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Prediction of Central Venous Catheter-associated Deep Venous Thrombosis in Pediatric Critical Care Settings by Haomin Li; Yang Lu; Xian Zeng; Cangcang Fu; Huilong Duan; Qiang Shu; Jihua Zhu is a Health Professions article available to read on EtoBox.

What is Prediction of Central Venous Catheter-associated Deep Venous Thrombosis in Pediatric Critical Care Settings about?

## Abstract ## Background An increase in the incidence of central venous catheter (CVC)-associated deep venous thrombosis (CADVT) has been reported in pediatric patients over the past decade. At the same time, current screening guidelines for venous thromboembolism risk have low sensitivity for CADVT in hospitalized children. This study utilized a multimodal deep learning model to predict CADVT before it occurs. ## Methods Children who were admitted to intensive care units (ICUs) between December 2015 and December 2018 and with CVC placement at least 3 days were included. The variables analyzed included demographic characteristics, clinical conditions, laboratory test results, vital signs and medications. A multimodal deep learning (MMDL) model that can handle temporal data using long short-term memory (LSTM) and gated recurrent units (GRUs) was proposed for this prediction task. Four benchmark machine learning models, logistic regression (LR), random forest (RF), gradient boosting decision tree (GBDT) and a published cutting edge MMDL, were used to compare and evaluate the models with a fivefold cross-validation approach. Accuracy, recall, area under the ROC curve (AUC), and avera

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Author

Haomin Li; Yang Lu; Xian Zeng; Cangcang Fu; Huilong Duan; Qiang Shu; Jihua Zhu

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Health Professions (Health Sciences)