Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Model calibration concerning risk coefficients of driving safety field model

Driving safety field(DSF) model has been proposed to represent comprehensive driving risk formed by interactions of driver-vehicle-road in mixed traffic environment. In this work, we establish an optimization model based on grey relation degree analysis to calibrate risk coefficients of DSF model. To solve the optimum solution, a genetic algorithm is employed. Finally, the DSF model is verified through a real-world driving experiment. Results show that the DSF model is consistent with driver's hazard perception and more sensitive than TTC. Moreover, the proposed DSF model offers a novel way for criticality assessment and decision-making of advanced driver assistance systems and intelligent connected vehicles.

Construction of a risk score prognosis model based on hepatocellular carcinoma microenvironment

BACKGROUND Hepatocellular carcinoma(HCC)is a common cancer with a poor prognosis.Previous studies revealed that the tumor microenvironment(TME)plays an important role in HCC progression,recurrence,and metastasis,leading to poor prognosis.However,the effects of genes involved in TME on the prognosis of HCC patients remain unclear.Here,we investigated the HCC microenvironment to identify prognostic genes for HCC.AIM To identify a robust gene signature associated with the HCC microenvironment to improve prognosis prediction of HCC.METHODS We computed the immune/stromal scores of HCC patients obtained from The Cancer Genome Atlas based on the ESTIMATE algorithm.Additionally,a risk score model was established based on Differentially Expressed Genes(DEGs)between high and lowimmune/stromal score patients.RESULTS The risk score model consisting of eight genes was constructed and validated in the HCC patients.The patients were divided into high-or low-risk groups.The genes(Disabled homolog 2,Musculin,C-X-C motif chemokine ligand 8,Galectin 3,B-cell-activating transcription factor,Killer cell lectin like receptor B1,Endoglin and adenomatosis polyposis coli tumor suppressor)involved in our risk score model were considered to be potential immunotherapy targets,and they may provide better performance in combination.Functional enrichment analysis showed that the immune response and T cell receptor signaling pathway represented the major function and pathway,respectively,related to the immune-related genes in the DEGs between high-and low-risk groups.The receiver operating characteristic(ROC)curve analysis confirmed the good potency of the risk score prognostic model.Moreover,we validated the risk score model using the International Cancer Genome Consortium and the Gene Expression Omnibus database.A nomogram was established to predict the overall survival of HCC patients.CONCLUSION The risk score model and the nomogram will benefit HCC patients through personalized immunotherapy.

Analysis of Length of Stay (LOS) Data from the Medical Records of Tertiary Care Hospital in Saudi Arabia for Five Diagnosis Related Groups: Application of Cox Prediction Model

Background: One of the main objectives of hospital managements is to control the length of stay (LOS). Successful control of LOS of inpatients will result in reduction in the cost of care, decrease in nosocomial infections, medication side effects, and better management of the limited number of available patients’ beds. The length of stay (LOS) is an important indicator of the efficiency of hospital management by improving the quality of treatment, and increased hospital profit with more efficient bed management. The purpose of this study was to model the distribution of LOS as a function of patient’s age, and the Diagnosis Related Groups (DRG), based on electronic medical records of a large tertiary care hospital. Materials and Methods: Information related to the research subjects were retrieved from a database of patients admitted to King Faisal Specialist Hospital and Research Center hospital in Riyadh, Saudi Arabia between January 2014 and December 2016. Subjects’ confidential information was masked from the investigators. The data analyses were reported visually, descriptively, and analytically using Cox proportional hazard regression model to predict the risk of long-stay when patients’ age and the DRG are considered as antecedent risk factors. Results: Predicting the risk of long stay depends significantly on the age at admission, and the DRG to which a patient belongs to. We demonstrated the validity of the Cox regression model for the available data as the proportionality assumption is shown to be satisfied. Two examples were presented to demonstrate the utility of the Cox model in this regard.

