Diagnosis and Management of Macrosomia and Shoulder Dystocia: A Comprehensive Review of Major Guidelines.

Importance: Macrosomia represents the most significant risk factor of shoulder dystocia (SD), which is a severe and emergent complication of vaginal delivery. They are both associated with adverse pregnancy outcomes. Objective: The aim of this study was to review and compare the most recently published influential guidelines on the diagnosis and management of fetal macrosomia and SD. Evidence Acquisition: A comparative review of guidelines from the American College of Obstetricians and Gynecologists (ACOG), the Royal College of Obstetricians and Gynaecologists, the National Institute for Health and Care Excellence, the Royal Australian and New Zealand College of Obstetricians and Gynaecologists (RANZCOG), and the Department for Health and Wellbeing of the Government of South Australia on macrosomia and SD was conducted. Results: The ACOG and RANZCOG agree that macrosomia should be defined as birthweight above 4000-4500 g regardless of the gestational age, whereas the National Institute for Health and Care Excellence defines macrosomia as an estimated fetal weight above the 95th percentile. According to ACOG and RANZCOG, ultrasound scans and clinical estimates can be used to rule out fetal macrosomia, although lacking accuracy. Routine induction of labor before 39 weeks of gestation with the sole indication of suspected fetal macrosomia is unanimously not recommended, but an individualized counseling should be provided. Exercise, appropriate diet, and prepregnancy bariatric surgery are mentioned as preventive measures. There is also consensus among the reviewed guidelines regarding the definition and the diagnosis of SD, with the "turtle sign" being the most common sign for its recognition as well as the poor predictability of the reported risk factors. Moreover, there is an overall agreement on the algorithm of SD management with McRoberts technique suggested as first-line maneuver. In addition, appropriate staff training, thorough documentation, and time keeping are crucial aspects of SD management according to all medical societies. Elective delivery for the prevention of SD is discouraged by all the reviewed guidelines. Conclusions: Macrosomia is associated not only with SD but also with maternal and neonatal complications. Similarly, SD can lead to permanent neurologic sequalae, as well as perinatal death if managed in a suboptimal way. Therefore, it is crucial to develop consistent international practice protocols for their prompt diagnosis and effective management in order to safely guide clinical practice and improve pregnancy outcomes.

Comprehensive analysis of macrosomia: exploring the association between first-trimester alanine aminotransferase and uric acid measurements in pregnant women.

OBJECTIVES: Investigating the relationship between liver enzymes, uric acid (UA), and macrosomia will benefit physicians in the early detection of complications that may emerge during/after pregnancy. The study analyzed liver enzyme activity and UA levels in first-trimester pregnant for the risk of macrosomia. METHODS: This retrospective cross-sectional research analyzed the data of pregnant women who gave birth between Jan 2021-2023. All data were extracted from medical records, and UA and AST-ALT were examined in all the participants. RESULTS: Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels were higher in the macrosomia (p<0.05). Similarly, UA levels were higher in the macrosomia (p<0.001). There was a moderate positive correlation between ALT and birth weight (r=0.168, p<0.01), while we found a strong positive correlation between UA and birth weight (r=0.355, p<0.01). In the ROC (receiver operating characteristic), Area Under the Curve (AUC) for ALT and UA was significant (p<0.0001) but not for AST (p=0.157). UA showed a predictive value for macrosomia with 68.1 % sensitivity and 63.8 % specificity at a 3.15 cut-off (AUC:0.689; p:0.0001; CI:0.644-0.725). CONCLUSIONS: These results indicate that ALT and UA may be potentially important in determining the risk of macrosomia. The UA had a more potent marker for macrosomia than ALT. The occurrence of macrosomia might be more closely related to the mother's metabolic syndrome rather than NAFLD.

Prediction of birthweight and risk of macrosomia in pregnancies complicated by diabetes.

