“INTERGROWTH21st vs customized fetal growth curves in the assessment of the neonatal nutritional status: a retrospective cohort study of gestational diabetes”

Gestational diabetes mellitus (GDM) is associated with increased incidence of adverse perinatal outcomes including newborns large for gestational age (LGA), macrosomia [1‐4], preeclampsia [5], polyhydramnios, stillbirth, and neonatal morbidity [6]. Newborns of mothers with GDM are heavier and have greater skinfold measures and adiposity than offspring of mothers without GDM. Later in life, children of diabetic mothers more frequently develop early overweight or obesity, type 2 diabetes, and metabolic syndrome [7‐9].

In pregnant woman with GDM, ultrasound is used to monitor growth as overnutrition is common and influences management. Prenatally, fetal overgrowth is suspected when the ultrasound estimated fetal weight (EFW) is abnormally elevated. An EFW higher than the 90th centile indicates an LGA fetus. In preterm fetuses, this method is more accurate than that based only on the absolute value of the EFW (EFW greater than 4000 or 4500 g) [10]. By considering gestational age at the time of ultrasound, excessive fetal growth can be identified before the term [11].

Traditionally, fetal growth has been evaluated by comparing estimated fetal weight with population-based reference curves. Similarly, recent reports of the INTERGROWTH21st Project recommend using a single standard for fetal growth and birthweight [12‐14].

Alternatively, a customized approach that uses a mathematical model of maternal anthropometric variables to predict the optimal weight at term of each fetus has gained strength recently [15, 16]. This optimal weight at term can be combined with a fetal proportionality weight curve to calculate a customized curve for each mother in each pregnancy that can be used to predict birth weight and fetal growth [17].

A few studies have compared INTERGROWTH21st and customized curves ability to identify fetuses at high risk of adverse perinatal outcomes, but not their ability to identify alterations in neonatal nutritional status [17‐21]. In this study, we used the ponderal index (PI) to assess the nutritional status of newborns of GDM mothers. We hypothesized that, in pregnant women with GDM, customized curves identify the nutritional status of the newborn more accurately than INTERGROWTH21st. This study aims to determine which method, INTERGROWTH21 or customized curves, better identifies newborns with an abnormal PI, as an indicator of the nutritional status of newborns of diabetic mothers.

This study recruited 234 pregnant women with GDM. In 3 cases the length of the newborn was missing, so 231 women remained in the study.

The maternal characteristics and perinatal outcomes are displayed in Table 1. The mean PI was 2.68 (SD: 0.26). The results of neonatal nutritional status are shown in Table 2. The incidence of newborns with a PI <10th centile was 8.7%, and the incidence of newborns with a PI >90th centile was 13.9%.

Table 3 shows the distribution of SGA, AGA and LGA newborns identified by each, INTERGROWTH21st and the customized, method. The rate of SGA newborns identified by INTERGROWTH21st was 4.8% versus 10.8% identified by our customized method. The rate of LGA newborns identified by INTERGROWTH21st was 26% vs 13.4% identified by the customized method (p < 0.001).

Most newborns classified as SGA by INTERGROWTH21st also were identified as SGA by the custom method (10 of 11). However, of the 25 newborns classified as SGA by the customized method, only 40% (10) was classified as SGA by INTERGROWTH21st and the remaining 60% was classified as AGA.

Ten of the 11 newborns classified as SGA according to INTERGROWTH21st were classified as SGA and 1 as AGA according to the custom method. In contrast, 48.3% of the 60 newborns classified as LGA according to INTERGROWTH21st were classified as AGA according to the custom method and only 51.7% were classified as LGA.

On the other hand, only 10 (40%) of the 25 newborns classified as SGA by the custom method were also classified as SGA by INTERGROWTH21st and the remaining 60% was classified as AGA. 100% of newborns classified as LGA by the custom method were also classified as LGA by INTERGROWTH21st.

It is important to note that, in the overlap, 33.3% of children classified as SGA according to the customized method and as AGA according to INTERGROWTH21st had an PI below the 10th centile.

It is important to clarify that, in the overlap, 86.2% of children classified as LGA by INTERGROWTH21st and as AGA by the custom method presented a normal IP and only 10.3% presented a PI>90th centile.

