Development of Z-scores for fetal cardiac dimensions from echocardiography

Abstract

Objectives: Z-scores for cardiac dimensions are well established in postnatal life, but have yet to be developed for fetal cardiac dimensions. These would be of real advantage to the clinician in accurately quantifying size and growth of cardiac dimensions and to the researcher by allowing mathematical comparison of growth in differing subgroups of a disease. The purpose of this observational study, conducted at tertiary fetal medicine and cardiology units, was to produce formulae and nomograms allowing computation of Z-scores for fetal cardiac dimensions from knowledge of femur length (FL), biparietal diameter (BPD) or gestational age (GA) using fetal echocardiography.

Methods: Seventeen fetal cardiac dimensions were measured in 130 pregnant women with singleton fetuses of gestational age 15-39 weeks. Regression equations were derived relating all dimensions to FL, BPD and GA. From the calculations, formulae were then developed allowing fetal cardiac Z-score computation.

Results: The relationships between cardiac dimensions and FL, BPD or GA were described following natural log transformation. From this analysis, FL (taken as an expression of fetal size) had the highest correlation to fetal cardiac dimensions. From the developed nomograms, Z-scores of specific fetal cardiac structures could be estimated from knowledge of the FL, BPD or GA and echocardiographically derived measurements.

Conclusions: This study allowed computation of Z-scores in fetal life for 17 cardiac dimensions from FL, BPD or GA. Previous studies of normal data allowed qualitative assessment of where abnormal cardiac dimensions lay with regard to the normal range. Z-scores from this study allow quantitative analysis of where such dimensions lie relative to the mean. This permits exact assessment of growth of fetal cardiac structures in normal hearts and particularly in congenitally abnormal hearts where quantitative assessment of the growth of cardiac structures is important in analyzing and planning treatment strategies.