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A retrospective cohort study of disease-related risk factors for central venous catheter-related symptomatic thrombosis in intensive care unit inpatients.

ABSTRACT:

Abstract

Central venous catheters (CVC) are widely used in critically ill patients given their benefits in monitoring vital signs, treatment administration, and renal replacement therapy in intensive care unit (ICU) patients, but these catheters have the potential to induce symptomatic catheter-related venous thrombosis (CRVT). This study reported the rate of symptomatic CRVT in ICU patients receiving CVC and analyzed the disease-related risk factors for symptomatic CRVT in ICU patients.A retrospective analysis was performed on the consecutive ICU 1643 critically ill patients with CVCs inserted from January 2015 to December 2019. Symptomatic CRVT was confirmed by ultrasound. CVCs were divided into 2 groups based on the presence of symptomatic CRVT, and the variables were extracted from the electronic medical record system. Logistic univariate and multivariate regression analyses were used to determine the disease-related risk factors of symptomatic CRVT.A total of 209 symptomatic CRVT events occurred among 2114 catheters. The rate of CRVT was 9.5 per 1000 catheter days. Univariate analysis revealed that trauma, major surgery, heart failure, respiratory failure, and severe acute pancreatitis were risk factors for symptomatic CRVT in the ICU. Multivariate analysis showed that trauma (odds ratio [OR], 2.046; 95% confidence interval [CI] [1.325-3.160], P = .001), major surgery (OR, 2.457; 95% CI [1.641-3.679], P = .000), and heart failure (OR, 2.087; 95% CI [1.401-3.111], P = .000) were independent disease-related risk factors for symptomatic CRVT in ICU. The C-statistic for this model was 0.61 (95% CI [0.57-0.65], P = .000).The incidence rate of symptomatic CRVT in the ICU population was 9.5 per 1000 catheter days. Trauma, major surgery, and heart failure are independent disease-related risk factors of symptomatic CRVT.

SUBMITTER: Kuang Z

PROVIDER: S-EPMC8294906 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThis study aimed to explore the risk factors of catheter-related thrombosis (CRT) in children in Southwest China who underwent central venous catheter (CVC) insertion.MethodsAn observational cohort study was conducted at a single tertiary center in southwest China between November 2019 and February 2020. All patients who received a CVC were enrolled and Doppler-ultrasound examination was performed weekly until CVC removal. All patients in this study were hospitalized and were observed and followed up in this hospital. Patient demographics, medication, biochemical indexes, catheter maintenance practice, activities after CVC placement data were analyzed. The Kaplan-Meier method was used to calculate the incidence of CRT, and the Cox regression model was used to analyze the factors influencing CRT.ResultsA total of 594 children were included in the study, and the median indwelling time was 10 days, with the shortest being 1 day and the longest 60 days. The overall incidence of CRT was 26.60% (158/594), the 15-day cumulative incidence rate was 30.81%, and the 45-day cumulative incidence rate was 46.27%. After 45 days, the incidence of CRT further increased. Age <12 months [hazard ratio (HR), 1.654; 95% confidence interval (CI): 1.171-2.338], use of 20% mannitol or glycerol fructose (HR, 1.593; 95% CI: 1.058-2.398), CVC placement by a pediatric intensive care unit (PICU) doctor (HR, 1.921; 95% CI: 1.347-2.740), placement length ≥9 cm (HR, 1.633; 95% CI: 1.142-2.336), and D-dimer >1.5 mg/L (HR, 1.451; 95% CI: 1.044-2.015) were risk factors for CRT. Limb exercises (HR, 0.660; 95% CI: 0.469-0.929) after placement was a protective factor for CRT.ConclusionsThe incidence of CRT was higher in children with CVCs, and the key duration of CRT monitoring should be within 15 and 45 days after placement. Patients with age <12 months, using 20% mannitol or glycerol fructose, insertion length ≥9 cm, D-dimer >1.5 mg/L before placement are more likely to happen CVC-CRT than other patient, and it is necessary to be highly vigilant and take preventive measures.

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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A retrospective cohort study of disease-related risk factors for central venous catheter-related symptomatic thrombosis in intensive care unit inpatients.

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