Role of endometriosis fertility index system in predicting non-IVF conception in patients with surgically documented endometriosis

Background: Endometriosis still remains an enigmatic disease. There are important reasons to stage endometriosis and to prognosticate the chances of pregnancy after a surgical management. The currently used revised AFS system has poor correlation with pregnancy rate. A scoring system-Endometriosis fertility index (EFI) to prognosticate the outcome was proposed few years back. The objective was to assess the usefulness of the EFI system in predicting pregnancy in patients with surgically documented endometriosis who attempt Non-IVF conception.Methods: Retrospective data was collected from 77 subjects with endometriosis who underwent laparoscopy and had documented least function (LF) score and EFI score. All were followed up until 12 months for the occurrence of a non IVF pregnancy.Results: Our study showed that the pregnancy rate was clearly higher in those with high EFI scores than those with low scores. A score of less than 4 was associated with significantly lower pregnancy rates than those with score above 5 (n=26, pregnancy rate- 11.54%) vs. (n=51, pregnancy rate 50.1%); p = 0.001)). Similarly, the pregnancy rate was significantly lower in those with LF score 1-3 (21.2%) as opposed to those with higher LF scores (p =0.029). Also, sensitivity analysis showed that higher EFI score was significantly associated with higher LF score (P <0.001).Conclusions: EFI is a useful clinical tool that predicts pregnancy with reasonable accuracy after endometriosis surgery. Its use clearly provides reassurance to those patients with good prognosis

Anti-mullerian hormone and antral follicle count as predictors of ovarian response in assisted reproduction

Objective: The objective of this study was to test the hypothesis that AMH and antral follicle count (AFC) are good predictors of ovarian response to controlled ovarian stimulation and to compare them. Materials and Methods: This observational cross-sectional study included 56 subjects aged between 25 and 42 years who were enrolled between 1 st January and 31 st December 2010 for their first intracytoplasmic sperm injection (ICSI) program. Baseline hormone profiles including serum levels of Estradiol (E2), Follicle-stimulating hormone (FSH), Luteinizing hormone (LH), and Anti-mullerian Hormone (AMH) were determined on day 3 of the previous cycle. The antral follicle count measurements were performed on days 3-5 of the same menstrual cycle. Antral follicles within the bilateral ovaries between 2-6 mm were recorded. The subjects were treated with long protocol for ovarian stimulation. Ovulation was induced with 10,000 IU of human chorionic gonadotropin (hCG) when at least 3 follicles attained the size of more than 17 mm. Transvaginal oocyte retrieval was performed under ultrasound guidance 36 hours after hCG administration. An oocyte count less than 4 and absence of follicular growth with controlled ovarian hyper stimulation was considered as poor ovarian response. Oocyte count of 4 or more was considered as normal ovarian response. Results: Statistical analysis was performed using SPSS software trail version 16.0. Subjects were divided into 2 groups, depending on the ovarian response. The mean oocyte counts were 12.27 ± 6.06 and 2.22 ± 1.24 in normal and poor responders, respectively, ( P = 001). Multiple regression analysis revealed AMH and antral follicle count as predictors of ovarian response (β coefficient ± SE for AMH was 1.618 ± 0.602 ( P = 0.01) and for AFC, it was, 0.528 ± 0.175 ( P = 0.004). AFC was found to be a better predictor of ovarian response compared to AMH in controlled ovarian hyper stimulation. Conclusion: The observations made in this study revealed that both AMH and AFC are good predictors of ovarian response; AFC being a better predictor compared to AMH

Predicting Autonomic Dysfunction in Anxiety Disorder from ECG and Respiratory Signals Using Machine Learning Models

Anxiety is a cognitive, behavioural, and biological response that prepares the individual to handle the stresses and conflicts of everyday life. The excessive appearance of this biological response is diagnosed as an anxiety disorder, which is often associated with Autonomic dysfunction (ADy). ADy is difficult to study in clinics with very few parameters available. Detection of ADy may not be possible/difficult in anxiety disorder with the existing method. In this study, we built machine learning models to identify ADy in subjects with anxiety using properties extracted from ECG and respiratory signals. For each dataset, statistical and frequency domain features were estimated from ECG and respiratory signals. Supervised machine learning (ML) algorithms were used to classify the subjects. Out of 23 features estimated, 11 were found to be statistically significant for the classification. We segmented the signals into 5, 10, and 30 minutes intervals to build generalized models. To overcome data imbalance, ensemble techniques like boosting was used. The highest accuracy was obtained in the SVM, Random forest and Gradient Boosting classifiers (cross-validation accuracy of 82.2%, 81.64% and 79.06% and; AUC of 0.81, 0.76 and 0.84) for 10 and 30 minutes segmented datasets. Our results showed that the features extracted from the ECG signal are a good marker for diagnosing ADy in patients with anxiety disorder. Further, a deep neural network-based model can be implemented that may achieve better accuracy for classification provided with the cost of a large number of datasets and computation time