A Cohort Model for Ash Mortality Risk Due to Potential Emerald Ash Borer Infestation

Emerald ash borer (Agrilus planipennis Fairmaire) (Coleoptera: Buprestidae) is a phloem-feeding beetle native to Asia that is causing widespread mortality of ash trees in eastern North America. In this study, we quantify ash mortality risk associated with potential anthropogenic-induced introduction of Emerald Ash Borer (EAB) in North Dakota. The cohort model is calibrated with data from Ohio using weighting across factors—proximity to existing ash stands, campgrounds, roads and rails—to get a more accurate assessment of overall ash mortality risk. These factors are known to be associated with introduction of EAB to unaffected areas. Two protocols, a) “detection trees” and b) EAB traps are utilized to investigate EAB presence. Ash mortality risk maps such as the ones produced here may guide the placement of traps. Although North Dakota regions of high density ash tree stands are few, the resulting relative ash mortality risk map displays: a) very high risk areas around the Turtle Mountains and Theodore Roosevelt National Park and b) regions of high relative risk along the main riparian corridors. The applicability of risk maps such as the one developed may aid in assessing areas that may require significant monitoring.

基于GRA-ISM-HMM的广州市肉及肉制品安全风险评估

该研究旨在利用广州食品安全风险监测2015年至2020年针对肉及肉制品样本的检测数据,构建肉及肉制品的安全风险评估模型,从而了解广州市该段时间内肉及肉制品的食品安全风险及其时变特点。该研究采取灰色关联分析方法和解释结构模型建立风险指数,并基于该指标值作为隐马尔可夫模型的观测值探讨观测背后的隐含风险状态。分析结果显示,2015~2020年所有样本综合风险指数结果都在[0,0.45]之间,总体风险都较小,其中2019年风险最高;将风险指数进行等级划分,显示2015~2020年风险等级为[1,2,2,2,3,1];通过HMM分析得到这六年的隐藏风险等级为[0,1,1,1,2,0],与观测风险结果一致,且HMM预测2021年风险等级为1,即表明广州肉及肉制品风险往良好态势发展。最后,进行风险差异原因分析,发现各肉制品分类之间有差异,其中腊肠、鸡肉和腊肉的风险指数较高于其他种类,而2019年增加腊肠和腊肉的检测是风险增加的一个原因。总体来说,广州肉及肉制品风险较小,但依旧需要监督改善。

长江上游重庆段流域生态风险评价——基于RRM模型

通过建立长江重庆段流域相对风险模型(RRM),探讨了快速城市化化对长江重庆段流域生态环境的影响.结果表明:2010年相较于2006年,长江重庆段流域的综合生态风险有所减小.综合生态风险的结果表明,尽管城市工业用地的潜在风险不断加大,但是环境保护措施,对风险防范意识的加强对缓解风险起到了显著的作用.尤其是在重庆市主城区所处的风险单元流域第3区,其生态风险值在2010年显著降低.但是相较2006年,2010年长江重庆市主城区下游流域的生态风险压力明显升高.

心脏植入式电子设备感染风险预测模型的系统评价

目的:系统评价心脏植入式电子设备(CIED)植入术后设备感染(DRI)的风险预测模型。方法:通过计算机检索PubMed、Embase、Web of Science、Cochrane图书馆、CINAHL、中国生物医学文献数据库、中国知网、维普网、万方数据库中与CIED植入术后DRI风险预测模型相关的文献,检索时间为从建库至2023年12月2日。由2名研究者独立筛选文献、提取资料并完成纳入文献的偏倚风险与适用性评价。结果:共纳入16项研究,模型总体适用性较好,但偏倚风险较高,ROC曲线的AUC为0.67~0.96。11项研究完成了内部验证,5项研究进行了外部验证。囊袋和(或)电极重置/装置升级、肾功能不全或肾功能衰竭、年龄、植入埋藏式心脏复律除颤器或心脏再同步化治疗、使用抗凝药是DRI的预测因子。结论:目前CIED植入术后DRI风险预测模型整体性能较好,适用性较好,但偏倚风险较高。需在数据来源、变量筛选、模型评价等方面提高研究质量,开展前瞻性队列研究,完善现有模型的外部验证,并积极研发适用于我国人群的预测模型。

