UNASSIGNED:本研究旨在开发和验证一种特定的风险分层列线图模型,用于使用容易获得的人口统计学指标预测住院肺癌患者的静脉血栓栓塞(VTE)。临床和治疗特点,从而根据VTE风险水平指导血栓预防的个体化决策。
UNASSIGNED:我们对2016年1月至2021年12月住院的新诊断肺癌患者进行了回顾性病例对照研究。队列中包括234例发生PTE的患者和936例非VTE患者。将患者随机分为衍生组(70%,165例VTE患者和654例非VTE患者)和验证组(30%,69例VTE患者和282例非VTE患者)。使用Youden指数建立截止值。单因素和多因素回归分析用于确定与VTE相关的独立危险因素。方差膨胀因子(VIF)用于模型中协变量的共线性诊断。通过一致性指数(C指数)对模型进行了验证,接收器工作特性曲线(ROC)和Hosmer-Lemeshow拟合优度测试的校准图。通过决策曲线分析(DCA)评估模型的临床实用性。Further,列线图模型与当前模型的比较(Khorana,Caprini,帕多瓦和COMPASS-CAT)通过使用DeLong检验比较ROC曲线进行。
未经评估:预测列线图模型包括11个变量:体重指数(BMI)定义的超重(24-28):[优势比(OR):1.90,95%置信区间(CI):1.19-3.07],腺癌(OR:3.00,95%CI:1.88-4.87),III-IV期(OR:2.75,95CI:1.58-4.96),中心静脉导管(CVC)(OR:4.64,95CI:2.86-7.62),D-二聚体水平≥2.06mg/L(OR:5.58,95CI:3.54-8.94),PT水平≥11.45秒(OR:2.15,95%CI:1.32-3.54),Fbg水平≥3.33g/L(OR:1.76,95CI:1.12-2.78),TG水平≥1.37mmol/L(OR:1.88,95CI:1.19-2.99),ROS1重排(OR:2.87,95CI:1.74-4.75),化疗史(OR:1.66,95CI:1.01-2.70)和放疗史(OR:1.96,95CI:1.17-3.29)。共线性分析,证明变量之间没有共线性。所得模型在推导组(AUC0.865,95%CI:0.832-0.897)和验证组(AUC0.904,95CI:0.869-0.939)中显示出良好的预测性能。校准曲线和DCA显示,风险分层列线图具有良好的一致性和临床实用性。Futher,特定VTE风险-分层列线图模型的ROC曲线下面积(0.904;95%CI:0.869-0.939)显著高于KRS,Caprini,帕多瓦模型和COMPASS-CAT模型(Z=12.087、11.851、9.442、5.340,P均<0.001)。
未经评估:高性能列线图模型包含了可用的临床参数,遗传和治疗因素已经建立,能准确预测肺癌住院患者发生VTE的风险,指导患者血栓预防的个体化决策。值得注意的是,在对这些患者的VTE风险进行分层方面,新型列线图模型比常规临床实践中现有的广为接受的模型显著更有效.未来基于社区的前瞻性研究和来自多个临床中心的研究需要进行外部验证。
UNASSIGNED: This study aimed to develop and validate a specific risk-stratification nomogram model for the prediction of venous thromboembolism(VTE) in hospitalized patients with lung cancer using readily obtainable demographic, clinical and therapeutic characteristics, thus guiding the individualized decision-making on
thromboprophylaxis on the basis of VTE risk levels.
UNASSIGNED: We performed a retrospective
case-control study among newly diagnosed lung cancer patients hospitalized between January 2016 and December 2021. Included in the cohort were 234 patients who developed PTE and 936 non-VTE patients. The patients were randomly divided into the derivation group (70%, 165 VTE patients and 654 non-VTE patients) and the validation group (30%, 69 VTE patients and 282 non-VTE patients). Cut off values were established using a Youden´s Index. Univariate and multivariate regression analyses were used to determine independent risk factors associated with VTE. Variance Inflation Factor(VIF) was used for collinearity diagnosis of the covariates in the model. The model was validated by the consistency index (C-index), receiver operating characteristic curves(ROC) and the calibration plot with the Hosmer-Lemeshow goodness-of-fit test. The clinical utility of the model was assessed through decision curve analysis(DCA). Further, the comparison of nomogram model with current models(Khorana, Caprini, Padua and COMPASS-CAT) was performed by comparing ROC curves using the DeLong\'s test.
UNASSIGNED: The predictive nomogram modle comprised eleven variables: overweight(24-28) defined by body mass index (BMI): [odds ratio (OR): 1.90, 95% confidence interval (CI): 1.19-3.07], adenocarcinoma(OR:3.00, 95% CI: 1.88-4.87), stageIII-IV(OR:2.75, 95%CI: 1.58-4.96), Central venous catheters(CVCs) (OR:4.64, 95%CI: 2.86-7.62), D-dimer levels≥2.06mg/L(OR:5.58, 95%CI:3.54-8.94), PT levels≥11.45sec(OR:2.15, 95% CI:1.32-3.54), Fbg levels≥3.33 g/L(OR:1.76, 95%CI:1.12-2.78), TG levels≥1.37mmol/L (OR:1.88, 95%CI:1.19-2.99), ROS1 rearrangement(OR:2.87, 95%CI:1.74-4.75), chemotherapy history(OR:1.66, 95%CI:1.01-2.70) and radiotherapy history(OR:1.96, 95%CI:1.17-3.29). Collinearity analysis with demonstrated no collinearity among the variables. The resulting model showed good predictive performance in the derivation group (AUC 0.865, 95% CI: 0.832-0.897) and in the validation group(AUC 0.904,95%CI:0.869-0.939). The calibration curve and DCA showed that the risk-stratification nomogram had good consistency and clinical utility. Futher, the area under the ROC curve for the specific VTE risk-stratification nomogram model (0.904; 95% CI:0.869-0.939) was significantly higher than those of the KRS, Caprini, Padua and COMPASS-CAT models(Z=12.087, 11.851, 9.442, 5.340, all P<0.001, respectively).
UNASSIGNED: A high-performance nomogram model incorporated available clinical parameters, genetic and therapeutic factors was established, which can accurately predict the risk of VTE in hospitalized patients with lung cancer and to guide individualized decision-making on
thromboprophylaxis. Notably, the novel nomogram model was significantly more effective than the existing well-accepted models in routine clinical practice in stratifying the risk of VTE in those patients. Future community-based prospective studies and studies from multiple clinical centers are required for external validation.