冠状动脉旁路移植术后手术部位感染风险预测模型及管理策略

目的:探讨影响冠状动脉旁路移植术(CABG)后手术部位感染的主要危险因素,并构建定量列线图风险预测模型,提出针对性管理策略。方法:回顾性总结2015年5月至2019年5月于南京医科大学附属盐城市第三人民医院接受CABG患者共302例作为模型组,比较感染与未感染者临床资料(主要包括性别、年龄、基础疾病史、体质指数、美国麻醉师协会(ASA)评分、常规皮肤准备、围术期抗菌药物不合理使用、手术时间>4 h、手术室探视和连续使用同一手术室),再经多因素Logistic回归分析筛选主要危险因素并构建列线图风险预测模型。纳入2019年6月至2020年12月共211例CABG患者作为验证组,接受感染管理策略。采用受试者工作曲线(ROC)分析列线图模型评估模型组与验证组感染发生的效能。结果:模型组共诊断11例患者发生感染(3.64%,11/302),单因素分析显示感染者年龄高于未感染者,且感染者基础疾病史(高血压和糖尿病)和肥胖者比例增高,ASA评分升高,常规皮肤准备方法、围术期抗菌药物不合理使用、手术时间>4 h、手术室探视、连续使用同一手术室比例均较未感染者增高(P均<0.05)。Logistic回归分析显示,高龄(OR=1.58、95%CI:1.12~2.53、P=0.011)、基础疾病史(OR=2.63、95%CI:2.12~3.06、P=0.001)、围术期抗菌药物不合理使用(OR=2.01、95%CI:1.55~2.69、P=0.002)、手术时间>4 h(OR=3.11、95%CI:2.68~3.59、P=0.001)和手术室探视(OR=1.24、95%CI:1.01~1.85、P=0.024)均为CABG术后手术部位感染的主要危险因素。应用R软件根据主要危险因素(高龄、基础疾病、抗菌药物不合理使用、手术时间>4 h、手术室探视)的权重(β值)进行定量赋值建立列线图模型。验证组共诊断感染者2例(0.95%,2/211),显著低于模型组(Fisher’s确切概率法,取单侧P=0.047)。ROC分析显示,列线图模型预测模型组和验证组发生感染的准确性分别为0.895和0.864;Hosmer-Lemeshow检验显示拟合度良好。结论:CABG术后手术部位感染的发生与多个临床因素有关,如高龄、基础疾病史、围术期抗菌药物不合理使用、手术时间>4 h和手术室探视,医护人员应充分认知并采取严格的感染管理措施以减少感染的发生。     

肝移植术后早期感染危险因素分析及预测

目的探讨肝移植术后早期(≤1个月)感染发生的危险因素并建立列线图预测模型。 方法回顾性分析2016年1月至2020年12月宁波大学附属李惠利医院肝移植中心200例同种异体肝移植受者临床资料。根据纳入和排除标准共收集181例受者的人口学数据、临床资料和病原菌检测结果,根据术后早期是否发生感染分为感染组(n＝96)和非感染组(n＝85)。分析受者术后早期感染菌群分布特点和相关危险因素,构建列线图并评价其拟合度、区分度和临床实用性。正态分布计量资料采用独立样本t检验比较,不符合正态分布计量资料采用Mann-Whitney U检验比较。分类变量采用χ²检验或Fisher确切概率法。采用Logistic回归分析进行多因素分析。采用R语言(4.1.2)软件rms包构建列线图模型,并通过Bootstrap自抽样法对模型进行内部验证;采用Hosmer-Lemeshow检验、校准曲线、受试者工作特征(ROC)曲线下面积、一致性指数(C指数)及临床决策曲线分析来评价列线图的校准度、区分度及临床实用性。P<0.05为差异有统计学意义。 结果纳入研究的181例受者中,肝移植术后早期感染发生率为53.0%(96/181),96例感染组受者共检出病原菌132株,以革兰阴性菌最为常见(42.4%)。受者术后2周内感染发生率最高(70.8%,68/96),感染常见部位为肺部和血行感染。多因素Logistic回归分析结果显示,受者女性(OR＝4.235,95%CI：1.577～11.370)、MELD评分≥20(OR＝3.742,95%CI：1.296～10.805)、Chlid-Pugh分级C级(OR＝3.346,95%CI：1.263～8.862)、术后呼吸机使用时间(OR＝1.036,95%CI：1.009～1.063)是肝移植术后早期感染的独立危险因素。根据上述独立危险因素建立列线图预测模型,经Bootstrap法进行内部验证,Hosmer-Lemeshow检验无统计学意义(χ²＝7.236,P>0.05),校正曲线贴近于理想曲线,预测模型与观测值具有较好的拟合度。C指数和ROC曲线下面积均为0.800(95%CI：0.735～0.865),模型具有良好的区分度。模型临床决策曲线在较广的阈值概率范围内(0.2～1.0),高于采用单一危险因素预测,显示该模型具有临床实用性。 结论肝移植受者性别、MELD评分、Child-Pugh分级和术后呼吸机使用时间是移植术后早期感染的独立危险因素,列线图对移植术后早期感染的预测效果良好。     

