Is Patient Satisfaction After Total Knee Arthroplasty Predictable Using Patient Characteristics and Preoperative Patient-Reported Outcomes?

BackgroundDissatisfaction after total knee arthroplasty (TKA) remains a difficult problem. Patient characteristics and preoperative patient-reported outcomes (PROs) are potential predictors of satisfaction one year after TKA. Being able to predict the outcome preoperatively might reduce the number of less satisfied patients.MethodsA retrospective cohort study on prospectively collected data of 1239 primary TKA patients (ASA I-II, BMI <35) was performed. Primary outcome was degree of patient satisfaction one year after TKA (Numeric Rating Scale (NRS) 0-10). Secondary outcomes were degree of patient satisfaction six months and two years after TKA and being dissatisfied (NRS 0-6) or satisfied (NRS 7-10) at all three time points. Multivariate linear and binary logistic regression analyses were executed with patient characteristics and preoperative PROs as potential predictors.ResultsOne year after TKA, median NRS satisfaction score was 9.0 (8.0-10.0) and 1117 (90.2%) patients were satisfied. BMI, degree of medial cartilage damage, previous knee surgery, Knee injury and Osteoarthritis Outcome Score-Physical Function Short Form score, EQ VAS score, and anxiety were identified as predictors of the degree of patient satisfaction (P = .000, R² = 0.027). Models on secondary outcomes reported R² of 1.7%-7.1% (P < .05). All models showed bad agreement between observed and predicted values for lower NRS satisfaction scores and being dissatisfied.ConclusionThe degree of patient satisfaction and the chance of being dissatisfied or satisfied six months, one, and two years after TKA are predictable by patient characteristics and preoperative PROs but not at a reliability level that is clinically useful.     

基于近、中红外光谱技术的热毒宁注射液制备过程中金银花浓缩过程含量预测研究

目的 采用近红外光谱（near infrared spectroscopy,NIRS）、中红外光谱（mid-infrared spectroscopy,MIRS）技术实现对热毒宁注射液制备过程中金银花浓缩过程绿原酸、新绿原酸、隐绿原酸、异绿原酸A、异绿原酸B、异绿原酸C、断氧化马钱子苷和固含量8个质控指标的含量预测,并对比2种技术的预测效果。方法 收集热毒宁注射液制备过程中金银花浓缩过程样本,进行NIRS、MIRS采集和含量测定,优选最佳光谱预处理方法和特征波段,采用偏最小二乘（partial least square,PLS）法建立8个质控指标的含量预测模型,并比较8个质控指标的NIRS、MIRS模型性能,得到8个最优含量预测模型,并对其进行外部验证。结果 NIRS技术对绿原酸、隐绿原酸、异绿原酸C、断氧化马钱子苷、固含量的预测效果更好,平均相对预测误差（average relative prediction error,ARPE）分别为1.57%、1.88%、4.13%、3.79%、0.94%,故选用NIRS模型作为这5个质控指标的最佳模型;MIRS技术对新绿原酸、异绿原酸A、异绿...     

基于生物信息学探讨SARS-CoV-2介导免疫炎症反应诱发动脉粥样硬化进展的潜在机制与防治中药筛选

目的 应用生物信息学技术,从免疫炎症角度探索严重急性呼吸综合征冠状病毒-2(severe acute respiratory syndrome coronavirus-2,SARS-CoV-2)感染诱导动脉粥样硬化（atherosclerosis,AS）进展的核心靶点及重要通路,进而预测潜在防治中药。方法 从基因表达数据库（Gene Expression Omnibus,GEO）中获取新型冠状病毒肺炎患者和动脉粥样硬化患者芯片数据,利用“limmar”包及“Venn”包筛选2种疾病的共同差异表达基因（differentially expressed genes,DEGs）,对共同DEGs进行基因本体论（gene ontology,GO）和京都基因与基因组百科全书（Kyoto encyclopedia of genes and genomes,KEGG）分析,注释其功能及重要通路。对2组基因集进行免疫细胞和免疫功能打分,评估免疫细胞浸润水平。利用STRING数据库,构建蛋白-蛋白互作（protein-protein interaction,PPI）网络;通过Cytoscape的CytoH...     

