Maternal Risk Modeling in Critical Care-Development of a Multivariable Risk Prediction Model for Death and Prolonged Intensive Care.

OBJECTIVES: We aimed to develop and validate an accurate risk prediction model for both mortality and a combined outcome of mortality and morbidity for maternal admissions to critical care. DESIGN: We used data from a high-quality prospectively collected national database, supported with literature review and expert opinion. We tested univariable associations between each risk factor and outcome. We then developed two separate multivariable logistic regression models for the outcomes of acute hospital mortality and death or prolonged ICU length of stay. We validated two parsimonious risk prediction models specific for a maternal population. SETTING: The Intensive Care National Audit and Research Centre Case Mix Programme is the national clinical audit for adult critical care in England, Wales, and Northern Ireland. PATIENTS: All female admissions to adult general critical care units, for the period January 1, 2007-December 31, 2016, 16-50 years old, and admitted either while pregnant or within 42 days of delivery-a cohort of 15,480 women. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We aimed to develop and validate an accurate risk prediction model for both mortality and a combined outcome of mortality and morbidity for maternal admissions to critical care. For the primary outcome of acute hospital mortality, our parsimonious risk model consisting of eight variables had an area under the receiver operating characteristic of 0.96 (95% CI, 0.91-1.00); these variables are commonly available for all maternal admissions. For the secondary composite outcome of death or ICU length of stay greater than 48 hours, the risk model consisting of 17 variables had an area under the receiver operating characteristic of 0.80 (95% CI, 0.78-0.83). CONCLUSIONS: We developed risk prediction models specific to the maternal critical care population. The models compare favorably against general adult ICU risk prediction models in current use within this population.

Severe acute maternal morbidity trends in Victoria, 2001-2017.

BACKGROUND: The incidence of severe acute maternal morbidity (SAMM) is one method of measuring the complexity of maternal health and monitoring maternal outcomes. Monitoring trends may provide a quantitative method for assessing health care at local, regional, or jurisdictional levels and identify issues for further investigation. AIMS: Identify temporal trends for SAMM event rates and maternal outcomes over 17 years in the state of Victoria, Australia. MATERIALS AND METHODS: All maternal public health service admissions were extracted from an administrative dataset from July 2000 to June 2017. SAMM-related diagnoses were defined by matching as closely as possible with published definitions. Outcomes included annual SAMM event rates, hospital survival, and hospital length of stay (LOS). Temporal trends were analysed using mixed-effects generalised linear models. RESULTS: There were 854 777 live births and 1.21 million pregnancy-related hospital admissions which included 34 008 SAMM events in 29 273 records and in 3.42% (95%CI = 3.39-3.46) of births. Most common were severe pre-eclampsia (0.87% of births), severe postpartum haemorrhage (0.59%), and sepsis (0.62%). SAMM-related admissions were associated with longer LOS and higher mortality risk (P < 0.001). Maternal mortality ratio remained unchanged at 8.6 fatalities per 100 000 births (P = 0.65). CONCLUSION: Over 17 years, there was a significant increase in birth rate and SAMM-related events in Victoria. Administrative data may provide a pragmatic approach for monitoring SAMM-related events in maternal health services.

Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?

BACKGROUND: Preterm birth is a clinical event significant but difficult to predict. Biomarkers such as fetal fibronectin and cervical length are effective, but the often are used only for women with clinically suspected preterm risk. It is unknown whether routinely collected data can be used in early pregnancy to stratify preterm birth risk by identifying asymptomatic women. This paper tries to determine the value of the Victorian Perinatal Data Collection (VPDC) dataset in predicting preterm birth and screening for invasive tests. METHODS: De-identified VPDC report data from 2009 to 2013 were extracted for patients from Barwon Health in Victoria. Logistic regression models with elastic-net regularization were fitted to predict 37-week preterm, with the VPDC antenatal variables as predictors. The models were also extended with two additional variables not routinely noted in the VPDC: previous preterm birth and partner smoking status, testing the hypothesis that these two factors add prediction accuracy. Prediction performance was evaluated using a number of metrics, including Brier scores, Nagelkerke's R(2), c statistic. RESULTS: Although the predictive model utilising VPDC data had a low overall prediction performance, it had a reasonable discrimination (c statistic 0.646 [95% CI: 0.596-0.697] for 37-week preterm) and good calibration (goodness-of-fit p = 0.61). On a decision threshold of 0.2, a Positive Predictive Value (PPV) of 0.333 and a negative predictive value (NPV) of 0.941 were achieved. Data on previous preterm and partner smoking did not significantly improve prediction. CONCLUSIONS: For multiparous women, the routine data contains information comparable to some purposely-collected data for predicting preterm risk. But for nulliparous women, the routine data contains insufficient data related to antenatal complications.

Pregnant women's knowledge of weight, weight gain, complications of obesity and weight management strategies in pregnancy.

BACKGROUND: Obesity is increasingly common in the obstetric population. Maternal obesity and excess gestational weight gain (GWG) are associated with increased perinatal risk. There is limited published data demonstrating the level of pregnant women's knowledge regarding these problems, their consequences and management strategies.We aimed to assess the level of knowledge of pregnant women regarding: (i) their own weight and body mass index (BMI) category, (ii) awareness of guidelines for GWG, (iii) concordance of women's own expectations with guidelines, (iv) knowledge of complications associated with excess GWG, and (v) knowledge of safe weight management strategies in pregnancy. METHODS: 364 pregnant women from a single center university hospital antenatal clinic were interviewed by an obstetric registrar. The women in this convenience sample were asked to identify their weight category, their understanding of the complications of obesity and excessive GWG in pregnancy and safe and/or effective weight management strategies in pregnancy. RESULTS: Nearly half (47.8%) of the study population were overweight or obese. 74% of obese women underestimated their BMI category. 64% of obese women and 40% of overweight women overestimated their recommended GWG. Women's knowledge of the specific risks associated with excess GWG or maternal obesity was poor. Women also reported many incorrect beliefs about safe weight management in pregnancy. CONCLUSIONS: Many pregnant women have poor knowledge about obesity, GWG, their consequences and management strategies. Bridging this knowledge gap is an important step towards improving perinatal outcomes for all pregnant women, especially those who enter pregnancy overweight or obese.