Is the sFlt-1/PlGF ratio efficient in predicting adverse neonatal outcomes in small-for-gestational-age newborns? A prospective observational multicenter cohort study.

Introduction: Fetuses with growth abnormalities are at an increased risk of adverse neonatal outcomes. The aim of this study was to investigate if placental growth factor (PlGF), soluble fms-like tyrosine kinase-1 (sFlt-1), or the sFlt-1/PlGF ratio were efficient predictive factors of adverse neonatal outcomes in small-for-gestational-age (SGA) newborns. Methods: A prospective observational multicenter cohort study was performed between 2020 and 2023. At the time of the SGA fetus diagnosis, serum angiogenic biomarker measurements were performed. The primary outcome was an adverse neonatal outcome, diagnosed in the case of any of the following: <34 weeks of gestation: mechanical ventilation, sepsis, necrotizing enterocolitis, intraventricular hemorrhage grade III or IV, and neonatal death before discharge; ≥34 weeks of gestation: Neonatal Intensive Care Unit hospitalization, mechanical ventilation, continuous positive airway pressure, sepsis, necrotizing enterocolitis, intraventricular hemorrhage grade III or IV, and neonatal death before discharge. Results: In total, 192 women who delivered SGA newborns were included in the study. The serum concentrations of PlGF were lower, leading to a higher sFlt-1/PlGF ratio in the adverse outcome group. No significant differences in sFlt-1 levels were observed between the groups. Both PlGF and sFlt-1 had a moderate correlation with adverse neonatal outcomes (PlGF: R - 0.5, p < 0.001; sFlt-1: 0.5, p < 0.001). The sFlt-1/PlGF ratio showed a correlation of 0.6 (p < 0.001) with adverse outcomes. The uterine artery pulsatility index (PI) and the sFlt-1/PlGF ratio were identified as the only independent risk factors for adverse outcomes. An sFlt-1/PlGF ratio of 19.1 exhibited high sensitivity (85.1%) but low specificity (35.9%) in predicting adverse outcomes and had the strongest correlation with them. This ratio allowed the risk of adverse outcomes to be assessed as low with approximately 80% certainty. Discussion: The sFlt-1/PlGF ratio seems to be an efficient predictive tool in adverse outcome risk assessment. More studies on large cohorts of SGA-complicated pregnancies with and without preeclampsia are needed to develop an optimal and detailed formula for the risk assessment of adverse outcomes in SGA newborns.

Deep learning for estimation of fetal weight throughout the pregnancy from fetal abdominal ultrasound.

BACKGROUND: Fetal weight is currently estimated from fetal biometry parameters using heuristic mathematical formulas. Fetal biometry requires measurements of the fetal head, abdomen, and femur. However, this examination is prone to inter- and intraobserver variability because of factors, such as the experience of the operator, image quality, maternal characteristics, or fetal movements. Our study tested the hypothesis that a deep learning method can estimate fetal weight based on a video scan of the fetal abdomen and gestational age with similar performance to the full biometry-based estimations provided by clinical experts. OBJECTIVE: This study aimed to develop and test a deep learning method to automatically estimate fetal weight from fetal abdominal ultrasound video scans. STUDY DESIGN: A dataset of 900 routine fetal ultrasound examinations was used. Among those examinations, 800 retrospective ultrasound video scans of the fetal abdomen from 700 pregnant women between 15 6/7 and 41 0/7 weeks of gestation were used to train the deep learning model. After the training phase, the model was evaluated on an external prospectively acquired test set of 100 scans from 100 pregnant women between 16 2/7 and 38 0/7 weeks of gestation. The deep learning model was trained to directly estimate fetal weight from ultrasound video scans of the fetal abdomen. The deep learning estimations were compared with manual measurements on the test set made by 6 human readers with varying levels of expertise. Human readers used standard 3 measurements made on the standard planes of the head, abdomen, and femur and heuristic formula to estimate fetal weight. The Bland-Altman analysis, mean absolute percentage error, and intraclass correlation coefficient were used to evaluate the performance and robustness of the deep learning method and were compared with human readers. RESULTS: Bland-Altman analysis did not show systematic deviations between readers and deep learning. The mean and standard deviation of the mean absolute percentage error between 6 human readers and the deep learning approach was 3.75%±2.00%. Excluding junior readers (residents), the mean absolute percentage error between 4 experts and the deep learning approach was 2.59%±1.11%. The intraclass correlation coefficients reflected excellent reliability and varied between 0.9761 and 0.9865. CONCLUSION: This study reports the use of deep learning to estimate fetal weight using only ultrasound video of the fetal abdomen from fetal biometry scans. Our experiments demonstrated similar performance of human measurements and deep learning on prospectively acquired test data. Deep learning is a promising approach to directly estimate fetal weight using ultrasound video scans of the fetal abdomen.

Polish Female Cancer Survivors' Experiences Related to Fertility Preservation Procedures.

Purpose: The aim of the study was to assess the experience of Polish female cancer patients related to fertility preservation (FP) after gonadotoxic treatment. Methods: A cross-sectional study was performed among young women, who were diagnosed with a neoplasm. The questionnaire was distributed via the Internet. Results: The study group consisted of 299 women. Most of them had breast cancer (34%) or Hodgkin lymphoma (20%). The most popular treatment regimen was a combination of surgery, chemotherapy, and radiation (30%). Almost 78% of study participants underwent potentially gonadotoxic treatment. Fifty-seven percent of study participants admitted that oncologist had not informed them about FP methods, while only 19% were encouraged to visit fertility specialists. Seventeen percent of women decided to preserve fertility before malignancy treatment, 11.8% of women cryopreserved oocytes, 16% cryopreserved embryos, 16% cryopreserved ovarian tissue and 55% had ovarian suppression. Conclusion: The access to fertility counseling in Poland is limited. Emphasis should be placed on the coordination between cancer treatment centers and reproductive specialists.

sFlt-1/PlGF Ratio in Prediction of Short-Term Neonatal Outcome of Small for Gestational Age Neonates.

BACKGROUND: Small for gestational age is a pregnancy complication associated with a variety of adverse perinatal outcomes. The aim of the study was to investigate if sFlt-1/PlGF ratio is related to adverse short-term neonatal outcome in neonates small for gestational age in normotensive pregnancy. METHODS: A prospective observational study was conducted. Serum sFlt-1/PlGF ratio was measured in women in singleton gestation diagnosed with fetus small for gestational age. Short-term neonatal outcome analyzed in the period between birth and discharge home. RESULTS: Eighty-two women were included. Women with sFlt-1/PlGF ratio ≥33 gave birth to neonates with lower birthweight at lower gestational age. Neonates from high ratio group suffered from respiratory disorders and NEC significantly more often. They were hospitalized at NICU more often and were discharged home significantly later. sFlt-1/PlGF ratio predicted combined neonatal outcome with sensitivity of 73% and specificity of 82.2%. CONCLUSIONS: sFlt-1/PlGF ratio is a useful toll in prediction of short-term adverse neonatal outcome in SGA pregnancies.