Interpretation
In our study, the SMFM definition identified 115 cases (4.91%) of FGR,
the ACOG definition identified 63 cases (2.7%) of FGR, while the ISUOG
definition identified 48 cases (2.05%) of FGR. However, when Molina et
al.(12) and Roeckner et al.(13)analyzed the data of the Redefining Fetal Growth Restriction (RFGR)
project, the ACOG definition identified 8.63% (91/1055) of FGR cases,
the SMFM definition identified 13.0% (137/1054) of FGR cases, and the
ISUOG definition identified 5.2.% (55/1055, 55/1054) of FGR cases, with
percentages that were higher than the incidence observed in this study.
The reason of the higher incidence was that the population enrolled in
the RFGR program was at high risk of developing FGR. High risk factors
included uterine height inconsistent with gestational age, hypertension,
diabetes, and other chronic kidneys, blood vessel, and hemoglobin
diseases. In their study, pregnant women with chronic hypertension and
pre-pregnancy diabetes accounted for 14% and 8% of the sample,
respectively. Nevertheless, the samples in our study were from the
general population. The percentages of chronic hypertension and
pregestational diabetes mellitus in this study were 1.1% and 1.3%,
respectively Furthermore, our institution offered third trimester
ultrasound as a routine protocol, which provided sufficient ultrasound
biometric information for the calculation of EFW in the late pregnancy.
The reason why SGA was selected as a primary outcome is that SGA is
associated with ANO and adult cardiovascular diseases and metabolic
diseases (16; 17). In addition, it has a clear
definition (birth weight less than the 10th percentile). Therefore, many
studies have used SGA as one of the indicators of the accuracy of FGR
predictions.(11; 12; 13). The results from this study
indicate that the performance of the SMFM-FGR definition to predict SGA
was higher than those of
the
ACOG-FGR and ISUOG-FGR criteria (AUC: 0.69 vs. 0.62 and 0.60), mainly
because the sensitivity of the SMFM-FGR criteria to detect SGA was
significantly higher than those of the ACOG-FGR and ISUOG-FGR criteria
(40.8% vs. 24.5%, 20%). In 2018, Blue et al. compared the ability of
RCOG (same as SMFM-FGR) and ACOG criteria for FGR to predict SGA and
found that the SMFM-FGR criteria were slightly better than those of the
ACOG-FGR criteria (AUC: 0.78 vs. 0.76)(11). When
comparing the SMFM-FGR and ISUOG-FGR criteria, Roeckner et al. also
found that the sensitivity of the SMFM-FGR criteria to predict SGA was
significantly higher than that of the ISUOG-FGR criteria (54.7% vs.
28.8%), while the specificity was slightly lower than that of the
ISUOG-FGR criteria (93.3% vs. 98.4%)(13).
As is known that not all SGA cases are pathological. Most of SGAs are
constitutional. Using the SGA alone to assess compare the predictive
ability of different FGR definition was comprehensive. Considering that
FGR fetuses were at an increased risk of fetal and neonatal mortality
and complications due to their reduced growth potential, there were also
a few studies which used neonatal complications and mortality as primary
outcome, thereby the concept of composite ANO being proposed(12; 13). When Molina et al. compared the ability of
the ACOG-FGR and ISUOG-FGR criteria to predict ANO, the performance of
the ISUOG-FGR criteria was superior to that of the ACOG-FGR criteria
(sensitivity: 10.1% vs. 9.3%; specificity: 95.5% >
91.5%) (12). In our study, the sensitivity of the
ACOG-FGR criteria for predicting ANO was slightly higher than that of
the ISUOG-FGR criteria, but the specificity was slightly lower, and the
AUC of the two groups was similar
(0.552).
When Roeckner et al. compared the SMFM-FGR and ISUOG-FGR criteria, they
found that, although the sensitivity of the SMSM-FGR criteria was higher
than that of the ISUOG criteria, the specificity was lower than that of
the ISUOG criteria, and the AUC of the two groups was 0.51 vs. 0.53. In
this study, the sensitivity of the SMFM-FGR criteria for predicting ANO
was higher than that of the ISUOG criteria, but the specificity was
slightly lower, and the final AUC for the SMFM-FGR criteria was slightly
higher. However, the differences in the medical resources and
capabilities of different institutions to deal with neonatal
complications also led to differences in ANO between studies.
Many studies have shown that different growth curves produce different
percentiles, thereby affecting the rates of detection of SGA and
LGA.(18; 19). In addition, different ethnic groups
have different growth potentials. For example, the average birth weight
of newborns in India is 2.9 kg, and the average birth weight of newborns
in the UK is 3.5 kg, with a difference of 600 g.(20).
Therefore, the National Institute of Child Health and Human Development
(NICHD) developed different growth curves for Caucasian, Black,
Hispanic, and Asian fetuses.(21) Because there is no
authoritative local growth curve in China, the EFW percentile in this
study was determined based on the growth curve of Asian women developed
by the NIICHD(21; 22). The percentiles for newborns
are based on newly released data of newborns in nine cities in southern
and northern China in 2020.(23).