Prenatal diagnosis of tetralogy of Fallot with pulmonary atresia using: Fetal Intelligent Navigation Echocardiography (FINE)

<p>Tetralogy of Fallot with pulmonary atresia, a severe form of tetralogy of Fallot, is characterized by the absence of flow from the right ventricle to the pulmonary arteries. This cardiac abnormality is challenging and complex due to its many different anatomic variants. The main source of variability is the pulmonary artery anatomy, ranging from well-formed, confluent pulmonary artery branches to completely absent native pulmonary arteries replaced by major aorto-pulmonary collateral arteries (MAPCAs) that provide all of the pulmonary blood flow. Since the four-chamber view is usually normal on prenatal sonography, the diagnosis may be missed unless additional cardiac views are studied. Fetal Intelligent Navigation Echocardiography (FINE) is a novel method developed recently that allows automatic generation of nine standard fetal echocardiography views in normal hearts by applying "intelligent navigation" technology to spatiotemporal image correlation volume datasets. We report herein for the first time, two different cases of tetralogy of Fallot with pulmonary atresia having variable sources of pulmonary blood flow in which the prenatal diagnosis was made successfully using the FINE method. Virtual Intelligent Sonographer Assistance (VIS-Assistance^®) and automatic labeling (both features of FINE) were very helpful in making such diagnosis.</p

Personalized third-trimester fetal growth evaluation: comparisons of individualized growth assessment, percentile line and conditional probability methods

<p><i>Objective</i>: To compare third-trimester size trajectory prediction errors (average transformed percent deviations) for three individualized fetal growth assessment methods.</p> <p><i>Methods</i>: This study utilized longitudinal measurements of nine directly measured size parameters in 118 fetuses with normal neonatal growth outcomes. Expected value (EV) function coefficients and variance components were obtained using two-level random coefficient modeling. Growth models (IGA) or EV coefficients and variance components (PLM and CPM) were used to calculate predicted values at ∼400 third-trimester time points. Percent deviations (Þv) calculated at these time points using all three methods were expressed as percentages of IGA MA-specific reference ranges [transformed percent deviations (TÞv)]. Third-trimester TÞv values were averaged (aTÞv) for each parameter. Mean ± standard deviation’s for sets of aTÞv values derived from each method (IGA, PLM and CPM) were calculated and compared.</p> <p><i>Results</i>: Mean aTÞv values for nine parameters were: (i) IGA: −4.3 to 5.2% (9/9 not different from zero); (ii) PLM: −32.7 to 25.6% (4/9 not different from zero) and (iii) CPM: −20.4 to 17.4% (5/9 not different from zero). Seven of nine systematic deviations from zero were statistically significant when IGA values were compared to either PLM or CPM values. Variabilities were smaller for IGA when compared to those for PLM or CPM, with (i) 5/9 being statistically significant (IGA versus PLM), (ii) 2/9 being statistically significant (IGA versus CPM) and (iii) 5/9 being statistically significant (PLM versus CPM).</p> <p><i>Conclusions</i>: Significant differences in the agreement between predicted third-trimester size parameters and their measured values were found for the three methods tested. With most parameters, IGA gave smaller mean aTÞv values and smaller variabilities. The CPM method was better than the PLM approach for most but not all parameters. These results suggest that third-trimester size trajectories are best characterized by IGA in fetuses with normal growth outcomes.</p

Single and Serial Fetal Biometry to Detect Preterm and Term Small- and Large-for-Gestational-Age Neonates: A Longitudinal Cohort Study

