The March issue of Ultrasound in Obstetrics & Gynecology includes a systematic review evaluating the yield of prenatal exome sequencing in agenesis of the corpus callosum, a study comparing singleton and twin growth charts in the prediction of neonatal morbidity in twin pregnancy, a prospective study updating the M6 risk-prediction model in pregnancy of unknown location and an analysis of a new machine-learning model for predicting first-trimester pre-eclampsia in a Latin American population.
Please see below a selection of articles from the March issue of the Journal chosen specially by the UOG team. To view all UOG content, become an ISUOG member today or login and upgrade.
Diagnostic yield of exome sequencing in prenatal agenesis of corpus callosum: systematic review and meta-analysis
Agenesis of the corpus callosum has a heterogeneous etiology and is associated with many genetic variants and syndromes, which complicates prenatal diagnosis and parental counseling. This systematic review and meta-analysis by Mustafa et al. investigates the incremental diagnostic yield of exome sequencing following negative chromosomal microarray in prenatally diagnosed cases of agenesis of the corpus callosum. The authors identified over 100 associated pathogenic/likely pathogenic variants in over 80 genes, with the highest diagnostic yield in cases with an additional extracranial (55%) or cranial (43%) anomaly. These findings underscore the importance of reducing diagnostic turnaround time to reap the benefits of exome sequencing in routine clinical practice.
Incidence of neonatal morbidity in small-for-gestational-age twins based on singleton and twin charts
Twin-specific charts are often promoted for growth evaluation in twin pregnancy on the grounds that they avoid overdiagnosis of growth restriction and provide a stronger association between small-for-gestational age (SGA) and adverse outcome. In this study, Wright et al. counter each of these claims to advance their case for the use of singleton charts in twins. Using a singleton chart to define SGA at 36 weeks, they report higher growth-related neonatal morbidity in SGA twins compared with SGA singletons. Moreover, the greater diagnostic accuracy of twin charts over singleton charts was observed in both twins and singletons. These findings support the consistent use of singleton reference standards to classify fetal size.
Updating M6 pregnancy of unknown location risk-prediction model including evaluation of clinical factors
The multivariable M6 model is recognized as the most accurate method of risk prediction in pregnancy of unknown location, but is based on decades-old data and focuses solely on biochemical markers. In this study, Kyriacou et al. use a large prospective dataset from eight centers to update the M6 model and assess the value of incorporating clinical factors into its risk-prediction algorithm. The updated model showed excellent negative predictive value and sensitivity for predicting high-risk outcomes, namely ectopic pregnancy and persistent pregnancy of unknown location. The addition of maternal demographic and clinical factors offered some benefit when progesterone level was unsuitable or unavailable. The authors recommend integrating the updated M6 model into a two-step triage protocol to safely manage affected women in early pregnancy units.
Performance of machine-learning approach for prediction of pre-eclampsia in a middle-income country
Latin American women have been noticeably underrepresented in the cohorts used to develop and validate current prediction models for pre-eclampsia. In this prospective study set in Mexico City, Torres-Torres et al. develop a machine-learning model for the first-trimester prediction of pre-eclampsia. Using maternal characteristics and locally derived multiples of the median values for mean arterial pressure, uterine artery pulsatility index and placental growth factor, their model had high accuracy and detection rates for preterm and early-onset pre-eclampsia. The authors conclude that coupling a machine-learning approach, which limits the number of input variables required, with population-specific biomarker reference ranges could improve pre-eclampsia prediction in diverse and low-resource settings.
Coming up next month…
- A systematic review assessing the diagnostic accuracy of prenatal ultrasound in coarctation of the aorta. Preview the Accepted Article.
- A Danish cohort study on the safety of fetal reduction in quadruplet pregnancy. Preview the Accepted Article.
- A study testing the performance of the IETA-1 prediction model for intracavitary uterine pathology in women without abnormal uterine bleeding. Preview the Accepted Article.