Oxytocin augmentation and neurotransmitters in prolonged delivery: An experimental appraisal

Malvasi, Antonio; Ballini, Andrea; Tinelli, Andrea; Fioretti, Bernard; Vimercati, Antonella; Gliozheni, Elko; Baldini, Giorgio Maria; Cascardi, Eliano; Dellino, Miriam; Bonetti, Monica; Cicinelli, Ettore; Vitagliano, Amerigo; Damiani, Gianluca Raffaello

doi:10.1016/j.eurox.2023.100273

Cited by 3 publications

(1 citation statement)

References 90 publications

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“…The sympathetic pathway is the visceral–sensory pathway and is responsible for the motility of the uterine body. The parasympathetic pathway is the visceral–motor pathway responsible for the closure of the internal uterine orifice [ 9 ]. The cervix is more innervated than the uterine body, and its innervation remains constant during pregnancy [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Localization of Catecholaminergic Neurofibers in Pregnant Cervix as a Possible Myometrial Pacemaker

Malvasi,

Baldini,

Cicinelli

et al. 2024

IJMS

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In eutocic labor, the autonomic nervous system is dominated by the parasympathetic system, which ensures optimal blood flow to the uterus and placenta. This study is focused on the detection of the quantitative presence of catecholamine (C) neurofibers in the internal uterine orifice (IUO) and in the lower uterine segment (LUS) of the pregnant uterus, which could play a role in labor and delivery. A total of 102 women were enrolled before their submission to a scheduled cesarean section (CS); patients showed a singleton fetus in a cephalic presentation outside labor. During CS, surgeons sampled two serial consecutive full-thickness sections 5 mm in depth (including the myometrial layer) on the LUS and two randomly selected samples of 5 mm depth from the IUO of the cervix. All histological samples were studied to quantify the distribution of A nerve fibers. The authors demonstrated a significant and notably higher concentration of A fibers in the IUO (46 ± 4.8) than in the LUS (21 ± 2.6), showing that the pregnant cervix has a greater concentration of A neurofibers than the at-term LUS. Pregnant women’s mechanosensitive pacemakers can operate normally when the body is in a physiological state, which permits normal uterine contractions and eutocic delivery. The increased frequency of C neurofibers in the cervix may influence the smooth muscle cell bundles’ activation, which could cause an aberrant mechano-sensitive pacemaker activation–deactivation cycle. Stressful circumstances (anxiety, tension, fetal head position) cause the sympathetic nervous system to become more active, working through these nerve fibers in the gravid cervix. They might interfere with the mechano-sensitive pacemakers, slowing down the uterine contractions and cervix ripening, which could result in dystocic labor.

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Section: Introductionmentioning

confidence: 99%

Localization of Catecholaminergic Neurofibers in Pregnant Cervix as a Possible Myometrial Pacemaker

Malvasi,

Baldini,

Cicinelli

et al. 2024

IJMS

Self Cite

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Dystocia, Delivery, and Artificial Intelligence in Labor Management: Perspectives and Future Directions

Malvasi,

Malgieri,

Stark

et al. 2024

JCM

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Labor management remains a critical issue in obstetrics, with dystocic labor presenting significant challenges in both management and outcomes. Recent advancements in intrapartum ultrasound have facilitated substantial progress in monitoring labor progression. This paper explores the integration of artificial intelligence (AI) into obstetric care, focusing on the Artificial Intelligence Dystocia Algorithm (AIDA) for assessing spatial dystocia during labor. The AIDA utilizes intrapartum ultrasonography to measure four geometric parameters: the angle of progression, the degree of asynclitism, the head–symphysis distance, and the midline angle. These measurements are analyzed using machine learning techniques to predict delivery outcomes and stratify risk. The AIDA classification system categorizes labor events into five classes, providing a nuanced assessment of labor progression. This approach offers several potential advantages, including objective assessment of fetal position, earlier detection of malpositions, and improved risk stratification, placing labor events within a broader context of labor dystocia and obstetric care and discussing their potential impact on clinical practice. This paper serves as a more comprehensive overview and discussion of the AIDA approach, its implications, perspectives, and future directions. However, challenges such as the technological requirements, training needs, and integration with clinical workflows are also addressed. This study emphasizes the necessity for additional validation across diverse populations and careful consideration of its ethical implications. The AIDA represents a significant advancement in applying AI to intrapartum care, potentially enhancing clinical decision-making and improving outcomes in cases of suspected dystocia. This paper explicates the key methodological approaches underpinning the AIDA, illustrating the integration of artificial intelligence and clinical expertise. The innovative framework presented offers a paradigm for similar endeavors in other medical specialties, potentially catalyzing advancements in AI-assisted healthcare beyond obstetrics.