基于生物信息学构建口腔鳞状细胞癌免疫基因的预后模型

目的旨在构建免疫相关基因(IRGs)的风险预测模型,以预测口腔鳞状细胞癌(OSCC)患者的预后。方法应用生物信息学技术分析OSCC的转录组测序数据,鉴定出差异表达的IRGs(DEIRGs)。通过Cox回归分析构建DEIRGs的风险预测模型,并对其预测能力进行评估。分析该模型与临床病理和免疫细胞浸润的相关性。结果通过比较OSCC和正常样本共鉴定出3634个差异表达基因,其中包括330个DEIRGs(FDR<0.05,|logFC|>1)。单因素Cox回归分析筛选出与预后相关的20个DEIRGs(P<0.05),多因素Cox回归分析筛选出其中15个DEIRGs用于构建风险预测模型。该模型可作为OSCC患者的独立预后因素(P<0.001),预测患者预后的能力具有较高的准确性(AUC=0.732),并与临床分期(t=-3.484,P<0.001)、B细胞(Cor=-0.180,P=0.002)和CD4^(+)T细胞(Cor=-0.127,P=0.026)密切相关。结论基于15个预后相关DEIRGs构建的风险预测模型能够有效地预测OSCC患者的预后,可帮助临床医生为不同风险的OSCC患者选择个性化的治疗策略。

基于TCGA数据库构建肝细胞癌双硫死亡相关基因(DRGs)预后风险模型及评价

目的基于癌症基因组图谱(the cancer genome atlas,TCGA)数据库构建肝细胞癌(hepatocellular carcinoma,HCC)双硫死亡相关基因(disulfidptosis-related genes,DRGs)预后风险模型及评价。方法通过生物信息学方法分析TCGA数据库中371例HCC样本及50例癌旁样本中15个DRGs的表达情况,并进行基因本体(gene ontology,GO)功能注释和京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes,KEGG)富集分析、Kaplan-Meier(KM)生存分析;通过单因素COX回归分析筛选出有统计学意义的DRGs,通过LASSO回归分析及多因素COX回归分析筛选出关键DRGs构建预后风险模型,并根据风险评分将HCC患者分为高风险组和低风险组,制作KM生存曲线和时间依赖的受试者工作特征(receiver operator characteristic,ROC)曲线进行验证评价。结果与癌旁样本相比,HCC样本15个DRGs中FLNA,MYH9,TLN1,ACTB,MYL6,CAPZB,DSTN,ACTN4,SLC7A11,INF2,CD2AP,PDLIM1和FLNB均表达上调,且差异具有统计学意义(t=1793~6310,均P<0.001);经GO功能注释和KEGG富集分析显示,DRGs主要与肌动蛋白细胞骨架和细胞黏附相关的生物过程或途径密切相关。经KM生存分析结果显示,SLC7A11,INF2,CD2AP,MYL6,ACTB高表达组生存率低于低表达组[HR=1.46(1.03~2.07)~1.93(1.36~2.75),均P<0.05]。通过单因素COX回归分析、LASSO分析及多因素COX回归分析构建预后风险模型riskscore=(0.247×SLC7A11)+(0.289×INF2)+(0.076×CD2AP)+(0.06×MYL6);计算样本的风险评分,风险评分越高,预后不良的HCC患者人数越多;KM生存分析显示高风险组的总生存率比低风险组低;1,3,5年的AUC值分别为0.709,0.661和0.648;通过多因素COX回归分析表明SLC7A11[HR=1.832(1.274~2.636),P=0.001]是独立的预后危险因素。结论四个DRGs构建的预后风险模型在预测HCC患者预后情况有一定的作用。

