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Symptomatic catheter-associated thrombosis in pediatric trauma patients: Choose your access wisely.

ABSTRACT: BACKGROUND:Traumatic injury and the presence of a central venous catheter are 2 of the strongest risk factors for venous thromboembolism in children. The purpose of this study was to determine the incidence of symptomatic, catheter-associated thrombosis in critically injured children. We hypothesized that femoral venous catheters are associated with a greater rate of thrombotic complications when compared with all other central venous access points. METHODS:We reviewed a retrospective cohort (2006-2016) of injured children (?18 years) admitted to a pediatric intensive care unit with central access placed ?7 days from admission. Symptomatic, catheter-associated thrombosis was determined by radiographic evidence. Poisson regression was used to compare the incidence of catheter-associated thrombosis per 1,000 catheter days between femoral and nonfemoral catheters. All comparisons were 2-tailed with ? = 0.05. RESULTS:We examined 209 pediatric trauma patients with central access (65% femoral, 19% subclavian, 11% arm vein, and 5% internal jugular). Femoral catheters were removed earlier (median [interquartile range] 4 [2-7] vs 8 [3-12] days, P < .001) and were larger in diameter (5 Fr [4-7] vs 4 Fr [4-4], P < .001) when compared with all other catheters. Catheter-associated thrombosis was more frequent in femoral versus nonfemoral catheters (18.4 vs 3.5 per 1,000 catheter days, P = .01). CONCLUSION:Femoral venous catheters are associated with a greater incidence of symptomatic, catheter-associated thrombosis in pediatric trauma patients. When central venous access is indicated for injured children, the femoral site should be avoided. If a femoral venous catheter is necessary, use of a smaller catheter should be considered.

SUBMITTER: McLaughlin CM

PROVIDER: S-EPMC7169988 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Symptomatic catheter-associated thrombosis in pediatric trauma patients: Choose your access wisely.

McLaughlin Cory M CM Barin Erica N EN Fenlon Michael M Azen Colleen C Deakers Timothy W TW Stein James E JE Bliss David W DW Upperman Jeffrey S JS Jensen Aaron R AR

Surgery 20190913 6

<h4>Background</h4>Traumatic injury and the presence of a central venous catheter are 2 of the strongest risk factors for venous thromboembolism in children. The purpose of this study was to determine the incidence of symptomatic, catheter-associated thrombosis in critically injured children. We hypothesized that femoral venous catheters are associated with a greater rate of thrombotic complications when compared with all other central venous access points.<h4>Methods</h4>We reviewed a retrospec ...[more]

PMID: 31526580

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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.

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Symptomatic catheter-associated thrombosis in pediatric trauma patients: Choose your access wisely.

Publications

Symptomatic catheter-associated thrombosis in pediatric trauma patients: Choose your access wisely.

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