提供科学依据。方法:采用前瞻性调查的方法,对2008年11月～2011年11月在我院实施PICC患者的相关信息采用PICC临床观察表记录,将我们所关注的30个因素引入多因素Logistic回归模型中进行分析。结果:475例PICC患者中有62例(13.05%)发生非正常拔管,不能达到预期的留置时间,平均留置时间21.48 d。引入分析的30项指标中,多因素Logistic回归分析显示8项指标与非正常拔管的发生有相关性(P<0.05)。结论:PICC导管的留置受多种因素影响,应在PICC置管前后针对相关因素采取有效的预防措施,如加强无菌隔离技术、提高一针穿刺成功率、合理使用抗菌药物和强化局部护理等,有助于长期有效、安全地应用PICC。     

新生儿缺血缺氧性脑病相关危险因素的分析

目的：探讨新生儿缺血缺氧性脑病（ HIE）发生的相关危险因素。方法回顾性分析绍兴市上虞妇幼保健院2008年1月至2013年1月收治的350例HIE患儿临床资料，另选取同期顺产的200例非HIE高危新生儿作为对照组，应用单因素分析和Logistic回归分析筛选HIE发生的相关危险因素。结果多因素Logistic回归显示分娩前因素只有妊娠期糖尿病与HIE相关（RR＝1．43，95％CI：1．08～2．12，P＜0．05），其他HIE独立预测因素均为分娩期因素，即羊水粪染、肩先露、脐带绕颈、产程延长、胎心监护异常、器械助产、急诊剖宫产、1min Apgar＜3分和5min Apgar＜5分（RR值分别为1．97、6．31、2．26、1．84、2．41、1．91、1．21、17．48和20．07，95％CI分别为1．38～3．67、3．44～12．42、1．52～5．20、1．09～4．65、1．14～5．96、1．24～4．77、1．04～2．39、8．21～174．26和7．26～164．28，均P＜0．05）。结论新生儿HIE与母体、胎儿等多种因素相关，其中分娩期是关键，其他因素通过影响分娩期因素起作用。     

Utilising allelic dropout probabilities estimated by logistic regression in casework

Some advanced methods for DNA profile interpretation require a probability for the event of dropout. Methods have been suggested based on logistic regression. Two of these respectively use a proxy for template that is constant across loci and one that is modelled using an exponential curve. Both of these methods allow different modelling constants from each locus. A variant of the model using an exponential curve is discussed. This variant constrains the constants to be the same for every locus. We test these two methods and the variant by developing the constants (training) on one set of data and testing them on another. This mimics the likely use in casework. We find that the new variant appears to be the most useful in that it performs better than the other two options when trained on one data set and used on another. The hypothesised reason for this is that locus to locus variation in amplification efficiency varies with time, multimix batch, or from sample to sample.     

护理教学中影响护生评判性思维能力因素分析及对策探讨

目的 探讨护理教学中影响护生评判性思维能力的因素及对策效果分析.方法 以我校在读的2009级全日制护理学专业的398名护生为研究对象,采用单因素Logistic回归分析、多因素Logistic回归分析方法,通过课堂气氛、实习等影响护理教学中护士评判性思维能力培养的最重要因素,展开针对性教学,探寻效果.结果 采用CTDI-CV进行评定,与培养实施前相比较,实施1年后评判性思维能力总分及各项指标均升高明显,差异具有,显著意义(P＜0.05).结论 通过有针对性的活跃课堂气氛,增加实习等实践机会,可有效提高护生的评判性思维能力.     

转移性骨肿瘤患者医源性诊断延迟的相关因素研究

目的:分析影响转移性骨肿瘤患者医源性诊断延迟的相关因素,为转移性骨肿瘤的早期诊断提供参考依据。方法:采用多分类无序反应变量logistic回归分析2006年1月～2011年11月转移性骨肿瘤患者入院时性别、年龄、首诊症状、首诊医院等级、首诊科室、首诊是否病理检查、首诊至病理检查的时间、首诊至确诊经过几个科室及首诊至确诊间有无治疗处理等对转移性骨肿瘤患者医源性诊断延迟的影响。结果:126例患者,医源性诊断延迟率为(72.2%91/126);诊断延迟1～88周,中位数5周。首诊是否进行病理检查、首诊至病理检查时间、首诊至确诊经过几个科室、确诊前有无进行治疗与医源性诊断延迟间存在显著性相关(P<0.01)。结论:首诊即在专业骨科诊断室进行并作病理检查,尽可能减少采取确诊前非针对性治疗,可减少医源性诊断延迟的发生。     

哮喘健康教育的临床效果

目的研究哮喘健康教育各个方面对临床效果的影响，为系统规范的进行哮喘健康教育提供科学参考。方法对我院呼吸科104例哮喘病患者进行健康教育，出院时以问卷调查的方式，对患者掌握健康教育5个方面的效果进行评价。用Logistic回归分析影响复发住院率的因素。结果疾病知识、饮食注意、日常活动、正确用药和心理情绪合格率分别为43．75％，52．86％，76．68％，70．64％，67．46％；复发住院率22．12％；日常活动、疾病知识和正确用药对应的OR值为0．176，0．451，0．791，差异有统计学意义（P〈0．05），表明是复发住院的重要影响因素。饮食注意和心理情绪OR值均小于1，差异无统计学意义（P〉0．05）。结论对于哮喘的健康教育需要更加规范化，专业化，需要着重注意对日常活动、疾病知识和正确用药等方面有针对性地进行健康教育。