股骨头坏死行全髋关节置换术后输血的危险因素分析及列线图预测模型建立

目的：探讨股骨头坏死行全髋关节置换术后输血的危险因素,构建此类患者术后输血风险的列线图预测模型。方法：回顾性研究2019年1月至2020年12月因股骨头坏死行初次全髋关节置换的237例患者的病例资料,根据术后是否输血分为输血组60例,未输血组177例,术后输血率为25.3%。收集两组性别、体重指数（BMI）、麻醉方式、术前血红蛋白水平、手术时长、出血量等资料,运用多因素logistic回归分析法分析导致术后输血的危险因素,而后利用R软件构建股骨头坏死全髋关节置换术后输血的列线图预测模型,并绘制受试者操作特征（ROC）曲线来评估列线图模型区分度。结果：多因素分析显示,骨质疏松、术前血红蛋白降低、手术时长、出血量、术后1 d血钙降低是股骨头坏死行全髋关节置换术后输血的危险因素。通过R软件绘制列线图预测模型及ROC曲线,得到曲线下面积（AUC）为0.891,表明该预测模型有较好的区分度,预测术后输血风险与实际发生风险一致性较好。结论：基于骨质疏松、术前血红蛋白降低、手术时长、出血量、术后1 d血钙降低5项独立危险因素构建的预测股骨头坏死患者行全髋关节置换术后输血风险的列线图模型具有良好的区分...     

冠状动脉造影或经皮冠状动脉介入术前患者临床列线图预测术后发生对比剂急性肾损伤的风险

目的构建接受冠状动脉造影(CAG)或经皮冠状动脉介入(PCI)治疗患者术前临床指标的列线图,据此预测术后发生对比剂急性肾损伤(CI-AKI)的风险。方法收集245例接受CAG或PCI治疗患者的术前临床资料。采用LASSO回归方法筛选与CI-AKI相关的临床特征,据以构建列线图预测模型,计算CI-AKI风险分数。绘制ROC曲线,计算风险评分临界值。结果 245例中,34例(34/245,13.88%)发生CI-AKI。LASSO回归分析显示性别、糖尿病史、乳酸脱氢酶水平、超敏C反应蛋白,饮酒年限、慢性肾脏病及其分期、脑卒中史、急性心肌梗死及收缩压是CI-AKI风险评分和预测模型的危险因素。CI-AKI列线图预测效能较好,其风险评分临界值为-1.953。结论列线图可用于术前预测患者接受CAG或PCI治疗后发生CI-AKI的风险。     

老年患者腹壁切口疝修补术后复发的危险因素探讨及风险列线图模型的建立

目的探讨老年患者腹壁切口疝修补术后复发的危险因素，并建立预测老年患者腹壁切口疝修补术后复发的风险列线图模型。 方法选取2014年1月至2019年12月于南京大学附属鼓楼医院进行诊治的260例行腹壁切口疝修补术老年患者作为研究对象，分析所选患者的临床资料，根据是否复发将所选患者分为复发组和正常组，采用Logistic回归分析筛选老年患者腹壁切口疝修补术后复发的危险因素，并建立老年患者腹壁切口疝修补术后复发的风险列线图模型。 结果260例腹壁切口疝修补术老年患者中术后复发患者36例（13.85%）。单因素分析结果显示，复发组和正常组患者性别、年龄、疝类型、疝部位、补片型号、固定补片、手术类型及饮酒史等资料差异均无统计学意义（P>0.05），而手术时间、医师水平、术后血肿、吸烟史及肥胖等资料差异均有统计学意义（P<0.05）。Logistic回归分析结果显示，手术时间≥120 min、医师水平、术后血肿、有吸烟史及肥胖等为老年患者腹壁切口疝修补术后复发的独立危险因素（P<0.05），均和老年患者腹壁切口疝修补术后复发高度相关。基于手术时间、医师水平、术后血肿、吸烟史及肥胖等老年患者腹壁切口疝修补术后复发的独立危险因素，建立预测老年患者腹壁切口疝修补术后复发的风险列线图模型，C-index指数为0.775（95% CI：0.728~0.823），预测值与实测值基本一致，说明本列线图的辨别力较好，列线图模型预测老年患者腹壁切口疝修补术后复发的受试者工作特征曲线显示，曲线下面积为0.807，表明本研究列线图的预测价值较高。 结论手术时间≥120 min、医师水平、术后血肿、有吸烟史及肥胖等为老年患者腹壁切口疝修补术后复发的独立危险因素，本研究所建立的列线图有助于预测老年患者腹壁切口疝修补术后复发的发生风险。     

