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Competing Interests: The authors declare that they have no conflicts of interest.

Objectives: Preeclampsia is characterized by maternal systemic inflammation and coagulation activation, akin to the sepsis syndrome. Recombinant human activated protein C (rhAPC; drotrecogin alfa [activated]) may modify disease progression to safely prolong pregnancies and improve perinatal outcomes. Both maternal and perinatal risks are highest remote from term., Study Design: Open-label, single arm safety and efficacy trial of rhAPC in consenting pregnant women with severe early-onset preeclampsia. Disease severity-matched rhAPC-naïve controls were identified from an existing database. An additional six women were recruited as biomarker controls., Main Outcome Measures: Primary safety outcome: incidence of peripartum bleeding; primary efficacy outcome: duration of pregnancy after enrolment., Results: Twelve (31.6%) of 38 eligible women consented; 3 did not receive the infusion due to staffing. Therefore, 9 women received rhAPC (24 μg/kg/hr for ≤96 h antenatally). No safety issues were identified. There was a marginal prolongation in eligibility-to-delivery intervals for women receiving rhAPC (Mantel-Cox p = 0.052; Gehan-Breslow-Wilcoxon p = 0.049). Compared with both the pre-infusion phase in the rhAPC-treated women themselves and with fullPIERS rhAPC-naïve women, rhAPC was associated with increased urine output during the infusion (6/9 vs 1/9 had urine output >100 mL/h during the infusion, Fisher's exact p = 0.003)., Conclusions: These data support further investigation of APC in women with severe early-onset preeclampsia; recombinant and purified human APC is available. In addition, these data will inform the design and implementation of randomized controlled trials aiming to modify and/or moderate the proinflammatory and proacoagulant state of preeclampsia., (Copyright © 2018. Published by Elsevier B.V.)

Background: The hypertensive disorders of pregnancy are a leading cause of maternal and perinatal mortality and morbidity. The ability to predict these complications using simple tests could aid in management and improve outcomes. We aimed to systematically review studies that reported on potential predictors of adverse maternal outcomes among women with a hypertensive disorder of pregnancy., Methods: We searched MEDLINE, Embase and CINAHL (inception - December 2016) for studies of predictors of severe maternal complications among women with a hypertensive disorder of pregnancy. Studies were selected in a two-stage process by two independent reviewers, excluding those reporting only on adverse fetal outcomes. We extracted data on study and test(s) characteristics and outcomes. Accuracy of prediction was assessed using sensitivity, specificity, likelihood ratios and area under the receiver operating curve (AUROC). Strong evidence of prediction was taken to be a positive likelihood ratio >10 or a negative likelihood ratio <0.1, and for multivariable models, an AUROC ≥0.70. Bivariate random effects models were used to summarise performance when possible., Results: Of 32 studies included, 28 presented only model development and four examined external validation. Tests included symptoms and signs, laboratory tests and biomarkers. No single test was a strong independent predictor of outcome. The most promising prediction was with multivariable models, especially when oxygen saturation, or chest pain/dyspnea were included., Conclusion: Future studies should investigate combinations of tests in multivariable models (rather than single predictors) to improve identification of women at high risk of adverse outcomes in the setting of the hypertensive disorders of pregnancy., (Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: To assess the performance of the Acute Physiology and Chronic Health Evaluation II (APACHE II) mortality prediction model in pregnant and recently pregnant women receiving critical care in low-, middle-, and high-income countries during the study period (1985-2015), using a structured literature review., Data Sources: Ovid MEDLINE, Embase, Web of Science, and Evidence-Based Medicine Reviews, searched for articles published between 1985 and 2015., Study Selection: Twenty-five studies (24 publications), of which two were prospective, were included in the analyses. Ten studies were from high-income countries (HICs), and 15 were from low- and middle-income countries (LMICs). Median study duration and size were six years and 124 women, respectively., Data Synthesis: ICU admission complicates 0.48% of deliveries, and pregnant and recently pregnant women account for 1.49% of ICU admissions. One quarter were admitted while pregnant, three quarters of these for an obstetric indication and for a median of three days. The median APACHE II score was 10.9, with a median APACHE II-predicted mortality of 16.6%. Observed mortality was 4.6%, and the median standardized mortality ratio was 0.36 (interquartile range 0.23 to 0.73). The standardized mortality ratio was < 0.9 in 24 of 25 studies. Women in HICs were more frequently admitted with a medical comorbidity but were less likely to die than were women in LMICs., Conclusion: The APACHE II score consistently overestimates mortality risks for pregnant and recently pregnant women receiving critical care, whether they reside in HICs or LMICs. There is a need for a pregnancy-specific outcome prediction model for these women., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To develop and internally validate a prognostic model for perinatal death that could guide community-based antenatal care of women with a hypertensive disorder of pregnancy (HDP) in low-resourced settings as part of a mobile health application., Study Design: Using data from 1688 women (110 (6.5%) perinatal deaths) admitted to hospital after 32weeks gestation with a HDP from five low-resourced countries in the miniPIERS prospective cohort, a logistic regression model to predict perinatal death was developed and internally validated. Model discrimination, calibration, and classification accuracy were assessed and compared with use of gestational age alone to determine prognosis., Main Outcome Measures: Stillbirth or neonatal death before hospital discharge., Results: The final model included maternal age; a count of symptoms (0, 1 or ⩾2); and dipstick proteinuria. The area under the receiver operating characteristic curve was 0.75 [95% CI 0.71-0.80]. The model correctly identified 42/110 (38.2%) additional cases as high-risk (probability >15%) of perinatal death compared with use of only gestational age <34weeks at assessment with increased sensitivity (48.6% vs. 23.8%) and similar specificity (86.6% vs. 90.0%)., Conclusion: Using simple, routinely collected measures during antenatal care, we can identify women with a HDP who are at increased risk of perinatal death and who would benefit from transfer to facility-based care. This model requires external validation and assessment in an implementation study to confirm performance., (Copyright © 2015 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. All rights reserved.)