BACKGROUND: Antenatal detection of accelerated fetal growth and macrosomia in pregnancies complicated by diabetes mellitus is important for patient counseling and management. Sonographic fetal weight estimation is the most commonly used tool to predict birthweight and macrosomia. However, the predictive accuracy of sonographic fetal weight estimation for these outcomes is limited. In addition, an up-to-date sonographic fetal weight estimation is often unavailable before birth. This may result in a failure to identify macrosomia, especially in pregnancies complicated by diabetes mellitus where care providers might underestimate fetal growth rate. Therefore, there is a need for better tools to detect and alert care providers to the potential risk of accelerated fetal growth and macrosomia. OBJECTIVE: This study aimed to develop and validate prediction models for birthweight and macrosomia in pregnancies complicated by diabetes mellitus. STUDY DESIGN: This was a completed retrospective cohort study of all patients with a singleton live birth at ≥36 weeks of gestation complicated by preexisting or gestational diabetes mellitus observed at a single tertiary center between January 2011 and May 2022. Candidate predictors included maternal age, parity, type of diabetes mellitus, information from the most recent sonographic fetal weight estimation (including estimated fetal weight, abdominal circumference z score, head circumference-to-abdomen circumference z score ratio, and amniotic fluid), fetal sex, and the interval between ultrasound examination and birth. The study outcomes were macrosomia (defined as birthweights >4000 and >4500 g), large for gestational age (defined as a birthweight >90th percentile for gestational age), and birthweight (in grams). Multivariable logistic regression models were used to estimate the probability of dichotomous outcomes, and multivariable linear regression models were used to estimate birthweight. Model discrimination and predictive accuracy were calculated. Internal validation was performed using the bootstrap resampling technique. RESULTS: A total of 2465 patients met the study criteria. Most patients had gestational diabetes mellitus (90%), 6% of patients had type 2 diabetes mellitus, and 4% of patients had type 1 diabetes mellitus. The overall proportions of infants with birthweights >4000 g, >4500 g, and >90th percentile for gestational age were 8%, 1%, and 12%, respectively. The most contributory predictor variables were estimated fetal weight, abdominal circumference z score, ultrasound examination to birth interval, and type of diabetes mellitus. The models for the 3 dichotomous outcomes had high discriminative accuracy (area under the curve receiver operating characteristic curve, 0.929-0.979), which was higher than that achieved with estimated fetal weight alone (area under the curve receiver operating characteristic curve, 0.880-0.931). The predictive accuracy of the models had high sensitivity (87%-100%), specificity (84%-92%), and negative predictive values (84%-92%). The predictive accuracy of the model for birthweight had low systematic and random errors (0.6% and 7.5%, respectively), which were considerably smaller than the corresponding errors achieved with estimated fetal weight alone (-5.9% and 10.8%, respectively). The proportions of estimates within 5%, 10%, and 15% of the actual birthweight were high (52.3%, 82.9%, and 94.9%, respectively). CONCLUSION: The prediction models developed in the current study were associated with greater predictive accuracy for macrosomia, large for gestational age, and birthweight than the current standard of care that includes estimated fetal weight alone. These models may assist care providers in counseling patients regarding the optimal timing and mode of delivery.

Effects of different dietary patterns during pregnancy on birth outcomes and glucose parameters in women with gestational diabetes mellitus: A systematic review and meta-analysis.

PURPOSE: Dietary interventions are the cornerstone of gestational diabetes mellitus (GDM) treatment. This study aimed to evaluate the effects of dietary patterns during pregnancy on birth outcomes and glucose parameters in women with GDM. METHODS: PubMed, Embase, and The CoChrane Library were searched from the time of database creation to November 30, 2021, along with manual searches. Data analyses were performed using Stata 15.4 software. RESULTS: From 2461 studies, 27 RCTs involving 1923 women were eligible. The pooled results showed that dietary pattern interventions during pregnancy reduced birth weight (WMD: -0.14 kg; 95% CI: -0.24, -0.00), hemoglobin A1 C (HbA1 C) (WMD: -0.19, 95% CI: -0.34, -0.05), and macrosomia incidence (RR 0.65 [95% CI 0.48, 0.88]). Low glycemic index (GI) diet reduced macrosomia incidence (RR 0.31 [95% CI 0.11, 0.93]) and fasting plasma glucose (FPG) levels (WMD: -0.10 mmol/L; 95% CI: -0.14, -0.05); a low carbohydrate (CHO) diet reduced large for gestational age (LGA) incidence (RR 0.33 [95% CI 0.13, 0.82]) and HbA1 C (WMD: -0.32; 95% CI: -0.51, -0.14); dietary approaches to stop hypertension (DASH) diet reduced birth weight (WMD:-0.59 kg; 95% CI: -0.64, -0.55), insulin use (RR 0.31 [95% CI 0.18, 0.56), macrosomia incidence (RR 0.12 [95% CI 0.03, 0.50]), and cesarean sections incidence (RR 0.57 [95% CI 0.40, 0.82]). CONCLUSION: Dietary patterns during pregnancy can improve certain birth outcomes and glycemic parameters. Due to limitations in the quality and number of included studies, the above findings still need to be validated by further randomized controlled trials with high quality and large samples.