进展期胃癌病人发生腹腔脱落细胞学阳性的危险因素分析及风险预测评分模型建立

目的探讨影响进展期胃癌病人腹腔脱落细胞学阳性的危险因素,建立腹腔脱落细胞学阳性的风险预测评分模型。方法收集分析河北医科大学第四医院2012年2月至2018年12月期间开展的一项前瞻性、多中心、开放、随机对照Ⅲ期临床试验(NCT01516944)中的225例进展期胃癌病人临床病理资料,所有病人术中均行腹腔镜探查及腹腔脱落细胞学检查(500 mL生理盐水冲洗),检测腹腔脱落癌细胞的阳性率,分析影响进展期胃癌病人腹腔脱落细胞学阳性的临床病理因素。结果225例胃癌病人腹腔冲洗液中脱落细胞学阳性者32例,阳性率为14.22%。单因素分析结果显示,病人的年龄、肿瘤直径,浸润深度(cT分期)、组织学类型、肿瘤部位、淋巴结转移情况(cN分期)以及血清中肿瘤标志物癌胚抗原(CEA)、癌抗原72-4(CA72-4)、癌抗原19-9(CA19-9)的表达情况均是发生腹腔脱落细胞学阳性的危险因素(P<0.05);多因素分析显示,影响进展期胃癌病人发生腹腔脱落细胞学阳性的独立危险因素为:肿瘤浸润深度为cT4a~cT4b期[OR=20.954,95%置信区间(CI):(1.610,12.651),P=0.020];CEA表达阳性[OR=7.695,95%CI(1.307,8.669),P=0.001];CA72-4表达阳性[OR=7.551,95%CI(2.255,25.283),P=0.001];CA19-9表达阳性[OR=3.317,95%CI(1.011,10.883),P=0.048]。根据多因素Logistic回归结果建立风险预测方程:logit(p)=-7.088+3.042×X1(肿瘤浸润深度)+2.041×X2(CEA)+1.199×X3(CA19-9)+2.022×X4(CA72-4),采用Hosmer-Lemeshow检验检测回归方程的拟合优度(P=0.614)。采用受试者工作特征(ROC)曲线评价回归方程的区分度,曲线下面积为0.844[95%CI(0.751,0.936),P=0.000]。病人术前肿瘤浸润深度为cT4a~cT4b期、血清中肿瘤标志物CEA、CA72-4、CA19-9表达阳性其发生腹腔脱落细胞学阳性风险评分分别为3、2、2、1分,病人发生腹腔脱落细胞学阳性的概率:评分≥4分者为19.44%,评分<4分者为4.94%。结论进展期胃癌病人CEA、CA72-4、CA19-9水平升高以及浸润程度为cT4a~cT4b期则提示可能存在腹腔脱落细胞学阳性。同时联合上述指标建立风险评分模型,能够有效预测进展期胃癌病例中发生腹腔脱落细胞学阳性的高风险病人。     

基于网络药理学的三仙汤治疗骨质疏松症作用机制研究

目的通过网络药理学方法探讨三仙汤干预骨质疏松症(OP)的有效成分、作用靶点及信号通路等,从而明确其作用机制。方法应用中药系统药理学数据库与分析平台(TCMSP)检索获得三仙汤中3味药物的化学成分,通过Uniprot数据库,将已筛选出的靶点名称进行标准化。通过Genecards、OMIM数据库检索骨质疏松症的相关靶基因。将药物靶点与疾病靶点取交集,筛选得到疾病-药物成分共同靶蛋白在String网站构建蛋白-蛋白相互作用(PPI)网络模型。在Cytoscape软件中绘制药物、疾病、靶基因网络图,并进行基因GO功能和KEGG通路富集分析,探讨三仙汤治疗骨质疏松症的作用机制。结果通过数据库筛选得到三仙汤的28种有效成分以及与骨质疏松症相关的靶点127个,其中蛋白激酶、白介素6、血管内皮生长因子等基因为PPI网络中的核心基因; GO富集分析显示靶点主要影响核受体活性、转录因子活性、类固醇激素受体活性、细胞因子受体结合、蛋白质异二聚活性、血红素结合等生物过程;以及影响AGE-RAGE信号通路、流体剪切应力与动脉粥样硬化通路、IL-17信号通路、前列腺癌通路、肿瘤坏死因子信号通路、乙型肝炎等信号通路。结论三仙汤中的有效成分通过关键信号通路作用于核心基因,影响细胞分化、凋亡、炎症、氧化应激等多种生物学过程发挥抗骨质疏松的作用,为其后续研究提供了科学依据。     

