Quantifying the association of sperm DNA fragmentation with assisted reproductive technology outcomes: An umbrella review.

BACKGROUND: Systematic reviews and meta-analyses are instrumental in shaping clinical practice. However, their findings can sometimes be marred by discrepancies and potential biases, thereby diluting the strength of the evidence presented. Umbrella reviews serve to comprehensively assess and synthesise these reviews, offering a clearer insight into the quality of the evidence presented. In the context of the relationship between sperm DNA fragmentation (SDF) and assisted conception outcomes, there is a divergence in the literature. Some reviews suggest a clear cause-and-effect linkage, whereas others present conflicting or inconclusive results. OBJECTIVES: In this umbrella review we aimed to synthesise the evidence collated in systematic reviews and meta-analyses summarising the association of SDF with assisted reproductive technology (ART) outcomes. SEARCH STRATEGY: After preregistration (https://doi.org/10.17605/OSF.IO/6JHDP), we performed a comprehensive search of the PubMed, Scopus, Cochrane Library, Web of Science and Embase databases. We conducted a search for systematic reviews on the association between SDF and ART without any restrictions on language or publication date. SELECTION CRITERIA: Systematic reviews and meta-analyses assessing the association between SDF and ART outcomes were eligible. DATA COLLECTION AND ANALYSIS: We assessed the quality of the included reviews using AMSTAR 2 and ROBIS, and determined the degree of overlap of primary studies between reviews estimating the corrected covered area (CCA), adjusted for structural missingness. We evaluated the most recent reviews assessing the association of SDF with live birth, pregnancy, miscarriage, implantation, blastulation and fertilisation. The synthesis of evidence was harmonised across all included quantitative syntheses, re-estimating the odds ratio (eOR) in random-effects meta-analyses with 95% confidence intervals (95% CIs) and 95% prediction intervals (95% PIs). We categorised the evidence strength into convincing, highly suggestive, suggestive, weak or nonsignificant, according to the meta-analysis re-estimated P-value, total sample size, I2 statistic for heterogeneity, small study effect, excess significance bias and the largest study significance. MAIN RESULTS: We initially captured and screened 49 332 records. After excluding duplicate and ineligible articles, 22 systematic reviews, 15 of which were meta-analyses, were selected. The 22 reviews showed a moderate degree of overlap (adjusted CCA 9.2%) in their included studies (overall n = 428, with 180 unique studies). The 15 meta-analyses exhibited a high degree of overlap (adjusted CCA = 13.6%) in their included studies (overall n = 274, with 118 unique studies). AMSTAR 2 categorised the quality of evidence in 18 reviews as critically low and the quality of evidence in four reviews as low. ROBIS categorised all the reviews as having a high risk of bias. The re-estimated results showed that the association of SDF with live birth was weak in one and nonsignificant in four meta-analyses. Similarly, the association of SDF with pregnancy, miscarriage, implantation, blastulation and fertilisation was also weak or nonsignificant. The association of high SDF with different ART outcomes was also weak or nonsignificant for different interventions (IVF, ICSI and IUI) and tests. CONCLUSIONS: This umbrella review did not find convincing or suggestive evidence linking SDF with ART outcomes. Caution should be exercised in making any claims, policies or recommendations concerning SDF.

Endometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis.

