State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China.
Xinjiang Key Laboratory of Cardiovascular Disease, Clinical Medical Research Institute, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China.
J Thromb Thrombolysis. 2024 Jan;57(1):29-38. doi: 10.1007/s11239-023-02846-2. Epub 2023 Jun 23.
VT (Ventricular Thrombus) is a serious complication of dilated cardiomyopathy (DCM). Our goal is to develop a nomogram for personalized prediction of incident VT in DCM patients.
1267 patients (52.87 ± 11.75 years old, 73.8% male) were analyzed retrospectively from January 01, 2015, to December 31, 2020. A nomogram model for VT risk assessment was established using minimum absolute contraction and selection operator (LASSO) and multivariate logistic regression analysis, and its effectiveness was validated by internal guidance. The model was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). We compared the performance in predicting VT between nomogram and CHA2DS2, CHA2DS2- VASc or ATRIA by AUC, akaike information criterion (AIC), bayesian information criterion (BIC), net reclassification index (NRI), and integrated discrimination index (IDI).
89 patients (7.02%) experienced VT. Multivariate logistic regression analysis revealed that age, left ventricular ejection fraction (LVEF), uric acid (UA), N-terminal precursor B-type diuretic peptide (NT-proBNP), and D-dimer (DD) were important independent predictors of VT. The nomogram model correctly separates patients with and without VT, with an optimistic C score of 0.92 (95%CI: 0.90-0.94) and good calibration (Hosmer-Lemeshow χ = 11.51, P = 0.12). Our model showed improved prediction of VT compared to CHA2DS2, CHA2DS2-VASc or ATRIA (all P < 0.05).
The novel nomogram demonstrated better than presenting scores and showed an improvement in predicting VT in DCM patients.
VT（心室血栓）是扩张型心肌病（DCM）的严重并发症。我们的目标是开发一种列线图，用于对 DCM 患者发生 VT 的事件进行个性化预测。
回顾性分析了 2015 年 1 月 1 日至 2020 年 12 月 31 日期间的 1267 名患者（52.87±11.75 岁，男性占 73.8%）。使用最小绝对值收缩和选择算子（LASSO）和多变量逻辑回归分析建立了用于 VT 风险评估的列线图模型，并通过内部指导对其有效性进行了验证。通过受试者工作特征曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）对模型进行评估。我们通过 AUC、Akaike 信息准则（AIC）、贝叶斯信息准则（BIC）、净重新分类指数（NRI）和综合判别指数（IDI）比较了列线图和 CHA2DS2、CHA2DS2-VASc 或 ATRIA 在预测 VT 方面的性能。
89 名患者（7.02%）发生了 VT。多变量逻辑回归分析显示，年龄、左心室射血分数（LVEF）、尿酸（UA）、N 末端前体 B 型利钠肽（NT-proBNP）和 D-二聚体（DD）是 VT 的重要独立预测因子。该列线图模型正确地区分了有无 VT 的患者，乐观的 C 评分为 0.92（95%CI：0.90-0.94），校准良好（Hosmer-Lemeshow χ=11.51，P=0.12）。与 CHA2DS2、CHA2DS2-VASc 或 ATRIA 相比，我们的模型显示出对 VT 预测的改善（均 P<0.05）。
该新型列线图在预测 DCM 患者 VT 方面表现优于现有评分，并显示出改善。