Evaluating the effect of gestational diabetes mellitus on macrosomia based on the characteristics of oral glucose tolerance test.

BACKGROUND: GDM is always treated as a homogenous disease ignoring the different metabolic characteristics in oral glucose tolerance test (OGTT). We assessed the effect of GDM on macrosomia based on the different characteristics of OGTT. METHODS: We retrospectively divided 998 GDM pregnant women into 7 groups, Group A1: abnormal OGTT0h; Group A2: abnormal OGTT1h; Group A3: abnormal OGTT2h; Group B1: abnormal OGTT0h+1h; Group B2: abnormal OGTT0h+2h; Group B3: abnormal OGTT1h+2h; Group C: abnormal OGTT0h+1h+2h. RESULTS: The incidence of macrosomia in group C (21.92%) was higher than other groups. The OR of OGTT0h+1h+2h was significant (OGTT1h: OR = 1.577, 95% CI: 0.791, 3.145; OGTT2h: OR = 1.151, 95% CI: 0.572, 2.313; OGTT0h+1h: OR = 1.346, 95% CI: 0.584, 3.101; OGTT0h+2h: OR = 1.327, 95% CI: 0.517, 3.409; OGTT1h+2h: OR = 0.771, 95% CI: 0.256, 2.322; OGTT0h+1h+2h: OR = 4.164, 95% CI: 2.095, 8.278) when comparing with OGTT0h. Subgroup analysis showed abnormal OGTT0h+1h+2h might contribute more to macrosomia in pre-pregnancy BMI ≥ 24 kg/m2 than those with BMI < 24 kg/m2. CONCLUSION: The effect of abnormal OGTT0h+1h+2h on macrosomia was significantly greater than other OGTT characteristics, especially for those with pre-pregnancy BMI ≥ 24 kg/m2. Individualized management of GDM based on OGTT characteristics and pre-pregnancy BMI might be needed.

Development and validation of a new antenatal warning score system (DRRiP) for neonatal complications of gestational diabetes.

OBJECTIVE: To evaluate the DRRiP (Diabetes Related Risk in Pregnancy) score warning system as a tool for predicting neonatal morbidity in gestational diabetes. METHODS: A retrospective observational cohort study. By applying nine parameters from an antenatal trichotomy of glycemic, ultrasound, and clinical characteristics, DRRiP scores were calculated and assigned to each patient using a checklist tool. Logistic regression models were used to evaluate the association between DRRiP score and adverse fetal outcomes, after adjusting for maternal age and body mass index (calculated as weight in kilograms divided by the square of height in meters). RESULTS: In all, 627 women were studied. DRRiP score was an excellent predictor of macrosomia and shoulder dystocia (both areas under the receiver operating characteristics curves [AUROC] = 0.86), and a modest predictor of preterm delivery, hyperbilirubinemia, neonatal intensive care unit admission and a composite of either of the studied events (AUROC range 0.63-0.69). For the composite outcome, the sensitivity of an amber trigger score of 1 was 68.7% (95% confidence interval [CI] 62.27%-74.63%) and specificity was 48.87% (95% CI 43.85%-53.9%). Specificity at a red trigger score of 3 (89.7%) and a graded increase in post-test probability (90.7% risk at a score of 5) were highly encouraging. CONCLUSION: DRRiP score offers reasonable discriminative performance that could be clinically useful for meaningful risk stratification when making delivery plans.

Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia.

