Small-for-Gestational-Age Birth Weight Risk Stratification Using First-Trimester Fetal Cardiac Parameters

To apply unsupervised machine learning techniques to first-trimester fetal cardiac data to enhance early risk stratification of small-for-gestational-age (SGA) birth weight.

This was a prospective cohort study that enrolled patients up to 13 6/7 weeks of gestation without fetal, umbilical cord, or placental abnormalities. At the first-trimester ultrasonogram, the chest area, heart area, ventricular inlet lengths, and spectral and color Doppler of the atrioventricular valves were assessed. An unsupervised machine learning technique, k-means clustering, was applied to sort fetuses into risk groups for SGA birth weight , defined as a birth weight less than the 10th percentile for gestational age. Candidate variables were selected with regression analyses, and the elbow method was used to determine the optimal number of clusters. Cumulative rates of outcomes were plotted with Kaplan-Meier analysis, and model performance was tested with area under the curve values with repeated cross-validation.

Six hundred seventeen pregnancies were included in the analysis, with 45 (7.3%) patients delivering a neonate with SGA birth weight. z-scores of the chest area ( P =.031) and tricuspid valve E/A ratio ( P <.001) showed an independent association with SGA birth weight and were used in the clustering algorithm. An unsupervised machine learning algorithm blinded to the outcome identified three risk clusters: low (n=202), intermediate (n=217), and high (n=198). The rates of SGA birth weight (1.2%, 5.4%, and 14.4%, respectively, P <.001) and nonreassuring fetal heart rate tracings (3.6%, 5.4%, and 8.6%, respectively, P =.039) differed significantly among the three risk clusters. Area under the curve values of the model in cross-validation samples were 0.71 (95% CI, 0.64-0.77). Using the low-risk cluster as a threshold, the model specificity was 95.5% and sensitivity was 35.0% for ruling out SGA birth weight. The negative predictive value for ruling out SGA birth weight was 99.0%.

Unsupervised machine learning of first-trimester fetal cardiac parameters can effectively stratify risk for SGA birth weight.