背景:在Behçet病(BD)中,轻度至重度瓣膜返流(VR)会引起严重的并发症,从而导致心力衰竭并最终死亡。VR的准确预测在BD受试者的早期阶段对于改善预后至关重要。因此,这项研究旨在开发一种可以在BD过程中早期检测VR的列线图。
方法:对72例诊断为Behçet病(BD)的患者进行评估,以评估心脏瓣膜反流作为主要结果。反流的严重程度被归类为轻度,中度,或严重。与诊断标准相关的参数用于开发模型1。stepAIC的组合,最佳子集,并采用随机森林方法来识别VR的独立预测因子,从而建立模型2并创建列线图来预测BD中VR的概率。接收器工作特性(ROC)和决策曲线分析(DCA)用于评估模型性能。
结果:34例患者经历了轻度至重度VR事件。使用五个变量建立了模型2,包括动脉受累,性别,住院年龄,平均动脉压,和皮肤损伤。与模型1(0.635,95%CI:0.512-0.757)相比,模型2的ROC(0.879,95%CI:0.793-0.966)显着改善。DCA表明模型2比模型1更可行和临床适用。
结论:建立了预测Behçet病患者VR的预测模型和列线图。该模型的良好性能可以帮助我们识别潜在的心力衰竭高危人群。要点•在这项研究中,评估了BD中VR的预测因子,并建立了早期预测BD患者VR发生的风险预测模型。•VR预测模型包括以下指标:动脉受累,性别,住院年龄,平均动脉压,和皮肤损伤。•我们开发的风险模型比使用诊断标准参数构建的模型更好,更优化,可视化和个性化模型,一个列线图,为临床医生提供了一种简单直观的实际预测工具。
BACKGROUND: In Behçet\'s disease (BD), mild-to-severe valvular regurgitation (VR) poses a serious complication that contributes significantly to heart failure and eventually death. The accurate prediction of VR is crucial in the early stages of BD subjects for improved prognosis. Accordingly, this study aimed to develop a nomogram that can detect VR early in the course of BD.
METHODS: One hundred seventy-two patients diagnosed with Behçet\'s disease (BD) were conducted to assess cardiac valve regurgitation as the primary outcome. The severity of regurgitation was classified as mild, moderate, or severe. The parameters related to the diagnostic criteria were used to develop model 1. The combination of stepAIC, best subset, and random forest approaches was employed to identify the independent predictors of VR and thus establish model 2 and create a nomogram for predicting the probability of VR in BD. Receiver operating characteristics (ROC) and decision curve analysis (DCA) were used to evaluate the model performance.
RESULTS: Thirty-four patients experienced mild-to-severe VR events. Model 2 was established using five variables, including arterial involvement, sex, age at hospitalization, mean arterial pressure, and skin lesions. In comparison with model 1 (0.635, 95% CI: 0.512-0.757), the ROC of model 2 (0.879, 95% CI: 0.793-0.966) was improved significantly. DCA suggested that model 2 was more feasible and clinically applicable than model 1.
CONCLUSIONS: A predictive model and a nomogram for predicting the VR of patients with Behçet\'s disease were developed. The good performance of this model can help us identify potential high-risk groups for heart failure. Key Points • In this study, the predictors of VR in BD were evaluated, and a risk prediction model was developed for the early prediction of the occurrence of VR in patients with BD. • The VR prediction model included the following indexes: arterial involvement, sex, age at hospitalization, mean arterial pressure, and skin lesions. • The risk model that we developed was better and more optimized than the models built with diagnostic criteria parameters, and visualizing and personalizing the model, a nomogram, provided clinicians with an easy and intuitive tool for practical prediction.