基于NK相关基因的急性髓细胞白血病预后风险评估模型的建立

目的:通过分析NK细胞相关基因(NRGs)在急性髓细胞白血病(AML)中的表达模式及其与预后的关系,构建新型风险评分模型以评估AML患者的预后。方法:基于生物信息学的方法下载TCGA数据库中AML转录组数据。根据NRGs的表达不同,通过无监督聚类将AML患者分组,并分别分析不同亚组AML患者的生存差异、基因功能富集情况和免疫相关分析。根据亚组的差异表达基因,通过Cox生存分析以及LASSO回归分析构建基于NRGs的预后评估模型。最后收集AML患者的骨髓样本进行转录组测序,进一步验证预后评估模型中各基因与AML的相关性。结果:单因素Cox筛选与预后相关的NRGs,并根据其表达情况将AML患者分成3个亚组。生存分析以及富集分析、免疫浸润分析、免疫检查点分析均发现亚组2具有更好的临床预后,并且免疫微环境明显重塑。进一步根据三个亚组的差异表达基因,通过Cox联合LASSO回归分析构建风险评分模型,此风险评分模型与AML的临床预后显著相关。临床病例的转录组测序结果表明,风险模型的6个AML危险基因中,ADGRG1、LSP1、PDE7B在复发患者中的表达量较缓解患者上调,保护基因TBX1的表达量则下调。结论:本研究构建的AML患者风险评分模型可作为一种新的预后评估手段,具有较好的预测价值。

基于TCGA构建胃癌免疫相关基因预后风险模型及相关分析

目的对TCGA数据库胃癌数据集进行分析,构建基于免疫相关基因的预后风险模型。方法下载TCGA数据库中胃癌组织和癌旁组织中的基因表达数据及患者相关临床资料,进行数据整理、合并、表达差异分析。与ImmPort数据库取交集获得差异表达的免疫相关基因(IRGs),并进行GO功能富集分析和KEGG通路富集分析。按照排除标准,将胃癌样本随机分为Train组(224例)、Test组(111例),利用Train组构建免疫相关基因预后风险模型并用Test组进行检验。将胃癌表达数据按照评估模型分为高、低风险2组,进行免疫浸润,分析2组免疫细胞表达水平的差异。观察2组免疫检查点表达差异及免疫治疗相关结果。结果共得到238个差异表达的IRGs。GO功能富集分析结果示差异表达的IRGs主要参与免疫球蛋白生产、免疫反应分子介质的产生等生物学过程。KEGG通路富集分析结果示差异表达的IRGs主要参与细胞因子-细胞因子受体相互作用、神经活性配体-受体相互作用等通路。通过数据分析,得到6个IRGs(MPO APOH IGHD3-16 CGB5 GHR PRKCG)构建的预后风险模型。Train组和Test组中高风险组生存率明显低于低风险组(P<0.05)。Train组ROC第3年曲线下面积为0.692,Test组ROC第3年曲线下面积为0.658。该模型可作为独立预测因子评估胃癌患者的预后。高、低风险组中幼稚B细胞、活化的记忆CD4+T细胞、静止肥大细胞、活化肥大细胞表达具有明显差异。低风险组PD-L1表达增高,抗PD-1治疗效果更加显著。结论基于TCGA确定了一个胃癌免疫相关基因预后风险模型,可以较好地评估胃癌患者的预后情况并指导个体化治疗。

基于N-K和SNA模型的物流仓储安全风险因素分析

为提高物流仓储的安全风险管理水平,降低事故发生率,收集2008—2022年的174起物流仓储安全事故,采用N-K模型进行风险因素耦合分析,借助社会网络模型实现风险因素关系可视化,并进行中心度和可达性分析。结果表明:多因素耦合导致物流仓储安全事故发生的概率最大;控制人—管双风险因素耦合可有效避免物流仓储安全事故发生;安全意识淡薄、安全投入不足、安全教育不到位是物流仓储安全事故的关键风险因素,需重点防范。

Study on the correlation between ambient environment-meteorological factors and the number of visits of acute otitis media,Lanzhou,China

