用 Nomogram 分析口咽癌的预后

目的：改进美国肿瘤学会（American Joint Committee on Cancer,AJCC）口咽癌预后评分系统并绘制其可视化的 Nomogram,从而使口咽癌预后评分更加精准高效。方法：在 AJCC 第七版评分系统的基础上增加常用临床指标,采用 Cox 回归建立模型,并进一步用 Nomogram 可视化该评分系统,使其更加直观快捷。结果：与 AJCC 第七版相比,添加性别、年龄、肿瘤位置、治疗方式四个变量后的新评分系统具有更高的预测精度（一致性指数 C －index 0．7304 vs 0．7028,P ＜0．05）,新系统的 Nomogram 可以个性化的预测每个患者的生存率。结论：建立了一个新的口咽癌5年和10年预后评分系统,新的评分系统具有很好的预测效果。Nomogram 提供了一个个性化的可视生存概率预测图。     

Renal cell carcinoma: A nomogram for the CT imaging-inclusive prediction of indolent,non-clear cell renal cortical tumours

AimTo develop a nomogram from clinical and computed tomography (CT) data for pre-treatment identification of indolent renal cortical tumours.Patients and methodsA total of 1201 consecutive patients underwent dedicated contrast-enhanced CT prior to nephrectomy for a renal cortical tumour between January 2000 and July 2011. Two radiologists evaluated all tumours on CT for size, necrosis, calcification, contour, renal vein invasion, collecting system invasion, contact with renal sinus fat, multicystic tumour architecture, nodular enhancement, and the degree of nephrographic phase enhancement. CT and clinical predictors (gender, body mass index [BMI], age) were incorporated into the nomogram. We employed multivariable logistic regression analysis to predict tumour type and internally validated the final model using the data from reader 1. External validation was performed by using all data from reader 2. We applied Wilcoxon rank sum test and Fisher’s exact test to investigate for differences in tumour size, BMI, age, and differences in CT imaging features between patients with aggressive and those with indolent tumours.Results63.6% (764/1201) of patients had clear-cell or other aggressive non-clear-cell RCC (i.e. papillary RCC type 2, unclassified RCC) and 36.4% (437/1201) had indolent renal cortical tumours (i.e. papillary RCC type 1, chromophobe RCC, angiomyolipoma, or oncocytoma). On CT, indolent tumours were significantly smaller (p < 0.001) than aggressive tumours and significantly associated with well-defined tumour contours (p < 0.001). Aggressive RCC were significantly associated with necrosis, calcification, renal vein invasion, collecting system invasion, contact with renal sinus fat, multicystic tumour architecture, and nodular enhancement (all, p < 0.001). The nomogram’s concordance index (C-index) was 0.823 after internal and 0.829 after external validation.Concluding statementWe present a nomogram based on 1201 patients combining CT features with clinical data for the prediction of indolent renal cortical tumours. When externally validated, this nomogram resulted in a C-index of 0.829.     

Development and Validation of an RNA Binding Protein-associated Prognostic Model for Hepatocellular Carcinoma

Background and AimsThe survival rate of patients with hepatocellular carcinoma is variable. The abnormal expression of RNA-binding proteins (RBPs) is closely related to the occurrence and development of malignant tumors. The primary aim of this study was to identify RBPs related to the prognosis of liver cancer and to construct a prognostic model of liver cancer.MethodsWe downloaded the hepatocellular carcinoma gene sequencing data from The Cancer Genome Atlas (cancergenome.nih.gov/) database, constructed a protein-protein interaction network, and used Cytoscape to realize the visualization. From among 325 abnormally expressed genes for RBPs, 9 (XPO5, enhancer of zeste 2 polycomb repressive complex 2 subunit [EZH2], CSTF2, BRCA1, RRP12, MRPL54, EIF2AK4, PPARGC1A, and SEPSECS) were selected for construction of the prognostic model. Then, we further verified the results through the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo/) database and in vitro experiments.ResultsA prognostic model was constructed, which determined that the survival time of patients in the high-risk group was significantly shorter than that of the low-risk group (p<0.01). Univariate and multivariate Cox regression analysis suggested that the risk score was an independent prognostic factor (p<0.01). We also constructed a nomogram based on the risk score, survival time, and survival status. At the same time, we verified the high expression and cancer-promoting effects of EZH2 in tumors.ConclusionsSurvival, receiver operating characteristic curve and independent prognostic analyses demonstrated that we constructed a good prognostic model, which might be useful for estimating the survival of patients with hepatocellular carcinoma.     