基于SEER数据库验证淋巴结清扫术对N0期胆囊癌患者预后的影响

目的 基于监测、流行病学和最终结局（SEER）数据库评估淋巴结清扫术对N0期胆囊癌患者预后的影响。方法 下载、整理并分析于2004—2015年明确诊断为胆囊癌患者的临床资料，使用R语言将患者分为淋巴结清扫组（LNR）和淋巴结未清扫组（non-LNR），结局指标为肿瘤特异性生存率（CSS）。通过倾向性评分（PSM）保证两组基线资料可比性，采用单因素和多因素Cox回归筛选危险因素并构建相关列线图，Kaplan-Meier法描述生存曲线，Log-rank检验比较生存差异。采用一致性指数（C-index）、ROC曲线和校准曲线对列线图进行评估，同时应用综合判别改善指数（IDI）比较列线图与AJCC分期的临床适用性。结果 经PSM，本研究纳入2 272例患者。Cox回归筛选出年龄、分化程度、AJCC分期、T分期与LNR为独立危险因素。列线图预测准确性为0.738，标准误为0.007。与AJCC分期相比，模型预测1、3和5年的CSS的IDI分别为0.084、0.111和0.115，具有正改善作用。根据列线图计算得分，Kaplan-Meier法显示高分险与低分险患者的预后差异有统计学意义，有较好的临...     

术前血清γ-谷氨酰转肽酶与血小板比值预测乙型肝炎病毒相关性肝细胞癌患者预后的预测模型构建

目的 探讨术前血清γ-谷氨酰转肽酶与血小板比值（gamma-glutamyl transpeptidase to platelet ratio,GPR）与行根治性切除术的乙型肝炎病毒相关性肝细胞癌（hepatocellular carcinoma,HCC）患者（简称“HCC患者”）预后的关系并建立列线图预测模型。方法 根据纳入和排除标准，回顾性收集2012年1月15日至2018年12月15日期间咸阳市中心医院肝胆外科收治的HCC患者的临床病理资料。应用受试者操作特征曲线确定GPR的最佳临界值，据此将患者分为低GPR组（GPR≤最佳临界值）和高GPR组（GPR>最佳临界值）；应用Kaplan-Meier法绘制生存曲线进行生存分析。应用单因素和多因素Cox比例风险回归（简称“Cox回归”）模型分析影响HCC患者总生存期的风险因素，根据筛选出的风险因素构建列线图预测模型，采用一致性指数和校准曲线评估它预测HCC患者3年和5年累积总生存率的效能。结果 共纳入213例患者，GPR的最佳临界值为0.906，其中低GPR组和高GPR组分别为114例和99例。Kaplan-Meier生存曲线分析...     

