Dataset Information

Abnormal location of umbilical venous catheter due to Scimitar syndrome.

ABSTRACT: Scimitar syndrome is a rare congenital anomaly where the right pulmonary veins return to the inferior vena cava (IVC) just below the diaphragm. On chest X-ray (CXR), an IVC catheter will be in a bizarre location outside the heart if it inadvertently passes into the scimitar vein rather than into the right atrium.

SUBMITTER: Mart CR

PROVIDER: S-EPMC4189247 | biostudies-other | 2014 Sep

REPOSITORIES: biostudies-other

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Abnormal location of umbilical venous catheter due to Scimitar syndrome.

Mart Christopher R CR Van Dorn Charlotte S CS

Annals of pediatric cardiology 20140901 3

Scimitar syndrome is a rare congenital anomaly where the right pulmonary veins return to the inferior vena cava (IVC) just below the diaphragm. On chest X-ray (CXR), an IVC catheter will be in a bizarre location outside the heart if it inadvertently passes into the scimitar vein rather than into the right atrium. ...[more]

PMID: 25298705

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Project description:BackgroundAn 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.MethodsChildren 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 average precision (AP) were used to evaluate the discrimination of each model at three time points (24 h, 48 h and 72 h) before CADVT occurred. Brier score and Spiegelhalter's z test were used measure the calibration of these prediction models.ResultsA total of 1830 patients were included in this study, and approximately 15% developed CADVT. In the CADVT prediction task, the model proposed in this paper significantly outperforms both traditional machine learning models and existing multimodal deep learning models at all 3 time points. It achieved 77% accuracy and 90% recall at 24 h before CADVT was discovered. It can be used to accurately predict the occurrence of CADVT 72 h in advance with an accuracy of greater than 75%, a recall of more than 87%, and an AUC value of 0.82.ConclusionIn this study, a machine learning method was successfully established to predict CADVT in advance. These findings demonstrate that artificial intelligence (AI) could provide measures for thromboprophylaxis in a pediatric intensive care setting.

Dataset Information

Abnormal location of umbilical venous catheter due to Scimitar syndrome.

Publications

Abnormal location of umbilical venous catheter due to Scimitar syndrome.

Similar Datasets

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