Padua风险评估模型对内科住院患者静脉血栓栓塞症的评估价值

  目的  调查内科住院患者静脉血栓栓塞症(venous thromboembolism, VTE)现状, 探究Padua风险评估模型是否适用于内科住院患者。   方法  回顾分析2016年5月17日至9月9日北京协和医院内科住院患者基本信息和VTE危险因素、预防措施及发生情况, 比较住院期间及出院后3个月内发生与未发生VTE患者的异同点, 评估危险因素与VTE事件相关性。使用Padua风险评估模型对患者进行VTE风险分层并采用Logistic回归分析评估其预测价值。   结果  共3115例患者纳入本研究, 症状性VTE发生率为2.5%。Padua模型敏感度为83.3%, 特异度为62.3%, 模型分层下高危患者占比38.8%。Padua模型中高龄、急性心肌梗死/缺血性卒中、肥胖、近期创伤和(或)手术这4项危险因素在VTE及非VTE患者间无统计学差别(P>0.05), 而模型未包含的危险因素如雌、孕激素、输血、机械通气与VTE发生显著相关(P均 < 0.01)。Padua模型高危患者接受抗凝药物预防及机械预防的比例显著高于低危患者(P < 0.01), 其中85.3%的高危患者未接受抗凝药物或机械预防, 亦未发生VTE。   结论   Padua模型特异度低, 模型中多项危险因素及其权重分布不合理, 对内科住院患者VTE风险分层的价值有限。

Value of Padua Risk Assessment Model in Evaluating Venous Thromboembolism of Hospitalized Patients in the Department of Internal Medicine

  Objective   The aim of this study was to investigate the status of venous thromboembolism(VTE)in patients in the department of internal medicine and to evaluate whether Padua risk assessment modelcan be applied to this patient population.   Methods   Baseline information, risk factors, prevention, and the incidence of VTE in in-patients of the department of internal medicine were collected and analyzed at Peking Union Medical College Hospital from May 17, 2016, to September 9, 2016. Patients with or without VTE were compared during hospitalization and within 3 months after discharge. Correlations between risk factors and VTE events were calculated and risk stratification was calculated by Padua risk assessment model. The predictive value was assessed by Logistic regression analysis.   Results   A total of 3115 patients were enrolled in this study. The incidence of VTE was 2.5%(78/3115). The sensitivity and specificity of Padua risk assessment model were 83.3% and 62.3%, respectively, and 38.8% of patients had a high risk of VTE based on this model. The four risk factors embodied in the model including elderly age(≥ 70 years), acute myocardial infarction or ischemic stroke, obesity(body mass index ≥ 30 kg/m2), and recent(≤ 1 month)trauma and/or surgery, showed no statistical difference between patients with and without VTE(P>0.05). However, other risk factors that are not included in Padua risk assessment model, i.e. use of estrogen or progesterone, blood transfusion, and mechanical ventilation showed statistically different between VTE and non-VTE patients(P < 0.01). The ratio of receiving preventive treatment with anticoagulant drugs or machines was significantly higher in high-risk patients than those in low-risk ones(P < 0.01); 85.3% of high-risk patients did not receive anticoagulant or mechanical prevention and did not have VTE either.   Conclusions   Padua risk assessment model shows low specificity. Several risk factors and their weight distribution in the model are not suitable, which leads to the limitation of this model in VTE risk assessment for in-patients of the department of internal medicine.