血液灌流后急性中毒患者深静脉血栓形成预测模型的建立与验证:回顾性分析

Xiuqin Li, Jing Liu, S. Cui, T. Jian, Shuang Ma, Longke Shi, Ying Lin, Juan Zhang, Yingying Zheng, Yanxia Zhang, X. Jian, X. Luan, B. Kan
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引用次数: 1

摘要

目的建立并验证一种个体化的预测模型,以确定急性中毒患者血液灌流术后深静脉血栓形成的概率。方法将2017年10月至2019年2月在中国某医院住院的311例急性中毒患者纳入发展组。在2019年2月至5月期间入院的80例急性中毒患者被纳入验证组。检查深静脉血栓形成的独立危险因素。利用回归系数建立了个体化预测模型。结果留置导管天数、转运时留置导管、血清同型半胱氨酸升高、血脂异常是急性中毒患者血液灌流后深静脉血栓形成的独立危险因素。开发组和验证组的受试者工作特征曲线下面积分别为0.713和0.702,表明预测模型具有较好的判别能力。两组校正带均较理想。结论该预测模型对急性中毒患者深静脉血栓的发生有中等预测作用。在临床实践中,该模型可与常见的血栓形成风险评估模型相结合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and validation of a prediction model of deep venous thrombosis for patients with acute poisoning following hemoperfusion: a retrospective analysis
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.
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