INTRODUCTION: Gestational diabetes mellitus (GDM), a metabolism-related pregnancy complication, is significantly associated with an increased risk of macrosomia. We hypothesized that maternal circulating metabolic biomarkers differed between women with GDM and macrosomia (GDM-M) and women with GDM and normal neonatal weight (GDM-N), and had good prediction performance for GDM-M. METHODS: Plasma samples from 44 GDM-M and 44 GDM-N were analyzed using Olink Proseek multiplex metabolism assay targeting 92 biomarkers. Combined different clinical characteristics and Olink markers, LASSO regression was used to optimize variable selection, and Logistic regression was applied to build a predictive model. Nomogram was developed based on the selected variables visually. Receiver operating characteristic (ROC) curve, calibration plot, and clinical impact curve were used to validate the model. RESULTS: We found 4 metabolism-related biomarkers differing between groups [CLUL1 (Clusterin-like protein 1), VCAN (Versican core protein), FCRL1 (Fc receptor-like protein 1), RNASE3 (Eosinophil cationic protein), FDR <  0.05]. Based on the different clinical characteristics and Olink markers, a total of nine predictors, namely pre-pregnancy body mass index (BMI), weight gain at 24 gestational weeks (gw), parity, oral glucose tolerance test (OGTT) 2 h glucose at 24 gw, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw, were identified by LASSO regression. The model constructed using these 9 predictors displayed good prediction performance for GDM-M, with an area under the ROC of 0.970 (sensitivity = 0.955, specificity = 0.886), and was well calibrated (P Hosmer-Lemeshow test = 0.897). CONCLUSION: The Model included pre-pregnancy BMI, weight gain at 24 gw, parity, OGTT 2 h glucose at 24 gw, HDL and LDL at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw had good prediction performance for predicting macrosomia in women with GDM.

Maternal insulin-like growth factors and insulin-like growth factor-binding proteins for macrosomia prediction in diabetic and nondiabetic pregnancy: A prospective study.

OBJECTIVE: Attributable to the insulin-like growth factor (IGF) axis involvement in fetal growth regulation, possible contribution of the maternal IGF axis to antenatal fetal macrosomia diagnosis is a subject of particular interest in diabetic pregnancy. METHODS: A total of 130 women were prospectively enrolled in a longitudinal single-center cohort study. The four study groups were: type 1 diabetes (n = 40), type 2 diabetes (n = 35), gestational diabetes (n = 40), and control (n = 15). IGF-1 and IGF-2 and insulin-like growth factor-binding protein (IGFBP) 1, 3, 6, and 7 serum levels were analyzed in 11- to 14-week and 30- to 34-week samples with a specific immunoassay. RESULTS: In mothers of large-for-gestational-age neonates (90th percentile), higher (median test) first-trimester IGF-1 (P = 0.007) and lower IGFBP-1 (P = 0.035) were observed. The IGF-1/IGFBP-1 ratio was positively associated with neonatal weight (r = 0.434, P < 0.001). Receiver operating characteristic analysis revealed an association between large for gestational age and the first-trimester IGF-1 (area under the curve [AUC] = 0.747, P < 0.001), IGFBP-1 (AUC = 0.334, P = 0.011), and IGF-1/IGFBP-1 ratio (AUC = 0.750, P < 0.001). IGF-1/IGFBP-1 ratio had better performance for prediction of birth weight over 4000 g (AUC = 0.822, P < 0.001). CONCLUSION: The authors detected different first-trimester IGF-1 and IGF-1/IGFBP-1 thresholds applicable for either supposition or rejection of macrosomia diagnosis. Further investigation is needed to determine how the maternal IGF axis can contribute to fetal macrosomia prediction.

Various screening and diagnosis approaches for gestational diabetes mellitus and adverse pregnancy outcomes: a secondary analysis of a randomized non-inferiority field trial.