To investigate the correlation between environmental-meteorological factors and daily visits for acute otitis media(AOM)in Lanzhou,China.Methods:Data were collected in 2014e2016 by the Departments of Otolaryngology-Head and Neck Surgery at two hospitals in Lanzhou.Relevant information,including age,sex and visiting time,was collected.Environmental data included air quality index,PM10,PM2.5,O3,CO,NO2 and SO2,and meteorological data included daily average temperature(T,C),daily mean atmospheric pressure(AP,hPa),daily average relative humidity(RH,%)and daily mean wind speed(W,m/s).The SPSS22.0 software was used to generate Spearman correlation coefficients in descriptive statistical analysis,and the R3.5.0 software was used to calculate relative risk(RR)and to obtain exposure-response curves.The relationship between meteorological-environmental parameters and daily AOM visits was summarized.Results:Correlations were identified between daily AOM visits and CO,O3,SO2,CO,NO2,PM2.5 and PM10 levels.NO2,SO2,CO,AP,RH and T levels significantly correlated with daily AOM visits with a lag exposure-response pattern.The effects of CO,NO2,SO2 and AP on daily AOM visits were significantly stronger compared to other factors(P<0.01).O3,W,T and RH were negatively correlated with daily AOM visits.The highest RR lagged by 3e4 days.Conclusions:The number of daily AOM visits appeared to be correlated with short-term exposure to mixed air pollutants and meteorological factors from 2014 through 2016 in Lanzhou.

经尿道前列腺切除术后并发导尿管相关膀胱刺激征的风险预测列线图模型构建研究

目的探讨经尿道前列腺切除术(TURP)后并发导尿管相关膀胱刺激征(CRBD)的危险因素,并构建风险预测列线图模型。方法回顾性分析2020年1月至2022年2月海南省人民医院收治的246例行TURP治疗的患者的临床资料。根据术后是否并发CRBD将其分为并发组(n=148)和未并发组(n=98)。分析TURP后并发CRBD的影响因素,并建立风险预测列线图模型。结果TURP后并发CRBD的影响因素包括年龄>65岁、麻醉后留置导尿管、药物催醒、术后尿路感染、使用氯胺酮/艾司氯胺酮类药物、使用抗胆碱能药物(P<0.05);基于上述影响因素构建列线图模型,根据列线图低、中、高风险临界值分为TURP后并发CRBD低、中、高风险(<79.63分、79.63~160.23分、>160.23分);受试者工作特征(ROC)曲线分析显示,列线图预测TURP后并发CRBD的曲线下面积(AUC)为0.810(95%CI:0.756~0.857);采用Bootstrap自抽样法内部验证的一致性指数为0.798,该列线图模型区分度良好;校准曲线与标准曲线贴合较好。结论年龄>65岁、麻醉后留置导尿管、药物催醒、术后尿路感染、使用氯胺酮/艾司氯胺酮类药物、使用抗胆碱能药物均是TURP后并发CRBD的影响因素,据此构建的风险列线图模型具有良好的准确度、区分度和预测效果。

血液净化患儿中心静脉导管相关深静脉血栓风险预测模型的构建及验证

目的:构建血液净化患儿中心静脉导管相关深静脉血栓(central venous catheter-related deep venous thrombosis,CRT)风险预测模型,并检验模型的预测能力。方法:便利选取2018年1月—2021年6月吉林省某三级甲等医院儿童重症监护病房行血液净化治疗的286例患儿的临床资料作为建模集,根据是否发生CRT分为CRT组和非CRT组,采用Logistic回归分析构建风险预测模型,并绘制列线图。采用Hosmer-Lemeshow检验判断模型的拟合优度,采用受试者工作特征曲线(receiver operator characteristic curve,ROC)下面积(the area under the ROC curve,AUC)检验模型的预测效能;同时选取2021年7月—2022年3月行血液净化治疗的70例患儿临床资料作为验证集进行模型外部验证。结果:年龄(OR=1.063)、管路凝血(OR=3.420)、导管功能障碍(OR=2.097)、导管留置时间(OR=1.131)、抗凝不达标(OR=1.838)是血液净化患儿CRT的独立危险因素;小儿危重症评分(OR=0.431)是血液净化患儿CRT的保护因素。Hosmer-Lemeshow检验结果显示,χ^(2)=11.354,P=0.182,建模集AUC为0.765,95%CI(0.709,0.820),约登指数为0.477,最佳截断点为0.369,敏感度82.30%,特异度65.40%;验证集AUC为0.745,95%CI(0.627,0.863),最佳截断点0.578,灵敏度为64.50%,特异度为76.90%。结论:该风险预测模型预测效果良好,结合临床实际可为临床评估血液净化患儿CRT的发生风险提供参考。

