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Accuracy and Completion Rate of the Fetal Anatomic Survey in the Super Obese Parturient
Author(s) -
Simmons Pamela M.,
Wendel Michael P.,
Whittington Julie R.,
San Miguel Kelly,
Ounpraseuth Songthip T.,
Magann Everett F.
Publication year - 2021
Publication title -
journal of ultrasound in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 91
eISSN - 1550-9613
pISSN - 0278-4297
DOI - 10.1002/jum.15582
Subject(s) - medicine , body mass index , ultrasound , logistic regression , obstetrics , gestational age , fetus , mass index , pregnancy , radiology , biology , genetics
Objective To determine the completion rate of ultrasound in with a body mass index (BMI) ≥ 50 to women with BMI 18.5 to 29.9. Study Design This study was a retrospective cohort study. Women with a singleton pregnancy, age 18 to 45 with a BMI ≥50 that delivered between 2013–2016 were compared to women with a BMI 18.5 to 29.9 during that same time period to assess the accuracy and, as a second aim, the completion rate of the fetal anatomic survey. Data were analyzed using two‐sample t test, chi‐square test, or logistic regression as appropriate. Results Eighty‐one cases with a BMI ≥50 were compared with 81 patients with a BMI 18.5 to 29.9. Maternal demographics and timing (gestational age) at the time of the ultrasound were similar between groups. In women with a BMI 18.5 to 29.9, completion of anatomy was 58% of the time with the first ultrasound, 81% with second ultrasound, and 84% with the third ultrasound. In women with BMI ≥50, completion of anatomy was 10% of the time with the first ultrasound, 33% with the second ultrasound, and 42% with the third ultrasound. Each time frame was statistically significant. Agreement level on the accuracy to detect fetal anomalies between groups were not statistically significant between the groups. Conclusion In women with a BMI ≥50 compared to women with BMI of 18.8 to 29.9, more ultrasounds are needed to complete the anatomic survey although overall accuracy in fetal anomaly detection is similar.