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Predicting deep venous thrombosis in pregnancy: external validation of the LEFT clinical prediction rule.

ABSTRACT: The assessment of clinical probability represents an important step in the diagnostic strategy of patients with suspected deep vein thrombosis. The recently derived LEFt clinical prediction rule for pregnant women combines three variables: symptoms in the left leg (L), calf circumference difference of 2 centimeters or over (E for edema) and first trimester presentation (Ft) but is lacking an external validation. The LEFt rule was computed among pregnant women with suspected deep vein thrombosis who were included in a multicenter prospective diagnostic management outcome study. We calculated the proportion of women and the prevalence of deep vein thrombosis in each probability group, along with the diagnostic performances of the LEFt rule. All variables needed to compute the rule could be retrieved in 157 of the 167 pregnant women with suspected deep vein thrombosis. The prevalence of confirmed deep vein thrombosis was 13 of 157 (8.3%). The LEFt rule was negative in 46 (29%) women. A deep vein thrombosis was diagnosed in 13 of 111 (11.7%, 95% Confidence Interval (CI): 8.3-20.9%) of women with at least one of the LEFt criteria, as compared with none of 46 (0.0%, 95%CI: 0.0-7.9%) of women with none of the LEFt criteria. These results suggest that a negative LEFt rule accurately identifies pregnant women in whom the proportion of confirmed deep vein thrombosis appears to be very low. The rule should not be used as stand-alone test for excluding DVT during pregnancy, but might rather be implemented in a diagnostic strategy in association with D-dimer measurement and compression ultrasonography.

SUBMITTER: Righini M

PROVIDER: S-EPMC3659985 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Predicting deep venous thrombosis in pregnancy: external validation of the LEFT clinical prediction rule.

Righini Marc M Jobic Christelle C Boehlen Françoise F Broussaud Jean J Becker François F Jaffrelot Morgan M Blondon Marc M Guias Bruno B Le Gal Grégoire G

Haematologica 20121012 4

The assessment of clinical probability represents an important step in the diagnostic strategy of patients with suspected deep vein thrombosis. The recently derived LEFt clinical prediction rule for pregnant women combines three variables: symptoms in the left leg (L), calf circumference difference of 2 centimeters or over (E for edema) and first trimester presentation (Ft) but is lacking an external validation. The LEFt rule was computed among pregnant women with suspected deep vein thrombosis ...[more]

PMID: 23065510

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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

Predicting deep venous thrombosis in pregnancy: external validation of the LEFT clinical prediction rule.

Publications

Predicting deep venous thrombosis in pregnancy: external validation of the LEFT clinical prediction rule.

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