Association between three-dimensional transvaginal sonographic markers and outcome of pregnancy of unknown location: a pilot study

Predicting the Outcome of a Pregnancy of Unknown Location: What Can the Endometrial Stripe Thickness Reveal?

Objectives:

To investigate the role of the uterine endometrial thickness and sonographic pattern as potential predictors for a pregnancy with an unknown location (PUL) and its possible outcomes.

Materials and Methods:

A convenient sample of 330 symptomatic extra preposition female patients were enrolled in this study with a diagnosis of PUL. The clinical variables of endometrial stripe thickness and endometrial sonographic pattern were determined with transvaginal sonography. These sonographic examinations (it seems that examination in this context is a countable noun) were conducted during the first 24 hours of referral, and their predictive values for PUL outcomes (normal intrauterine pregnancy (IUP), ectopic pregnancy (EP), and pregnancy loss) were assessed after clinical follow-up sessions provided a definite outcome. The statistical significance was set a priori at a P value < .05.

Results:

The mean initial endometrial stripe thickness among participants, with a normal IUP, was more than those patients with an abnormal pregnancy outcome ( P < .05). The optimum cut-off value for predicting a an EP, compared to a normal IUP was 11 mm and had a sensitivity of 73.3% and specificity of 39% ( P < .001). These results also demonstrated no statistically significant relationship between the PUL outcome and the categories of endometrial sonographic pattern ( P = .15).

Conclusion:

In this large cohort of patients, the endometrial stripe thickness of more than 11 mm, among those who were symptomatic and deemed as PUL, had the potential to predict ectopic pregnancy as an unlikely diagnosis.

Multifactor Prediction of Embryo Transfer Outcomes Based on a Machine Learning Algorithm

In vitro fertilization-embryo transfer (IVF-ET) technology make it possible for infertile couples to conceive a baby successfully. Nevertheless, IVF-ET does not guarantee success. Frozen embryo transfer (FET) is an important supplement to IVF-ET. Many factors are correlated with the outcome of FET which is unpredictable. Machine learning is a field of study that predict various outcomes by defining data attributes and using relevant data and calculation algorithms. Machine learning algorithm has been widely used in clinical research. The present study focuses on making predictions of early pregnancy outcomes in FET through clinical characters, including age, body mass index (BMI), endometrial thickness (EMT) on the day of progesterone treatment, good-quality embryo rate (GQR), and type of infertility (primary or secondary), serum estradiol level (E2) on the day of embryo transfer, and serum progesterone level (P) on the day of embryo transfer. We applied four representative machine learning algorithms, including logistic regression (LR), conditional inference tree, random forest (RF) and support vector machine (SVM) to build prediction models and identify the predictive factors. We found no significant difference among the models in the sensitivity, specificity, positive predictive rate, negative predictive rate or accuracy in predicting the pregnancy outcome of FET. For example, the positive/negative predictive rate of the SVM (gamma = 1, cost = 100, 10-fold cross validation) is 0.56 and 0.55. This approach could provide a reference for couples considering FET. The prediction accuracy of the present study is limited, which suggests that there may be some other more effective predictors to be developed in future work.

The effectiveness of uterine parametrics measured by three-dimensional transvaginal sonography in the diagnosis of uterine cavity distortions

Background

To analyze the application values of quantitative parametrics measured by three-dimensional transvaginal sonography (3D-TVS) in the diagnosis of uterine cavity distortions.

Methods

A retrospective analysis was performed on consecutive 113 patients with septate uteruses, as diagnosed consistently by both 3D-TVS and hysteroscopic surgery, between Jan 2016 and Jan 2018. The intrauterine quantitative parametrics were compared between an infertility group and a miscarriage group. Receiver operating characteristic (ROC) curve analysis and evaluation indicators of diagnostic tests were plotted to analyze the relationships between parametrics and uterine cavity distortions.

Results

There were no significant differences in septum width, septum length, uterine angle, remaining uterine cavity length, and distortion rate between the infertility and miscarriage groups (P>0.05). The distortion rate was positively correlated with septum length, but showed a negative correlation with uterine angle and remaining uterine cavity length (all P<0.05). The area under the curve (AUC) of septum length for predicting a distortion rate higher than 50% was 0.969; with a cutoff value of 2.15 cm, and a diagnostic accuracy of 97.14%, sensitivity was 81.80% and specificity was 98.90%, respectively. Patients with lower septum length coupled with a higher remaining uterine cavity length had a lower risk of uterine cavity distortion.

Conclusions

Septum length was shown to have the most important effect on uterine cavity distortion of all the uterine parametrics measured by 3D-TVS. Patients with septum lengths higher than 2.15 cm indicated a distortion rate of more than 50%, and tend to experience adverse pregnancy outcomes.