Validation of machine-learning model for first-trimester prediction of pre-eclampsia using cohort from PREVAL study.

OBJECTIVE: Effective first-trimester screening for pre-eclampsia (PE) can be achieved using a competing-risks model that combines risk factors from the maternal history with multiples of the median (MoM) values of biomarkers. A new model using artificial intelligence through machine-learning methods has been shown to achieve similar screening performance without the need for conversion of raw data of biomarkers into MoM. This study aimed to investigate whether this model can be used across populations without specific adaptations. METHODS: Previously, a machine-learning model derived with the use of a fully connected neural network for first-trimester prediction of early (< 34 weeks), preterm (< 37 weeks) and all PE was developed and tested in a cohort of pregnant women in the UK. The model was based on maternal risk factors and mean arterial blood pressure (MAP), uterine artery pulsatility index (UtA-PI), placental growth factor (PlGF) and pregnancy-associated plasma protein-A (PAPP-A). In this study, the model was applied to a dataset of 10 110 singleton pregnancies examined in Spain who participated in the first-trimester PE validation (PREVAL) study, in which first-trimester screening for PE was carried out using the Fetal Medicine Foundation (FMF) competing-risks model. The performance of screening was assessed by examining the area under the receiver-operating-characteristics curve (AUC) and detection rate (DR) at a 10% screen-positive rate (SPR). These indices were compared with those derived from the application of the FMF competing-risks model. The performance of screening was poor if no adjustment was made for the analyzer used to measure PlGF, which was different in the UK and Spain. Therefore, adjustment for the analyzer used was performed using simple linear regression. RESULTS: The DRs at 10% SPR for early, preterm and all PE with the machine-learning model were 84.4% (95% CI, 67.2-94.7%), 77.8% (95% CI, 66.4-86.7%) and 55.7% (95% CI, 49.0-62.2%), respectively, with the corresponding AUCs of 0.920 (95% CI, 0.864-0.975), 0.913 (95% CI, 0.882-0.944) and 0.846 (95% CI, 0.820-0.872). This performance was achieved with the use of three of the biomarkers (MAP, UtA-PI and PlGF); inclusion of PAPP-A did not provide significant improvement in DR. The machine-learning model had similar performance to that achieved by the FMF competing-risks model (DR at 10% SPR, 82.7% (95% CI, 69.6-95.8%) for early PE, 72.7% (95% CI, 62.9-82.6%) for preterm PE and 55.1% (95% CI, 48.8-61.4%) for all PE) without requiring specific adaptations to the population. CONCLUSIONS: A machine-learning model for first-trimester prediction of PE based on a neural network provides effective screening for PE that can be applied in different populations. However, before doing so, it is essential to make adjustments for the analyzer used for biochemical testing. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Prevalence of neutralizing antibodies against SARS-CoV-2 using a rapid serological test in health workers of a Spanish Department of Health in Alicante (Spain) before the booster dose of the vaccine.

AIM: To study the prevalence of neutralizing antibodies in healthcare workers and healthcare support personnel after the administration of the second dose of the BNT162b2 vaccine (Pfizer-BioNTech). MATERIALS AND METHODS: In December 2021, we undertook a study in the Health Department in Orihuela, Alicante (Spain), which consists of 1500 workers. We collected demographic variables about the study participants, and we performed a "point-of-care" immunochromatography test to measure the presence of neutralizing antibodies (OJABIO® SARS-CoV-2 Neutralizing Antibody Detection Kit, manufactured by Wenzhou OJA Biotechnology Co., Ltd. Wenzhou, Zhejiang, China) before the administration of the third dose of the vaccine. RESULTS: We obtained complete information about 964 (64%) workers, which consisted of 290 men and 674 women. The average age was 45,8 years (min. 18, max. 68) and the average time since the last dose of the vaccine was 40,5 weeks (min. 1,71, max. 47,71). A total of 131 participants (13,5%) had suffered infection by SARS-CoV-2 confirmed using RT-PCR. The proportion of participants who showed presence of neutralizing antibodies was 38,5%. In the multivariable analysis, the time since the last dose of the vaccine (aOR week: 1,07; 95%CI: 1,04; 1,09) and previous infection by SARS-CoV-2 (aOR: 3,7; 95CI: 2,39; 5,63) showed a statistically significant association with the presence of neutralizing antibodies. CONCLUSIONS: The time since the administration of the last dose of the vaccine and the previous infection by SARS-CoV-2 determined the presence of neutralizing antibodies in 38,5% of the healthcare workers and support workers.

Endothelial dysfunction in gestational hypertension induced by catechol-O-methyltransferase inhibition.

The present study evaluated whether catechol-O-methyltransferase inhibition in pregnant rats results in increased blood pressure and vascular endothelial dysfunction as a consequence of decreased nitric oxide bioavailability. Pregnant Sprague-Dawley rats were given entacapone (a catechol-O-methyltransferase inhibitor) by gavage from the 10th to the 20th day of pregnancy. Blood pressure was measured by plethysmography in the tail artery. Vascular endothelial function and NO release were assessed both in the absence and in the presence of tempol. Systolic blood pressure increased significantly in pregnant rats treated with entacapone compared with untreated pregnant rats on days 14 (143 ± 4 versus 122 ± 3 mmHg) and 19 of gestation (129 ± 4 versus 115 ± 5 mmHg). Both conductance (aortic rings) and resistance vessels (mesenteric small arterial vessels) from entacapone-treated pregnant rats showed diminished relaxation in response to acetylcholine compared with vessels from vehicle-treated pregnant and virgin rats. In mesenteric arterioles, this endothelial dysfunction was abolished in the presence of l-NAME, indicating that it was caused by reduced NO availability, and it also improved in the presence of tempol, suggesting increased oxidative stress in hypertensive pregnant rats. Endothelial release of nitric oxide induced by calcium ionophore (A23187) was significantly greater in aortas from vehicle-treated pregnant rats than in aortas from pregnant rats given entacapone. This endothelial dysfunction seen in hypertensive rats was prevented by addition of tempol. The present study provides evidence that catechol-O-methyltransferase inhibition in pregnant rats produces arterial hypertension and endothelial dysfunction due to reduced nitric oxide bioavailability.