肝移植术后二次气管插管的危险因素及预测模型的建立

目的 探讨肝移植术后二次气管插管的危险因素,建立并验证预测模型。方法 回顾性分析2016年1月至2021年12月在中山大学附属第一医院接受肝移植术的500例患者的临床资料,分为二次气管插管组（R组,21例）和对照组（C组,479例）。比较两组患者围手术期临床资料,分析肝移植术后二次气管插管的危险因素及预后情况。建立列线图预测模型,并运用受试者工作特征（ROC）曲线和拟合优度检验进行评价。结果 500例接受肝移植术的患者术后二次气管插管发生率为4.2%(21/500)。多因素Logistic回归分析结果显示,术前终末期肝病模型（MELD）评分高、术后48 h血肌酐（Scr）升高、术后丙氨酸氨基转移酶（ALT）峰值是术后二次气管插管的独立危险因素。将上述危险因素绘制ROC曲线,曲线下面积为0.790(95%CI:0.677～0.903,P<0.001)。制作肝移植术后二次气管插管的列线图预测模型,根据得分实现对肝移植术后二次气管插管的初步预测。结论 接受肝移植术的患者,术前MELD评分高、术后48 h Scr升高、术后ALT峰值是术后二次气管插管的独立危险因素。     

预测胆囊癌根治性切除术预后的列线图的构建及验证

目的 构建可有效预测胆囊癌根治性切除术患者预后的列线图。方法 从SEER（Surveillance,Epidemiology,and End Results Program）数据库提取2004-2013年共832例胆囊癌根治术后患者的回顾性临床病理资料,采用随机抽样法将数据分为训练集（n=608）和验证集（n=224）。使用Cox比例风险回归模型筛选影响预后的独立危险因素,通过训练集数据构建预测模型,基于列线图获得每例患者的综合生存概率得分。利用Bootstrap方法重复1 000次采样对训练集进行内部验证,然后利用验证集完成外部验证。一致性指数（C-index）及校正曲线用以评估模型的预测精度。结果 训练集的中位疾病相关生存（diseasespecific survival,DSS）时间为22.07个月,术后1、3、5年DSS率分别为67.6%、36.8%、29.2%。多因素分析提示,性别、年龄、分化程度、淋巴结转移、肿瘤直径、侵袭范围、远处转移是影响胆囊癌根治术后预后的独立危险因素（P < 0.05）。内部验证的C-index为0.76（95%CI 0.74～0.78）,术后5年DDS校正曲线提示在观察值与预测值之间有良好的一致性。本列线图的预测能力优于AJCC（American Joint Commissionon Cancer）分期（C-index 0.70,95%CI 0.67～0.73）,两组之间比较有统计学意义（P < 0.001)。结论 列线图模型可准确、直观地预测胆囊癌根治术后患者的生存概率。     

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。     

术前全身炎症反应指数和纤维蛋白原在肾癌切除术后肿瘤预后的影响

目的:探讨肾癌患者术前全身炎症反应指数(systemic inflammation response index,SIRI)和纤维蛋白原(fibrinogen,FIB)在预后评估中的临床价值。方法:回顾性分析2011年1月—2014年12月我院收治肾癌患者的临床及术后随访资料;采用受试者工作曲线(ROC)确定SIRI和FIB的最佳临界值。分析低SIRI组与高SIRI组和低FIB组与高FIB组临床特征间的差异;运用Kaplan-Meier法计算生存率,通过log-rank检验比较组间的生存率差异;通过Cox比例风险回归模型分析影响肾癌预后的影响因素。采用所有5年生存率的独立预后指标,包括Fuhrman分级,SIRI值,FIB值建立列线图,预测患者5年生存率。结果:本研究共收集188例肾癌患者,ROC曲线确定SIRI和FIB的最佳临界值分别为1.81和3.42 g/L,分为高SIR组、低SIRI组,高FIB组、低FIB组。术前SIRI水平与组间患者肿瘤大小、TNM分期、Fuhrman分级、淋巴结转移、高血压病史密切相关(P0.05);术前FIB水平与组间患者年龄、TNM分期、Fuhrman分级、淋巴结转移、有无高血压密切相关(P0.05)。Cox多因素回归分析结果显示,SIRI1.81、FIB3.42 g/L、Fuhrman分级是影响肾癌预后的独立危险因素。列线图结果发现可以预测患者术后5年生存率。结论:高SIRI和FIB是影响肾癌预后不良的独立危险因素,对预测患者术后生存状况有积极的作用。     