乳腺癌化疗致周围神经病变的列线图模型 构建与评价

目的 探讨乳腺癌患者化疗致周围神经病变（chemotherapy-induced peripheral neuropathy,CIPN）的影响因素,建立预测CIPN的列线图模型。方法 选择2019年7月至2022年1月福建省肿瘤医院治疗的431例乳腺癌化疗患者作为建模组,构建CIPN列线图模型;另选取89例乳腺癌患者作为验证组,比较两组患者的危险因素并绘制受试者工作特征曲线。结果 多因素分析显示：年龄、糖尿病、体质量指数（body mass index,BMI）、化疗前焦虑、化疗次数是CIPN的独立危险因素（P<0.05）;建模组和验证组的受试者工作特征曲线下面积分别为0.723（95%CI：0.676～0.770）和0.771（95%CI：0.676～0.866）。结论 本研究构建的列线图模型对乳腺癌化疗致周围神经病变的诊断具有较好的精确度和区分度。     

TIPS术治疗的肝硬化并发EVB患者医院内死亡风险预测模型的构建与应用*

目的 构建肝硬化门静脉高压症并发食管胃底静脉曲张破裂出血(EVB)患者医院内死亡的风险预测模型。方法 2018年6月～2020年6月我院收治的107例肝硬化门静脉高压症并发EVB患者,均接受经颈静脉肝内门体静脉分流术(TIPS)治疗。应用Logistic回归分析影响患者死亡的危险因素,基于独立影响因素构建医院内死亡的风险预测模型,应用Bootstrap法对预测模型进行内部验证,应用受试者工作特征曲线(ROC)下面积(AUC)评估预测模型的预测效能。结果 本组患者医院内死亡25例(23.4%),生存82例;单因素分析显示,死亡患者Child-Pugh分级、出血部位、门静脉内径、肝性脑病和失血性休克发生率等与生存患者比,均存在显著性差异(P＜0.05),多因素Logistic回归分析显示,门静脉内径(OR=2.201,95%CI:1.544～3.139)、肝性脑病(OR=3.093,95%CI:1.731～5.524)和失血性休克(OR=1.101,95%CI:1.040～1.165)是影响患者医院内死亡的独立危险因素(P＜0.05);对所构建的列线图预测模型,经内部验证,其C-index为0.937(95%CI:0.734～0.879),具有良好的区分度;应用ROC曲线分析显示,预测模型的曲线下面积(AUC)为0.896(95%CI:0.796～0.958,P＜0.001),其预测的敏感度和特异度分别为91.3%和88.1%。结论 了解影响肝硬化门静脉高压症并发EVB患者医院内死亡的独立危险因素,并据此构建的风险预测列线图模型具有良好的区分度和预测效能,有助于临床对高风险患者的筛查和及时处理。     

呼出气一氧化氮和血嗜酸性粒细胞对哮喘患者气道高反应性程度的预测价值

目的分析呼出气一氧化氮(fraction of exhaled nitric oxide, FeNO)水平和血嗜酸性粒细胞(blood eosinophil, B-Eos)计数对哮喘患者气道高反应性(airway hyperresponsiveness, AHR)程度的预测价值,并探索AHR严重程度的预测模型。 方法选择2014年1月至2019年12月于我院首诊为哮喘的患者1 347例,将其中520例具有FeNO和B-Eos的纳入主要研究人群。依据乙酰甲胆碱激发试验(methacholine challenge test, MCT)结果,分为重度AHR组(MCT为中度或重度阳性183例和轻度AHR组(MCT为极轻度或轻度阳性337例。然后分析两组差异,用Logistic回归构建预测模型,最后绘制重度AHR风险的列线图和森林图。 结果重度AHR组的FeNO和B-Eos均高于轻度AHR组(73 vs. 36 ppb、394 vs. 243个/μl,P<0.001)。Logistic回归示年龄、性别、FEV₁/FVC、B-Eos、FeNO为重度AHR的独立危险因素,将它们纳入回归模型,其灵敏度为49.7%,特异度为87.8%。受试者工作特征曲线示模型的曲线下面积明显高于单独的FeNO或B-Eos(0.797 vs. 0.715或0.644,P<0.001)。重度AHR风险的亚组分析示：随着FeNO或B-Eos的增高风险逐步增高(趋势检验P<0.001);女性的风险为男性的1.57倍(P＝0.041),而低FEV₁/FVC组(<70%)为正常组的3.38倍(P<0.001)。 结论在哮喘患者中单独的FeNO或B-Eos对重度AHR具有中等程度的预测效能,通过多因素回归模型构建的列线图可以用于预测重度AHR的概率。     

Nomogram for sample size calculation on a straightforward basis for the kappa statistic

PurposeKappa is a widely used measure of agreement. However, it may not be straightforward in some situation such as sample size calculation due to the kappa paradox: high agreement but low kappa. Hence, it seems reasonable in sample size calculation that the level of agreement under a certain marginal prevalence is considered in terms of a simple proportion of agreement rather than a kappa value. Therefore, sample size formulae and nomograms using a simple proportion of agreement rather than a kappa under certain marginal prevalences are proposed.MethodsA sample size formula was derived using the kappa statistic under the common correlation model and goodness-of-fit statistic. The nomogram for the sample size formula was developed using SAS 9.3.ResultsThe sample size formulae using a simple proportion of agreement instead of a kappa statistic and nomograms to eliminate the inconvenience of using a mathematical formula were produced.ConclusionsA nomogram for sample size calculation with a simple proportion of agreement should be useful in the planning stages when the focus of interest is on testing the hypothesis of interobserver agreement involving two raters and nominal outcome measures.     