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AIDA (Artificial Intelligence Dystocia Algorithm) in Prolonged Dystocic Labor: Focus on Asynclitism Degree

Malvasi,

Malgieri,

Cicinelli

et al. 2024

J. Imaging

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Asynclitism, a misalignment of the fetal head with respect to the plane of passage through the birth canal, represents a significant obstetric challenge. High degrees of asynclitism are associated with labor dystocia, difficult operative delivery, and cesarean delivery. Despite its clinical relevance, the diagnosis of asynclitism and its influence on the outcome of labor remain matters of debate. This study analyzes the role of the degree of asynclitism (AD) in assessing labor progress and predicting labor outcome, focusing on its ability to predict intrapartum cesarean delivery (ICD) versus non-cesarean delivery. The study also aims to assess the performance of the AIDA (Artificial Intelligence Dystocia Algorithm) algorithm in integrating AD with other ultrasound parameters for predicting labor outcome. This retrospective study involved 135 full-term nulliparous patients with singleton fetuses in cephalic presentation undergoing neuraxial analgesia. Data were collected at three Italian hospitals between January 2014 and December 2020. In addition to routine digital vaginal examination, all patients underwent intrapartum ultrasound (IU) during protracted second stage of labor (greater than three hours). Four geometric parameters were measured using standard 3.5 MHz transabdominal ultrasound probes: head-to-symphysis distance (HSD), degree of asynclitism (AD), angle of progression (AoP), and midline angle (MLA). The AIDA algorithm, a machine learning-based decision support system, was used to classify patients into five classes (from 0 to 4) based on the values of the four geometric parameters and to predict labor outcome (ICD or non-ICD). Six machine learning algorithms were used: MLP (multi-layer perceptron), RF (random forest), SVM (support vector machine), XGBoost, LR (logistic regression), and DT (decision tree). Pearson’s correlation was used to investigate the relationship between AD and the other parameters. A degree of asynclitism greater than 70 mm was found to be significantly associated with an increased rate of cesarean deliveries. Pearson’s correlation analysis showed a weak to very weak correlation between AD and AoP (PC = 0.36, p < 0.001), AD and HSD (PC = 0.18, p < 0.05), and AD and MLA (PC = 0.14). The AIDA algorithm demonstrated high accuracy in predicting labor outcome, particularly for AIDA classes 0 and 4, with 100% agreement with physician-practiced labor outcome in two cases (RF and SVM algorithms) and slightly lower agreement with MLP. For AIDA class 3, the RF algorithm performed best, with an accuracy of 92%. AD, in combination with HSD, MLA, and AoP, plays a significant role in predicting labor dystocia and labor outcome. The AIDA algorithm, based on these four geometric parameters, has proven to be a promising decision support tool for predicting labor outcome and may help reduce the need for unnecessary cesarean deliveries, while improving maternal-fetal outcomes. Future studies with larger cohorts are needed to further validate these findings and refine the cut-off thresholds for AD and other parameters in the AIDA algorithm.

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Oxytocin augmentation and neurotransmitters in prolonged delivery: An experimental appraisal

Cited by 3 publications

References 90 publications

Localization of Catecholaminergic Neurofibers in Pregnant Cervix as a Possible Myometrial Pacemaker

Localization of Catecholaminergic Neurofibers in Pregnant Cervix as a Possible Myometrial Pacemaker

Dystocia, Delivery, and Artificial Intelligence in Labor Management: Perspectives and Future Directions

AIDA (Artificial Intelligence Dystocia Algorithm) in Prolonged Dystocic Labor: Focus on Asynclitism Degree

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