Abstract
Background
To establish and verify a nomogram model that can predict the risk of macrosomia in patients with gestational diabetes mellitus (GDM).
Methods
Data of patients with GDM who delivered their babies in Shanxi Bethune Hospital between November 2020 and February 2022 were analyzed. Multifactor logistic regression analysis was used to screen the independent risk factors for macrosomia. The model was constructed by R software. The area under the receiver operating characteristic curve (AUC) and goodness-of-fit analysis were used to evaluate its efficiency and accuracy. The clinical application value was evaluated using the decision curve analysis (DCA).
Results
A total of 991 patients with GDM were enrolled for modeling. Multigravida, pre-pregnancy body mass index, family history of hypertension, abdominal circumference, and biparietal diameter were independent risk factors for macrosomia, and the prediction model was established. The AUC in the training and test set were 0.93 (0.89–0.97) and 0.90 (0.84–0.96), respectively, and the difference was not statistically significant. The DCA suggested that the model has a high clinical application value.
Conclusion
The nomogram model for predicting macrosomia in patients with GDM was established. The model has certain accuracy and is expected to be a quantitative tool to guide clinical decision of delivery timing, individualized labor monitoring, and delivery mode.
Background
Gestational diabetes mellitus (GDM) is a common metabolic complication during pregnancy. It can lead to various adverse outcomes for both the mother and infants [Citation1–3]. The randomized controlled studies have shown that the incidence of macrosomia among patients with GDM was significantly higher than that of the normal population, indicating that hyperglycemia was a highly co-related factor [Citation4,Citation5]. Macrosomia can cause serious childbirth injuries, such as shoulder dystocia, neonatal asphyxia, stillbirth, soft birth canal laceration, postpartum hemorrhage, and pelvic floor dysfunction, and improved cesarean section rate [Citation6]. Therefore, a method to accurately predict the occurrence of macrosomia is extremely needed. As a practical visualization tool, nomogram can be used to identify whether patients are at high risk of developing a given disease or complication and quantify the individual risk. Previously, scholars have constructed prediction models to assess macrosomia in patients with GDM; however, there are certain limitations. This study aimed to establish a nomogram model that could predict the risk of macrosomia in patients with GDM, which could implement individualized labor monitoring and provide a reference for key clinical decisions.
Methods
Study design
Patients with GDM who underwent prenatal obstetric examinations and delivery in Shanxi Bethune Hospital and Shanxi Academy of Medical Sciences, Shanxi Bethune Hospital affiliated to Shanxi Medical University, from November 2020 to February 2022 were selected. Clinical data, including maternal characteristics, laboratory indicators, ultrasound indicators, and newborn birth weight, were collected. This was a retrospective cohort study and approved by the Ethics Committee.
The exclusion criteria were as follows: (1) cases of multiple pregnancy, premature birth, and combined gestational hypertension and (2) incomplete medical records.
Finally, 991 cases were analyzed and randomly divided into training (686 cases) and test (305 cases) sets at a ratio of 7:3. The patients were assigned to the macrosomia and non-macrosomia groups according to the condition of their newborns.
Diagnostic criteria for GDM and macrosomia
GDM is characterized by abnormal glucose metabolism during pregnancy. The 75-g oral glucose tolerance test (OGTT) is performed on pregnant women at 24–28 weeks of gestation. The blood sugar thresholds for fasting and 1 and 2 h after taking sugar were 5.1, 10.0, and 8.5 mmol/L, respectively. GDM was diagnosed when the blood sugar level was greater than or equal to the corresponding threshold at any time.
Macrosomia was diagnosed when the baby’s birth weight was >4,000 g [Citation7,Citation8].
Variables included for analysis
The population variables included age, pre-pregnancy body mass index (BMI), gestational weight gain (GWG), multigravida, family history of diabetes mellitus, family history of hypertension, history of GDM, history of macrosomia, polycystic ovary syndrome (PCOS), assisted reproductive technology (ART), insulin therapy, and polyhydramnios. The laboratory parameters included three values of the OGTT and fasting plasma glucose (FPG) in the third trimester. The indexes in obstetric ultrasound before delivery included the following: biparietal diameter (BPD), head circumference (HC), femur length (FL), abdominal circumference (AC), and amniotic fluid index (AFI). The development of macrosomia was the main parameter of this study.