Carotid intima–media thickness value distributions in The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)

Objective

Carotid intima–media thickness (IMT) is a noninvasive measurement of early atherosclerosis. Most IMT studies have involved populations with low rates of racial blending. The aim of the present article is to describe IMT value distributions and analyze the influence of sex and race on IMT values in a large Brazilian sample, a setting with a high rate of racial admixture.

Methods

The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) is a multicenter cohort of 15,105 adult (aged 35–74 years) civil servants in six Brazilian cities. Baseline assessment included IMT measurements in both common carotid arteries. Race was self-reported. We studied the association between sex and race with IMT values using multiple linear regression models. We conducted analyses in all and low-risk individuals, defined as those without classical cardiovascular risk factors.

Results

We analyzed complete IMT data from 10,405 ELSA-Brasil participants. We present nomograms by age for all and low-risk individuals, stratified by sex and race. We found that men had significantly higher maximal IMT values compared with women (β = 0.058; P < 0.001). This association remained for low-risk individuals (β = 0.027; P = 0.001). In addition, Brown and White individuals had lower maximal IMT values compared with Black individuals for all (β = −0.034 and β = −0.054, respectively; P < 0.001) and low-risk individuals (β = −0.027; P = 0.013 and β = −0.035; P < 0.001, respectively).

Conclusion

We found significantly higher IMT values in men. We found significantly higher IMT values in Black individuals than White and Brown individuals. These results persisted when analyses were restricted to low-risk individuals.     

非糖尿病老年患者术中血糖异常风险预测模型的构建及验证

目的 分析非糖尿病老年患者术中血糖异常的影响因素,构建列线图风险预测模型。方法 采用便利抽样法,选取2021年3月—11月在北京市某三级甲等医院住院的607例非糖尿病老年手术患者作为调查对象,采用单因素分析和Logistic回归分析确定术中血糖异常的危险因素,建立风险预测模型并绘制列线图。采用受试者操作特征曲线（receiver operating characteristic,ROC）和Hosmer-Lemeshow检验验证模型预测效果,采用Bootstrap法对模型进行内部验证。结果 Logistic回归分析显示,麻醉类型、手术时间、进入手术室血糖、年龄是术中血糖异常的独立预测因素。建立术中血糖异常的预测公式如下：Logit P=-12.810+0.066×年龄+1.966×进入手术室血糖+0.008×手术时间-2.778×麻醉类型。ROC曲线下面积为0.815,最佳临界值为0.765。灵敏度和特异度分别为83.00%和67.00%。HosmerLemeshow检验的结果 为χ2=5.557(P=0.697),模型预测曲线与理想曲线拟合良好,表明模型具有良好的预测能力。外部验证表明...     

A Nomogram for Predicting the Likelihood of Additional Nodal Metastases in Breast Cancer Patients With a Positive Sentinel Node Biopsy

Background:The standard of care for breast cancer patients with sentinel lymph node (SLN) metastases includes complete axillary lymph node dissection (ALND). However, many question the need for complete ALND in every patient with detectable SLN metastases, particularly those perceived to have a low risk of non-SLN metastases. Accurate estimates of the likelihood of additional disease in the axilla could assist greatly in decision-making regarding further treatment.Methods:Pathological features of the primary tumor and SLN metastases of 702 patients who underwent complete ALND were assessed with multivariable logistic regression to predict the presence of additional disease in the non-SLNs of these patients. A nomogram was created using pathological size, tumor type and nuclear grade, lymphovascular invasion, multifocality, and estrogen-receptor status of the primary tumor; method of detection of SLN metastases; number of positive SLNs; and number of negative SLNs. The model was subsequently applied prospectively to 373 patients.Results:The nomogram for the retrospective population was accurate and discriminating, with an area under the receiver operating characteristic (ROC) curve of 0.76. When applied to the prospective group, the model accurately predicted likelihood of non-SLN disease (ROC, 0.77).Conclusions:We have developed a user-friendly nomogram that uses information commonly available to the surgeon to easily and accurately calculate the likelihood of having additional, non-SLN metastases for an individual patient.Drs. Manasseh and Bevilacqua contributed equally to the work.Dr. Bevilacqua is currently affiliated with Hospital Sírio Libanes, Instituto Brasileiro de Controle do Câncer, and Disciplina de Cirurgia Geral, Departamento de Cirurgia, Faculdade de Medicina da Univerdidade de Sao Paulo. São Paulo, Brazil; Dr. Boolbol is currently affiliated with Beth Israel Medical Center, New York, New York.