Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models.

This study aimed to develop a model for predicting the completion of clinical trials involving pregnant women using the Cox proportional hazard model and neural network model (DeepSurv) and to compare the predictive performance of both methods. We collected data on 819 clinical trials performed on pregnant women and intervention studies using at least one drug as intervention from 2009 to 2018 from ClinicalTrials.gov. The Cox proportional hazard model and DeepSurv were used to develop models that predict clinical trial completion. The concordance index (C-index) was used to evaluate the predictive performance. The Cox proportional hazard model revealed that a sample size of n ≥ 329 (hazard ratio [HR] = 0.53), very high human development index (HDI) country (HR = 0.28), abortion (HR = 3.30), labor (HR = 2.16), and iron deficiency anemia (HR = 2.29) were significantly related to the probability of clinical trial completion (all p value < 0.01). The C-index of the model development dataset and test dataset were 0.72 and 0.73, respectively. DeepSurv model consisted of one hidden layer with 16 nodes. DeepSurv showed the C-index comparable to the Cox proportional hazard model. The C-index of the training dataset and test dataset were 0.76 and 0.72, respectively. Further a nomogram that calculate a probability of clinical trial completion at 1 year, 3 years, and 5 years was developed. Both the Cox proportional hazard model and DeepSurv yielded sufficient predicting performance. We hope that this study will contribute to the execution of future clinical trials in pregnant women.

Gender Equity and Vertically Transmitted Infections: A Country-Level Analysis Across 153 Countries.

Purpose: Gender inequality is a barrier to education toward women and accessibility to health facilities, which are important for preventing vertical transmission. This study was conducted to analyze the impact of gender equity on vertically transmitted infections (hepatitis viruses, human immunodeficiency virus [HIV], and syphilis) using country-level indicators. Methods: The relationship between the Global Gender Gap Index (GGGI), which is indicator of gender equity, and vertical transmission was analyzed. GGGI scores were collected from 153 countries in 2020. Vertical transmission included 10 outcomes for hepatitis viruses, HIV, and syphilis. Generalized linear model (GLM) was used for analyzing the relationship. Other predictors included skilled birth attendant and country income. Results: The median GGGI score was 0.706 (interquartile range, 0.664-0.736). GLM showed that the GGGI score was significantly associated with the incidence of both chronic hepatitis B and C in under 5 years (both p<0.001). For HIV, GGGI score was significantly associated with the pregnant women with unknown HIV status (p=0.001), no early infant diagnosis (p=0.027), and final transmission rate (p=0.005). There was no significant predictor for pregnant women who have not received antiretroviral therapy for prevention of mother-to-child transmission. All syphilis indicators have improved in high-income countries compared to low-income countries. GGGI score had a significant association only with no syphilis screening (p<0.001). Conclusions: A lower GGGI score was associated with higher vertical transmission of hepatitis and HIV. The improvement of gender equity might prevent vertical transmission of these viruses. Further intervention studies are warranted to verify the results.