日间经皮肾镜碎石取石术患者延迟出院的危险因素分析和列线图构建

目的探讨日间经皮肾镜碎石取石术（PCNL）患者延迟出院的危险因素,并构建风险预测列线图。 方法回顾性审查370例日间PCNL患者的电子病历,根据患者是否在24 h内出入院,将其分为延迟出院组（61例）和正常出院组（309例）。采用单因素分析比较两组的一般资料、术后1 h生命体征和血液学指标。通过Logistic回归分析确定延迟出院的危险因素,绘制列线图以预测延迟出院的风险,应用校准曲线评估列线图的内部校准性能。 结果370例患者中有61例（16.5%）出现延迟出院,18例（4.9%）住院时间超过48 h。Logistic回归分析显示年龄>65岁（OR：3.992,95%CI：1.792~8.892,P=0.001）、通道数量（OR：3.139,95%CI：1.548~6.367,P=0.002）、术中严重出血（OR：12.207,95%CI：2.761~53.973,P=0.001）、术后1 h体温>38℃（OR：4.623,95%CI：1.445~14.789,P=0.010）、中性粒细胞比率>75%（OR：1.951,95%CI：1.008~3.777,P=0.047）和钾<3.5 mmol/L（OR：5.469,95%CI：1.641~18.226,P=0.006）是日间PCNL患者延迟出院的独立危险因素。延迟出院预测模型的ROC曲线下面积为0.784,通过危险因素构建的列线图具有良好的区分度和准确性。 结论基于年龄、通道数量、术中严重出血、术后感染和低钾血症构建的延迟出院风险预测列线图预测性能良好,可帮助医护人员快速识别术后延迟出院高风险患者,以便尽早实施医疗干预。     

Nomogram for predicting recurrence and metastasis of stage IA lung adenocarcinoma treated by videoassisted thoracoscopic lobectomy

ObjectiveThis study aimed to construct a nomogram to effectively predict recurrence and metastasis in patients with stage 1A lung adenocarcinoma after video-assisted thoracoscopic surgery (VATS) lobectomy.MethodsOur study included 337 patients. The 3-year recurrence-free survival rate and the 5-year recurrence-free survival (5-RFS) rate were analyzed. Multivariate Cox proportional hazards regression was conducted to identify independent risk factors. We established a nomogram and performed Harrell’s Concordance index, calibration plots, integrated discrimination improvement, and decision curve analyses to assess its discrimination and calibration.ResultsThe median follow-up time was 45 months. In a multivariate analysis, tumor diameter, pathological subtype, preoperative carcinoembryonic antigen level, and preoperative CYFRA21-1 level were independent prognostic factors for RFS (P < 0.05). These risk factors were used to construct a nomogram to predict postoperative recurrence and metastasis in these patients. Internal verification was performed using the bootstrap method. The C-index was 0.946 (95% confidence interval: 0.923–0.970), indicating that the model had a good predictive performance. Using the nomogram and X-tile software, the patients were divided into two groups: the high-risk (5-RFS rate, 0.10–0.90) and low-risk groups (5-RFS rate, 0.90–0.99); the difference in the RFS rate between the groups was significant (χ2 = 86.705, P < 0.001).ConclusionsOur nomogram had a better predictive ability for recurrence and metastasis in patients with stage 1A lung adenocarcinoma after VATS lobectomy resection than the Tumor–Node–Metastasis staging system and other predictive models. This nomogram can help provide individualized treatment strategies and follow-up times.     