Table 4 shows the ability of INTERGROWTH21st and our customized method to identify a PI <10th centile in newborns classified as SGA or a PI >90th centile in newborns classified as LGA. Identifying malnutrition, both PPV and NPV were superior using the customized method than using INTERGROWTH21st.

Figure 1 shows the ROC curves of both methods for the detection of a PI <10th centile. The AUC obtained with INTERGROWTH21st was 0.550 while using the custom method we obtained an AUC of 0.648. Although the customized method obtained a higher AUC, the comparison of both curves using the deLong method showed that the differences found were not statistically significant (p = 0.18).

Identifying children with a PI > p90 (overnutrition) the PPV of the customized method to was significantly higher than that of INTERGROWTH21st. For detecting overnutrition, the PPV of the customized method was 51.61% (95% CI 32.41–70.82) versus 32.20% (95% CI 19.43–44.97) for INTERGROWTH21st. However, NPVs of the two methods were very similar.

Identifying overnutrition (PI>90th centile), the LR+ of the customized method was superior for identifying newborns with a PI > p90.

Figure 2 shows the ROC curves obtained by both methods in the identification of newborns with a PI >90th centile. The customized method obtained an AUC that was slightly higher than that obtained by INTERGROWTH21st (0.71 vs. 0.68). However, this difference was not statistically significant (p = 0.72).

The classification of fetal nutritional status via an appropriate ultrasound standard is important to guide pregnancy management. A fetus incorrectly classified as SGA or LGA will induce the clinician to intensify the monitoring of the pregnant woman and, in the specific case of the GDM, even to modify the diet or treat with insulin. Therefore, we believe that the clinician should choose the curve that best identifies newborns with true alterations in nutritional status (malnutrition or overnutrition).

This study shows that in newborns of mothers with GDM, the rates of SGA and LGA differ by the reference curve used, INTERGROWTH21st or customized. The SGA rate using INTERGROWTH21st was 4.8%, significantly lower than 10.8% observed using customized curves. In contrast, the LGA rate using INTERGROWTH21st was 26%, compared to 13.4% using our customized curves as the reference. Therefore, in our population, the customized method identifies more SGA while INTERGROWTH21st identifies more LGA.

These SGA results were consistent with those recently published by Francis et al. [15] who reported overall SGA and LGA rates of 10.5 and 9.5%, respectively, using customized curves. Using INTERGROWTH21st Francis et al. observed an overall SGA rate, 4.4%, very similar to the 4.7% rate of our sample and, like our study, they find an unexpectedly high LGA rate of 20% (Similar to our 25%, if we take into account that we have analyzed a diabetic population).

Similarly, Anderson et al. [21], reported a significantly lower SGA rates using INTERGROWTH21st versus customized curves (4.5% vs 11.6%); additionally, in their cohort, Anderson et al. had a customised LGA rate of 8.9% and INTERGROWTH21st LGA rate of 20.8%, with wide variation by ethnicity (European women 23.7%, Indian 6.8% and Pacific 32.%) [NH Anderson, personal communication].

The use of PI to assess the nutritional status of the newborn presents certain limitations as it not only evaluates fat mass, but also head size, lean mass and bone mass, hence potentially limiting its accuracy in reflecting adiposity. However, accurate measures of body composition are usually costly. A recent work published by Chen et al. [33] informs us that although skinfold measures may have more discriminative power in terms of total body adiposity, simple anthropometric measures (like PI) correlated strongly with neonatal adiposity and conclude that these simple measures could be of value in large epidemiological studies.

We found that the SGA and LGA classifications by each method (customized vs INTERGROWTH21st) reflect differences in their ability to identify true alterations in the PI as an indicator of the neonatal nutritional status.

Likewise, the accuracy of the customized curves for identification of newbornt with a PI <10th centile was greater than that of INTERGROWTH21st, LR + of 3.86 vs 2.74, respectively. That is, using customized curves, it is 3.86 times more likely that a malnourished newborn is classified as SGA than a normally nourished newborn is classified as SGA.

