摘要
目的比较汇集队列风险方程(PCE)和改良Framingham卒中风险评估量表(R-FSP)在健康体检人群卒中风险评估中的应用效果,为预防人群卒中提供指导依据。方法 2017年1~12月随机抽取于宜宾市第二人民医院健康管理中心体检的40~70岁健康体检者1 277名,采用R-FSP和PCE评估其10年卒中风险,比较M-FSP和PCE在卒中风险评估应用中的一致性。结果采用R-FSP和PCE评估1 277名健康体检者的10年卒中风险概率分别为5.8%和6.1%。R-FSP预测卒中风险男性(6.0%)高于女性(5.5%),PCE预测结果也一致(男vs女:6.5%vs 5.7%),R-FSP预测BMI≥24 kg/m2者卒中风险(6.5%)高于BMI<24 kg/m2(5.5%),PCE预测结果也一致(≥24 kg/m2vs <24 kg/m2:6.8%vs 5.9%);R-FSP预测40~50岁、50~60岁和60~70岁年龄段体检者卒中风险概率分别为3.6%、5.9%和9.2%,PCE预测结果分别为4.0%、6.0%、9.9%,差异均有统计学意义(P<0.05);R-FSP和PCE评估卒中风险结果有高度一致性,其Pearson相关系数和组内相关系数(ICC)分别为0.719和0.757 (P<0.05)。结论 R-FSP和PCE均能较好地预测卒中发生风险概率,研究者可以根据实际情况选择合适的预测工具。
Objective To compare the applications of pooled cohort risk equations(PCE) and revised Framingham stroke risk profile(R-FSP) in predicting the risk of stroke among physical examiners, and to provide effective strategy for preventing stroke. Methods From January 2017 to December 2017, 1 277 physical examiners were selected randomly from No.2 Peoples' Hospital of Yibin to assess their 10 years stroke risk by R-FSP and PCE, and the consistency of R-FSP and PCE in stroke risk assessment was compared. Results The probability of stroke predicted by R-FSP and PCE was 5.8% and 6.1% respectively among 1 277 physical examiners, with male(6.0%) higher than female(5.5%) by R-FSP, and the same tendency predicted by PCE(male vs female: 6.5% vs 5.7%); the probability of stroke predicted by R-FSP for the patients with BMI≥24 kg/m2(6.5%) was higher than BMI<24 kg/m2(5.5%), with the same tendency predicted by PCE(≥24 kg/m2 vs <24 kg/m2: 6.8% vs 5.9%). The probability of stroke predicted by R-FSP for 40-50 years old, 50-60 years old and 60-70 years old were respectively 3.6%, 5.9%, 9.2% versus corresponding 4.0%, 6.0%, 9.9%predicted by PCE(all P<0.05). The results of stroke risk predicted by R-FSP and PCE was high consistency with Pearson correlation coefficient(0.719) and intraclass correlation coefficient(0.757), P<0.05. Conclusion R-FSP and PCE are suited to predict the probability of stroke with high consistency, and researchers could select appropriate prediction tools according to distinctive situation.
引文
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