The birth weight of macrosomia influence the accuracy of ultrasound estimation of fetal weight at term

Evaluation of sonographic and clinical measures in early versus late third trimester for birth weight prediction

OBJECTIVE

To evaluate the optimal timing for fetal weight estimation during the third trimester.

METHODS

This retrospective cohort study involved fetal weight estimations from both early (28+0-36+6 weeks) and late (37+0 weeks and beyond) third trimester. These estimations were converted to predicted birth weights using the gestation-adjusted projection formula. Birth weight predictions were compared with actual birth weights, to identify the most effective timing for weight prediction.

RESULTS

The study included 3549 cases, revealing mean percentage errors (MPE) of -3.69% for early sonographic assessments, -2.5% for late sonographic assessments, and -1.9% for late clinical assessments. A significant difference was found between early and late sonographic estimations (P < 0.001), whereas late sonographic and clinical assessments did not differ significantly (P = 0.771). Weight predictions for fetuses below the 10th and above the 90th centiles were less accurate than for those within the 10th-90th centiles (P < 0.001). In women with obesity, late clinical estimations were less precise (MPE of -5.85) compared with non-obese women (MPE of -1.66, P < 0.001). For women with diabetes, early sonographic estimations were more accurate (MPE of -1.31) compared with non-diabetic patients (MPE of -3.94, P < 0.001) though this difference did not persist later in pregnancy.

CONCLUSION

Sonographic and clinical weight predictions in the late third trimester were more accurate than earlier third-trimester sonographic assessments, hence continuous follow up and assessments closer to term are important. In women with diabetes, no adjustments in weight prediction methods are necessary. Accurately predicting birth weights for abnormally small or large fetuses remains challenging, indicating the need for improved screening and diagnostic strategies.

Risk of adverse pregnancy outcomes in pregnant women with gestational diabetes mellitus by age: a multicentric cohort study in Hebei, China

Gestational diabetes mellitus (GDM) is an unique metabolic disorder that occurs during pregnancy. Both GDM and advanced age increase the risk of adverse pregnancy outcomes. This study used a GDM cohort study to investigate the role of age in the adverse pregnancy outcomes for pregnant women with GDM. From 2015 to 2021, 308,175 pregnant women were selected, and the data received from 22 hospitals by the Hebei Province Maternal Near Miss Surveillance System. There were 24,551 pregnant women with GDM that were divided into five groups by age (20-24, 25-29, 30-34, 35-39, 40-44 years old). Because the prevalence of adverse pregnancy outcomes was lower in pregnant women with GDM aged 25-29, they were used as a reference group (P < 0.05). Compared with GDM women aged 25-29 years, GDM women aged 35-44 years had a significant higher risk of cesarean delivery (aOR: 2.86, 95% CI 2.52-3.25) (P < 0.001), abnormal fetal position (aOR: 1.78, 95% CI 1.31-2.37) (P < 0.001), pre-eclampsia (aOR: 1.28, 95% CI 1.01-1.61) (P < 0.05), macrosomia (aOR: 1.25, 95% CI 1.08-1.45) (P < 0.05), and large for gestational age (LGA) (aOR: 1.16, 95% CI 1.02-1.31) (P < 0.05), GDM women aged 40-44 years had a higher risk of placenta previa (aOR: 2.53, 95% CI 1.01-6.35) (P < 0.05), anemia (aOR: 3.45, 95% CI 1.23-9.68) (P < 0.05) and small for gestational age (aOR: 1.32, 95% CI 1.01-1.60) (P < 0.05). Advanced maternal age was an independent risk factor for abnormal fetal position, pre-eclampsia, anemia, macrosomia, and LGA in pregnant women with GDM.

Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.

The Effect of Timing and Methods for the Diagnosis of Gestational Diabetes Mellitus on Obstetric Complications

Aim. To compare the impact of the time and method of diagnosis on gestational diabetes mellitus (GDM) in women who gave birth at the Hospital of the Lithuanian University of Health Sciences (LUHS) Kauno klinikos. Methods. A retrospective study was performed using data from the Department of Obstetrics and Gynecology of the LUHS Birth Registry to analyze the data of women who gave birth and had GDM in 2020-2021. The subjects were divided based on the type of diagnosis: GDM was diagnosed either at the first antenatal visit when fasting plasma glycemia (FPG) was ≥5.1 mmol/L (early diagnosis group) or after OGTT at 24 + 0 - 28 + 6 weeks of gestation when at least one pathological glycemic index was observed: fasting glycemia 5.1-6.9 mmol/L or 1-h glycemia ≥10.0 mmol/L or 2 h glycemia 8.5-11.0 mmol/L (late diagnosis group). The results were processed using IBM SPSS. Results. The early diagnosis group had 1254 (65.7%) women, the late diagnosis group had 654 (34.3%). More primigravida women were in the late diagnosis group (p = 0.017) while more multigravida were in the early diagnosis group (p = 0.033). The early diagnosis group had more obese women (p = 0.001), including those with a BMI > 40 (p = 0.001). In the early diagnosis group, GDM was more frequently diagnosed in women who gained <11 kg (p = 0.005), while in the late diagnosis group->16 kg (p = 0.001). FPG was higher in the early diagnosis group (p = 0.001). Glycemia was more commonly corrected with lifestyle changes in the late diagnosis group (p = 0.001), and with additional insulin therapy in the early diagnosis group (p = 0.001). Polyhydramnios and preeclampsia were more common in the late diagnosis group (p = 0.027 and p = 0.009). There were more large-for-gestational-age neonates in the late diagnosis group (p = 0.005). Macrosomia was more common in the late diagnosis group (p = 0.008). Conclusions. GDM is more commonly diagnosed with OGTT in primigravida women. Higher pregestational weight and BMI has an impact on the early diagnosis of GDM and need for insulin therapy with lifestyle changes. Late diagnosis of GDM is connected with obstetric complications.