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Background/Objectives : Prolonged prelabour rupture of membranes (PROMs), and the resulting inflammatory response, can contribute to the occurrence of adverse neonatal outcomes, especially for early-preterm neonates. This prospective study aimed to measure neonates' inflammatory markers in the first 72 h of life based on ROM duration. The second aim was to examine the relationship between PROMs, serum inflammatory markers, and composite adverse neonatal outcomes after controlling for gestational age (GA). Methods : Data from 1026 patients were analyzed considering the following groups: group 1 (ROM < 18 h, n = 447 patients) and group 2 (ROM > 18 h, n = 579 patients). These groups were further segregated depending on the GA at the moment of membranes' rupture into subgroup 1 (<33 weeks of gestation and 6 days, n = 168 patients) and subgroup 2 (at least 34 completed weeks of gestation, n = 858 patients). Multiple logistic regressions and interaction analyses adjusted for GA considering five composite adverse neonatal outcomes and predictors were employed. Results : PROMs and high c-reactive protein (CRP) values significantly increased the risk of composite outcome 1 occurrence by 14% (95%CI: 1.03-1.57, p < 0.001). PROMs and high CRP values increased the risk of composite outcome 5 by 14% (95%CI: 1.07-1.78, p < 0.001), PROM and leukocytosis by 11% (95%CI: 1.02-1.59, p = 0.001), and PROMs and high PCT values by 21% (95%CI: 1.04-2.10, p < 0.001). Conclusions : The combination of PROMs and high CRP values significantly increased the risk of all evaluated adverse composite outcomes in early-preterm neonates and should point to careful monitoring of these patients.

Background/Objectives : There is a constant need to improve the prediction of adverse neurodevelopmental outcomes in growth-restricted neonates who were born prematurely. The aim of this retrospective study was to evaluate the predictive performance of a three-layered neural network for the prediction of adverse neurodevelopmental outcomes determined at two years of age by the Bayley Scales of Infant and Toddler Development, 3rd edition (Bayley-III) scale in prematurely born infants by affected by intrauterine growth restriction (IUGR). Methods : This observational retrospective study included premature newborns with or without IUGR admitted to a tertiary neonatal intensive care unit from Romania, between January 2018 and December 2022. The patients underwent assessment with the Amiel-Tison scale at discharge, and with the Bailey-3 scale at 3, 6, 12, 18, and 24 months of corrected age. Clinical and paraclinical data were used to construct a three-layered artificial neural network, and its predictive performance was assessed. Results : Our results indicated that this type of neural network exhibited moderate predictive performance in predicting mild forms of cognitive, motor, and language delays. However, the accuracy of predicting moderate and severe neurodevelopmental outcomes varied between moderate and low. Conclusions : Artificial neural networks can be useful tools for the prediction of several neurodevelopmental outcomes, and their predictive performance can be improved by including a large number of clinical and paraclinical parameters.

Background and Objectives : Polycystic ovary syndrome (PCOS) is a complex disorder that can negatively impact the obstetrical outcomes. The aim of this study was to determine the predictive performance of four machine learning (ML)-based algorithms for the prediction of adverse pregnancy outcomes in pregnant patients diagnosed with PCOS. Materials and Methods : A total of 174 patients equally divided into 2 groups depending on the PCOS diagnosis were included in this prospective study. We used the Mantel-Haenszel test to evaluate the risk of adverse pregnancy outcomes for the PCOS patients and reported the results as a crude and adjusted odds ratio (OR) with a 95% confidence interval (CI). A generalized linear model was used to identify the predictors of adverse pregnancy outcomes in PCOS patients, quantifying their impact as risk ratios (RR) with 95% CIs. Significant predictors were included in four machine learning-based algorithms and a sensitivity analysis was employed to quantify their performance. Results : Our crude estimates suggested that PCOS patients had a higher risk of developing gestational diabetes and had a higher chance of giving birth prematurely or through cesarean section in comparison to patients without PCOS. When adjusting for confounders, only the odds of delivery via cesarean section remained significantly higher for PCOS patients. Obesity was outlined as a significant predictor for gestational diabetes and fetal macrosomia, while a personal history of diabetes demonstrated a significant impact on the occurrence of all evaluated outcomes. Random forest (RF) performed the best when used to predict the occurrence of gestational diabetes (area under the curve, AUC value: 0.782), fetal macrosomia (AUC value: 0.897), and preterm birth (AUC value: 0.901) in PCOS patients. Conclusions : Complex ML algorithms could be used to predict adverse obstetrical outcomes in PCOS patients, but larger datasets should be analyzed for their validation.

(1) Background: Trial of labor after cesarean (TOLAC) can be associated with significant maternal and neonatal complications, and the aim of this retrospective study was to calculate the risks and probabilities of these complications in two tertiary maternity centers in Romania. (2) Methods: A total of 216 patients who attempted TOLAC were included in the study and were segregated into two groups, depending on TOLAC success. Medical records were assessed, and clinical data were used to determine the maternal and neonatal risks and complications, using multinomial logistic regression and postestimation predictions. (3) Results: Our data indicated that patients who had a failed TOLAC had significantly higher risks and probabilities of uterine rupture, either complete or incomplete; intensive care unit (ICU) admission; and emergency hysterectomy. The newborns of these mothers had significantly higher risks and probabilities of low Apgar score at birth, neonatal intensive care unit (NICU) admission, and invasive ventilation. (4) Conclusions: Failed TOLAC could lead to significant maternal and neonatal complications, and women who attempt this procedure should be monitored in a tertiary center where a multidisciplinary team and an emergency operating room are available.