RESUMO
Objective To establish a pediatric echocardiographic normal reference system based on clinical BigData and overcome limitations such as insufficient sample size and diverse in methods of normalization . Methods Measurements were extracted from total 71 831 pediatric echocardiography reports in the past 5 years by using the Natural Language Processing ( NLP) technology . Among them ,a total of 12 732 reports were labeled as normal and were used to establish the normal reference system . A local regression ( LOESS ) approach was used to optimize both the reference value and variance across 5 grow th variables ( aortic diameter ,left atrium diameter ,left ventricle end‐diastolic endocardial diameter ,left main coronary artery diameter ,and right main coronary artery diameter) . T wo Z scores adjusted for age/sex and body surface area ( BSA ) were established respectively . In addition , 4 459 echocardiography reports with BSA information were used to evaluate these two Z scores . Results T wo Z scores generated from 4 459 reports showed pretty good normal distribution . T here were close strong correlations among two Z scores with Z scores generated based on the Pediatric Heart Network ( PHN ) . T he average correlation coefficient between BSA‐adjust Z scores and PHN Z scores was 0 .954 . T he average correlation coefficient between age/sex‐adjust Z scores and PHN Z scores was 0 .895 . T he results of this project were available as Z score calculator using the following link :http ://hdb .nbscn .org/zscore . Conclusions BigData provides a more efficient and better approach to establish normal reference systems in pediatric echocardiography .
