ARRS 2022 Abstracts

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E1359. Variation in Fetal Weight Percentile Estimates
Authors
  1. Garvit Khatri; University of Washington
  2. Michael Richardson; University of Washington
  3. Manjiri Dighe; University of Washington
  4. Theodore Dubinsky; University of Washington
Objective:
The fetal weight percentile is based on three parameters: estimated fetal weight, gestational age, and ethnic growth charts. All these parameters are prone to standard deviations and errors; however, the paradox is that using gestational age, which has error, and a fetal weight that has error, the weight percentile is generated and generally reported without any error and to a very high level of precision. Many guidelines use an absolute cut off of the 10th and 90th percentiles to identify growth restriction and macrosomia. This variation can lead a fetus being mistakenly classified as having intrauterine growth retardation (IUGR) or macrosomia. We used quantile-quantile (QQ) plots to analyze our data. QQ plots are helpful for comparing the shapes of distributions and give a graphical view of how properties such as location, scale, and skewness are similar or different in the two distributions. The goal of this study was to compare the estimated weight percentile with the actual observed percentile for each gestational age.

Materials and Methods:
After IRB approval, the radiology information system database was retrospectively searched for all obstetrical ultrasound reports obtained during the late second and third trimesters from July 1, 2014 until July 1, 2020. Demographic information as well as fetal weight and weight percentile information were obtained from these reports. QQ plots were created for all gestational ages and all ethnicities.

Results:
Our study included 6259 ultrasounds in 4060 patients. In terms of ethnicity divisions, there were 3434 (54.8%) White patients, 499 (7.9%) Native Americans, 403 (6.4 %) Black, 91 (1.4%) Asian, and 29 (0.4%) Hispanic, and ethnic data were unavailable for 1803 (28.8%) patients. Mean age of the total group was 31.68 years (range 15–53 years). When all subjects were considered, the median values in our QQ plots approximated the line of identity. However, there was considerable variation for a given estimate, implying that estimated fetal weight percentiles are only very rough predictors of the actual percentile. For example, if a patient were estimated to be at the 50th percentile, 90% of the time the actual percentile lay between 32 and 69.

Conclusion:
In summary, all three of the parameters on which the fetal weight percentile estimates are based are prone to variations and error. We propose that fetal weight percentiles should also be reported with an estimate of their error. This error should be documented, so as to improve screening of each fetus. Mid third trimester ultrasound exams are the best time to evaluate fetal weight and weight percentile, and perhaps these should not be reported earlier in gestation. The recognition of the large error associated with the assignment of a weight percentile is very important in those clinical situations where the absolute weight and Dopplers suggest a healthy baby, but an erroneous weight percentile suggest IUGR or macrosomia.