INTRODUCTION: We evaluate which screening and diagnostic approach resulted in the greatest reduction in adverse pregnancy outcomes due to increased treatment. RESEARCH DESIGN AND METHODS: This study presents a secondary analysis of a randomized community non-inferiority trial conducted among pregnant women participating in the GULF Study in Iran. A total of 35 430 pregnant women were randomly assigned to one of the five prespecified gestational diabetes mellitus (GDM) screening protocols. The screening methods included fasting plasma glucose (FPG) in the first trimester and either a one-step or a two-step screening method in the second trimester of pregnancy. According to the results, participants were classified into 6 groups (1) First-trimester FPG: 100-126 mg/dL, GDM diagnosed at first trimester; (2) First trimester FPG: 92-99.9 mg/dL, GDM diagnosed at first trimester; (3) First trimester FPG: 92-99.9 mg/dL, GDM diagnosed at second trimester; (4) First trimester FPG: 92-99.9 mg/dL, healthy at second trimester; (5) First trimester FPG<92 mg/dL, GDM diagnosed at second trimester; (6) First trimester FPG<92 mg/dL, healthy at second trimester. For our analysis, we initially used group 6, as the reference and repeated the analysis using group 2, as the reference group. The main outcome of the study was major adverse maternal and neonatal outcomes. RESULTS: Macrosomia and primary caesarean section occurred in 9.8% and 21.0% in group 1, 7.8% and 19.8% in group 2, 5.4% and 18.6% in group 3, 6.6% and 21.5% in group 4, 8.3% and 24.0% in group 5, and 5.4% and 20.0% in group 6, respectively. Compared with group 6 as the reference, there was a significant increase in the adjusted risk of neonatal intensive care unit (NICU) admission in groups 1, 3, and 5 and an increased risk of macrosomia in groups 1, 2, and 5. Compared with group 2 as the reference, there was a significant decrease in the adjusted risk of macrosomia in group 3, a decreased risk of NICU admission in group 6, and an increased risk of hyperglycemia in group 3. CONCLUSIONS: We conclude that screening approaches for GDM reduced the risk of adverse pregnancy outcomes to the same or near the same risk level of healthy pregnant women, except for the risk of NICU admission that increased significantly in groups diagnosed with GDM compared with healthy pregnant women. Individuals with slight increase in FPG (92-100 mg/dL) at first trimester, who were diagnosed as GDM, had an even increased risk of macrosomia in comparison to those group of women with FPG 92-100 mg/dL in the first trimester, who were not diagnosed with GDM, and developed GDM in second trimester TRIAL REGISTRATION: IRCT138707081281N1 (registered: February 15, 2017).

Umbilical Cord Biometry and Fetal Abdominal Skinfold Assessment as Potential Biomarkers for Fetal Macrosomia in a Gestational Diabetes Romanian Cohort.

Backgroundand Objectives: Gestational diabetes mellitus (GDM) is a pregnancy-associated pathology commonly resulting in macrosomic fetuses, a known culprit of obstetric complications. We aimed to evaluate the potential of umbilical cord biometry and fetal abdominal skinfold assessment as screening tools for fetal macrosomia in gestational diabetes mellitus pregnant women. Materials and methods: This was a prospective case−control study conducted on pregnant patients presenting at 24−28 weeks of gestation in a tertiary-level maternity hospital in Northern Romania. Fetal biometry, fetal weight estimation, umbilical cord area and circumference, areas of the umbilical vein and arteries, Wharton jelly (WJ) area and abdominal fold thickness measurements were performed. Results: A total of 51 patients were enrolled in the study, 26 patients in the GDM group and 25 patients in the non-GDM group. There was no evidence in favor of umbilical cord area and WJ amount assessments as predictors of fetal macrosomia (p > 0.05). However, there was a statistically significant difference in the abdominal skinfold measurement during the second trimester between macrosomic and normal-weight newborns in the GDM patient group (p = 0.016). The second-trimester abdominal circumference was statistically significantly correlated with fetal macrosomia at term in the GDM patient group with a p value of 0.003, as well as when considering the global prevalence of macrosomia in the studied populations, 0.001, when considering both populations. Conclusions: The measurements of cord and WJ could not be established as predictors of fetal macrosomia in our study populations, nor differentiate between pregnancies with and without GDM. Abdominal skinfold measurement and abdominal circumference measured during the second trimester may be important markers of fetal metabolic status in pregnancies complicated by GDM.

Performance of whole-genome promoter nucleosome profiling of maternal plasma cell-free DNA for prenatal noninvasive prediction of fetal macrosomia: a retrospective nested case-control study in mainland China.