<div><p>Objectives</p><p>To assess the value of single and serial fetal biometry for the prediction of small- (SGA) and large-for-gestational-age (LGA) neonates delivered preterm or at term.</p><p>Methods</p><p>A cohort study of 3,971 women with singleton pregnancies was conducted from the first trimester until delivery with 3,440 pregnancies (17,334 scans) meeting the following inclusion criteria: 1) delivery of a live neonate after 33 gestational weeks and 2) two or more ultrasound examinations with fetal biometry parameters obtained at ≤36 weeks. Primary outcomes were SGA (<5^th centile) and LGA (>95^th centile) at birth based on INTERGROWTH-21^st gender-specific standards. Fetus-specific estimated fetal weight (EFW) trajectories were calculated by linear mixed-effects models using data up to a fixed gestational age (GA) cutoff (28, 32, or 36 weeks) for fetuses having two or more measurements before the GA cutoff and not already delivered. A screen test positive for single biometry was based on Z-scores of EFW at the last scan before each GA cut-off so that the false positive rate (FPR) was 10%. Similarly, a screen test positive for the longitudinal analysis was based on the projected (extrapolated) EFW at 40 weeks from all available measurements before each cutoff for each fetus.</p><p>Results</p><p>Fetal abdominal and head circumference measurements, as well as birth weights in the Detroit population, matched well to the INTERGROWTH-21^st standards, yet this was not the case for biparietal diameter (BPD) and femur length (FL) (up to 9% and 10% discrepancy for mean and confidence intervals, respectively), mainly due to differences in the measurement technique. Single biometry based on EFW at the last scan at ≤32 weeks (GA IQR: 27.4–30.9 weeks) had a sensitivity of 50% and 53% (FPR = 10%) to detect preterm and term SGA and LGA neonates, respectively (AUC of 82% both). For the detection of LGA using data up to 32- and 36-week cutoffs, single biometry analysis had higher sensitivity than longitudinal analysis (52% vs 46% and 62% vs 52%, respectively; both p<0.05). Restricting the analysis to subjects with the last observation taken within two weeks from the cutoff, the sensitivity for detection of LGA, but not SGA, increased to 65% and 72% for single biometry at the 32- and 36-week cutoffs, respectively. SGA screening performance was higher for preterm (<37 weeks) than for term cases (73% vs 46% sensitivity; p<0.05) for single biometry at ≤32 weeks.</p><p>Conclusions</p><p>When growth abnormalities are defined based on birth weight, growth velocity (captured in the longitudinal analysis) does not provide additional information when compared to the last measurement for predicting SGA and LGA neonates, with both approaches detecting one-half of the neonates (FPR = 10%) from data collected at ≤32 weeks. Unlike for SGA, LGA detection can be improved if ultrasound scans are scheduled as close as possible to the gestational-age cutoff when a decision regarding the clinical management of the patient needs to be made. Screening performance for SGA is higher for neonates that will be delivered preterm.</p></div

SGA and LGA prediction performance for the entire study population.

<p>Sensitivity of predicting small-for-gestational–age (SGA; <5th percentile) (left) and large-for-gestational–age neonates (LGA; >95th percentile) (right) for multiple GA cutoffs. Bars show sensitivity at a 10% false positive rate when using data up to a given GA cutoff (x-axis) to predict the outcome of infants delivered after that cutoff. The number of controls/cases based on which sensitivity is estimated is given under each cutoff. Blue bars correspond to single biometry analysis (last available sample) while red bars are used for longitudinal analysis. * denotes a significant difference in sensitivity for the given cutoff (p<0.05).</p

A comparison of this study data and INTERGROWTH 21^st standards.

<p>Longitudinal measurements of abdominal circumference (AC) (top left), head circumference (HC) (top right), biparietal diameter (BPD) (bottom left), and femur length (FL) (bottom right), as well as 90% confidence intervals, derived from both the Hutzel population used in this study (red) and the INTERGROWTH-21^st study (blue).</p

Distribution of gestational ages at the final evaluation before each gestational age cutoff.

<p>Distribution of gestational ages at the final evaluation before each gestational age cutoff.</p

Demographic characteristics of the studied cohort.

<p>Demographic characteristics of the studied cohort.</p

SGA and LGA prediction performance for a restricted study population.

<p>Sensitivity of predicting small-for-gestational–age (SGA; <5th percentile) (left) and large-for-gestational–age neonates (LGA; >95th percentile) (right) for multiple GA cutoffs when restricting analysis to subjects with the last measurement before each cutoff within two weeks from the cutoff.</p

Estimated fetal weight Z-scores as a function of gestational age at delivery and outcome.

<p>Estimated fetal weight Z-scores for the last available sample at ≤32 weeks of gestation for all pregnancies (top) and those with the last sample between 30 and 32 weeks (bottom) as a function of gestational age at delivery. Horizontal lines denote the Z-score cutoff that leads to a false positive rate of 10% for each outcome (small- or large-for-gestational-age newborns) separately.</p

Summary of Participant and Data Characteristics.

<p>Younger fetuses are defined as GA <31 weeks, older fetuses are defined as GA≥31 weeks.</p><p>*denotes significant p-values. Abbreviations: GA, gestational age; MRI, magnetic resonance imaging; M, male; F, female; SD, standard deviation; IQR, interquartile range.</p