A predictive model of macrosomic birth based upon real-world clinical data from pregnant women

Macrosomia refers to a birth weight that reaches 4000 g. It affects approximately 3–15% of pregnancies and frequently leads to adverse pregnancy outcomes such as shoulder dystocia, postpartum hemorrhage, and birth fractures [1]. A reliable prenatal predictor of macrosomia would therefore be of great significance for the optimization of clinical management and for improving maternal and infant outcomes.

Fetal weight is traditionally estimated by abdominal examination or ultrasonographic evaluation in obstetric practice [2]. The accuracy of abdominal examination is influenced by obesity, uterine fibroids, the amount of amniotic fluid, and clinical experience, and as ultrasonography is a practical method used to estimate the fetal weight (EFW), the post-test probability of identifying macrosomia varies from 15 to 79% with sonographic EFW [3]. This accuracy is significantly diminished with macrosomic infants, and low accuracy limits the predictive value of ultrasonograms.

Various predictive models based upon traditional statistical formulas or machine learning algorithms have been applied to the prediction of macrosomia in recent years. Daisuke et al. [4] developed an integer scoring system for excluding macrosomia using only maternal physical examination without sonographic information, and Wang et al. [5] constructed a random forest model that involved extra-pelvic measurement information and achieved improved sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (at 91.7, 91.7, and 95.3%, respectively). While the current predictive models obtained a higher degree of accuracy than the ultrasonographic and maternal abdominal evaluative methods, the models did not address comprehensive variables relevant to risk factors of macrosomia, and this might have weakened the overall accuracy of these models. Unusual indicators (e.g., carnitine metabolism or fetal soft tissue) were selected in some of the previous models, but these indicators are not always available, which is troubling to clinicians. Some models are exclusive in their predictions within a population of women with gestational diabetes mellitus (GDM) but neglect predictions in individuals without GDM [6]. Based on the aforementioned data, the use of comprehensive and readily available predictors—as well as the expansion of the study population—may augment the application and precision of the model.

In this study, we aimed to develop a more accurate, applicable, and stable model to predict the risk of macrosomia and employed a retrospective analysis of common clinical data that encompassed maternal characteristics, laboratory tests, and sonographic parameters in a large cohort of pregnant women. We believe that our study will provide a reference for the development of macrosomia prevention and appropriate intervention strategies.

In this single-center retrospective study, we developed and validated a nomogram model for the prediction of macrosomia among newborns and achieved satisfactory predictive effects based upon the clinical data from a large cohort of pregnant women.

Pregnancy is a complex process accompanied by substantial changes in sugar and lipid metabolism [9]. Considering that these indicators may be key factors that contribute to fetal weight changes, we combined the key indicators (including blood glucose and lipid parameters) with the general indicators (for example, demographic characteristics and fetal intuitive sonography image measurements) as variables in our model.

It has been proposed that maternal hyperglycemia leads to fetal hyperglycemia, stimulating maturation and hypertrophy of the fetal pancreas [9], and various studies have indicated that GDM constitutes one of the important factors affecting the onset and development of macrosomia [8, 10]. The results of our study revealed that the incidence of macrosomia was 6.38% (115/1802) for women with GDM and 5.65% (649/11,482) for women with non-GDM, numbers consistent with the previous studies [11]. Several authors have supported pregnant women’s blood glucose levels as strongly associated with the incidence of macrosomia, regardless of a diagnosis of GDM [12, 13]. We usually perform an OGTT on pregnant women between their 24th and 28th gestational weeks to diagnose GDM, as OGTT, FPG, GLU-1H, GLU-2H, and HBA1C are important indicators in the assessment of maternal blood glucose levels. Through multivariate analysis, fasting glucose and OGTT-1H were then utilized as indicators in our model.

Maternal serum lipids may comprise an important fuel in fetal overgrowth during the entire pregnancy [14]. Since Xue et al. hypothesized that elevated TG levels in early pregnancy but not in late pregnancy were crucial risk factors associated with the incidence of fetal macrosomia [15], we then chose the lipid index in early pregnancy as the variable we used for the prediction of macrosomia. When we herein collected four maternal lipid parameters (TC, TG, HDL, and LDL) in the first trimester (9th and 13th gestational weeks) of pregnancy, our results revealed that TGs showed high specificity in the prediction of macrosomia, and we, therefore, chose TGs as the predictive indicator in our model.