甲状腺乳头状癌患者侧颈区淋巴结转移影响因素分析

背景与目的：甲状腺乳头状癌（PTC）是甲状腺癌中占比最大的病理类型,PTC的侧颈区淋巴结转移（LLNM）是导致患者复发和再手术的主要原因。因此,本研究分析PTC患者的临床特征,探讨发生LLNM的影响因素,并构建临床预测模型,为制定合理的手术范围提供参考依据。方法：回顾性分析锦州医科大学附属第一医院2018年3月—2022年1月行手术治疗的PTC患者临床资料,比较发生LLNM与未发生LLNM患者临床病理因素的差异,将有统计学意义的因素纳入多因素Logistic回归分析,用R 4.1.3建立PTC患者LLNM风险预测列线图模型,并绘制校准曲线评价该模型的精准度,用ROC曲线界定LLNM独立危险因素的诊断截断值。结果：共纳入597例PTC患者,其中,187例（31.32%）发生LLNM。单因素分析显示,年龄、肿瘤直径、多发癌灶、腺外侵犯、颈中央区淋巴结转移（CLNM）与BRAFV600E基因突变为PTC患者发生LLNM的影响因素（均P<0.05）。年龄、肿瘤直径、发生腺外侵犯、存在CLNM和BRAFV600E基因突变是LLNM的独立危险因素（均P<0.05）。基于以上影响因素构建...     

BRCA1/2基因突变对乳腺癌保乳术后局部复发的影响及相关预后模型的构建

背景与目的 保乳手术现已成为乳腺癌的标准手术方式之一,保乳手术能够保留患者的乳房外形,极大地改善患者术后的心理状态和生活质量。BRCA1/2基因是与乳腺癌密切相关的易感基因,BRCA1/2基因突变对保乳术后乳腺癌患者局部复发的影响目前尚有争议。因此,本研究分析BRCA1/2基因突变与乳腺癌保乳术后局部复发的关系,并构建相关预测模型,预测保乳术后乳腺癌患者的无局部复发生存（LRFS）率,为乳腺癌患者保乳手术适应证的选择提供可靠的依据。方法 回顾性分析2014年6月—2016年6月于中国人民解放军空军军医大学第一附属医院进行保乳手术的189例乳腺癌患者临床资料,并比较不同临床病理特征下患者BRCA1/2基因突变的差异,通过单因素及多因素Cox等比例回归模型分析BRCA1/2基因突变及其它临床病理因素对乳腺癌患者保乳术后局部复发的影响,并构建列线图来预测患者的LRFS率。通过一致性指数（C-index）、受试者工作特征（ROC）曲线及曲线下面积（AUC）对模型进行内部验证,通过校准曲线评估模型的准确性,并通过临床决策曲线分析（DCA）评价模型的临床获益和应用价值。结果 BRCA1/2基因突变组和未突变组的年龄和分子分型进行差异有统计学意义（均P<0.05）。单因素Cox等比例回归模型分析结果显示,BRCA1/2突变、肿瘤分级、肿瘤大小、N分期及分子分型是保乳术后乳腺癌患者LRFS率的影响因素（均P<0.1）。多因素Cox等比例回归模型分析结果显示,BRCA1/2基因突变、肿瘤大小、N分期及分子分型是保乳术后乳腺癌患者局部复发的独立影响因素（P<0.05）。将这些因素纳入并建立LRFS率的列线图预测模型。模型的C-index为0.86,内部验证C-index为0.81。ROC曲线分析结果显示,模型的3、5年LRFS率预测的AUC分别为0.89、0.85;校准曲线显示列线图预测的LRFS率与实际LRFS率接近;DCA分析显示模型的临床获益及应用价值较高。结论 BRCA1/2基因突变与保乳术后乳腺癌患者的局部复发相关,基于BRCA1/2基因突变列线图模型能够准确地预测保乳术后乳腺癌患者的LRFS率,并为乳腺癌患者手术方式的选择提供有效的科学依据。     

A nomogram to predict residual cavity formation after thoracoscopic decortication in chronic tuberculous empyema

