Editorial Board

Objective: The internally validated fulIPIERS model predicts adverse maternal outcomes in women with pre-eclampsia within 48 h after eligibility. Our objective was to assess generalizability of this prediction model. Study design: External validation study using prospectively collected data from two tertiary care obstetric centers. Methods: The existing PETRA dataset, a cohort of women (n = 216) with severe early-onset pre-eclampsia, eclampsia, HELLP syndrome or hypertension-associated fetal growth restriction was used. The fulIPIERS model equation was applied to all women in the dataset using values collected within 48 h after inclusion. The performance (ROC area and R-squared) of the model, risk stratification and calibration were assessed from 48 h up to a week after inclusion. Results: Of 216 women in the PETRA trial, 73 (34%) experienced an adverse maternal outcome(s) at any time after inclusion. Adverse maternal outcome was observed in 32 (15%) cases within 48 h and 62 (29%) within 7 days after inclusion. The fulIPIERS model predicted adverse maternal outcomes within 48 h (AUC ROC 0.97, 95% CI: 0.87-0.99) and up to 7 days after inclusion (AUC ROC 0.80, 95% CI: 0.70-0.87). Conclusions: The fullPIERS model performed well when applied to the PETRA dataset. These results confirm the usability of the fulIPIERS prediction model as a 'rule-in' test for women admitted with severe pre-eclampsia, eclampsia, HELLP syndrome or hypertension-associated fetal growth restriction. Future research should focus on intervention studies that assess the clinical impact of strategies using the fullPIERS model. (C) 2014 Elsevier Ireland Ltd. All rights reserved

Development and internal validation of the multivariable CIPHER (Collaborative Integrated Pregnancy High-dependency Estimate of Risk) clinical risk prediction model

Background: Intensive care unit (ICU) outcome prediction models, such as Acute Physiology And Chronic Health Evaluation (APACHE), were designed in general critical care populations and their use in obstetric populations is contentious. The aim of the CIPHER (Collaborative Integrated Pregnancy High-dependency Estimate of Risk) study was to develop and internally validate a multivariable prognostic model calibrated specifically for pregnant or recently delivered women admitted for critical care.Methods: A retrospective observational cohort was created for this study from 13 tertiary facilities across five high-income and six low- or middle-income countries. Women admitted to an ICU for more than 24 h during pregnancy or less than 6 weeks post-partum from 2000 to 2012 were included in the cohort. A composite primary outcome was defined as maternal death or need for organ support for more than 7 days or acute life-saving intervention. Model development involved selection of candidate predictor variables based on prior evidence of effect, availability across study sites, and use of LASSO (Least Absolute Shrinkage and Selection Operator) model building after multiple imputation using chained equations to address missing data for variable selection. The final model was estimated using multivariable logistic regression. Internal validation was completed using bootstrapping to correct for optimism in model performance measures of discrimination and calibration.Results: Overall, 127 out of 769 (16.5%) women experienced an adverse outcome. Predictors included in the final CIPHER model were maternal age, surgery in the preceding 24 h, systolic blood pressure, Glasgow Coma Scale score, serum sodium, serum potassium, activated partial thromboplastin time, arterial blood gas (ABG) pH, serum creatinine, and serum bilirubin. After internal validation, the model maintained excellent discrimination (area under the curve of the receiver operating characteristic (AUROC) 0.82, 95% confidence interval (CI) 0.81 to 0.84) and good calibration (slope of 0.92, 95% CI 0.91 to 0.92 and intercept of −0.11, 95% CI −0.13 to −0.08).Conclusions: The CIPHER model has the potential to be a pragmatic risk prediction tool. CIPHER can identify critically ill pregnant women at highest risk for adverse outcomes, inform counseling of patients about risk, and facilitate bench-marking of outcomes between centers by adjusting for baseline risk

Pericyte heterogeneity identified by 3D ultrastructural analysis of the microvessel wall