Prognostic Nomogram for Patients with Hepatitis E Virus-related Acute Liver Failure: A Multicenter Study in China

Background and AimsTimely and effective assessment scoring systems for predicting the mortality of patients with hepatitis E virus-related acute liver failure (HEV-ALF) are urgently needed. The present study aimed to establish an effective nomogram for predicting the mortality of HEV-ALF patients.MethodsThe nomogram was based on a cross-sectional set of 404 HEV-ALF patients who were identified and enrolled from a cohort of 650 patients with liver failure. To compare the performance with that of the model for end-stage liver disease (MELD) scoring and CLIF-Consortium-acute-on-chronic liver failure score (CLIF-C-ACLFs) models, we assessed the predictive accuracy of the nomogram using the concordance index (C-index), and its discriminative ability using time-dependent receiver operating characteristics (td-ROC) analysis, respectively.ResultsMultivariate logistic regression analysis of the development set carried out to predict mortality revealed that γ-glutamyl transpeptidase, albumin, total bilirubin, urea nitrogen, creatinine, international normalized ratio, and neutrophil-to-lymphocyte ratio were independent factors, all of which were incorporated into the new nomogram to predict the mortality of HEV-ALF patients. The area under the curve of this nomogram for mortality prediction was 0.671 (95% confidence interval: 0.602–0.740), which was higher than that of the MELD and CLIF-C-ACLFs models. Moreover, the td-ROC and decision curves analysis showed that both discriminative ability and threshold probabilities of the nomogram were superior to those of the MELD and CLIF-C-ACLFs models. A similar trend was observed in the validation set.ConclusionsThe novel nomogram is an accurate and efficient mortality prediction method for HEV-ALF patients.     

基于术前IL-6、PGE2、TNF-c构建预测膀胱癌患者术后复发的列线图模型

摘要:目的基于术前 血清IL-6、前列腺素E2( PGE2)、TNF-a构建预测膀胱癌术后复发的列线图模型。方法回顾性收集2018年6月至2023年2月临平区第一人民医院收治的348例膀胱癌患者的临床资料,经计算机产生随机数表并以2:1比例将其分为训练集(232例)和验证集(116例)。所有患者均接受随访,将发生复发的患者纳入复发组,未发生复发的患者纳入未复发组。比较训练集复发组、未复发组血清IL-6、PGE2、TNF-a水平及一般资料;用Logistie 回归模型分析训练集膀胱癌术后复发的影响因素,并建立回归方程;用ROC曲线分析术前IL-6 PGE2、TNF-a单独及联合预测膀胱癌术后复发的效能;建立膀胱癌术后复发的风险预测列线图模型,并验证其效能。结果与未复发组比较, 复发组血清IL-6、PGE2、TNF-a水平升高,肿瘤直径增大,多发性肿瘤、肿瘤分期T2~T,肿瘤WHO病理学分级1I~川级的构成比升高,术后规律膀胱灌注的构成比降低(P<0.05)。Logistie 回归分析显示,术前血清IL-6、PGE2、TNF-a、肿瘤分期、肿瘤WHO病理学分级是膀胱癌术后复发的影响因素(P<0.05),并建立Logistic回归方程:Y=1.718X1+2.081X2+ 1.815X3+2.319X.+1.868Xs。ROC曲线显示,术前IL-6、PGE2、TNF-a预测膀胱癌术后复发的最佳截断点分别为0.60 ng/L、57.13 pg/mL、2. 10 ng/mL,三者单独及联合预测膀胱癌的ROC曲 线下面积(AUCR0C)分别为0.729、0.743 .0.733和0.825。基于训练集Logistic回归分析结构建立膀胱癌术后复发的风险预测列线图模型,该模型预测训练集验证集的敏感性分别为94.12% .90.20% ,特异性分别为90.06%、87.29% ,AUCROG分别为0.940、0.914 ; Bootstrap法内部验证结果显示,训练集、验证集的C-index分别为0.918( 95% CI:0.824~0.987)、0.901 ( 95% CI:0.835~0.957)。结论术前血清IL-6PGE2、TNF-ax水平是膀胱癌术后复发的影响因素,据此建立的风险预测列线图模型具有良好的预测效能。     