Open in a separate window OBJECTIVESThe goal of this study was to develop and validate a nomogram for predicting residual cavity formation after video-assisted thoracoscopic decortication in patients with chronic tuberculous empyema (CTE).METHODSWe retrospectively analysed patients who were diagnosed and treated for CTE at our hospital from January 2017 to December 2020. We used univariable and binary logistic regression analyses to identify independent risk factors. A predictive nomogram was developed and validated for predicting the risk of residual cavity formation after video-assisted thoracoscopic decortication in patients with CTE. The receiver operating characteristic (ROC) was used to evaluate the nomogram.RESULTSData from 103 patients were analysed. The contact area between the lung and empyema (P = 0.001, odds ratio [OR] 1.017, 95% confidence interval [CI] 1.007–1.028), calcification (P = 0.004, OR 0.12, 95% CI 0.029–0.501) and thickness of the pleura (P = 0.02, OR 1.315, 95% CI 1.045–1.654) were risk factors for residual cavity formation after video-assisted thoracoscopic decortication. A 50% residual cavity formation rate was used as the cut-off to validate the nomogram model. The area under the ROC curve for the nomogram was 0.891 (95% CI, 0.82–0.963). The sensitivity and specificity of the nomogram were 86.67% and 82.19%, respectively. The calibration curve indicated good consistency between the predicted and actual risks.CONCLUSIONSThe preliminary nomogram could contribute to preventing postoperative residual cavity formation and making appropriate surgical decisions.     

妇科恶性肿瘤患者术后深静脉血栓危险因素的Meta分析

背景与目的 妇科恶性肿瘤患者术后发生深静脉血栓（DVT）,可引起患者肺栓塞或猝死,严重威胁患者的生命,本研究通过Meta分析明确妇科恶性肿瘤患者术后发生DVT的危险因素,为预防和降低妇科恶性肿瘤患者术后DVT的发生提供循证依据。方法 计算机检索多个国内外数据库,搜集有关妇科恶性肿瘤患者术后DVT危险因素的队列研究或病例对照研究,检索时限均为建库至2021年3月,采用Revman 5.3软件进行Meta分析。结果 共纳入19篇文献,包含4 964例患者,其中病例组1 040例,对照组3 924例,共研究了36项危险因素。将其中10项危险因素进行了数据合并分析显示,既往有DVT史（OR=3.70,95% CI=2.15~6.35,P<0.001）、年龄大（OR=2.99,95% CI=1.85~4.82,P<0.001）、合并高血压（OR=2.25,95% CI=1.32~3.83,P=0.003）、手术时间长（OR=1.03,95% CI=1.02~1.04,P<0.001）、BMI增加（OR=1.87,95% CI=1.55~2.25,P<0.001）、术后卧床时间长（OR=3.17,95% CI=2.56~3.92,P<0.001）、纤维蛋白原高（OR=2.80,95% CI=2.26~3.47,P<0.001）、肿瘤分期晚（OR=2.56,95% CI=1.83~3.57,P<0.001）、发生淋巴结转移（OR=2.88,95% CI=1.58~5.25,P=0.000 6）、D-二聚体高（OR=2.52,95% CI=1.84~3.43,P<0.001）均为妇科恶性肿瘤患者术后发生危险因素。结论 临床医护人员应结合本研究结果所确定10项危险因素,积极识别术后易发生DVT的妇科恶性肿瘤高危人群,并提供针对性的措施预防或降低其术后发生DVT风险。     

基于血脂异常及相关因素的肝内胆管癌发生风险列线图预测模型构建

背景与目的：肝内胆管癌（ICC）起病隐匿,进展迅速,患者诊断时往往错过最佳手术时机。ICC的发生机制尚不明确,可能与多种因素有关,目前发现血脂异常可能是风险因素之一。因此,本研究探讨血脂异常及其他危险因素与ICC发生风险的关联,并构建列线图预测模型,以期对ICC高危人群实现早期预防并最终降低发病率。方法：回顾性分析2015年1月—2023年1月于浙江大学医学院附属邵逸夫医院普通外科就诊的5 906例肝脏手术患者,其中ICC患者和非癌症患者分别设为病例组和对照组,收集患者治疗前基本资料和生化指标,将血脂指标和其余风险因素纳入单因素、多因素回归分析,筛选ICC发生的独立风险因素,构建列线图预测模型评估各因素影响程度,采用受试者工作特征曲线（ROC）、校准曲线和决策曲线评估列线图模型临床预测效能。结果：共纳入351例ICC患者和2 145例非癌症患者,单因素分析显示,两组患者的性别、年龄与糖尿病、高血压、肝硬化、乙肝、胆管结石病史、血吸虫病史比例,以及血清甘油三酯、血清总胆固醇、血清高密度脂蛋白胆固醇（HDL-C）水平的差异均有统计学意义（均P<0.05）。Logistic多因素回归分...     

