An Yuqiong, Wang Xiaojuan, Wang Zhen, Chen Kundi, Nie Fang
Ultrasound Medical Center, Second Hospital of Lanzhou University, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.
Department of Heart Center, First Hospital of Lanzhou University, No.1 Donggang West Road, Chengguan District, Lanzhou, 730030, China.
Sci Rep. 2025 Jul 22;15(1):26561. doi: 10.1038/s41598-025-12043-1.
This study aimed to develop a nomogram for accurately predicting in-hospital mortality in patients with infective endocarditis (IE). We conducted a retrospective analysis of clinical, echocardiographic, and laboratory data from IE patients admitted between January 2010 and September 2024. 252 IE patients from the Second Hospital of Lanzhou University were included in the training cohort, while 65 IE patients from the First Hospital of Lanzhou University were enrolled for external validation. The least absolute shrinkage and selection operator (LASSO) regression method was used to identify factors associated with in-hospital mortality. A nomogram was constructed using multivariate logistic regression. Model performance was assessed using receiver operating characteristic (ROC) curve and calibration curve. Clinical utility was evaluated through decision curve analysis (DCA) and clinical impact curve (CIC). The nomogram included five independent risk factors: embolic events, vegetation size ≥ 10 mm, moderate or higher pulmonary hypertension, hydropericardium, and surgery. The area under the curve (AUC) of the nomogram in the training cohort was 0.850 (95% CI: 0.794-0.906), and external validation cohort was 0.819 (95% CI: 0.693-0.946). The calibration plot demonstrated excellent prediction consistency. Both DCA and CIC confirmed the clinical utility of the nomogram. We developed and validated a nomogram for predicting in-hospital mortality in patients with IE. The model demonstrated excellent performance and provided a useful tool to assist clinicians in decision-making and patient management.
本研究旨在开发一种列线图，以准确预测感染性心内膜炎（IE）患者的院内死亡率。我们对2010年1月至2024年9月期间收治的IE患者的临床、超声心动图和实验室数据进行了回顾性分析。兰州大学第二医院的252例IE患者被纳入训练队列，而兰州大学第一医院的65例IE患者被纳入外部验证。采用最小绝对收缩和选择算子（LASSO）回归方法识别与院内死亡率相关的因素。使用多变量逻辑回归构建列线图。使用受试者操作特征（ROC）曲线和校准曲线评估模型性能。通过决策曲线分析（DCA）和临床影响曲线（CIC）评估临床效用。列线图包括五个独立危险因素：栓塞事件、赘生物大小≥10 mm、中度或更高肺动脉高压、心包积液和手术。训练队列中列线图的曲线下面积（AUC）为0.850（95%CI：0.794 - 0.906），外部验证队列中为0.819（95%CI：0.693 - 0.946）。校准图显示出良好的预测一致性。DCA和CIC均证实了列线图的临床效用。我们开发并验证了一种用于预测IE患者院内死亡率的列线图。该模型表现出色，为临床医生的决策和患者管理提供了有用的工具。