脊柱结核病灶清除植骨融合内固定术后住院时间延长的危险因素及预测模型

【摘要】 目的：分析脊柱结核患者行病灶清除植骨融合内固定术后住院时间（length of stay,LOS）延长的危险因素,建立预测模型并进行验证。方法：回顾性分析2016年2月~2020年12月在西安交通大学附属红会医院行病灶清除植骨融合内固定术的152例脊柱结核患者的临床资料,根据患者术后LOS是否超过整体研究队列第75%分位的术后LOS分为LOS延长组（PLOS组）和LOS正常组（NLOS组）。对两组患者的性别、年龄、高血压、糖尿病、截瘫、抗凝史、结核耐药、术前抗结核时间、输血、手术部位、手术入路、融合椎体数目、手术时间、术中出血量（intraoperative blood loss,IBL）、术后并发症、输血费用、住院费用、C反应蛋白（C-reactive protein,CRP）、血沉（erythrocyte sedimentation rate,ESR）、白蛋白（albumin,ALB）、血常规、凝血功能等进行单因素分析。根据套索（Lasso）回归,选择与脊柱结核术后LOS延长显著相关的危险因素;随后将筛选出来的危险因素纳入多因素Logistic回归分析,最终依据多因素Logistic回归分析结果建立预测模型,并通过绘制列线图对模型进行可视化,以此来预测脊柱结核术后LOS延长的风险概率。使用自举法（Bootstrap）进行模型内部验证,绘制受试者工作特征（receiver operating characteristic,ROC）曲线、校准曲线和决策曲线分析（decision curve analysis,DCA）验证该模型的区分度、准确度以及临床适用性。结果：纳入研究的152例患者中位LOS为10d,75%LOS为14d,PLOS组96例,NLOS组56例。单因素分析显示,两组患者的年龄、高血压、糖尿病、抗凝史、结核耐药、术前抗结核时间、手术部位、手术入路、手术时间、IBL、术后并发症、CRP、ESR、术前ALB、血常规、凝血功能等均无统计学差异（P>0.05）,两组患者的性别、截瘫、输血、融合椎体数目、输血费用、住院费用差异有统计学差异（P<0.05）。将患者手术时间、IBL、术前Hb、术前ALB,按ROC的约登指数为分割点,手术时间临界值为198（min）、IBL临界值为1000（mL）、术前Hb临界值为118（g/L）、术前ALB 临界值为38.8（g/L）。筛选出与脊柱结核术后LOS延长密切相关的危险因素为女性、输血、融合椎体数目≥3、手术时间≥198min和IBL≥1000mL、术前Hb＜118g/L和术前ALB＜38.8g/L。多因素Logistic回归分析显示,女性、融合椎体数目≥3、手术时间≥198min和IBL≥1000ml是脊柱结核患者术后LOS延长的危险因素（P<0.05）。构建Logistic回归的可视化列线图模型,列线图中的预测因子包括女性、融合椎体数目、手术时间和IBL。进行1000次Bootstrap自助抽样以完成模型内部验证,C指数值为0.882,ROC曲线下面积（area under curve,AUC）为0.884（95%CI：0.782～0.985）。校准曲线显示模型的表观曲线与偏差校正后的曲线拟合良好。DCA曲线显示在0.2～0.9的阈值区间具有最大临床效益。结论：女性、融合椎体数目≥3、手术时间≥198min和IBL≥1000ml是脊柱结核患者行后路病灶清除植骨融合内固定术后LOS延长的主要危险因素,基于以上危险因素所绘制的连线图可以帮助医生做出临床决策并优化围术期管理。     

基于 SEER数据库建立预测卵巢浆液性囊腺癌术后患者生存时间列线图

目的：建立卵巢浆液性囊腺癌患者手术后生存时间预测模型并绘制列线图。方法：回顾性分析监测、流行病学和结果（SEER）数据库2010至2015年5906例诊断为卵巢浆液性囊腺癌手术后患者的资料，通过多因素Cox比例风险回归模型得到其远期存活的独立危险因素。采用R语言绘制患者术后3年和5年存活率的列线图，用受试者操作特征曲线及C指数检验模型的区分度，校准图检验其校准度，并对其独立危险因素进行生存分析。结果：Cox比例风险回归模型分析结果显示，年龄、种族、组织学分级（低分化和未分化）、T分期（T2a、T2b、T2c、T3a、T3b、T3c）、M分期（M1）是卵巢浆液性囊腺癌手术后患者预后的独立危险因素（均 P<0.01）。建立的列线图能迅速通过年龄、种族、组织学分级、T分期、M分期预测患者术后3年和5年的存活率。列线图C指数为0.688，预测患者术后3年和5年存活率的列线图的曲线下面积分别为0.708、0.716。校准图显示患者术后3年和5年存活率的列线图模型与实际模型一致性尚可。具有高危因素的患者生存时间短于具有低危因素的患者（ P<0.05）。 结论：本研究基于SEER数据库建立的预测卵巢浆液性囊腺癌术后患者生存时间的列线图有助于临床评估。     

基于风险管理的统计质量控制策略研究进展

实验医学旨为临床提供可靠的实验室检测结果,从而支持医疗决策和改善患者健康状况。为达到这个目的,实验室质量管理策略经历了不断的变化和进展。传统统计质量控制以误差检出率判断质控方案性能,六西格玛质量管理策略引进了西格玛度量作为过程指数,EP23-A提出了基于风险管理的质量控制计划,以及近年来为不断优化风险管理策略而提出了列线图和风险管理指数等图形工具和方法。本文总结以上质量管理工具,为实验室提供基于风险管理的统计质量控制策略及评价。     

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

BackgroundPrediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases.MethodsThis study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set.ResultsAge ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/.ConclusionThe prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.