Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries.

BACKGROUND: The process of childbirth is one of the most crucial events in the future health and development of the offspring. The vulnerability of parturients and fetuses during the delivery process led to the development of intrapartum monitoring methods and to the emergence of alternative methods of delivery. However, current monitoring methods fail to accurately discriminate between cases in which intervention is unnecessary, partly contributing to the high rates of cesarean deliveries worldwide. Machine learning methods are applied in various medical fields to create personalized prediction models. These methods are used to analyze abundant, complex data with intricate associations to aid in decision making. Initial attempts to predict vaginal delivery vs cesarean deliveries using machine learning tools did not utilize the vast amount of data recorded during labor. The data recorded during labor represent the dynamic process of labor and therefore may be invaluable for dynamic prediction of vaginal delivery. OBJECTIVE: We aimed to create a personalized machine learning-based prediction model to predict successful vaginal deliveries using real-time data acquired during the first stage of labor. STUDY DESIGN: Electronic medical records of labor occurring during a 12-year period in a tertiary referral center were explored and labeled. Four different models were created using input from multiple maternal and fetal parameters. Initial risk assessments for vaginal delivery were calculated using data available at the time of admission to the delivery unit, followed by models incorporating cervical examination data and fetal heart rate data, and finally, a model that integrates additional data available during the first stage of labor was created. RESULTS: A total of 94,480 cases in which a trial of labor was attempted were identified. Based on approximately 180 million data points from the first stage of labor, machine learning models were developed to predict successful vaginal deliveries. A model using data available at the time of admission to the delivery unit yielded an area under the curve of 0.817 (95% confidence interval, 0.811-0.823). Models that used real-time data increased prediction accuracy. A model that includes real-time cervical examination data had an initial area under the curve of 0.819 (95% confidence interval, 0.813-0.825) at first examination, which increased to an area under the curve of 0.917 (95% confidence interval, 0.913-0.921) by the end of the first stage. Adding the real-time fetal heart monitor data provided an area under the curve of 0.824 (95% confidence interval, 0.818-0.830) at first examination, which increased to an area under the curve of 0.928 (95% confidence interval, 0.924-0.932) by the end of the first stage. Finally, adding additional real-time data increased the area under the curve initially to 0.833 (95% confidence interval, 0.827-0.838) at the first cervical examination and up to 0.932 (95% confidence interval, 0.928-0.935) by the end of the first stage. CONCLUSION: Real-time data acquired throughout the process of labor significantly increased the prediction accuracy for vaginal delivery using machine learning models. These models enable translation and quantification of the data gathered in the delivery unit into a clinical tool that yields a reliable personalized risk score and helps avoid unnecessary interventions.

Prediction of vaginal birth after cesarean deliveries using machine learning.

BACKGROUND: Efforts to reduce cesarean delivery rates to 12-15% have been undertaken worldwide. Special focus has been directed towards parturients who undergo a trial of labor after cesarean delivery to reduce the burden of repeated cesarean deliveries. Complication rates are lowest when a vaginal birth is achieved and highest when an unplanned cesarean delivery is performed, which emphasizes the need to assess, in advance, the likelihood of a successful vaginal birth after cesarean delivery. Vaginal birth after cesarean delivery calculators have been developed in different populations; however, some limitations to their implementation into clinical practice have been described. Machine-learning methods enable investigation of large-scale datasets with input combinations that traditional statistical analysis tools have difficulty processing. OBJECTIVE: The aim of this study was to evaluate the feasibility of using machine-learning methods to predict a successful vaginal birth after cesarean delivery. STUDY DESIGN: The electronic medical records of singleton, term labors during a 12-year period in a tertiary referral center were analyzed. With the use of gradient boosting, models that incorporated multiple maternal and fetal features were created to predict successful vaginal birth in parturients who undergo a trial of labor after cesarean delivery. One model was created to provide a personalized risk score for vaginal birth after cesarean delivery with the use of features that are available as early as the first antenatal visit; a second model was created that reassesses this score after features are added that are available only in proximity to delivery. RESULTS: A cohort of 9888 parturients with 1 previous cesarean delivery was identified, of which 75.6% of parturients (n=7473) attempted a trial of labor, with a success rate of 88%. A machine-learning-based model to predict when vaginal delivery would be successful was developed. When features that are available at the first antenatal visit are used, the model showed a receiver operating characteristic curve with area under the curve of 0.745 (95% confidence interval, 0.728-0.762) that increased to 0.793 (95% confidence interval, 0.778-0.808) when features that are available in proximity to the delivery process were added. Additionally, for the later model, a risk stratification tool was built to allocate parturients into low-, medium-, and high-risk groups for failed trial of labor after cesarean delivery. The low- and medium-risk groups (42.4% and 25.6% of parturients, respectively) showed a success rate of 97.3% and 90.9%, respectively. The high-risk group (32.1%) had a vaginal delivery success rate of 73.3%. Application of the model to a cohort of parturients who elected a repeat cesarean delivery (n=2145) demonstrated that 31% of these parturients would have been allocated to the low- and medium-risk groups had a trial of labor been attempted. CONCLUSION: Trial of labor after cesarean delivery is safe for most parturients. Success rates are high, even in a population with high rates of trial of labor after cesarean delivery. Application of a machine-learning algorithm to assign a personalized risk score for a successful vaginal birth after cesarean delivery may help in decision-making and contribute to a reduction in cesarean delivery rates. Parturient allocation to risk groups may help delivery process management.

Pluripotency-independent induction of human trophoblast stem cells from fibroblasts.

