Caprini血栓风险评估模型筛选脑卒中后深静脉血栓形成的有效性

Retrospective study about validity of Caprini risk assessment scale in evaluation of deep venous thrombosis risk among patients after stroke

目的 分析Caprini血栓风险评估模型在脑卒中患者深静脉血栓形成(DVT)筛选中的有效性,探讨依据Caprini风险评分预防DVT发生的效果.方法 采用回顾性病例对照研究设计,选择青岛大学第三附属医院青岛市市立医院脑科中心2015年1月至2015年12月收治的5268例脑卒中患者中确诊的212例DVT患者作为DVT组,采用随机数字表法选择同时期入院的212例未发生DVT的脑卒中患者作为无栓组.采用Caprini血栓风险评估模型对两组患者进行回顾性风险评分、DVT危险度分级,并分析评分及危险度分级与实际发生DVT的相关关系.结果 DVT组患者Caprini评分明显高于无栓组[(7.7±2.5)比(4.8±2.4),P<0.001].DVT患者中,极高危患者所占比例最多(120例,57.1%)、其次为高危(47例,22.2%),高危及以上者合计占79.3%;无栓组中,低危所占比例最多(61例,28.8%)、其次为高危(60例,28.3%)、最低为极高危(42例,19.8%),两组构成之间差异具有统计学意义(P<0.001).基于Caprini量表危险因素的Logistic回归分析,结果显示:肥胖(体质指数≥25kg/m2)、下肢水肿(<1个月)、年龄(≥60岁)、需长期卧床(>72h)、DVT/肺血栓栓塞症疾病史、血栓家族史等6个危险因素是脑卒中患者并发DVT的主要危险因素.脑卒中患者Caprini危险度分级与DVT发病风险关系分析显示:极高危和高危的脑卒中患者是DVT发病的高危人群,DVT的发病风险分别为低危患者的12.756倍和2.136倍.结论 基于Caprini血栓风险评估模型可以很好的预测脑卒中患者DVT的发病风险,值得在临床中推广应用.

Objective To analyze the validity of Caprini risk assessment scale in evaluation of deep vein thrombolysis (DVT) risk among patients after stroke, and to explore the effect based on Caprini risk score to prevent DVT. Methods A retrospective case-control study design was used to collect 212 cases of DVT patients after stroke admitted to the the third affiliated hospital of Qingdao university from January 2015 to December 2015 as DVT group, 212 patients admitted during the same period of non DVT patients were randomly selected as control group. The risks score were retrospectively assessed with the Caprini risk assessment model, and the relationship between the DVT risk classification and the DVT was analyzed. Results Caprini score of patients in DVT group was significantly higher than that in the control group ((7.7 ±2.5) vs. (4.8 ±2.4), P<0.001)In DVT patients, the largest proportion of high-risk patients (120 cases, 57.1%), followed by high risk (47 cases, 22.2%), high-risk and above accounted for 79.3% of total;in the no thrombus group, low risk accounted for the largest proportion (61 cases, 28.8%), followed by high risk (60 cases, 28.3%), the lowest was very high (42 cases, 19.8%), the difference was statistically significant between the two groups (P<0.001). Caprini risk assessment scale of risk factors as independent variables were analyzed by logistic regression analysis. The results showed that obesity (body mass index greater than or equal to 25 kg/m2), lower extremity edema (<1 months), age (over 60 years), bedrest time longer than 72h, DVT/PTE disease history and family history of thrombosis were the major risk factors of the occurrence of DVT among patients after stroke. The relationship between Caprini risk classification and DVT risk in patients with stroke showed that the very high risk and high risk of stroke patients were at high risk of DVT population, the risk of DVT were 12.756 times and 2.136 times respectively. Conclusion Caprini risk assessment model can effectively assess the risk of DVT among patients after stroke, and can be clinically applicable.