There is still no curative treatment for the spontaneous preterm prelabor rupture of membranes (sPPROM), the main cause of premature birth. Here, we summarize the most recent methods and materials used for sealing membranes after sPPROM. A literature search was conducted between 2013 and 2023 on reported newborns after membranes were sealed or on animal or tissue culture models. Fourteen studies describing the outcomes after using an amniopatch, an immunologic sealant, or a mechanical cervical adapter were included. According to these studies, an increase in the volume of amniotic fluid and the lack of chorioamnionitis demonstrate a favorable neonatal outcome, with a lower incidence of respiratory distress syndrome and early neonatal sepsis, even if sealing is not complete and stable. In vivo and in vitro models demonstrated that amniotic stem cells, in combination with amniocytes, can spontaneously repair small defects; because of the heterogenicity of the data, it is too early to draw a thoughtful conclusion. Future therapies should focus on materials and methods for sealing fetal membranes that are biocompatible, absorbable, available, easy to apply, and easily adherent to the fetal membrane.

Background/Objectives: Fetal growth restriction (FGR) is a leading cause of perinatal morbidity and mortality. Accurate surveillance and timely delivery are critical to improving outcomes. This narrative review examines the role of computerized cardiotocography (cCTG) and short-term variation (STV) interpretation in the monitoring of FGR and its integration with Doppler velocimetry and the biophysical profile (BPP). Methods: A comprehensive literature search of PubMed, Scopus, and Web of Science was performed for studies published up to 2021 using combinations of terms related to FGR, CTG, STV, and Doppler surveillance. Eligible sources included original studies, systematic reviews, and international guidelines. Case reports, intrapartum-only monitoring, and studies involving major anomalies were excluded. Results: Reduced STV consistently correlates with fetal compromise, abnormal Doppler findings, and adverse perinatal outcomes. In early-onset FGR (<32 weeks), ductus venosus abnormalities often coincide with or precede STV reduction; combined use supports optimal timing of delivery. In late-onset FGR (≥32 weeks), STV changes are less pronounced and require integration with cerebroplacental ratio, variability indices, and trend-based interpretation. Longitudinal evaluation offers greater prognostic value than isolated measurements. However, heterogeneity in thresholds, fragmented outcome data, and system-specific definitions limit standardization and comparability across studies. Conclusions: cCTG provides an objective and adjunct to Doppler and BPP in the surveillance of FGR, a tool for obstetrician needs. Its greatest utility lies in serial, integrated assessment, supported by gestational age-specific reference ranges. Future advances should include standardized STV thresholds, large outcome-linked databases, and artificial intelligence-driven tools to refine decision-making and optimize delivery timing.

Background/Objectives: Antepartum computerized cardiotocography (cCTG) represents an essential tool for assessing fetal well-being. This study aimed to comparatively evaluate antepartum cCTG-derived indices across high-risk pregnancies to identify distinctive fetal autonomic and reactivity profiles. Methods: A comparative analysis of antepartum cCTG parameters was conducted. The cohort included pregnancies beyond 28 weeks of pregnancy, 169 cases of hypertensive disorders of pregnancy (HDP), 146 of gestational diabetes mellitus (GDM), 86 of intrahepatic cholestasis (ICP), and 87 low-risk pregnancies as controls. Results: Baseline FHR remained within the physiological range across all groups (110–160 bpm; p > 0.05). Dynamic cCTG parameters exhibited clear pathology-dependent alterations. Short-term variability (STV) showed a stepwise decline from controls to ICP and GDM, reaching its lowest values in HDP (mean 1.08 bpm; p < 0.00001), accompanied by an increased proportion of epochs with STV < 1 bpm. Long-term variability suppression (LTV < 5 bpm) was significantly higher in GDM and HDP (p = 0.0077). Acceleration frequency decreased across all pathological groups, with the most pronounced reduction observed in HDP, whereas fetal movements were paradoxically elevated in both GDM and HDP. Total decelerations were more frequent in ICP and HDP; however, repetitive, late, prolonged, and >5 min decelerations remained rare and did not differ significantly between groups. Conclusions: HDP showed the most unfavorable cCTG profiles, consistent with impaired fetal autonomic regulation and chronic subclinical hypoxemia. GDM and ICP had moderate changes, suggesting milder adaptive responses. These findings emphasize the value of quantitative cCTG in differentiating fetal autonomic patterns in high-risk pregnancies and the importance of tailored surveillance strategies.

Background/Objectives: The management of high-risk pregnancies remains a major clinical challenge, particularly regarding the optimal timing of delivery, which has significant implications for both perinatal outcomes and healthcare costs. In this context, computerized cardiotocography (cCTG) offers an objective, non-invasive and cost-effective method for fetal surveillance, providing quantitative measures of heart rate dynamics that reflect autonomic regulation and oxygenation status. This study aimed to develop and validate regression-based machine learning models capable of predicting the duration of neonatal hospitalization—an objective and quantifiable indicator of neonatal well-being—using cCTG parameters obtained outside of labor, binary clinical variables describing the presence or absence of pregnancy pathologies, and gestational age at monitoring and at delivery. Methods: A total of 694 singleton high-risk pregnancies complicated by gestational diabetes, preexisting diabetes, intrahepatic cholestasis of pregnancy, pregnancy-induced or preexisting hypertension, or fetal growth restriction were enrolled. Twenty clinically relevant features derived from cCTG recordings and perinatal data were used to train and evaluate four regression algorithms: Random Forest, CatBoost, XGBoost, and LightGBM against a linear regression model with Ridge regularization serving as a benchmark. Results: Random Forest achieved the highest generalization performance (test R² = 0.8226; RMSE = 3.41 days; MAE = 2.02 days), outperforming CatBoost (R² = 0.7059), XGBoost (R² = 0.6911), LightGBM (R² = 0.6851) and the linear regression benchmark with Ridge regularization (R² = 0.5699) while showing a consistent train–validation–test profile (0.9428 → 0.8042 → 0.8226). The error magnitude (≈2 days on average) is clinically interpretable for neonatal resource planning, supporting the model’s practical utility. These findings justify selecting Random Forest as the final predictor and its integration into a clinician-facing application for real-time length-of-stay estimation. Conclusions: Machine learning models integrating cCTG features with maternal clinical factors can accurately predict neonatal hospitalization duration in pregnancies complicated by maternal or fetal disease. This approach provides a clinically interpretable and non-invasive decision support tool that may enhance delivery planning, optimize neonatal resource allocation, and improve perinatal care outcomes.