Confident identification of pericytes (PCs) remains an obstacle in the field, as a single molecular marker for these unique perivascular cells remains elusive. Adding to this challenge is the recent appreciation that PC populations may be heterogeneous, displaying a range of morphologies within capillary networks. We found additional support on the ultrastructural level for the classification of these PC subtypes—“thin-strand” (TSP), mesh (MP), and ensheathing (EP)—based on distinct morphological characteristics. Interestingly, we also found several examples of another cell type, likely a vascular smooth muscle cell, in a medial layer between endothelial cells (ECs) and pericytes (PCs) harboring characteristics of the ensheathing type. A conserved feature across the different PC subtypes was the presence of extracellular matrix (ECM) surrounding the vascular unit and distributed in between neighboring cells. The thickness of this vascular basement membrane was remarkably consistent depending on its location, but never strayed beyond a range of 150–300 nm unless thinned to facilitate closer proximity of neighboring cells (suggesting direct contact). The density of PC-EC contact points (“peg-and-socket” structures) was another distinguishing feature across the different PC subtypes, as were the apparent contact locations between vascular cells and brain parenchymal cells. In addition to this thinning, the extracellular matrix (ECM) surrounding EPs displayed another unique configuration in the form of extensions that emitted out radially into the surrounding parenchyma. Knowledge of the origin and function of these structures is still emerging, but their appearance suggests the potential for being mechanical elements and/or perhaps signaling nodes via embedded molecular cues. Overall, this unique ultrastructural perspective provides new insights into PC heterogeneity and the presence of medial cells within the microvessel wall, the consideration of extracellular matrix (ECM) coverage as another PC identification criteria, and unique extracellular matrix (ECM) configurations (i.e., radial extensions) that may reveal additional aspects of PC heterogeneity

Role of community engagement in maternal health in rural Pakistan: Findings from the CLIP randomized trial

Background: Community-based strategies to promote maternal health can help raise awareness of pregnancy danger signs and preparations for emergencies. The objective of this study was to assess change in birth preparedness and complication readiness (BPCR) and pregnant women\u27s knowledge about pre-eclampsia as part of community engagement (CE) activities in rural Pakistan during the Community Level Interventions for Pre-eclampsia (CLIP) Trial.Methods: The CLIP Trial was a cluster randomized controlled trial that aimed to reduce maternal and perinatal morbidity and mortality using CE strategies alongside mobile health-supported care by community health care providers. CE activities engaged pregnant women at their homes and male stakeholders through village meetings in Hyderabad and Matiari in Sindh, Pakistan. These sessions covered pregnancy complications, particularly pre-eclampsia/eclampsia, BPCR and details of the CLIP intervention package. BPCR was assessed using questions related to transport arrangement, permission for care, emergency funds, and choice of facility birth attendant for delivery during quarterly household surveys. Outcomes were assessed via multilevel logistic regression with adjustment for relevant confounders with effects summarized as odds ratios and 95% confidence intervals.Results: There were 15 137 home-based CE sessions with pregnant women and families (n = 46 614) and 695 village meetings with male stakeholders (n = 7784) over two years. The composite outcomes for BPCR and pre-eclampsia knowledge did not differ significantly between trial arms. However, CE activities were associated with improved pre-eclampsia knowledge in some areas. Specifically, pregnant women in the intervention clusters were twice as likely to know that seizures could be a complication of pregnancy (odds ratio (OR) = 2.17, 95% confidence interval (CI) = 1.11, 4.23) and 2.5 times more likely to know that high blood pressure is potentially life-threatening during pregnancy (OR = 2.52, 95% CI = 1.31, 4.83) vs control clusters.Conclusions: The findings suggested that a CE strategy for male and female community stakeholders increased some measures of knowledge regarding complications of pre-eclampsia in low-resource settings. However, the effect of this intervention on long-term health outcomes needs further study.Trial registration: Clinical Trials.gov - INCT01911494

Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model): a modelling study.

