Lei Song, Yu Chenyu, Li Li, Liu Mei, Yang Mou, Hu Hongde
Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
Affiliated Hospital of Sichuan Nursing Vocational College, The Third People's Hospital of Sichuan Province, Chengdu, Sichuan, China.
Pacing Clin Electrophysiol. 2025 Sep;48(9):1024-1036. doi: 10.1111/pace.70014. Epub 2025 Aug 8.
Left ventricular thrombus (LVT) is a severe complication associated with increased risks of systemic embolism and mortality. Despite advancements in anticoagulant therapy, optimal management strategies and risk factors for all-cause mortality remain unclear. This study aims to develop a predictive model to assess mortality risk in LVT patients and guide clinical decision-making.
This retrospective cohort study included LVT patients diagnosed at West China Hospital (June 2018-June 2023). Patients were classified into survival and mortality groups based on all-cause mortality during follow-up. A total of 459 patients were included, randomly divided into training (n = 322) and validation (n = 137) sets. Logistic regression analysis identified seven independent predictors of mortality, which were used to construct a nomogram-based risk prediction model. The model demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.846 in the training set and 0.791 in the validation set. Key mortality predictors included elevated B-type natriuretic peptide (BNP) (OR 3.359, 95% CI 1.827-6.176, p = 0.0001), lower albumin levels (OR 0.930, 95% CI 0.882-0.981, p = 0.0077), absence of antithrombotic therapy (OR 0.468, 95% CI 0.303-0.723, p = 0.0006), and presence of malignant tumors (OR 6.199, 95% CI 1.593-24.129, p = 0.0085).
A novel mortality prediction model for LVT patients was developed, offering a valuable tool for risk assessment and treatment optimization. This model provides a valuable tool for risk assessment and treatment optimization in Asian populations, particularly in China. Further validation is required to confirm its clinical utility.
左心室血栓（LVT）是一种严重并发症，与全身栓塞和死亡风险增加相关。尽管抗凝治疗取得了进展，但全因死亡率的最佳管理策略和风险因素仍不明确。本研究旨在建立一个预测模型，以评估LVT患者的死亡风险并指导临床决策。
这项回顾性队列研究纳入了在华西医院确诊的LVT患者（2018年6月至2023年6月）。根据随访期间的全因死亡率将患者分为生存组和死亡组。共纳入459例患者，随机分为训练集（n = 322）和验证集（n = 137）。逻辑回归分析确定了七个独立的死亡预测因素，用于构建基于列线图的风险预测模型。该模型在训练集中的受试者操作特征曲线下面积（AUC）为0.846，在验证集中为0.791，显示出良好的区分度。关键的死亡预测因素包括B型利钠肽（BNP）升高（OR 3.359，95%CI 1.827 - 6.176，p = 0.0001）、白蛋白水平降低（OR 0.930，95%CI 0.882 - 0.981，p = 0.0077）、未进行抗栓治疗（OR 0.468，95%CI 0.303 - 0.723，p = 0.0006）以及存在恶性肿瘤（OR 6.199，95%CI 1.593 - 24.129，p = 0.0085）。
开发了一种新的LVT患者死亡预测模型，为风险评估和治疗优化提供了有价值的工具。该模型为亚洲人群，尤其是中国人群的风险评估和治疗优化提供了有价值的工具。需要进一步验证以确认其临床实用性。