BACKGROUND: Fetal macrosomia is common occurrence in pregnancy, which is associated with several adverse prognosis both of maternal and neonatal. While, the accuracy of prediction of fetal macrosomia is poor. The aim of this study was to develop a reliable noninvasive prediction classifier of fetal macrosomia. METHODS: A total of 3600 samples of routine noninvasive prenatal testing (NIPT) data at 12+ 0-27+ 6 weeks of gestation, which were subjected to low-coverage whole-genome sequencing of maternal plasma cell-free DNA (cfDNA), were collected from three independent hospitals. We identified set of genes with significant differential coverages by comparing the promoter profiling between macrosomia cases and controls. We selected genes to develop classifier for noninvasive predicting, by using support vector machine (SVM) and logistic regression models, respectively. The performance of each classifier was evaluated by area under the curve (AUC) analysis. RESULTS: According to the available follow-up results, 162 fetal macrosomia pregnancies and 648 matched controls were included. A total of 1086 genes with significantly differential promoter profiling were found between pregnancies with macrosomia and controls (p < 0.05). With the AUC as a reference，the classifier based on SVM (CMA-A2) had the best performance, with an AUC of 0.8256 (95% CI: 0.7927-0.8586). CONCLUSIONS: Our study provides that assessing the risk of fetal macrosomia by whole-genome promoter nucleosome profiling of maternal plasma cfDNA based on low-coverage next-generation sequencing is feasible.

Analytical Comparison of Risk Prediction Models for the Onset of Macrosomia Based on Three Statistical Methods.

Method: A retrospective selection of 93 women who were hospitalized in our hospital from March 2019 to May 2022 with a singleton pregnancy and delivered at term with macrosomia were the study group. And 356 women who delivered a normal size baby during the same period were the control group. The variables that were associated with the onset of macrosomia were screened from maternal medical records. Logistic regression models, random forest, and CART decision tree models were developed using the screened variables as input variables and whether they were macrosomia as outcome variables, respectively. The performance of the three models was evaluated by accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC). Result: The risk prediction models for the onset of macrosomia, logistic regression model, random forest model, and decision tree, were successfully developed, with accuracies of 0.904, 1.000, and 0.901 in the training set and 0.926, 0.582, and 0.852 in the validation set, respectively. The AUC in the training set were 0.898, 1.000, and 0.789, and in the validation set were 0.906, 0.913, and 0.731, respectively. In general, the logistic regression model has the highest diagnostic efficiency, followed by the random forest model. Conclusion: Logistic regression models have high application value in the assessment of predicting the risk of macrosomia, and it is suggested that the advantages of logistic regression models and random forest models should be combined in future studies and applications to make them work better in the prediction of the risk of macrosomia.

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

BACKGROUND: Fetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction. METHODS: In the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We split the original dataset into a training set (n = 9382) and a validation set (n = 4021) at a 7:3 ratio to generate and validate our model. The candidate variables, including maternal characteristics, laboratory tests, and sonographic parameters were compared between the two groups. A univariate and multivariate logistic regression was carried out to explore the independent risk factors for macrosomia in pregnant women. Thus, the regression model was adopted to establish a nomogram to predict the risk of macrosomia. Nomogram performance was determined by discrimination and calibration metrics. All the statistical analysis was analyzed using R software. RESULTS: We compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P < 0.05), including biparietal diameter (BPD), head circumference (HC), femur length (FL), amniotic fluid index (AFI) at the last prenatal examination, pre-pregnancy body mass index (BMI), and triglycerides (TG). Values for the areas under the curve (AUC) for the nomogram model were 0.917 (95% CI, 0.908-0.927) and 0.910 (95% CI, 0.894-0.927) in the training set and validation set, respectively. The internal and external validation of the nomogram demonstrated favorable calibration as well as discriminatory capability of the model. CONCLUSIONS: Our model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.

Is there a residual risk of large-for-gestational-age infant related to gestational diabetes mellitus when it is treated?

