Dataset Information

Central Venous Catheter Cannulation in Pediatric Anesthesia and Intensive Care: A Prospective Observational Trial.

ABSTRACT: Currently, ultrasound-guided central venous catheter (CVC) insertion is recommended in pediatric patients. However, the clinical practice may vary. The primary aim of this study was the overall success rate and the first attempt success rate in ultrasound-guided CVC insertion versus anatomic-based CVC insertion in pediatric patients. The secondary aim was the incidence of associated complications and the procedural time. The physician could freely choose the cannulation method and venous approach. Data were collected for 10 months. Overall, 179 patients were assessed for eligibility and 107 patients were included. In almost half of the patients (48.6%), the percutaneous puncture was performed by real-time ultrasound navigation. In 51.4% of the patients, the puncture was performed by the landmark method. The overall success rate was 100% (n = 52) in the real-time ultrasound navigation group, 96.4% (n = 53) in the landmark insertion group, (p = 0.496). The first percutaneous puncture success rate was 57.7% (n = 30) in the real-time ultrasound navigation group and 45.5% (n = 25) in the landmark insertion group, (p = 0.460). The data show a higher overall success rate and the first success rate in the US-guided CVC insertion group, but the difference was not statistically significant.

SUBMITTER: Vafek V

PROVIDER: S-EPMC9688764 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Central Venous Catheter Cannulation in Pediatric Anesthesia and Intensive Care: A Prospective Observational Trial.

Vafek Václav V Skříšovská Tamara T Kosinová Martina M Klabusayová Eva E Musilová Tereza T Kramplová Tereza T Djakow Jana J Klučka Jozef J Kalina Jiří J Štourač Petr P

Children (Basel, Switzerland) 20221023 11

Currently, ultrasound-guided central venous catheter (CVC) insertion is recommended in pediatric patients. However, the clinical practice may vary. The primary aim of this study was the overall success rate and the first attempt success rate in ultrasound-guided CVC insertion versus anatomic-based CVC insertion in pediatric patients. The secondary aim was the incidence of associated complications and the procedural time. The physician could freely choose the cannulation method and venous approac ...[more]

PMID: 36360339

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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

Central Venous Catheter Cannulation in Pediatric Anesthesia and Intensive Care: A Prospective Observational Trial.

Publications

Central Venous Catheter Cannulation in Pediatric Anesthesia and Intensive Care: A Prospective Observational Trial.

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