Objective: The Canadian Perinatal Network (CPN) maintains an ongoing national database focused on threatened very preterm birth. The objective of the network is to facilitate between-hospital comparisons and other research that will lead to reductions in the burden of illness associated with very preterm birth., Methods: Women were included in the database if they were admitted to a participating tertiary perinatal unit at 22+0 to 28+6 weeks' gestation with one or more conditions most commonly responsible for very preterm birth, including spontaneous preterm labour with contractions, incompetent cervix, prolapsing membranes, preterm prelabour rupture of membranes, gestational hypertension, intrauterine growth restriction, or antepartum hemorrhage. Data were collected by review of maternal and infant charts, entered directly into standardized electronic data forms and uploaded to the CPN via a secure network., Results: Between 2005 and 2009, the CPN enrolled 2524 women from 14 hospitals including those with preterm labour and contractions (27.4%), short cervix without contractions (16.3%), prolapsing membranes (9.4%), antepartum hemorrhage (26.1%), and preterm prelabour rupture of membranes (23.0%). The mean gestational age at enrolment was 25.9 ± 1.9 weeks and the mean gestation age at delivery was 29.9 ± 5.1 weeks; 57.0% delivered at < 29 weeks and 75.4% at < 34 weeks. Complication rates were high and included serious maternal complications (26.7%), stillbirth (8.2%), neonatal death (16.3%), neonatal intensive care unit admission (60.7%), and serious neonatal morbidity (35.0%)., Conclusion: This national dataset contains detailed information about women at risk of very preterm birth. It is available to clinicians and researchers who are working with one or more CPN collaborators and who are interested in studies relating processes of care to maternal or perinatal outcomes.

