Dataset Information

Development and validation of a prediction model of deep venous thrombosis for patients with acute poisoning following hemoperfusion: a retrospective analysis.

ABSTRACT:

Objective

To develop and confirm an individualized predictive model to ascertain the probability of deep venous thrombosis in patients with acute poisoning after undergoing hemoperfusion.

Methods

Three hundred eleven patients with acute poisoning who were admitted to a hospital in China between October 2017 and February 2019 were included in the development group. Eighty patients with acute poisoning who were admitted between February and May 2019 were included in the validation group. The independent risk factors for deep venous thrombosis were examined. An individualized predictive model was developed using regression coefficients.

Results

The number of catheter indwelling days, having a catheter while being transported, elevated serum homocysteine concentrations, and dyslipidemia were independent risk factors for deep venous thrombosis following hemoperfusion in patients with acute poisoning. The areas under the receiver operating characteristic curve of the development and validation groups were 0.713 and 0.702, respectively, which suggested that the prediction model had good discrimination capacity. The calibration belts of the two groups were ideal.

Conclusions

Our prediction model has a moderate predictive effect for the occurrence of deep venous thrombosis in patients with acute poisoning. In clinical practice, this model could be combined with a common thrombosis risk assessment model.

SUBMITTER: Li X

PROVIDER: S-EPMC9021492 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a prediction model of deep venous thrombosis for patients with acute poisoning following hemoperfusion: a retrospective analysis.

Li Xiuqin X Liu Jing J Cui Siqi S Jian Tianzi T Ma Shuang S Shi Longke L Lin Ying Y Zhang Juan J Zheng Yingying Y Zhang Yanxia Y Jian Xiangdong X Luan Xiaorong X Kan Baotian B

The Journal of international medical research 20220401 4

<h4>Objective</h4>To develop and confirm an individualized predictive model to ascertain the probability of deep venous thrombosis in patients with acute poisoning after undergoing hemoperfusion.<h4>Methods</h4>Three hundred eleven patients with acute poisoning who were admitted to a hospital in China between October 2017 and February 2019 were included in the development group. Eighty patients with acute poisoning who were admitted between February and May 2019 were included in the validation g ...[more]

PMID: 35437041

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Project description:AbstractDeep venous thrombosis (DVT) is associated with high mortality in coronavirus disease 2019 (COVID-19) but there remains uncertainty about the benefit of anti-coagulation prophylaxis and how to decide when ultrasound screening is indicated. We aimed to determine parameters predicting which COVID-19 patients are at risk of DVT and to assess the benefit of prophylactic anti-coagulation.Adult hospitalized patients with positive severe acute respiratory syndrome coronavirus-2 reverse transcription-polymerase chain reaction (RT-PCR) undergoing venous duplex ultrasound for DVT assessment (n = 451) were retrospectively reviewed. Clinical and laboratory data within 72 hours of ultrasound were collected. Using split sampling and a 10-fold cross-validation, a random forest model was developed to find the most important variables for predicting DVT. Different d-dimer cutoffs were examined for classification of DVT. We also compared the rate of DVT between the patients going and not going under thromboprophylaxis.DVT was found in 65 (14%) of 451 reverse transcription-polymerase chain reaction positive patients. The random forest model, trained and cross-validated on 2/3 of the original sample (n = 301), had area under the receiver operating characteristic curve = 0.91 (95% confidence interval [CI]: 0.85-0.97) for prediction of DVT in the test set (n = 150), with sensitivity = 93% (95%CI: 68%-99%) and specificity = 82% (95%CI: 75%-88%). The following variables had the highest importance: d-dimer, thromboprophylaxis, systolic blood pressure, admission to ultrasound interval, and platelets. Thromboprophylaxis reduced DVT risk 4-fold from 26% to 6% (P < .001), while anti-coagulation therapy led to hemorrhagic complications in 14 (22%) of 65 patients with DVT including 2 fatal intra-cranial hemorrhages. D-dimer was the most important predictor with area under curve = 0.79 (95%CI: 0.73-0.86) by itself, and a 5000 ng/mL threshold at 7 days postCOVID-19 symptom onset had 75% (95%CI: 53%-90%) sensitivity and 81% (95%CI: 72%-88%) specificity. In comparison with d-dimer alone, the random forest model showed 68% versus 32% specificity at 95% sensitivity, and 44% versus 23% sensitivity at 95% specificity.D-dimer >5000 ng/mL predicts DVT with high accuracy suggesting regular monitoring with d-dimer in the early stages of COVID-19 may be useful. A random forest model improved the prediction of DVT. Thromboprophylaxis reduced DVT in COVID-19 patients and should be considered in all patients. Full anti-coagulation therapy has a risk of life-threatening hemorrhage.

Dataset Information

Development and validation of a prediction model of deep venous thrombosis for patients with acute poisoning following hemoperfusion: a retrospective analysis.

Objective

Methods

Results

Conclusions

Publications

Development and validation of a prediction model of deep venous thrombosis for patients with acute poisoning following hemoperfusion: a retrospective analysis.

Similar Datasets

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