Predicting Thromboembolism in Hospitalized Patients with Ventricular Thrombus

TL;DR

This study identifies risk factors and builds a model to predict thromboembolism in hospitalized patients with ventricular thrombus.

Contribution

A novel prediction model using Lasso regression and logistic regression to forecast thromboembolism risk in ventricular thrombus patients.

Findings

The model achieved an AUC of 0.930 in training and 0.839 in validation.

Protuberant thrombus and diabetes history were significant risk factors.

High ejection fraction and lack of antiplatelet therapy were linked to lower risk.

Abstract

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, 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).

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.

NCT 05006677.

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