ICU患者人工气道相关压力性损伤风险列线图预测模型的构建

目的探讨ICU患者人工气道相关压力性损伤的危险因素并构建预测风险列线图模型。方法选取2020年12月-2021年7月,浙江省某三级甲等医院ICU收治的留置人工气道患者650例,将压力性损伤发生组(n=131)和未发生压力性损伤组(n=519)的各项指标进行对比,通过单因素分析和多因素logistic回归分析,探索ICU患者人工气道相关压力性损伤发生的独立危险因素,构建列线图预测模型,应用Bootstrapping法评估模型预测效果。结果多因素logistic回归分析显示:人工气道的留置时间(OR=1.035)、使用声门下吸引导管(OR=2.878)、气管导管在口中的位置(OR=4.937)、插管时潮湿评分(OR=0.215)、插管时移动力评分(OR=0.225)、插管时摩擦力/剪切力评分(OR=0.170)是ICU患者人工气道相关压力性损伤发生的独立危险因素。根据上述6项危险因素构建的ICU患者人工气道相关压力性损伤风险列线图预测模型,一致性指数为0.842(95%CI为80.4%~87.9%),灵敏度为77.9%,特异度为78.4%,最大约登指数为0.563,对应的风险预测值为0.170,列线图预测临界值是177分。内部验证结果,一致性指数为0.835,灵敏度为79.2%,特异度为75.3%,模型Brier值为0.112,校正曲线表明,预测曲线和实际观察曲线基本吻合,决策曲线分析表明,模型有良好的临床获益。结论本研究构建的列线图能有效预测ICU患者人工气道相关压力性损伤的发生,具有一定临床指导意义。

基于TCGA构建口腔鳞癌自噬相关基因风险预测模型及验证

目的 利用癌症基因组图谱(TCGA)数据库构建口腔鳞状细胞癌(OSCC)的自噬相关基因(ARG)风险预测模型。方法 基于TCGA数据库筛选OSCC中差异表达ARG。依次行单因素Cox分析、Lasso回归分析和多因素Cox分析构建ARG风险预测模型,并根据模型风险评分(RS)的中位数将患者分为高、低风险组,组间生存差异通过Kaplan-Meier进行评估。单因素和多因素Cox分析检验模型在预测预后中的作用,受试者工作特征曲线(ROC)评估模型的准确性,最后分析模型与临床病理特征的相关性。Western-blot验证模型中基因BAK1在OSCC中的表达。结果 OSCC中共筛选出37个差异表达ARG(FDR<0.05,|logFC|>1)。行单因素Cox分析、Lasso回归分析及多因素Cox分析筛选出FADD、NKX2-3、BAK1作为预后相关差异表达ARG,用于风险预测模型的构建,即RS=(1.558 5×FADD)+(-0.557 8×NKX2-3)+(1.547 1×BAK1)。Kaplan-Meier生存分析表明,高风险组的5年生存率低于低风险组(P<0.001)。单因素和多因素Cox分析表明,预测模型有效且独立于其他临床因素(P<0.001)。ROC曲线结果显示,该模型诊断OSCC预后的曲线下面积为0.614。在较高T分期和较大年龄患者中可发现模型的RS增高(P<0.05)。Western-blot结果表明,在OSCC癌组织中BAK1蛋白表达高于癌旁组织(t=3.197,P=0.033)。结论 基于FADD、NKX2-3、BAK1等3个ARG构建的风险预测模型可作为OSCC患者预测预后的生物标志物,从而有助于对不同风险的患者进行个体化诊疗。

