RESUMO
Objective:To explore the characteristics of echocardiographic parameters among the many parameters of two-dimensional transthoracic echocardiography(2D-TTE) and three-dimensional speckle tracking imaging (3D-STI) that can be used for early identification of familial hypertrophic cardiomyopathy(FHCM) mutation gene carriers, and construct a Nomogram prediction model, in order to provide a diagnostic method for early identification of G+ P- patients for clinical practice.Methods:A total of 15 FHCM families admitted to the General Hospital of Ningxia Medical University from November 2017 to August 2022 were enrolled.Whole exome sequencing and Sanger sequencing technology were used for gene detection, among which 54 were G+ P- and 75 were G-P-. Stratified random sampling was used to divide the subjects into training set ( n=90) and test set ( n=39) according to the ratio of 7∶3. Philips iE33 ultrasonic diagnostic instrument and TomTec offline software were used to obtain relevant ultrasonic parameters. Lasso regression and Logistic regression were used to screen echocardiographic parameters and obtain independent risk factors for early prediction of G+ P-, based on which a Nomogram prediction model was established. Results:①Lasso-Logistic regression showed that global longitudinal strain(GLS) ( OR=1.739, 95% CI=1.305-2.316) and left ventricular outflow trac velocity time integral(LVOT-VTI) ( OR=1.358, 95% CI=1.072-1.722) could be used as independent risk factors for early prediction of G+ P-. ②The Nomogram prediction model was established based on the above indicators. After 1000 internal verifications of Bootstrap self-sampling, the C-indices of the training set and the test set were 0.885 (95% CI=0.816-0.954), 0.878 (95% CI=0.764-0.992), which had good internal consistency. ③The results of the calibration curve showed that the risk of G+ P- predicted by the Nomogram model was basically consistent with the actual risk (training set P=0.990, test set P=0.961); the clinical decision curve shows that under different threshold probabilities, using this prediction model to provide patients with clinical decision-making could bring benefits to patients. Conclusions:Echocardiographic parameters GLS and LVOT-VTI can be used as independent risk factors to predict FHCM mutation gene carriers. The Nomogram prediction model has good discrimination, goodness of fit and clinical benefit in identifying whether the family members of FHCM patients carry the mutation gene, and it can provide a new idea and evaluation method for the early identification of FHCM mutation gene carriers by echocardiography.