Background: Pre-eclampsia is a leading cause of maternal deaths. These deaths mainly result from eclampsia, uncontrolled hypertension, or systemic inflammation. We developed and validated the fullPIERS model with the aim of identifying the risk of fatal or life-threatening complications in women with pre-eclampsia within 48 h of hospital admission for the disorder., Methods: We developed and internally validated the fullPIERS model in a prospective, multicentre study in women who were admitted to tertiary obstetric centres with pre-eclampsia or who developed pre-eclampsia after admission. The outcome of interest was maternal mortality or other serious complications of pre-eclampsia. Routinely reported and informative variables were included in a stepwise backward elimination regression model to predict the adverse maternal outcome. We assessed performance using the area under the curve (AUC) of the receiver operating characteristic (ROC). Standard bootstrapping techniques were used to assess potential overfitting., Findings: 261 of 2023 women with pre-eclampsia had adverse outcomes at any time after hospital admission (106 [5%] within 48 h of admission). Predictors of adverse maternal outcome included gestational age, chest pain or dyspnoea, oxygen saturation, platelet count, and creatinine and aspartate transaminase concentrations. The fullPIERS model predicted adverse maternal outcomes within 48 h of study eligibility (AUC ROC 0·88, 95% CI 0·84-0·92). There was no significant overfitting. fullPIERS performed well (AUC ROC >0·7) up to 7 days after eligibility., Interpretation: The fullPIERS model identifies women at increased risk of adverse outcomes up to 7 days before complications arise and can thereby modify direct patient care (eg, timing of delivery, place of care), improve the design of clinical trials, and inform biomedical investigations related to pre-eclampsia., Funding: Canadian Institutes of Health Research; UNDP/UNFPA/WHO/World Bank Special Programme of Research, Development, and Research Training in Human Reproduction; Preeclampsia Foundation; International Federation of Obstetricians and Gynecologists; Michael Smith Foundation for Health Research; and Child and Family Research Institute., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Background: Subtle changes in monitored physiological signals might be used to guide clinical actions and give early warning of potential adverse events. Automated early warning systems could enhance the clinician's interpretation of data by instantaneously processing new information and presenting it within the context of previous observations. In this study, we tested algorithms for tracking the behaviour of dynamic physiological systems and automatically detecting key events over time., Methods: Algorithms were activated in real-time during anaesthesia to run context-sensitive monitoring of six variables (end-tidal PCO(2), heart rate, exhaled minute ventilation, non-invasive arterial pressure, respiratory rate, and oxygen saturation), alongside standard physiological monitors. The clinical evaluation included real-time feedback on each change point (change in the physiological trend) detected by the algorithms and the completion of a usability questionnaire., Results: Fifteen anaesthetists completed the evaluation during paediatric surgical cases. A total of 38 cases were evaluated, with a mean duration of 103 (102) min. The mean number of change points per case was 22.8 (23.4). Sixty-one per cent of all rated change points were considered clinically significant, and <7% were due to artifacts., Conclusions: The algorithms were able to detect a range of clinically significant physiological changes during paediatric anaesthesia, and were considered useful by participating anaesthetists. These findings indicate that automated detection of context-sensitive changes is possible and could be used by early warning systems during physiological monitoring. Further investigations are required to assess how this information can best be communicated to the anaesthetist.

Background: The Canadian Perinatal Network (CPN) was launched in 2005 as a national perinatal database project designed to identify best practices in maternity care. The inaugural project of CPN is focused on interventions that optimize maternal and perinatal outcomes in women with threatened preterm birth at 22+0 to 28+6 weeks' gestation., Objective: To examine existing data collection by perinatal health programs (PHPs) to inform decisions about shared data collection and CPN database construction., Methods: We reviewed the database manuals and websites of all Canadian PHPs and compiled a list of data fields and their definitions. We compared these fields and definitions with those of CPN and the Canadian Minimal Dataset, proposed as a common dataset by the Canadian Perinatal Programs Coalition of Canadian PHPs., Results: PHPs collect information on 2/3 of deliveries in Canada. PHPs consistently collect information on maternal demographics (including both maternal and neonatal personal identifiers), past obstetrical history, maternal lifestyle, aspects of labour and delivery, and basic neonatal outcomes. However, most PHPs collect insufficient data to enable identification of obstetric (and neonatal) practices associated with improved maternal and perinatal outcomes. In addition, there is between-PHP variability in defining many data fields., Conclusion: Construction of a separate CPN database was needed although harmonization of data field definitions with those of the proposed Canadian Minimal Dataset was done to plan for future shared data collection. This convergence should be the goal of researchers and clinicians alike as we construct a common language for electronic health records.

We have studied 42 female patients undergoing elective day-case surgery allocated randomly to two groups. After induction of anaesthesia an attempt was made to insert a laryngeal mask airway after application of cricoid pressure in one group or with no cricoid pressure in the other. The anaesthetist was unaware of the application, or not, of cricoid pressure. Successful insertion was achieved at the first or second attempt in 19 of the 22 patients in the non-cricoid pressure group, but in only three of the 20 patients in the cricoid pressure group (chi 2 18.62, P < 0.001). The laryngeal mask airway was then inserted successfully in all 17 patients after removal of cricoid pressure. The implications of having to remove cricoid pressure if a laryngeal mask airway is to be inserted are discussed.