Statistical analysis
Data were analyzed by R3.6.2 software. The measurement data meeting normality were presented as means ± standard deviations. A t-test was performed to analyze the difference between two independent groups. Non-normally distributed data were described using medians (percentiles), and the differences between the two groups were tested using the Wilcoxon rank sum test. The count data were expressed as the numbers of cases (%), and differences between the two groups were compared using the χ2 test or Fisher’s exact test. To establish and evaluate the predictive model, the initial data were randomly divided into training and test sets at a ratio of 7:3. The stepwise logistic regression (LR) method was used to analyze the multivariate LR model. The forest and nomogram models were developed based on the results. The area under the receiver operating characteristic curve (AUC evaluation) was used to predict the model performance, and the calibration curve was used to evaluate the model fitting. The decision curve analysis (DCA) was used to analyze the clinical value. p < .05 was considered statistically significant.
Results
Statistical description of the two datasets at baseline
A total of 991 medical records were included in this study, with 73 pregnant women who delivered newborns with macrosomia. There were 686 women in the training set, of whom 53 delivered newborns with macrosomia, and 305 women in the test set, of whom 20 delivered newborns with macrosomia. As shown in , a remarkable difference (p < .05) was presented in the factors, including pre-pregnancy BMI, family history of hypertension, polyhydramnios, multigravida, BPD, HC, AC, FL, and AFI before delivery. No difference was observed in other indicators.
Table 1. Statistical analysis of each index in the training dataset.
Forest map for the risk factor analysis of patients with GDM who delivered newborns with macrosomia
The meaningful variables in the aforementioned single factor analysis were included in the multifactor LR model based on stepwise regression for statistical analysis, and the forest map was drawn according to the analysis results, as shown in . The results demonstrated that pre-pregnancy BMI, family history of hypertension, multigravida, AC, and BPD were independent risk factors for macrosomia. The odds ratios (95% confidence intervals [CI]s) were 1.23 (1.10–1.38), 3.53 (1.54–8.21), 2.78 (1.29–6.10), 2.62 (1.96–3.63), and 20.30 (5.26–85.92), respectively (p < .05).
Figure 1. Forest Plot for risk factors analysis by multi-variates logistic regression.
Construction and evaluation of a nomogram prediction model for macrosomia in patients with GDM
Based on the results of multifactor LR analysis, R software was used to generate the monogram model to predict the risk of delivering newborns with macrosomia in patients with GDM ().
Figure 2. The nomogram model to predict macrosomia.
The AUC was used to evaluate the diagnostic efficacy of the nomogram model in the training and test sets. The results are shown in and . The AUC in the training and test sets were 0.93 (0.89–0.97) and 0.90 (0.84–0.96), respectively. No significant difference was observed between the two AUCs using the DeLong statistical test, indicating p = .368 > .05. The results indicated that the model was effective in both datasets.
Figure 3. The efficacy and calibration curve of the predictive model in the training dataset. The efficacy of the predictive model in training dataset.
Figure 4. The efficacy and calibration curve of the predictive model in the test dataset. The efficacy of the predictive model in test dataset.
The goodness-of-fit analysis was performed on the model. The results showed that the model presented a good consistency between the predicted risk and actual outcomes in the training dataset (Hosmer–Lemeshow test, χ2 = 11.283, p = .097) (). The same consistency was observed in the test dataset (Hosmer–Lemeshow test, χ2 = 3.426, p = .905) ().
Figure 5. The efficacy and calibration curve of the predictive model in the training dataset. Calibration curve of the predictive model in the training dataset.
Figure 6. The efficacy and calibration curve of the predictive model in the test dataset. Calibration curve of the predictive model in the test dataset.
DCA analysis of the nomogram prediction model
To better evaluate the clinical practical value of the nomogram model in the training and test sets, the DCA was used for analysis. The DCA can intuitively reflect each model or index under different probability threshold (Pt) values through the patient’s net clinical benefit (net benefit), that is, compared with no obstetric-related examinations at all, without adding excessive examinations, what percentage of pregnant women can be correctly judged according to a certain index or model diagnostic criteria.
As shown in and , when the Pt ≥ 8%, the use of this nomogram prediction model can gradually increase the net benefit of pregnant women compared with no other related obstetric examinations. In practice, when the Pt is set to 8%, the diagnostic model can detect eight more pregnant women who are likely to give birth to a newborn with macrosomia per 100 screening populations without excessively adding other tests, and does not increase the false-positive rate.
Figure 7. The DCA analysis of the model. The DCA analysis of the predictive model in training dataset.