OBJECTIVE: To establish a prognostic model for endometrial cancer (EC) that individualizes a risk and management plan per patient and disease characteristics. METHODS: A multicenter retrospective study conducted in nine European gynecologic cancer centers. Women with confirmed EC between January 2008 to December 2015 were included. Demographics, disease characteristics, management, and follow-up information were collected. Cancer-specific survival (CSS) and disease-free survival (DFS) at 3 and 5 years comprise the primary outcomes of the study. Machine learning algorithms were applied to patient and disease characteristics. Model I: pretreatment model. Calculated probability was added to management variables (model II: treatment model), and the second calculated probability was added to perioperative and postoperative variables (model III). RESULTS: Of 1150 women, 1144 were eligible for 3-year survival analysis and 860 for 5-year survival analysis. Model I, II, and III accuracies of prediction of 5-year CSS were 84.88%/85.47% (in train and test sets), 85.47%/84.88%, and 87.35%/86.05%, respectively. Model I predicted 3-year CSS at an accuracy of 91.34%/87.02%. Accuracies of models I, II, and III in predicting 5-year DFS were 74.63%/76.72%, 77.03%/76.72%, and 80.61%/77.78%, respectively. CONCLUSION: The Endometrial Cancer Individualized Scoring System (ECISS) is a novel machine learning tool assessing patient-specific survival probability with high accuracy.

Different azithromycin protocols for management of preterm prelabour rupture of membranes: a randomized clinical trial.

BACKGROUND: Preterm prelabor rupture of membranes is associated with polymicrobial infection; hence broad-spectrum antibiotics are recommended. Nowadays, Azithromycin is used instead of Erythromycin due to erythromycin shortages, its ease of administration, decreased cost, and better side effect profile. This study aimed to evaluate the efficacy of different azithromycin protocols for the conservative management of preterm prelabor rupture of membranes. METHODS: It was a single-blinded randomized clinical trial including pregnant women at 24-36+6 weeks with viable singleton pregnancies and confirmed preterm prelabor rupture of membranes from January 01, 2020, to June 01, 2021. The participants were randomized into two groups: Group I was made of women who received Azithromycin 1000 mg PO once, and Group II of women who received Azithromycin 500 mg PO once, followed by Azithromycin 250 mg PO daily for four days. The primary study outcome was the length of the latency period from the diagnosis of preterm prelabor rupture of membranes to delivery (days). RESULTS: The latency period in group I was significantly higher than that in Group II (5.80 ± 5.44 days vs. 2.88 ± 2.37; respectively, p = 0.0001). The mean gestational age at the time of delivery was significantly higher in Group I (p = 0.0001). However, postpartum endometritis and respiratory distress syndrome (RDS) rates were significantly higher in Group II (p = 0.003 and p = 0.0001, respectively). CONCLUSION: The higher dose of Azithromycin was associated with better maternal and neonatal outcomes. TRIAL REGISTRATION: Clinical trial identification number: Clinical trial.gov: NCT04202380 (17/ 12/ 2019). Date of registration: 1/1 /2020. Date of initial participant enrollment30 /1/2020. URL of the registration site: https://www. CLINICALTRIALS: gov/ct2/show/NCT04202380.

Middle-East OBGYN graduate education (MOGGE) foundation practice guidelines: use of labor charts in management of labor. Practice guideline no. 04-O-21.

Since the 50 s of the last century, labor charts have been proposed and appraised as a tool to diagnose labor abnormalities and guide decision-making. The partogram, the most widely adopted form of labor charts, has been endorsed by the world health organization (WHO) since 1994. Nevertheless, recent studies and systematic reviews did not support clinical significance of application of the WHO partogram. These results have led to further studies that investigate modifications to the structure of the partogram, or more recently, to reconstruct new labor charts to improve their clinical efficacy. This guideline appraises current evidence on use of labor charts in management of labor specially in low-resource settings.

Prediction of clinical outcomes in women with placenta accreta spectrum using machine learning models: an international multicenter study.