In a previous study by our team [22], carried out in an unselected population, the customized method was superior to the population-based for the identification of newborns with a PI at birth <10th centile. This superiority of the customized method was more evident in the highest scales of maternal weight and height.

Owen et al. [34] found a similar relationship between customized birth weight percentiles and neonatal malnutrition, but concluded that, in a low-risk population, the customized curves are only moderately useful in the identification of neonates with a low PI, with a positive likelihood ratio of 4.3 (95% CI: 2.5–7.1). Agarwal et al. [35] also found that the PI at birth was lower in newborns classified as SGA by customized curves than in SGA according to population curves. The apparent superiority of the customized method to detect a PI <10th centile should be interpreted with caution since the difference found between the RR was not statistically significant and when comparing the ROC curves of both methods the difference found was not statistically significant. The reality is that, with the prevalence of malnutrition found in the sample (8.9%) we would need at least 1200 individuals to reach a statistical power of 80%. This shows that the statistical non-significance could be due to an insufficient sample size.

Further, our analysis of the accuracy of each method for identification of newborns with a PI > p90 revealed that the customized method had a greater LR+, 5.40, than the LR+, 2.54, using INTERGROWTH21st. Hence, using customized curves, it is 5.40 times more likely that an over nourished newborn will be classified as LGA than a normally nourished newborn will be classified as LGA. Given that in GDM it is critical to identify fetal overnutrition, we consider of special relevance the differences found in the PPV of both methods to identify a PI > p90. Using our customized curves, the probability that a fetus classified as LGA has a PI >90th centile is 51.61% while using INTERGROWTH21st the probability drops to 32.20%. These results are consistent with those found by Gonzalez et al. [36] However, in our study, an analysis of the ROC curves shows that the AUC obtained by both methods is very similar and the small difference observed (0.70 vs. 0.68) is not statistically significant. Again, it should be noted that, with the prevalence of newborns with PI>90th centile, the lack of significance could be due to an insufficient sample size since 1603 individuals would have been required to have a statistical power of 80%.

Another aspect worth discussing is the low sensitivity of both methods to identify newborns under-nourished in fetuses classified as SGA. However, in the same case, the specificity is acceptable. In our opinion, this shows that the same cut-off point (10th centile for SGA) can classify a child as normal or small depending on the reference curve.

The relatively small sample lead to our primary limitations, including occasional RRs with overlapping or wide confidence intervals, which hampered their interpretation. However, the relative risks were usually large enough to be taken clinically relevant. It should be noted that the premises from which we started when estimating the sample size have not been fulfilled. In our estimation, we assumed a ratio between non-exposed / exposed of 8. This was based on the premise that we would find approximately 10% SGA and 10% LGA with each method. However, using INTERGROWTH21st, for example, we found 163 AGA and only 11 SGA. This makes the non-exposed / exposed ratio rise to 14.8. With this ratio, we would have needed 488 AGA newborns to obtain significance (and this to detect a minimum risk of 3; to detect a minimum risk of 2.5 - which is the observed one - we would need an even larger sample: 51 SGA and 751 AGA). Therefore, although in general our study seems to indicate that the customized method could surpass INTERGROWTH21st in the identification of alterations in nutritional status, we think that it is necessary to complement this study with a larger sample.

In addition, selection and information biases could affect the estimated of the performance of the two reference curves. We believe that our results can be extrapolated to other populations of pregnant women with adequate monitoring because obstetricians, endocrinologists, family doctors and primary care midwives monitored the pregnant woman with GDM using criteria for diagnosis, follow-up and treatment established by the Spanish Society of Gynecology and Obstetrics.

Overall, there does seem to be a difference in performance between the method customized and INTERGROWTH21st. Our results show that customized LGA and SGA may be better identifying nutritional status as assessed by PI in diabetic mothers. However, further studies with a larger sample size are necessary to increase the reliability of these findings.

In our opinion, if our results are confirmed, the greater capacity of the customized curves to identify newborns with PI > p90 may have important implication for monitoring pregnant women with GDM because intrauterine identification of overnutrition may indicate poor maternal metabolic control and the need for extreme dietary care or, even, insulin treatment.