基于HER-2相关基因构建风险模型用于膀胱癌生存预后评估

目的:探讨HER-2相关基因(the human epidermal growth factor receptor-2-related genes,HRGs)与膀胱癌生存预后的相关性,并基于HRGs构建一种膀胱癌患者生存预后的预测模型。方法:从癌症基因组图谱(the cancer genome atlas,TCGA)中下载膀胱肿瘤组织mRNA测序数据和临床数据,通过与分子签名数据库(the molecular signatures database,MSigDB)中HER-2相关的基因联合分析鉴定膀胱癌中的HRGs。利用单因素和多因素Cox回归分析进一步明确与膀胱癌生存相关的HRGs(P<0.05),并构建HRGs风险模型(HRGs risk score model,HRSM),根据风险评分取中位数将膀胱癌患者分成高风险组和低风险组。利用R语言对高风险和低风险组的患者进行生存分析,并对HRGs与临床特征的相关性进行分析。利用多因素Cox回归分析,验证影响膀胱癌患者预后的独立因素。计算HRSM的受试者工作特征曲线(receiver operating characteristic curve,ROC)下的面积(area under the curve,AUC),并构建诺模图(nomogram)对膀胱癌患者进行生存预测。利用TIMER数据库对HRSM和患者免疫细胞浸润相关性进行分析。结果:共鉴定到13个与患者生存相关的HRGs。通过多因素Cox回归分析,筛选出5个基因(BTC、CDC37、EGF、PTPRR和EREG)构建HRSM,高风险组的膀胱癌患者5年生存率明显低于低风险组患者。通过临床相关性分析发现,PTPRR的高表达与肿瘤分级、分期呈显著负相关,而EREG的高表达与肿瘤分级、分期呈正相关;EGF表达量的增加和患者的高级别有相关性,而CDC37的高表达却呈现出了相反的结果;BTC的表达与临床特征无显著相关性。通过对HRSM与免疫细胞的相关性分析发现,风险评分与树突状细胞、CD8+T细胞、CD4+T细胞、中性粒细胞和巨噬细胞的浸润呈正相关。结论:HRGs对膀胱癌患者的预后有重要作用,可能作为新的预测性生物标志物和治疗的潜在靶点。

胸外科老年患者PICC相关性血栓形成风险评估模型的构建及应用检验

目的构建胸外科老年患者PICC相关性血栓形成风险评估模型,并检验其预测效果。方法采用便利抽样法,选择2019年1月-2021年12月我科收治的老年患者200例为研究对象,借助一般资料问卷收集患者临床资料,统计形成PICC相关性血栓例数;采用单因素分析、多因素Logistic回归分析筛选PICC相关性血栓形成的危险因素;基于回归分析结果构建PICC相关性血栓形成风险评估模型,并检验模型预测准确率。结果200例胸外科老年患者中形成PICC相关性血栓28例,形成率为14.00%;多因素Logistic回归分析结果显示,胸外科老年患者PICC相关性血栓形成的独立危险因素为合并糖尿病、D-二聚体≥0.5mg/L、导管尖端位置上腔静脉上2/3、导管直径/静脉直径≥67%、穿刺次数>1次、护士工作年限<8年、置管宣教缺失(P<0.05)。Hosmer-Lemeshow检验结果显示,评估模型拟合优度良好(χ^(2)=0.481,P=0.488),ROC曲线下面积为0.904,95%CI为0.859~0.949,最大约登指数为0.600,灵敏度、特异度分别为71.40%、88.60%;模型预测准确率为91.25%。结论胸外科PICC置管老年患者伴有不同程度的相关性血栓形成风险,且PICC相关性血栓形成危险因素复杂,根据回归分析结果构建的风险评估模型,能实现对胸外科老年患者PICC相关性血栓形成风险的准确、有效评估,为临床预防护理提供参考。