Machine learning-based protein signatures for differentiating hypertensive disorders of pregnancy.

Hypertensive disorders of pregnancy (HDP) result in major maternal and fetal complications. Our study aimed to find a panel of protein markers to identify HDP by applying machine-learning models. The study was conducted on a total of 133 samples, divided into four groups, healthy pregnancy (HP, n = 42), gestational hypertension (GH, n = 67), preeclampsia (PE, n = 9), and ante-partum eclampsia (APE, n = 15). Thirty circulatory protein markers were measured using Luminex multiplex immunoassay and ELISA. Significant markers were screened for potential predictive markers by both statistical and machine-learning approaches. Statistical analysis found seven markers such as sFlt-1, PlGF, endothelin-1(ET-1), basic-FGF, IL-4, eotaxin and RANTES to be altered significantly in disease groups compared to healthy pregnant. Support vector machine (SVM) learning model classified GH and HP with 11 markers (eotaxin, GM-CSF, IL-4, IL-6, IL-13, MCP-1, MIP-1α, MIP-1ß, RANTES, ET-1, sFlt-1) and HDP with 13 markers (eotaxin, G-CSF, GM-CSF, IFN-gamma, IL-4, IL-5, IL-6, IL-13, MCP-1, MIP-1ß, RANTES, ET-1, sFlt-1). While logistic regression (LR) model classified PE with 13 markers (basic FGF, IL-1ß, IL-1ra, IL-7, IL-9, MIP-1ß, RANTES, TNF-alpha, nitric oxide, superoxide dismutase, ET-1, PlGF, sFlt-1) and APE by 12 markers (eotaxin, basic-FGF, G-CSF, GM-CSF, IL-1ß, IL-5, IL-8, IL-13, IL-17, PDGF-BB, RANTES, PlGF). These markers may be used to diagnose the progression of healthy pregnant to a hypertensive state. Future longitudinal studies with large number of samples are needed to validate these findings.

Comparative efficacy and safety of oral nifedipine with other antihypertensive medications in the management of hypertensive disorders of pregnancy: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Hypertensive disorders of pregnancy are the most frequently occurring medical condition during pregnancy, resulting in fetal and/or maternal morbidity and mortality. This meta-analysis compared the efficacy and safety of nifedipine with other antihypertensive medications used in hypertensive disorders of pregnancy. METHODOLOGY: A comprehensive search was performed using PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), and Google Scholar. The meta-analysis was carried out using Review Manager Software, and the pooled effect estimate was generated as standardized mean difference and odds ratio with 95% confidence interval and two-sided P -value. RESULTS: The meta-analysis was comprised of 22 randomized control trials with 2595 participants. It was found that meantime and number of doses required to achieve target blood pressure were lower in the nifedipine group ( P  < 0.05). Even though it is statistically insignificant, fetal APGAR (Appearance, Pulse, Grimace, Activity, and Respiration) scores less than seven favors nifedipine intervention. Furthermore, none of the fetal or maternal secondary outcomes were found significant. CONCLUSION: Nifedipine was found to be more effective than other antihypertensive medications to reduce blood pressure, particularly in patients with severe hypertension. However, future clinical studies, including real-world data are necessary to establish the safety profile of nifedipine concerning the fetal outcomes in hypertensive pregnant women.