基于Bevilacqua模型的乳腺癌保乳术患者术后淋巴水肿风险预测的临床研究

目的验证Bevilacqua乳腺癌术后淋巴水肿风险预测模型的临床适用性及可行性。方法回顾性分析2010年1月至2015年12月203例乳腺癌患者临床资料,临床数据分析使用统计学软件SPSS 24.0。Cox回归模型分析乳腺癌患者术后发生上肢淋巴水肿的危险因素,以P<0.05为有统计学意义;绘制ROC曲线,以曲线下面积检验模型预测效果;应用Hosmere-Lemeshow检验评估预测值与实际值的校准程度,以P>0.05为预测模型校准能力较好,预测与实际没有区别。结果所有患者随访共计62~86个月,中位随访时间70个月。术后5年内共发生上肢淋巴水肿患者45例(22.2%)。Cox回归模型分析结果显示,高身体质量指数(BMI)、接受过新辅助化疗、全腋窝淋巴结清扫、接受过放疗是上肢淋巴水肿的独立危险因素。Becilacqua上肢淋巴水肿风险预测模型ROC曲线分析结果显示,模型AUC值为0.711,95%CI(0.651~0.760),有较好的的预测效果。Hosmer-Lemeshow检验结果显示,风险预测模型预测风险与实际无明显差异(P=0.262),校准能力较好,与实际差别不大。结论Bevilacqua术后6个月淋巴水肿风险预测模型的准确性及适用性较高,可用于临床对乳腺癌保乳术后淋巴水肿的预测,可为预防淋巴水肿的发生制定干预决策提供参考。     

Derivation and validation of a novel comorbidity‐based delirium risk index to predict postoperative delirium using national administrative healthcare database

ObjectiveTo derive and validate a comorbidity‐based delirium risk index (DRI) to predict postoperative delirium.Data Source/Study SettingData of 506 438 hip fracture repair surgeries from 2006 to 2016 were collected to derive DRI and perform internal validation from the Premier Healthcare Database, which provided billing information on 20‐25 percent of hospitalizations in the USA. Additionally, data of 1 130 569 knee arthroplasty surgeries were retrieved for external validation.Study DesignThirty‐six commonly seen comorbidities were evaluated by logistic regression with the outcome of postoperative delirium. The hip fracture repair surgery cohort was separated into a training dataset (60 percent) and an internal validation (40 percent) dataset. The least absolute shrinkage and selection operator (LASSO) procedure was applied for variable selection, and weights were assigned to selected comorbidities to quantify corresponding risks. The newly developed DRI was then compared to the Charlson‐Deyo Index for goodness‐of‐fit and predictive ability, using the Akaike information criterion (AIC), Bayesian information criterion (BIC), area under the ROC curve (AUC) for goodness‐of‐fit, and odds ratios for predictive performance. Additional internal validation was performed by splitting the data by four regions and in 4 randomly selected hospitals. External validation was conducted in patients with knee arthroplasty surgeries.Data CollectionHip fracture repair surgeries, knee arthroplasty surgeries, and comorbidities were identified by using ICD‐9 codes. Postoperative delirium was defined by using ICD‐9 codes and analyzing billing information for antipsychotics (specifically haloperidol, olanzapine, and quetiapine) typically recommended to treat delirium.Principal FindingsThe derived DRI includes 14 comorbidities and assigns comorbidities weights ranging from 1 to 6. The DRI outperformed the Charlson‐Deyo Comorbidity Index with better goodness‐of‐fit and predictive performance.ConclusionsDelirium risk index is a valid comorbidity index for covariate adjustment and risk prediction in the context of postoperative delirium. Future work is needed to test its performance in different patient populations and varying definitions of delirium.     

Analysis of time‐to‐event for observational studies: Guidance to the use of intensity models

This paper provides guidance for researchers with some mathematical background on the conduct of time‐to‐event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time‐dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.     

Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches

Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi‐outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real‐world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction.