Evaluating fallopian tube patency and function using RT 3D-HyCoSy: A retrospective analysis
Year: 2024
Type: article
Study design: Observational Study
Abstract: Objectives This study aims to investigate transvaginal real-time three-dimensional hysterosalpingo-contrastsonography (RT 3D-HyCoSy) for evaluating fallopian tube patency and function. Design This retrospective analysis involved 120 infertile patients from June 2019 to June 2021 comparing the diagnostic performance of RT 3D-HyCoSy with Laparoscopic Meilan liquid test (LC) for tubal patency assessment. Participants Materials, Setting, Methods: Clinical data of participants undergoing RT 3D-HyCoSy and LC were retrospectively analyzed. Diagnostic agreement was assessed using statistical analysis including logistic regression and receiver operating characteristic (ROC) analysis to identify factors influencing pregnancy and evaluate predictive capability Results RT 3D-HyCoSy and LC show high diagnostic agreement (κ = 0.846, P < 0.05). RT 3D-HyCoSy has 91.95% sensitivity, 57.58% specificity, 82.50% accuracy, 85.11% PPV, and 73.08% NPV for tubal patency. In pregnant subjects, various parameters differ significantly from non-pregnant counterparts (P < 0.05). Logistic regression identifies factors influencing pregnancy (P < 0.05). The predictive equation's ROC AUC is 0.838 with 80.00% sensitivity and 78.00% specificity (P < 0.05). Limitations The retrospective design and sample size may limit generalizability. Future studies with larger cohorts are needed. External factors, including variations in clinical practices and patient characteristics, may also impact the outcomes. Conclusion Transvaginal RT 3D-HyCoSy is highly effective in assessing fallopian tube patency in infertility patients. Additionally, its contrast medium proves valuable in predicting pregnancy by evaluating parameters such as overflow time at the fimbriated extremity of the fallopian tube. Overall, this study highlights the potential of RT 3D-HyCoSy in non-invasively assessing tubal patency and predicting pregnancy outcomes, which could enhance infertility management.
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,400
OpenAlex ID: W4401690378