Construction of the Early Warning Model of Aortic Valve Dicuvalization in Premature Infants Based on the Quantitative Parameters of High Frequen-cy Ultrasound and Clinical Data
Objective To establish an early warning model of bicuspid aortic valve(BAV)malformation in prema-ture infants based on quantitative parameters of high frequency ultrasound and clinical data.Methods A total of 86 prema-ture infants with BAV treated from January 2020 to December 2022 were selected as the research group,and 86 preterm in-fants without BAV were selected as the control group according to a ratio of 1︰1.The clinical data,quantitative parameters of high frequency ultrasound and manifestations of the two groups were analyzed.The random forest model and multivariate Lo-gistic regression equation model were constructed,and the efficacy of the two models in predicting the onset of BAV in prema-ture infants was analyzed by receiver operating characteristic(ROC)curve.Results 25 hydroxyvitamin D,heart-type fatty acid binding protein(H-FABP),cardiac troponin I(cTnI),myocardial creatine kinase isoenzyme,family history,maximum blood flow velocity(Vmax)of the aortic valve by high frequency ultrasound,cross-valve pressure difference by high frequency ultrasound,aortic stenosis by high frequency ultrasound,aortic regregence by high frequency ultrasound,aortic involvement dilatation by high frequency ultrasound and age of mothers of preterm infants in the two groups were statistically significant(P<0.01).The early-warning model of BAV in premature infants was constructed using the random forest algorithm,and the importance was ranked according to the average accuracy decline,including Vmax by high frequency ultrasound,cross-valve pressure difference by high frequency ultrasound,aortic stenosis by high frequency ultrasound,H-FABP,cTnI,and family history from high to low in the preterm infants.The area under the curve(AUC),sensitivity and specificity of the model in predicting BAV in preterm infants were 0.974,0.954 and 0.988,respectively.Multivariate Logistic regression equation showed that Vmax by high frequency ultrasound,cross-valve pressure difference by high frequency ultrasound,aortic stenosis by high frequency ultrasound,H-FABP,family history and cTnI were the influencing factors for the incidence of BAV in preterm infants(P<0.01).The multivariate Logistic regression model predicted that the AUC,sensitivity and specificity of the model in predicting BAV in preterm infants were 0.924,0.919 and 0.930,respectively.Conclusion It is feasible to construct an early warning model of premature BAV based on quantitative parameters by high-frequency ultrasound and clinical data,and the prediction efficiency is good,which can provide convenience for clinical diagnosis and treatment.
万方数据
维普