Figure 8. The DCA analysis of the model. The DCA analysis of the predictive model in test dataset.
Discussion
With the adjustment of fertility policy, hyperglycemia has become the most common complication during pregnancy in China, and it is also an independent risk factor for macrosomia [Citation9]. Because macrosomia can cause several adverse maternal and infant outcomes, it is important to accurately predict the risk of macrosomia in patients with GDM. In this study, the data of 991 GDM cases were retrospectively analyzed to establish the nomogram model, and the accuracy and clinical value were also tested. The results showed that the model could help identify the high-risk patients in the third trimester, choose reasonable delivery time, make individualized labor monitoring, and guide them to make key clinical decisions.
Several factors, including the basic information, laboratory index, and ultrasound results, can affect the accuracy of prediction for macrosomia among patients with GDM. A number of studies have confirmed that maternal characteristics are highly correlated with the occurrence of macrosomia, such as pre-pregnancy BMI and GWG [Citation5,Citation9–12]. The probability of delivery of a newborn with macrosomia significantly increases in patients with a high pre-pregnancy BMI and excessive increase in the GWG. Generally speaking, obese patients tend to have a high-sugar and high-fat diet. It indicates that reasonable nutrition guidance during pregnant is needed, and effective body weight management can reduce adverse pregnant outcomes. Hypertension and diabetes are chronic diseases, and their pathological changes can involve the whole body’s tiny blood vessels, which are often mutually causal. Some studies have explored the relevant mechanisms of GDM in macrosomia. It has been reported that the expression of parathyroid hormone (PTH)-related protein (PTH-rP) and PTH/PTH-rP receptor (PTH-R1) were upregulated in the placenta of patients with GDM [Citation13]. This may promote the transportation of calcium through the placenta from the mother to the fetus, leading to adverse pregnancy outcomes [Citation13]. Sirico et al. reported that the vascular endothelial growth factor and CD1 expression levels increased in the placenta of patients with GDM and are also independently associated with maternal increased BMI and GWG [Citation14].
Recent studies have shown that the family history of hypertension and diabetes (especially first-degree relatives) increased the risk of GDM, thus increasing the incidence of macrosomia [Citation15–17]. Joyce et al. also found three gene loci highly related to the occurrence of macrosomia in patients with GDM [Citation16], suggesting that genetic factors cannot be ignored, which provides a new idea for predicting the risk of delivering newborns with macrosomia in patients with GDM. PCOS is a common reproductive endocrine disease among women of childbearing age, which is related to obesity, insulin resistance, and metabolic syndrome. The incidence of diabetes and cardiovascular diseases is higher than that of normal people [Citation18–20]. Several studies have shown that pregnant women with PCOS have a high probability of giving birth to a newborn with macrosomia, ranging from 6% to 18%; therefore, we put it into variables for analysis [Citation21–24]. In recent years, ART has been continuously improved and developed increasingly widely. Several studies show that women conceived by ART are more likely to have blood glucose metabolism disorder than those conceived naturally, and the risk of giving birth to infants who are large for gestational age (LGA) and with macrosomia is evidently increased [Citation25–27]. Because insulin does not pass through placenta tissue and has no adverse effects on pregnant women and fetuses, it is the first choice of drug treatment for patients whose blood sugar is not up to standard after nutrition management and exercise regulation. It can effectively help patients recover insulin balance and reduce the occurrence of adverse maternal and infant outcomes [Citation28]. Therefore, this study included insulin therapy in the analysis to explore whether insulin therapy can reduce the occurrence of macrosomia. Recent studies suggested that the higher the FPG level 2 weeks before delivery, the greater the risk of delivery of newborns who are LGA and with macrosomia [Citation29]; therefore, we included FPG in the third trimester of pregnancy in the variables for analysis.