Affecting 2-4% of pregnancies, pre-eclampsia is a leading cause of maternal death and morbidity worldwide. Using routinely available data, we aimed to develop and validate a novel machine learning-based and clinical setting-responsive time-of-disease model to rule out and rule in adverse maternal outcomes in women presenting with pre-eclampsia. We used health system, demographic, and clinical data from the day of first assessment with pre-eclampsia to predict a Delphi-derived composite outcome of maternal mortality or severe morbidity within 2 days. Machine learning methods, multiple imputation, and ten-fold cross-validation were used to fit models on a development dataset (75% of combined published data of 8843 patients from 11 low-income, middle-income, and high-income countries). Validation was undertaken on the unseen 25%, and an additional external validation was performed in 2901 inpatient women admitted with pre-eclampsia to two hospitals in south-east England. Predictive risk accuracy was determined by area-under-the-receiver-operator characteristic (AUROC), and risk categories were data-driven and defined by negative (-LR) and positive (+LR) likelihood ratios. Of 8843 participants, 590 (6·7%) developed the composite adverse maternal outcome within 2 days, 813 (9·2%) within 7 days, and 1083 (12·2%) at any time. An 18-variable random forest-based prediction model, PIERS-ML, was accurate (AUROC 0·80 [95% CI 0·76-0·84] vs the currently used logistic regression model, fullPIERS: AUROC 0·68 [0·63-0·74]) and categorised women into very low risk (-LR 0·2 and +LR 10·0; 11 [1·0%] women). Adverse maternal event rates were 0% for very low risk, 2% for low risk, 5% for moderate risk, 26% for high risk, and 91% for very high risk within 48 h. The 2901 women in the external validation dataset were accurately classified as being at very low risk (0% with outcomes), low risk (1%), moderate risk (4%), high risk (33%), or very high risk (67%). The PIERS-ML model improves identification of women with pre-eclampsia who are at lowest and greatest risk of severe adverse maternal outcomes within 2 days of assessment, and can support provision of accurate guidance to women, their families, and their maternity care providers. University of Strathclyde Diversity in Data Linkage Centre for Doctoral Training, the Fetal Medicine Foundation, The Canadian Institutes of Health Research, and the Bill & Melinda Gates Foundation. [Abstract copyright: Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

A risk prediction model for the assessment and triage of women with hypertensive disorders of pregnancy in low-resourced settings: the miniPIERS (Pre-eclampsia Integrated Estimate of RiSk) multi-country prospective cohort study.

BACKGROUND: Pre-eclampsia/eclampsia are leading causes of maternal mortality and morbidity, particularly in low- and middle- income countries (LMICs). We developed the miniPIERS risk prediction model to provide a simple, evidence-based tool to identify pregnant women in LMICs at increased risk of death or major hypertensive-related complications. METHODS AND FINDINGS: From 1 July 2008 to 31 March 2012, in five LMICs, data were collected prospectively on 2,081 women with any hypertensive disorder of pregnancy admitted to a participating centre. Candidate predictors collected within 24 hours of admission were entered into a step-wise backward elimination logistic regression model to predict a composite adverse maternal outcome within 48 hours of admission. Model internal validation was accomplished by bootstrapping and external validation was completed using data from 1,300 women in the Pre-eclampsia Integrated Estimate of RiSk (fullPIERS) dataset. Predictive performance was assessed for calibration, discrimination, and stratification capacity. The final miniPIERS model included: parity (nulliparous versus multiparous); gestational age on admission; headache/visual disturbances; chest pain/dyspnoea; vaginal bleeding with abdominal pain; systolic blood pressure; and dipstick proteinuria. The miniPIERS model was well-calibrated and had an area under the receiver operating characteristic curve (AUC ROC) of 0.768 (95% CI 0.735-0.801) with an average optimism of 0.037. External validation AUC ROC was 0.713 (95% CI 0.658-0.768). A predicted probability ≥25% to define a positive test classified women with 85.5% accuracy. Limitations of this study include the composite outcome and the broad inclusion criteria of any hypertensive disorder of pregnancy. This broad approach was used to optimize model generalizability. CONCLUSIONS: The miniPIERS model shows reasonable ability to identify women at increased risk of adverse maternal outcomes associated with the hypertensive disorders of pregnancy. It could be used in LMICs to identify women who would benefit most from interventions such as magnesium sulphate, antihypertensives, or transportation to a higher level of care

Temporal and external validation of the fullPIERS model for the prediction of adverse maternal outcomes in women with pre-eclampsia