INTRODUCTION: Placenta accreta spectrum is a major obstetric disorder that is associated with significant morbidity and mortality. The objective of this study is to establish a prediction model of clinical outcomes in these women. MATERIALS AND METHODS: PAS-ID is an international multicenter study that comprises 11 centers from 9 countries. Women who were diagnosed with PAS and were managed in the recruiting centers between 1 January 2010 and 31 December 2019 were included. Data were reanalyzed using machine learning (ML) models, and 2 models were created to predict outcomes using antepartum and perioperative features. ML model was conducted using python® programing language. The primary outcome was massive PAS-associated perioperative blood loss (intraoperative blood loss ≥2500 ml, triggering massive transfusion protocol, or complicated by disseminated intravascular coagulopathy). Other outcomes include prolonged hospitalization >7 days and admission to the intensive care unit (ICU). RESULTS: 727 women with PAS were included. The area under curve (AUC) for ML antepartum prediction model was 0.84, 0.81, and 0.82 for massive blood loss, prolonged hospitalization, and admission to ICU, respectively. Significant contributors to this model were parity, placental site, method of diagnosis, and antepartum hemoglobin. Combining baseline and perioperative variables, the ML model performed at 0.86, 0.90, and 0.86 for study outcomes, respectively. Ethnicity, pelvic invasion, and uterine incision were the most predictive factors in this model. DISCUSSION: ML models can be used to calculate the individualized risk of morbidity in women with PAS. Model-based risk assessment facilitates a priori delineation of management.

Placenta accreta risk-antepartum score in predicting clinical outcomes of placenta accreta spectrum: A multicenter validation study.

OBJECTIVE: To validate the use of placenta accreta risk-antepartum (PAR-A) score as a predictive tool of clinical outcomes of placenta accreta spectrum (PAS). METHODS: This is a prospective study, conducted in six PAS specialized centers in six different countries. The study was conducted between October 1, 2020 and March 31, 2021. Women who were provisionally diagnosed with PAS during pregnancy were considered eligible. A machine-learning-based PAR-A score was calculated. Diagnostic performance of the PAR-A score was evaluated using a receiver operating characteristic curve, for perioperative massive blood loss and admission to intensive care unit (ClinicalTrials.gov identifier NCT04525001). RESULTS: Of 97 eligible women, 86 were included. PAS-associated massive blood loss occurred in 10 patients (11.63%). Median PAR-A scores of massive blood loss in the current cohort were 8.9 (interquartile range 6.9-14.1). In predicting massive blood loss, the area under the curve of PAR-A scores was 0.85 (95% confidence interval [CI] 0.74-0.95), which was not significantly different from the original cohort (P = 0.2). PAR-A score prediction of intensive care unit admission was slightly higher compared with the original cohort (0.88, 95% CI 0.81-0.95; P = 0.06). CONCLUSION: PAR-A score is a novel scoring system of PAS outcomes, which showed external validity based on current data.

Prediction of success of uterus-preserving management in women with placenta accreta spectrum (CON-PAS score): A multicenter international study.

OBJECTIVE: To create a model for prediction of success of uterine-preserving procedures in women with placenta accreta spectrum (PAS). METHODS: PAS-ID is a multicenter study that included 11 centers from 9 countries. Women with PAS, who were managed between January 1, 2010 and December 31, 2019, were retrospectively included. Data were split into model development and validation cohorts, and a prediction model was created using logistic regression. Main outcome was success of uterine preservation. RESULTS: Out of 797 women with PAS, 587 were eligible. Uterus-preserving procedures were successful in 469 patients (79.9%). Number of previous cesarean sections (CS) was inversely associated with management success (adjusted odds ratio [aOR] 0.02, 95% confidence interval [CI] 0.001-3.63 with five previous CS). Other variables were complete placental invasion (aOR 0.14, 95% CI 0.05-0.43), type of CS incision (aOR 0.04, 95% CI 0.01-0.25 for classical incision), compression sutures (aOR 2.48, 95% CI 1.00-6.16), accreta type (aOR 3.76, 95% CI 1.13-12.53), incising away from placenta (aOR 5.09, 95% CI 1.52-16.97), and uterine resection (aOR 102.57, 95% CI 3.97-2652.74). CONCLUSION: The present study provides a prediction model for success of uterine preservation, which may assist preoperative and intraoperative decisions, and promote incorporation of uterine preservation procedures in comprehensive PAS protocols.