Yang Qing, Quan Xin, Lang Xinyue, Liang Yan
National Clinical Research Center of Cardiovascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China.
Emergency Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China.
Rev Cardiovasc Med. 2022 Nov 30;23(12):390. doi: 10.31083/j.rcm2312390. eCollection 2022 Dec.
Thromboembolism is associated with mortality and morbidity in patients with ventricular thrombus. Early detection of thromboembolism is critical. This study aimed to identify potential predictors of patient characteristics and develop a prediction model that predicted the risk of thromboembolism in hospitalized patients with ventricular thrombus.
We performed a retrospective cohort study from the National Center of Cardiovascular Diseases of China between November 2019 and December 2021. Hospitalized patients with an initial diagnosis of ventricular thrombus were included. The primary outcome was the rate of thromboembolism during the hospitalization. The Lasso regression algorithm was performed to select independent predictors and the multivariate logistic regression was further verified. The calibration curve was derived and a nomogram risk prediction model was built to predict the occurrence of thromboembolism.
A total of 338 eligible patients were included in this study, which was randomly split into a training set (n = 238) and a validation set (n = 100). By performing Lasso regression and multivariate logistic regression, the prediction model was established including seven factors and the area under the receiving operating characteristic was 0.930 in the training set and 0.839 in the validation set. Factors associated with a high risk of thromboembolism were protuberant thrombus (odds ratio (OR) 5.03, 95% confidential intervals (CI) 1.14-23.83, = 0.033), and history of diabetes mellitus (OR 6.28, 95% CI 1.59-29.96, = 0.012), while a high level of left ventricular ejection fraction along with no antiplatelet therapy indicated a low risk of thromboembolism (OR 0.95, 95% CI 0.89-1.01, = 0.098; OR 0.26, 95% CI 0.05-1.07, = 0.083, separately).
A prediction model was established by selecting seven factors based on the Lasso algorithm, which gave hints about how to forecast the probability of thromboembolism in hospitalized ventricular thrombus patients. For the development and validation of models, more prospective clinical studies are required.
NCT05006677.
血栓栓塞与心室血栓患者的死亡率和发病率相关。早期发现血栓栓塞至关重要。本研究旨在确定患者特征的潜在预测因素，并建立一个预测模型，以预测住院心室血栓患者发生血栓栓塞的风险。
我们于2019年11月至2021年12月在中国国家心血管病中心进行了一项回顾性队列研究。纳入初次诊断为心室血栓的住院患者。主要结局是住院期间血栓栓塞的发生率。采用Lasso回归算法选择独立预测因素，并进一步进行多因素逻辑回归验证。绘制校准曲线并建立列线图风险预测模型，以预测血栓栓塞的发生。
本研究共纳入338例符合条件的患者，随机分为训练集（n = 238）和验证集（n = 100）。通过Lasso回归和多因素逻辑回归，建立了包含七个因素的预测模型，训练集的受试者工作特征曲线下面积为0.930，验证集为0.839。与血栓栓塞高风险相关的因素为突出血栓（比值比（OR）5.03，95%置信区间（CI）1.14 - 23.83，P = 0.033）和糖尿病史（OR 6.28，95% CI 1.59 - 29.96，P = 0.012），而左心室射血分数高且未接受抗血小板治疗表明血栓栓塞风险低（分别为OR 0.95，95% CI 0.89 - 1.01，P = 0.098；OR 0.26，95% CI 0.05 - 1.07，P = 0.083）。
基于Lasso算法选择七个因素建立了预测模型，为预测住院心室血栓患者血栓栓塞的概率提供了线索。对于模型的开发和验证，需要更多的前瞻性临床研究。
NCT05006677。
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