OBJECTIVE: The hyperglycaemia and adverse pregnancy outcomes (HAPO) study, where hyperglycaemia was untreated, showed a continuous association between large-for-gestational-age (LGA) infant and seven increasing categories of fasting plasma glucose (PG), 1-hour and 2-hour PG values after a 75 g oral glucose tolerance test at 24-32 gestational weeks. We evaluated whether the excess risk persisted in the 6th and 7th glucose categories - corresponding to women treated for gestational diabetes mellitus (GDM). PATIENTS AND METHODS: We included 7,190 women meeting the HAPO criteria, of whom 655 (9.2%) were treated for GDM (dietary education in all; insulin therapy in 150 (20.3%)). We evaluated the adjusted odds ratio (aOR) for each glucose category (reference 1st category) for LGA infant. RESULTS: The aOR for LGA linearly increased from the 1st to 5th categories of fasting, 1-hour and 2-hour PG. Specifically, the aORs for the 5th category were 2.20 (95% confidence interval 1.41-3.44), 2.25 (1.11-4.59), and 2.51 (1.63-3.85), respectively. The aORs for the 6th category were globally stable at 2.52 (1.46-4.36), 2.87 (1.48-5.54), and 2.47 (1.46-4.16), respectively. The same was true for the 7th category: 1.41 (0.56-3.55), 2.84 (1.03-7.86), and 3.53 (1.77-7.06), respectively. CONCLUSION: We confirmed the association between increasing PG category and LGA infant in women without GDM. We did not observe a residual risk of LGA infant in women treated for GDM in our hospital, irrespective of elevated fasting, 1-hour, or 2-hour PG diagnosis. The risk of LGA infant was globally similar to that in women with high normal glucose values.

Can alanine aminotransferase measured in early pregnancy predict macrosomia?

Non-alcoholic fatty liver disease (NAFLD) is a part of the metabolic syndrome and is associated with adverse pregnancy outcomes. The aim of this study was to determine whether unexplained elevated ALT in early pregnancy has any effect in the prediction of large for gestational age (LGA) infants. In this study, the relationship between birth weights of LGA babies and babies with normal weight for gestational age (AGA) and ALT values measured in early pregnancy was evaluated. While a positive, moderately strong, statistically significant correlation was found between infant birth weight and ALT levels in LGA babies this correlation was continued when GDM was not detected and ALT levels were below 36 U/L. Foetal macrosomia, which can develop in advanced gestational weeks, can be predicted with this cheap, easy and simple method that can be checked in the first trimester and pregnancy follow-up can be shaped accordingly.IMPACT STATEMENTWhat is already known on this subject? It is suggested that asymptomatic high ALT values measured in the first trimester can predict a macrosomic foetus.What do the results of this study add? Asymptomatic elevated ALT values measured in the first trimester can predict a macrosomic foetus.What are the implications of these findings for clinical practice and/or further research? Macrosomic foetus development can be predicted with abnormal results obtained with this simple, cheap and easy measurement method measured in the first trimester and pregnancy follow-up can be managed accordingly.

[Reducing rate of macrosomia in Iceland in relation to changes in the labor induction rate].

AIM: Diabetes and prolonged pregnancy are risk factors of macrosomia. The aim was to explore the relationship between the increased rate of labor induction and macrosomia in Iceland. Changes in the incidence proportion of macrosomia was estimated by gestational age. Further, the association between labor induction and macrosomia was estimated in reference to expectant management. MATERIAL AND METHODS: Data from the Iceland birth registry on 92,424 singleton births from 1997 to 2018 was used in this cohort study. Macrosomia was defined as birth weight more than 4.5 kg. The incidence proportion during three periods, 1997-2004, 2005-2011, 2012-2018, was calculated and stratified by gestational age. The relative risk reduction of macrosomia over time was calculated with log-binomial regression, using the first period as reference. The risk and relative risk of macrosomia compared with expectant management was estimated and adjusted for diabetes. RESULTS: The total number of macrosomic infants was 5110 and of those only 313 had a mother with diabetes. The incidence proportion of macrosomia was 6.5% during the period 1997-2004, but 4.6% during 2012-2018. A relative risk reduction of macrosomia over time was seen for deliveries after estimated due date. Labor induction decreased the risk of macrosomia, but the association persisted after adjustment for diabetes. CONCLUSION: The rate of macrosomia decreased in Iceland during the last two decades, but only a small proportion of macrosomic infants had a mother with diabetes. Labor induction decreased the risk of macrosomia, an association which seemed independent of diabetes.