Human trophoblast stem cells (hTSCs) can be derived from embryonic stem cells (hESCs) or be induced from somatic cells by OCT4, SOX2, KLF4 and MYC (OSKM). Here we explore whether the hTSC state can be induced independently of pluripotency, and what are the mechanisms underlying its acquisition. We identify GATA3, OCT4, KLF4 and MYC (GOKM) as a combination of factors that can generate functional hiTSCs from fibroblasts. Transcriptomic analysis of stable GOKM- and OSKM-hiTSCs reveals 94 hTSC-specific genes that are aberrant specifically in OSKM-derived hiTSCs. Through time-course-RNA-seq analysis, H3K4me2 deposition and chromatin accessibility, we demonstrate that GOKM exert greater chromatin opening activity than OSKM. While GOKM primarily target hTSC-specific loci, OSKM mainly induce the hTSC state via targeting hESC and hTSC shared loci. Finally, we show that GOKM efficiently generate hiTSCs from fibroblasts that harbor knockout for pluripotency genes, further emphasizing that pluripotency is dispensable for hTSC state acquisition.

Conventional ICSI vs. physiological selection of spermatozoa for ICSI (picsi) in sibling oocytes.

BACKGROUND: Physiological selection of spermatozoa for ICSI (PICSI) is a sperm selection method based on sperm binding to hyaluronic acid. Previous studies on the effect of hyaluronic acid binding assays on fertilization and embryo quality have shown inconsistent results. Previous sibling oocyte studies have not found a significant improvement in fertilization or embryo development with hyaluronic acid binding assays. OBJECTIVE: To compare fertilization and embryo development between standard intracytoplasmic sperm injection (ICSI) and PICSI in sibling oocytes. MATERIALS AND METHODS: This is a retrospective analysis of all in vitro fertilization (IVF) cycles between January 2017 and April 2020 in which sibling oocytes were randomly fertilized by both ICSI and PICSI. Fertilization rate and the rate of embryos eligible for transfer were compared. RESULTS: Forty-five IVF cycles, in which 257 oocytes were fertilized with PICSI and 294 with standard ICSI, were compared. Most of the patients included in the study had previous failures of fertilization, poor embryonic development, implantation failure, or miscarriage. All but two of the patients had at least one previous unsuccessful IVF cycle. Both fertilization rates (71% vs. 83%) and transfer eligible embryo rates (38% vs. 51%) were significantly higher in PICSI fertilized oocytes (p = 0.008 and p = 0.01 respectively). DISCUSSION: Our study is the largest sibling oocyte study comparing ICSI and PICSI, and the first to find a significant improvement in fertilization and embryo quality with PICSI using sibling oocytes. The fact our cohort included almost exclusively couples with previous unsuccessful IVF cycles might suggest that PICSI should be used in selected cases. CONCLUSION: PICSI improves fertilization rates and transfer eligible embryo rates in sibling oocytes in a selected study group.

Elevated circulating micro-ribonucleic acid (miRNA)-200b and miRNA-429 levels in anovulatory women.

OBJECTIVE: To study the role of micro-RNA (miRNA)-200b and miRNA-429 in human ovulation and to measure their expression levels in ovulatory and anovulatory patients. DESIGN: Micro-RNA-200b and miRNA-429 expression analysis in human serum and granulosa cells at different phases of the ovulation cycle in normal cycling women and women undergoing assisted reproductive technology cycles. SETTING: University-affiliated hospital and academic research laboratory. PATIENT(S): Forty women (7 normally ovulating, 15 normally ovulating with pure male infertility factor, and 18 with polycystic ovary syndrome) were included in this study. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): The expression profile of circulating miRNAs and granulosa cells was assessed by means of real-time quantitative reverse transcription-polymerase chain reaction analysis. RESULT(S): We identified miRNA-200b and miRNA-429 in the sera of all women tested. These miRNA expression levels were elevated during the early follicular phase of the cycle compared with serum levels during the early luteal phase. Anovulatory women, diagnosed with polycystic ovary syndrome, expressed significantly higher levels of miRNA-200b and miRNA-429 compared with spontaneously ovulating women. Ovulation induction with exogenous gonadotropins during an IVF cycle reduced these levels to the levels measured in normal ovulating women. CONCLUSION(S): Our findings suggest an involvement of miRNA-200b and miRNA-429 in the pituitary regulation of human ovulation. Although it is unclear whether this altered miRNA expression profile is a cause or a result of anovulation, the levels of these molecules in the serum of anovulatory women may serve as serum biomarkers for the ovulation process.

Communication between residents and attending doctors on call after hours.

RATIONALE, AIMS AND OBJECTIVES: Off-hours medical care in hospitals is provided by residents, while attendings on call are available for assistance. This study evaluated the gap between residents' expectations and professional guidelines' requirements of attendings on call and what actually occurs during night shifts, while comparing surgical and medical specialties. METHODS: Two questionnaires based on professional guidelines were filled by residents. The first queried about residents' expectations of attendings on call, and the second asked about communication with the attendings during actual night shifts. RESULTS: While 91 (100%) of residents expected the attending on call to be available by phone during the shift, only 44 (48%) expected the attending to initiate contact, and only 17 (19%) expected the attending to visit the ward or emergency department (ED) without being requested to do so. In 127 shifts (84%), some form of communication occurred. Residents called their attendings during 105 shifts (70%). However, attendings initiated contact with residents at the beginning or during the shift in only 67 (44%) and 62 (41%) shifts, respectively, and initiated a visit to the ward/ED during the shift in only 41 cases (27%). Surgical attendings initiated contact in these three ways significantly more frequently than medical attendings [21 (28%) versus 46 (61%), 20 (26%) versus 42 (56%) and 4 (5%) versus 37 (50%), respectively; P < 0.001]. CONCLUSION: While communication during night shifts between residents and attendings occurs in most shifts, attendings initiate far less contact with residents than is required by the guidelines.