Open AccessDevelopment and validation of a prediction model of deep venous thrombosis for patients with acute poisoning following hemoperfusion: a retrospective analysis
Author(s) -
Xiuqin Li,
Jing Liu,
Siqi Cui,
Tianzi Jian,
Shuang Ma,
Longke Shi,
Ying Lin,
Juan Zhang,
Yingying Zheng,
Yanxia Zhang,
Xiangdong Jian,
Xiaorong Luan,
Baotian Kan
Publication year - 2022
Publication title -
journal of international medical research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 57
eISSN - 1473-2300
pISSN - 0300-0605
DOI - 10.1177/03000605221089779
Subject(s) - medicine , hemoperfusion , deep vein , venous thrombosis , thrombosis , receiver operating characteristic , surgery , hemodialysis
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.