Predicting Thromboembolism in Hospitalized Patients with Ventricular Thrombus

Abstract

Background: 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.

Methods: 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.

Results: 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, p = 0.033), and history of diabetes mellitus (OR 6.28, 95% CI 1.59-29.96, p = 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, p = 0.098; OR 0.26, 95% CI 0.05-1.07, p = 0.083, separately).

Conclusions: 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.

Clinical trial registration: NCT05006677.

Keywords: prediction model; thromboembolism; ventricular thrombus.

Fig. 1.

Tuning parameter (Lambda) selection in the Lasso Model used ten-fold cross-validation based on the minimum criteria (left dotted vertical line) or the 1 standard error criteria (right dotted vertical line).

Fig. 2.

ROC curvesof Model 2 for predicting the risk of thromboembolism. (A) Training set. (B) Validation set. ROC, receiver operating characteristic; AUC, area under the ROC curve.

Fig. 3.

Nomogram for the prediction of the outcome of thromboembolism in Model 2. Model 2: Prior SSE + Medical history of DM + Antiplatelet therapy + Thrombus morphology + Diastolic blood pressure + BMI + LVEF. SSE, stroke or systemic embolism; DM, diabetes mellitus; BMI, body mass index; LVEF, left ventricular ejection fraction.