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

Availability and use of magnesium sulphate at health care facilities in two selected districts of North Karnataka, India

Background: Pre-eclampsia and eclampsia are major causes of maternal morbidity and mortality. Magnesium sulphate is accepted as the anticonvulsant of choice in these conditions and is present on the WHO essential medicines list and the Indian National List of Essential Medicines, 2015. Despite this, magnesium sulphate is not widely used in India for pre-eclampsia and eclampsia. In addition to other factors, lack of availability may be a reason for sub-optimal usage. This study was undertaken to assess the availability and use of magnesium sulphate at public and private health care facilities in two districts of North Karnataka, India. Methods: A facility assessment survey was undertaken as part of the Community Level Interventions for Pre-eclampsia (CLIP) Feasibility Study which was undertaken prior to the CLIP Trials (NCT01911494). This study was undertaken in 12 areas of Belagavi and Bagalkote districts of North Karnataka, India and included a survey of 88 facilities. Data were collected in all facilities by interviewing the health care providers and analysed using Excel.Results: Of the 88 facilities, 28 were public, and 60 were private. In the public facilities, magnesium sulphate was available in six out of 10 Primary Health Centres (60%), in all eight taluka (sub-district) hospitals (100%), five of eight community health centres (63%) and both district hospitals (100%). Fifty-five of 60 private facilities (92%) reported availability of magnesium sulphate. Stock outs were reported in six facilities in the preceding six months – five public and one private. Twenty-five percent weight/volume and 50% weight/volume concentration formulations were available variably across the public and private facilities. Sixty-eight facilities (77%) used the drug for severe pre-eclampsia and 12 facilities (13.6%) did not use the drug even for eclampsia. Varied dosing schedules were reported from facility to facility.Conclusions: Poor availability of magnesium sulphate was identified in many facilities, and stock outs in some. Individual differences in usage were identified. Ensuring a reliable supply of magnesium sulphate, standard formulations and recommendations of dosage schedules and training may help improve use; and decrease morbidity and mortality due to pre-eclampsia/ eclampsia

Community-level interventions for pre-eclampsia (CLIP) in Pakistan: A cluster randomised controlled trial

Objectives: To reduce all-cause maternal and perinatal mortality and major morbidity through Lady Health Worker (LHW)-facilitated community engagement and early diagnosis, stabilization and referral of women with preeclampsia, an important contributor to adverse maternal and perinatal outcomes given delays in early detection and initial management.Study design: In the Pakistan Community-Level Interventions for Pre-eclampsia (CLIP) cluster randomized controlled trial (NCT01911494), LHWs engaged the community, recruited pregnant women from 20 union councils (clusters), undertook mobile health-guided clinical assessment for preeclampsia, and referral to facilities after stabilization.Main outcome measures: The primary outcome was a composite of maternal, fetal and newborn mortality and major morbidity.Findings: We recruited 39,446 women in intervention (N = 20,264) and control clusters (N = 19,182) with minimal loss to follow-up (3∙7% vs. 4∙5%, respectively). The primary outcome did not differ between intervention (26·6%) and control (21·9%) clusters (adjusted odds ratio, aOR, 1∙20 [95% confidence interval 0∙84-1∙72]; p = 0∙31). There was reduction in stillbirths (0·89 [0·81-0·99]; p = 0·03), but no impact on maternal death (1·08 [0·69, 1·71]; p = 0·74) or morbidity (1·12 [0·57, 2·16]; p = 0·77); early (0·95 [0·82-1·09]; p = 0·46) or late neonatal deaths (1·23 [0·97-1·55]; p = 0·09); or neonatal morbidity (1·22 [0·77, 1·96]; p = 0·40). Improvements in outcome rates were observed with 4-7 (p = 0·015) and ≥8 (p \u3c 0·001) (vs. 0) CLIP contacts.Interpretation: The CLIP intervention was well accepted by the community and implemented by LHWs. Lack of effects on adverse outcomes could relate to quality care for mothers with pre-eclampsia in health facilities. Future strategies for community outreach must also be accompanied by health facility strengthening.Funding: The University of British Columbia (PRE-EMPT), a grantee of the Bill & Melinda Gates Foundation (OPP1017337)

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

Economic and cost-effectiveness analysis of the community-level interventions for pre-eclampsia (CLIP) trials in India, Pakistan and Mozambique

Background: The Community-Level Interventions for Pre-eclampsia (CLIP) trials (NCT01911494) in India, Pakistan and Mozambique (February 2014-2017) involved community engagement and task sharing with community health workers for triage and initial treatment of pregnancy hypertension. Maternal and perinatal mortality was less frequent among women who received ≥8 CLIP contacts. The aim of this analysis was to assess the incremental costs and cost-effectiveness of the CLIP intervention overall in comparison to standard of care, and by PIERS (Pre-eclampsia Integrated Estimate of RiSk) On the Move (POM) mobile health application visit frequency.Methods: Included were all women enrolled in the three CLIP trials who had delivered with known outcomes by trial end. According to the number of POM-guided home contacts received (0, 1-3, 4-7, ≥8), costs were collected from annual budgets and spending receipts, with inclusion of family opportunity costs in Pakistan. A decision tree model was built to determine the cost-effectiveness of the intervention (vs usual care), based on the primary clinical endpoint of years of life lost (YLL) for mothers and infants. A probabilistic sensitivity analysis was used to assess uncertainty in the cost and clinical outcomes.Results: The incremental per pregnancy cost of the intervention was US$12.66 (India), US$11.51 (Pakistan) and US$13.26 (Mozambique). As implemented, the intervention was not cost-effective due largely to minimal differences in YLL between arms. However, among women who received ≥8 CLIP contacts (four in Pakistan), the probability of health system and family (Pakistan) cost-effectiveness was ≥80% (all countries).Conclusion: The intervention was likely to be cost-effective for women receiving ≥8 contacts in Mozambique and India, and ≥4 in Pakistan, supporting WHO guidance on antenatal contact frequency.Trial registration number: NCT01911494

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

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

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

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