As the main adverse outcome of GDM, models such as LR, decision tree, support vector machine, and artificial neural networks have been generated to compare the efficacy of predicting macrosomia [Citation30]. However, the related equations were complicated, and the clinical value was not high. A study conducted by domestic scholars predicted the risk of macrosomia in patients with GDM by constructing a nomogram model based on carnitine metabolism [Citation31]. However, in real clinical practice, it is difficult to include the substrates of carnitine metabolism as a routine prenatal assessment because it will significantly increase the economic burden of patients and increase medical consumption. Ultrasound is currently an important means of estimating fetal weight. The use of ultrasound to determine the estimated fetal weight (EFW) was first reported in 1975, and in 1985, Hadlock et al. published the most widely used EFW equation to date. The fetal BPD, HC, AC, and FL are important indicators for predicting fetal weight [Citation32,Citation33]. Some foreign scholars have constructed a nomogram model for predicting the risk of macrosomia in patients with GDM by combining the general information of patients and ultrasound data. Although the model showed high accuracy, only 194 cases were tested, and it did not undergo external verification. Zou et al. also combined the general information of patients and ultrasound data to construct a risk assessment model for the delivery of newborns with macrosomia in patients with GDM; however, the study did not include some of the aforementioned important variables into analysis, and the conclusions were limited [Citation34]. In addition to the common variables, such as age, pre-pregnancy BMI, GWG, history of GDM, history of macrosomia delivery, and ultrasound indicators, more factors, including FPG at the third trimester, PCOS, ART conception, and insulin treatment, were also included in our study. Taken together, we aimed to make the model as reliable and accurate as possible [Citation8,Citation10,Citation13,Citation35,Citation36].
A total of 991 patients with GDM were included in our study. Seventy-three of them gave birth to a newborn with macrosomia, and the incidence of macrosomia was 7.32%. The multivariate regression analysis showed that multigravida, pre-pregnancy BMI, family history of hypertension, AC, and BPD were independent risk factors for macrosomia, which was consistent with the results of the aforementioned studies [Citation5,Citation9–13]. In recent years, our hospital has actively adopted the “1-day outpatient clinic, five carriages” management model for patients with GDM. The GWG recorded before delivery has actually been controlled to a large extent, which may be one of the reasons why the probability of giving birth to a newborn with macrosomia in patients with GDM in our hospital is lower than that of several domestic and foreign studies and why GWG has not entered the final model [Citation34,Citation37]. Because nearly 70% of the pre-gestational diabetes mellitus (PGDM) group did not undergo the OGTT, this study did not include the PGDM populations. Finally, the results showed that the model had good consistency in both the training and test sets, and the decision curve also showed promising clinical value for the model.
The adjustment of dietary structure can change the physical conditions of patients with GDM (e.g. hypertension, hyperglycemia, and obesity), thereby altering adverse pregnancy outcomes. Some researchers reported that controlling diet reduced the risk of macrosomia in women with GDM or first pregnant women and the risk of pregnancy-induced hypertension [Citation38,Citation39]. A retrospective cohort study showed that the risk of macrosomia in patients with GDM treated with metformin was lower than that in diet control [Citation40]. It should be noted that dietary patterns may be influenced by society, culture, customs, and food supply. Considering these complicating factors, this study did not include diet during analysis. Moreover, a previous study reported that tall pregnant women have a higher risk of macrosomia than short pregnant women (OR, 2.77; 95% CI, 2.65–2.89) [Citation41]. Meanwhile, the paternal height and weight were also associated with the birth weight [Citation42]. It may be related to genetic factors. The fetuses of tall parents constitutionally have larger habitus than other fetuses in the general population. In this study, the habitus of parents were not included because of the incomplete information.
Our study has certain limitations. First, this study is not a multicenter study. Considering the differences in dietary structure and genes in different regions, races, and countries, it is necessary to include multicenter and larger-sample-size data in the future validation. Second, because our hospital does not use blood lipids as a routine screening item for pregnancy health care, this study cannot include blood lipids as variables for analysis. Third, some specific changes in the ultrasound parameters are not considered, such as the low umbilical artery pulsatility index in fetuses with macrosomia [Citation43]. We will focus on this issue in future research.
Conclusion
In general, the nomogram indicated high efficacy in predicting macrosomia in GDM with relevant clinical indexes. It can provide guidance for clinicians to choose delivery timing, formulate individualized labor management, and select delivery methods for patients. However, in actual work, we cannot rely on the model in isolation, and we need to make clinical decisions based on the actual situation of patients and comprehensive judgments to ensure the safety of mothers and infants.
Ethics approval and consent to participate
This study was approved by the Ethics Committee of Shanxi Bethune Hospital & Shanxi Academy of Medical Sciences, Shanxi Bethune Hospital affiliated to Shanxi Medical University.
Author contributions
Pengyu Sun and Tao Cao, designed the study, collected data and wrote the manuscript. Kang Liu, Xianmei Cui and Liang Zhang supervised the study, provided language help and writing assistance. All authors reviewed and commented on the manuscript. All authors approved the final version of the manuscript.
Availability of data and materials
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Funding
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