Development and external validation of a nomogram to predict lymph node invasion after robot assisted radical prostatectomy

BackgroundPrediction of lymph node invasion (LNI) after radical prostatectomy has been rarely assessed in robotically assisted laparoscopic radical prostatectomy (RALP) series. We aimed to develop and externally validate a pretreatment nomogram for the prediction of LNI following RALP in patients with high- and intermediate-risk prostate cancer.Methods1654 RALP patients were prospectively collected between 2009 and 2016 from academic and community hospitals. We included patients with intermediate- and high-risk prostate cancer who underwent pelvic lymph node dissection (e-PLND). Logistic regression analysis was applied to construct a nomogram to predict LNI. Centers were randomly assigned to the training cohort (80%) and validation cohort (20%). The discriminative accuracies were evaluated by the areas under the curve and by the calibration plot. The net benefit of the nomogram to predict LNI was assessed by decision curve analysis and a cut-off was proposed.ResultsIn total, 14% of the patients in our cohort had pN1 disease. Applying logistic regression analysis, the following covariates were chosen to develop the nomogram: initial PSA, clinical T stage, biopsy Gleason sum, and proportion of positive biopsy cores. The nomogram showed a median discriminative accuracy of 73% and excellent calibration. The net benefit of the model ranged between 7% and 51% predicted risk of LNI. A cut-off to perform e-PLND was set at 7%. This would permit a 29% of avoidable e-PLND, missing 9.4% of patients with LNI.ConclusionsWe developed and externally validated a nomogram to predict LNI in patients treated with RALP from a prospective, multi-institutional, nationwide series. A risk of LNI > 7% is proposed as cut-off above which e-PLND is recommended.     

Competing risk nomogram for predicting prognosis of patients with spinal and pelvic chordoma: A SEER-based retrospective study

Purpose

Recently, competing risk nomograms were widely applied to predict prognosis in numerous tumors other than chordoma. Here, we aimed to construct and validate a competing-risk-based prognostic nomogram to predict 3- and 5-year cancer-specific death (CSD) in patients with spinal and pelvic chordoma.

Methods

All chordoma patient data were abstracted from the Surveillance, Epidemiology, and End Results (SEER) resource, and a total of 485 chordoma patients were eventually included in this study. Multivariate competing risk model and multivariate Cox model were used to determine independent prognostic factors, respectively, and the results of the two models were compared. Nomogram was employed to visualize the competing risk model. The discrimination, calibration, and clinical utility of this model were evaluated by Harrell concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Ten-fold cross-validation was further utilized to validate the prognostic nomogram.

Results

Significant prognostic factors affecting CSD were age (P = 0.016), localized involvement (P < 0.0001), and radical resection (P < 0.001) in the multivariate competing risk model. C-indexes were 0.799 and 0.76, and AUC were 0.812 and 0.778 for 3- and 5-year CSD. Calibration plots demonstrated the nomogram was well-fitted, and DCA indicated good clinical utility. The nomogram showed good performance in the 10-fold cross-validation.

Conclusion

We successfully built the first competing-risk-based nomogram to predict clinical outcomes in patients with spinal and pelvic chordoma. This well-established nomogram hopes to help clinicians with precise prognostic assessment and thus improve clinical outcomes.

