Construction and analysis of a risk predictive model for carotid plaque shedding based on superb microvascular imaging blood flow grading indicators combined with serological indicators
Aim To explore the application value of a predictive model constructed based on superb microvascu-lar imaging(SMI)blood flow grading indicators and serological indicators in evaluating the risk of carotid plaque shedding.Methods A total of 122 patients diagnosed with carotid plaque in Lishui Central Hospital from February 2019 to February 2021 were selected.SMI was used to observe the blood flow grading and plaque characteristics in carotid plaque,and baseline clinical data of the patients were recorded.All patients were followed up for a period of 2 years,with the occur-rence of transient ischemic attack(TIA)or acute ischemic stroke(AIS)as the endpoint event,and were divided into plaque shedding group and non-shedding group.Clinical data of the two groups were compared and analyzed,and multiple regression analysis was conducted to identify the relevant factors affecting carotid plaque shedding.According to the SMI ultrasound characteristics and serological indicators,R software was adopted to establish the nomogram model and evaluate effectiveness of the model.Results During the 2-year follow-up period,21 TIA cases and 14 AIS cases were found in the remaining 112 patients excluding 10 lost to follow up.The SMI blood flow grading,neutrophil to lymphocyte ratio(NLR),matrix metalloproteinase-9(MMP-9),and low density lipoprotein cholesterol(LDLC)levels in the plaque shedding group were higher than those in the non-shedding group,and the differences were statistically significant(P＜0.05).Multivariate Logistic regression analysis showed that SMI blood flow grade 3(OR=38.095),LDLC(OR=19.730),NLR(OR=34.525)and MMP-9(OR=1.225)were independent risk factors for carotid plaque shedding(P＜0.05).The R software established a column chart model and applied it to the ROC curve analysis.The AUC of the col-umn chart model in early prediction of plaque shedding was 0.917,with a sensitivity of 82.86％and a specificity of 90.91％.Conclusion The predictive model constructed by combining blood flow grading within carotid artery plaques and serological indicators through SMI can provide early warning of plaque shedding and guide clinical early intervention to reduce the risk of TIA and AIS.
superb microvascular imagingcarotid artery plaqueplaque sheddingnomograph model
万方数据
维普