The most objective method currently employed to estimate fetal body weight is ultrasonographic (US) measurement, which encompasses over 30 different formulas for the US estimates to predict newborn birth weight [16‐18], with the most widely used being the Hadlock formula [19]. To generate sonographic fetal weight estimations with a lower error margin, many formulas have reflected disparate parameters of the fetus (fetal abdominal fat layer [20], shoulder soft-tissue thickness [21], biacromial diameter [22]), and some have even entailed 3D sonographic measurements [23]. Although such formulas and novel predictors may improve the accuracy of US evaluation, they nevertheless increase technical difficulty and sonication time. Considering the availability of predictors, we used B-ultrasonography 2 weeks prior to delivery to assess the predictive capability of our model.

The nomogram model can transform the cumbersome regression equation into a visually legible graph that is both convenient and rapid in its practical application [24]. Each predictor is given a point according to the woman’s characteristics, and the point total is then calculated to obtain the risk of macrosomia. Mazouni et al. developed a nomogram to predict macrosomia based on maternal demographic characteristics and US variables, with the model achieving moderate predictive ability at an AUC of 0.850 [25]. These authors’ sample size was, however, quite small and dismissed the influence of maternal metabolism. Although Sun et al. established a nomogram model combined with carnitine-related metabolic variables for predicting macrosomia in pregnant women with GDM [26], carnitine metabolism is not routinely used in the clinical setting, restricting its application. Shigemi et al. created a scoring system based upon the significant predictors of macrosomia without sonographic information [4], and their system exhibited a high negative predictive value of 0.996–1.000, while the positive predictive value for screening macrosomia was extremely low (0.003). Zou et al. [27] and Kang et al. [28] published models that could only be applied to women with GDM rather than to all pregnant women, and Ye et al. used ensemble methods (one comprising a machine-learning algorithm) to improve the prediction of fetal macrosomia [18]. Unfortunately, ensemble methods are cumbersome and limited in their practicability. In contrast, our model was applied to the entire population of pregnant women and displayed many advantages. First, the precision of our model met or exceeded the optimal predictive levels recorded in the literature [25, 27]. The areas under the ROC curves (AUCs) for the internal and external validation of our model were 91.7 and 91.0, respectively. In addition, using our model, we selected alternative but still routine clinical data that were easily accessible and relatively comprehensive. Macrosomia risk factors can be classified into three components: maternal characteristics, metabolic parameters, and US measurements. However, as most models only incorporate some of these three risk factors to predict the incidence of macrosomia, we posit that our predictive model is more generalizable, precise, and clinically suitable.

Our nomogram model could be a practical tool for clinical work. Once the model shows the possibility of macrosomia, suggesting that pregnant women might be in an over-nutrition condition and need strictly controlled weight gain by lifestyle, diet, exercise. Meanwhile, doctors enhance close monitoring and supervision on them. Nowadays, over-estimated fetal weight could result in over-classification of fetuses as macrosomic with unnecessary cesarean deliveries, under-estimated fetal weight could also pose a risk of dystocia or even stillbirth. Our model fits with the current strategy for precision medicine can guide the mode of delivery and provide assistance at birth. For example, if the model shows a high probability of macrosomia, we can arrange medical personnel, drugs and medical supplies ahead of time in order to prevent postpartum hemorrhage, shoulder dystocia, severe perineal lacerations etal actively. All in all, our model judges the macrosomia accurately and covers the clinical pregnancy management during the antenatal, intrapartum and postpartum periods. Significantly, our model will be a strong aidarm to enhance doctors and midwives’ decision confidence, as well as bring lower anxiety of pregnant women due to the uncertainty of their fetal weight. There were some limitations to the present study. First, we are a single obstetric hospital that principally covers low-to-moderate-risk pregnant women, and this cohort may not fully represent all obstetric practices in the community. Second, this was a retrospective study that lacked the validation of relevant variables, thus slightly reducing its overall credibility; in the next phase, we will include the relevant variables and appropriate influencing factors and initiate a prospective study. Finally, the occurrence of macrosomia is affected by many factors, including the environment. For example, some evidence suggests that exposure of pregnant women to air pollutants [29], such as PM_2.5, NO₂, and O₃, and passive smoking may also increase the fetal risk for macrosomia. We herein ignored the influences of environmental effects, lifestyle, work stress, and social relationships.

In summary, our proposed predictive nomogram model can be used effectively to prognosticate the incidence of macrosomia. The highly predictive sensitivity and specificity of our model can thus aid clinicians in reducing adverse pregnancy outcomes. In the future, we will convert the nomogram model into an electronic medical records system or mobile application for every pregnant woman to expand the potential value of this predictive model.