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Predictive Models for Normal Fetal Cardiac Structures
Anita Krishnan1, Jodi I Pike1, Robert McCarter1
1Children's National Heart Institute, Children's National Medical Center, Washington, District of Columbia.
Insights
This study developed predictive models for fetal cardiac Z scores using common biometric measurements. These new equations provide a standardized approach for diagnosing fetal heart conditions.
Area of Science:
- Fetal cardiology and diagnostic imaging.
- Quantitative analysis of fetal anatomy.
- Development of normative data for pediatric diagnostics.
Background:
- Clinicians lack standardized, universally accepted normative data for fetal cardiac structures.
- Variability in measurement standards across fetal cardiac centers hinders accurate diagnosis.
- This study addresses the need for reliable Z-score predictive models for fetal cardiac dimensions.
Purpose of the Study:
- To derive predictive models for Z scores of 13 common fetal cardiac structures.
- To establish age- and size-specific normative data for fetal cardiac measurements.
- To create a foundation for standardized diagnosis of fetal cardiac disease.
Main Methods:
- Utilized archived normal fetal echocardiograms (12-39 weeks gestation).
- Remeasured 13 cardiac dimensions by a blinded echocardiographer.
- Developed regression models using estimated gestational age (EGA) and other biometrics (biparietal diameter, femur length, estimated fetal weight).
Main Results:
- Estimated gestational age (EGA) provided the best predictive models for most cardiac dimensions (adjusted R² 0.72-0.893).
- Other biometric parameters like femur length, biparietal diameter, and estimated fetal weight were acceptable surrogates for EGA.
- Models often incorporated quadratic or cubic terms to account for curvilinearity.
Conclusions:
- Models based on EGA offer the best fit for determining normal fetal cardiac structure measurements.
- Alternative biometric parameters provide nearly equivalent predictive accuracy.
- These comprehensive Z-score results offer a preferable, standardized clinical tool and a basis for future research.
Background:
Clinicians rely on age- and size-specific measures of cardiac structures to diagnose cardiac disease. No universally accepted normative data exist for fetal cardiac structures, and most fetal cardiac centers do not use the same standards. The aim of this study was to derive predictive models for Z scores for 13 commonly evaluated fetal cardiac structures using a large heterogeneous population of fetuses without structural cardiac defects.
Methods:
The study used archived normal fetal echocardiograms in representative fetuses aged 12 to 39 weeks. Thirteen cardiac dimensions were remeasured by a blinded echocardiographer from digitally stored clips. Studies with inadequate imaging views were excluded. Regression models were developed to relate each dimension to estimated gestational age (EGA) by dates, biparietal diameter, femur length, and estimated fetal weight by the Hadlock formula. Dimension outcomes were transformed (e.g., using the logarithm or square root) as necessary to meet the normality assumption. Higher order terms, quadratic or cubic, were added as needed to improve model fit. Information criteria and adjusted R2 values were used to guide final model selection.
Results:
Each Z-score equation is based on measurements derived from 296 to 414 unique fetuses. EGA yielded the best predictive model for the majority of dimensions; adjusted R2 values ranged from 0.72 to 0.893. However, each of the other highly correlated (r > 0.94) biometric parameters was an acceptable surrogate for EGA. In most cases, the best fitting model included squared and cubic terms to introduce curvilinearity.
Conclusions:
For each dimension, models based on EGA provided the best fit for determining normal measurements of fetal cardiac structures. Nevertheless, other biometric parameters, including femur length, biparietal diameter, and estimated fetal weight provided results that were nearly as good. Comprehensive Z-score results are available on the basis of highly predictive models derived from gestational age or other biometrics as preferable for clinical reasons. These results supplant current equations and will provide a foundation for future multicenter collaborations.
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