A Cluster Randomized Noninferiority Field Trial of Gestational Diabetes Mellitus Screening.

CONTEXT: Although it is well-acknowledged that gestational diabetes mellitus (GDM) is associated with the increased risks of adverse pregnancy outcomes, the optimal strategy for screening and diagnosis of GDM is still a matter of debate. OBJECTIVE: This study was conducted to demonstrate the noninferiority of less strict GDM screening criteria compared with the strict International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria with respect to maternal and neonatal outcomes. METHODS: A cluster randomized noninferiority field trial was conducted on 35 528 pregnant women; they were scheduled to have 2 phases of GDM screening based on 5 different prespecified protocols including fasting plasma glucose in the first trimester with threshold of 5.1 mmol/L (92 mg/dL) (protocols A, D) or 5.6 mmol/L (100 mg/dL) (protocols B, C, E) and either a 1-step (GDM is defined if one of the plasma glucose values is exceeded [protocol A and C] or 2 or more exceeded values are needed [protocol B]) or 2-step approach (protocols D, E) in the second trimester. Guidelines for treatment of GDM were consistent with all protocols. Primary outcomes of the study were the prevalence of macrosomia and primary cesarean section (CS). The null hypothesis that less strict protocols are inferior to protocol A (IADPSG) was tested with a noninferiority margin effect (odds ratio) of 1.7. RESULTS: The percentages of pregnant women diagnosed with GDM and assigned to protocols A, B, C, D, and E were 21.9%, 10.5%, 12.1%, 19.4%, and 8.1%, respectively. Intention-to-treat analyses satisfying the noninferiority of the less strict protocols of B, C, D, and E compared with protocol A. However, noninferiority was not shown for primary CS comparing protocol E with A. The odds ratios (95% CI) for macrosomia and CS were: B (1.01 [0.95-1.08]; 0.85 [0.56-1.28], C (1.03 [0.73-1.47]; 1.16 [0.88-1.51]), D (0.89 [0.68-1.17]; 0.94 [0.61-1.44]), and E (1.05 [0.65-1.69]; 1.33 [0.82-2.00]) vs A. There were no statistically significant differences in the adjusted odds of adverse pregnancy outcomes in the 2-step compared with the 1-step screening approaches, considering multiplicity adjustment. CONCLUSIONS: The IADPSG GDM definition significantly increased the prevalence of GDM diagnosis. However, the less strict approaches were not inferior to other criteria in terms of adverse maternal and neonatal outcomes.

Does predelivery body mass index really matter in pregnancy?

OBJECTIVES: The aim of the study was to compare the perinatal outcome between the normal weight, overweight and obese pregnant women who delivered in the third-level center of reference. Moreover, the objective was to analyze the usefulness of predelivery body mass index (BMI) in prediction of preterm delivery, prolonged second stage of labor, instrumental vaginal delivery, cesarean section, fetal macrosomia, dystocia and newborn acidosis. MATERIAL AND METHODS: The retrospective study included 2104 patients, divided into three groups, with BMI between 18.5 and 24.9; 25.0 and 29.9; higher than or equal 30.0 kg/m2, respectively. The data were assessed from the medical history. RESULTS: The predelivery obesity increases the risk of cesarean section (aOR 1.63), macrosomia (aOR 8.89) and dystocia (aOR 3.40) in comparison to normal weight women. Moreover, the obese females had three times greater risk of having a macrosomic child (aOR 3.57) and 1.5 times greater risk of cesarean section (aOR 1.52) than overweight group. The role of predelivery BMI in the prediction of cesarean delivery (AUC 0.550; sensitivity 0.39; specificity 0.71, p < 0.001, cut-off value 28.7 kg/m2), macrosomia (AUC 0.714; sensitivity 0.66; specificity 0.70; p < 0.001, cut-off value 29.0 kg/m2) and dystocia (AUC 0.658; sensitivity 0.77; specificity 0.53, p < 0.001, cut-off value 27.0 kg/m2) was significant. CONCLUSIONS: The predelivery obesity increases the risk of cesarean section, macrosomia and shoulder dystocia and is a useful parameter in the prediction of perinatal outcomes. The establishing cut-off value for predelivery BMI was the lowest in prediction of shoulder dystocia.