The fullPIERS model is a risk prediction model developed to predict adverse maternal outcomes within 48 h for women admitted with pre-eclampsia. External validation of the model is required before implementation for clinical use. We assessed the temporal and external validity of the fullPIERS model in high income settings using five cohorts collected between 2003 and 2016, from tertiary hospitals in Canada, the United States of America, Finland and the United Kingdom. The cohorts were grouped into three datasets for assessing the primary external, and temporal validity, and broader transportability of the model. The predicted risks of developing an adverse maternal outcome were calculated using the model equation and model performance was evaluated based on discrimination, calibration, and stratification. Our study included a total of 2429 women, with an adverse maternal outcome rate of 6.7%, 6.6%, and 7.0% in the primary external, temporal, and combined (broader) validation cohorts, respectively. The model had good discrimination in all datasets: 0.81 (95%CI 0.75-0.86), 0.82 (95%CI 0.76-0.87), and 0.75 (95%CI 0.71-0.80) for the primary external, temporal, and broader validation datasets, respectively. Calibration was best for the temporal cohort but poor in the broader validation dataset The likelihood ratios estimated to rule in adverse maternal outcomes were high at a cut-off of >= 30% in all datasets. The fullPIERS model is temporally and externally valid and will be useful in the management of women with pre-eclampsia in high income settings although model recalibration is required to improve performance, specifically in the broader healthcare settings.Peer reviewe

Moving beyond silos: How do we provide distributed personalized medicine to pregnant women everywhere at scale? Insights from PRE-EMPT.

While we believe that pre-eclampsia matters-because it remains a leading cause of maternal and perinatal morbidity and mortality worldwide-we are convinced that the time has come to look beyond single clinical entities (e.g. pre-eclampsia, postpartum hemorrhage, obstetric sepsis) and to look for an integrated approach that will provide evidence-based personalized care to women wherever they encounter the health system. Accurate outcome prediction models are a powerful way to identify individuals at incrementally increased (and decreased) risks associated with a given condition. Integrating models with decision algorithms into mobile health (mHealth) applications could support community and first level facility healthcare providers to identify those women, fetuses, and newborns most at need of facility-based care, and to initiate lifesaving interventions in their communities prior to transportation. In our opinion, this offers the greatest opportunity to provide distributed individualized care at scale, and soon

Maternal and Newborn Health in Karnataka State, India: The Community Level Interventions for Pre-Eclampsia (CLIP) Trial's Baseline Study Results.

Existing vital health statistics registries in India have been unable to provide reliable estimates of maternal and newborn mortality and morbidity, and region-specific health estimates are essential to the planning and monitoring of health interventions. This study was designed to assess baseline rates as the precursor to a community-based cluster randomized control trial (cRCT)-Community Level Interventions for Pre-eclampsia (CLIP) Trial (NCT01911494; CTRI/2014/01/004352). The objective was to describe baseline demographics and health outcomes prior to initiation of the CLIP trial and to improve knowledge of population-level health, in particular of maternal and neonatal outcomes related to hypertensive disorders of pregnancy, in northern districts the state of Karnataka, India. The prospective population-based survey was conducted in eight clusters in Belgaum and Bagalkot districts in Karnataka State from 2013-2014. Data collection was undertaken by adapting the Maternal and Newborn Health registry platform, developed by the Global Network for Women's and Child Health Studies. Descriptive statistics were completed using SAS and R. During the period of 2013-2014, prospective data was collected on 5,469 pregnant women with an average age of 23.2 (+/-3.3) years. Delivery outcomes were collected from 5,448 completed pregnancies. A majority of the women reported institutional deliveries (96.0%), largely attended by skilled birth attendants. The maternal mortality ratio of 103 (per 100,000 livebirths) was observed during this study, neonatal mortality ratio was 25 per 1,000 livebirths, and perinatal mortality ratio was 50 per 1,000 livebirths. Despite a high number of institutional deliveries, rates of stillbirth were 2.86%. Early enrollment and close follow-up and monitoring procedures established by the Maternal and Newborn Health registry allowed for negligible lost to follow-up. This population-level study provides regional rates of maternal and newborn health in Belgaum and Bagalkot in Karnataka over 2013-14. The mortality ratios and morbidity information can be used in planning interventions and monitoring indicators of effectiveness to inform policy and practice. Comprehensive regional epidemiologic data, such as that provided here, is essential to gauge improvements and challenges in maternal health, as well as track disparities found in rural areas