     

Comparison of CTS5 risk model and 21-gene recurrence score assay in large-scale breast cancer population and combination of CTS5 and recurrence score to develop a novel nomogram for prognosis prediction

BackgroundBreast cancer is the most common malignancy in women. Clinical models such as Oncotype DX recurrence score (RS) and Clinical Treatment Score post–5 years (CTS5) model for survival prediction are crucial for clinical practice. However, it remains unclear whether CTS5 or RS would be a more powerful clinical model for recurrence risk evaluation. Therefore, we conducted the present study to compare the performance of CTS5 risk model and RS on different recurrence evaluation. And we further integrated the two models into a novel nomogram to improve the power for prognosis prediction.MethodsFemale patients with invasive hormone receptor positive breast cancer in the Surveillance, Epidemiology, and End Results Program (SEER) database with RS data available were included. The clinicopathological data were directly extracted from SEER database. Participants were divided into three subsets according to recurrence timing (<36 months, between 36 and 60 months, and >60 months) for model evaluation. Survival receiver operating characteristic curve and C-index were calculated to evaluate discrimination. Calibration curve were used to visual inspection for calibration. Model comparison was assessed by net reclassification index (NRI) method. Nomogram prognostic model was developed with the combination of CTS5 score and RS and also included other critical clinicopathological parameters.ResultsIn total, 64044 breast cancer patients were enrolled for analysis. The number of patients with survival <36 months (early recurrence subset), 36–60 months (intermediate recurrence subset) and >60 months (late recurrence subset) were 64044, 36878 and 15926, respectively. For model discrimination, CTS5 model was superior to RS for overall survival (OS) prediction (likelihood ratio test P < 0 0.001). RS model showed better performance for breast cancer specific survival (BCSS) in late recurrence subsets and worse performance in early and intermediate recurrence subsets than CTS5 (likelihood ratio test P < 0 0.001). For calibration, CTS5 model was superior to RS model for OS, which overestimated the recurrence risk in low-risk subgroup. Both models overestimated the risk for BCSS. In either early/intermediate/late recurrence patient subsets, there was no significant difference in NRI between two models in terms of both BCSS and OS, indicating the two models had comparable prognostic value. The nomogram which combined these two models largely improved the discrimination and calibration power (C-index 0.70–0.72).ConclusionsOur study proved the CTS5 risk model had comparable prognostic value as RS in HR + breast cancer patients. And the novel nomogram model had better discrimination and calibration than both CTS5 and RS, and future large-scale clinical trials are warranted for further validation.     

Predicting Non-home Discharge After Lung Surgery: Analysis of the General Thoracic Surgery Database

BackgroundAnticipating the need for non-home discharge (NHD) enables improved patient counseling and expedites placement, potentially reducing length of stay and hospital readmission. The objective of this study was to create a simple, preoperative, clinical prediction tool for NHD using The Society of Thoracic Surgeons General Thoracic Surgery Database (STS GTSD).MethodsThe STS GTSD was queried for patients who underwent elective anatomic lung cancer resection between 2009 and 2019. Exclusion criteria included age <18 years, percentage predicted diffusion capacity of the lung for carbon monoxide <20% or >150%, N3 or M1 disease, incomplete datasets, and mortality. The primary outcome was defined as discharge to an extended care, transitional care, rehabilitation center, or another hospital. Multivariable logistic regression was used to select risk factors and a nomogram for predicting risk of NHD was developed. The approach was cross-validated in 100 replications of a training set consisting of randomly selected two-thirds of the cohort and a validation set of remaining patients.ResultsA total of 35 948 patients from the STS GTSD met inclusion criteria. Final model variables used to derive the nomogram for NHD risk prediction included age (P < .001), percentage predicted diffusion capacity of the lung for carbon monoxide (P < .001), open surgery (P < .001), cerebrovascular history (P < .001), and Zubrod score (P < .001). The receiver operating characteristic curve, using sensitivities and specificities of the model, yielded area under the curve of 0.74. In 100 replicated cross-validations, out-of-sample area under the curve ranged from 0.72-0.76.ConclusionsUsing readily available preoperative variables, our nomogram prognosticates the risk of NHD after anatomic lung resection with good discriminatory ability. Such risk stratification can enable improved patient counseling and facilitate better planning of patients’ postoperative needs.