Helena J. Teede

Importance Monitoring and predicting optimal gestational weight gain (GWG) is important for maternal and child health. However, with recommendations based on total pregnancy GWG, available tools for real-time use in pregnancy care are lacking. These tools are prioritised by the WHO to enable healthcare providers to identify, monitor and target lifestyle interventions for those at high risk of suboptimal GWG and subsequent adverse health outcomes for mothers and babies. Objective This study aims to identify risk factors associated with GWG and to use these to develop an antenatal risk prediction tool for use during pregnancy to guide healthcare providers and women on optimal GWG, based on early pregnancy weight gain data. Design Routine health data from the Australian Monash Health Network birthing outcome system were used to analyse GWG in women of different body mass index (BMI) categories. Using data from 10 to 15, 15–20 and 15–25 weeks of pregnancy, we predicted the probability of women gaining inadequate or excessive total GWG by term. We used multinomial logistic regression to investigate associations between US National Academy of Medicine (NAM) classifications (inadequate, sufficient and excessive GWG) and BMI, age, country of birth (COB) by region, parity, socioeconomic status and visit frequency. Setting We used individual patient data routinely collected during care from one of the largest antenatal health networks in Australia. Participants The study included 17 397 women from 149 countries (based on the COB) of diverse socioeconomic backgrounds, with pregnancies between 2017 and 2021. Exposure Gestational weight gain. Results Overall, 31.5% gained below, 35.7% within and 32.8% above NAM GWG recommendations. Risk factors for excess GWG were higher BMI and maternal COB by region. Compared with the healthy BMI group, the overweight group has a 4.05 times higher adjusted relative risk of excess GWG (95% CI 3.37 to 4.80), and the obese group had a relative risk of 6.64 (95% CI 5.27 to 8.37). The risk prediction tool receiver operating characteristic curve was 0.81 for the 15–25 week, 0.80 for the 15–20 week and 0.69 for the 10–15 week GWG groups, with excellent performance in both discrimination and reliability. Conclusions and relevance From a large population of women from diverse socioeconomic backgrounds, we have identified risk factors for suboptimal GWG and developed and internally validated a risk prediction tool for attainment of recommended GWG from early pregnancy, with high performance. This tool is designed to enable clinicians to prospectively predict attainment of NAM GWG recommendations to guide risk stratification, monitoring and appropriate intervention for those at risk of suboptimal GWG.

Introduction Over one billion adults attend emergency departments (EDs) internationally every year, including 6.6 million in Australia. Up to half of these patients have a peripheral intravenous catheter (PIVC) inserted. Although healthcare workers believe that placing a cannula is helpful (‘just in case’), PIVCs often remain idle. PIVC insertion is painful for patients, takes clinicians’ attention away from other care, has adverse outcomes and causes major economic and environmental burden. Our aim is to codesign an implementation toolkit to reduce unnecessary PIVC insertions and improve other national quality indicators using an implementation science framework. Methods and analysis A stepped-wedge cluster-controlled trial will be conducted in nine ED sites (clusters) across Australia. The interventions will be codesigned with and adapted to sites based on local context. The interventions are evidence-based multimodal intervention (MMI) and aligned to the 2021 Australian Commission for Safety and Quality in Health Care National PIVC Clinical Care Standard. The Consolidated Framework for Implementation Research and Learning Health System will be used to guide implementation. Interventions will be phased across three steps (three sites per step), and each site will collect control and postintervention data using mainly routinely collected clinical data. Each site will be allocated to receive the intervention at one of three study steps. Implementation strategies will tailor broad clinician and consumer engagement, policy changes, education, audit and feedback and clinical champions, along with environment and equipment changes, to each site. The primary objective is to reduce the proportion of adult patients who have a PIVC inserted by 10%. We will evaluate the clinical, implementation and cost-effectiveness of the intervention. Study findings will be used to conduct a health economic analysis, develop an implementation toolkit and inform a sustainable roadmap for national roll-out. This will meet the needs of a diverse range of EDs nationally and internationally. Ethics and dissemination The protocol was approved by the Monash Health Human Research Ethics Committee (HREC Reference Number: HREC/100808/MonH-2023-390692(v3)). The outcomes of this trial will be disseminated through peer-reviewed publications, conference presentations and communication with study partners and stakeholders including professional colleges and the Australian Commission for Safety and Quality in Health Care.

Objective: Adverse lifestyle promotes escalating excess gestational weight gain (GWG) driving poor maternal and neonatal health outcomes. Recommended pregnancy lifestyle interventions rely on accurate assessment and prediction of GWG. A modelling technique to accommodate the complexities of GWG data and allow for the inclusion of maternal factors that influence the variation in GWG trajectory across pregnancy is necessary. We aimed to explore and determine the optimal statistical methods to accommodate data complexities such as nonlinearity, skewness and kurtosis and to model GWG trajectories from a large dataset of ethnically diverse pregnant women. Methods: This is a retrospective, observational study of routinely collected health data from women with singleton pregnancies from 2017 to 2021 delivering at one of the largest hospital networks in Australia, located in southeast Melbourne. Methods: There were 39 846 women with singleton pregnancies. Women had measurements taken during routine care at several time points throughout the pregnancy. Participants were from a diverse ethnic population, with the majority born overseas from 136 different countries (grouped into 12 world regions). Results: GWG was defined as the weight measured minus pre-pregnancy weight. Multiple statistical approaches were applied to model GWG trajectories: linear regression, cubic polynomial, neural network, generalised linear models and general additive model for location, scale and shape (GAMLSS) Box-Cox suite of models (including fitting fractional polynomials, cubic splines and penalised B-splines). Results: The dataset included 39 846 women and 109 339 GWG measurements. The two best-fitting models were derived using the GAMLSS Box-Cox t distribution: one with penalised B-splines and the other with cubic splines. Both models yielded the lowest Akaike information criterion and a generalised R-squared of 0.70. However, residual analysis indicated a preference for the model with penalised B-splines, making it the optimal choice. Using this optimal model, we demonstrate how to generate centile charts for the sample population. Conclusions: The optimal model developed will underpin our new epidemiological tool for the assessment and prediction of GWG. Using the model, individualised centile charts are relatively easy to produce, making them accessible to both healthcare providers and pregnant individuals. The visual nature of centile graphs makes it easier to see whether a woman's GWG is on track, which is helpful for making informed decisions about nutrition, lifestyle and healthcare.

Objective: To explore experiences of stigma related to gestational diabetes (GDM) among Australian women and collaboratively develop a comprehensive item pool to assess experienced and internalised GDM-specific stigma. Methods: A GDM Lived Experience Advisory Group (G-LEAG; n = 4) informed all aspects of a two-phase qualitative research process. Phase 1 included semi-structured online interviews with 20 women with current or recent GDM. Reflexive thematic analysis identified drivers and facilitators, markers, manifestations, impacts and protective mechanisms of GDM stigma. Findings informed the development of an item pool, debriefed and refined with a subset of 10 participants in Phase 2 interviews. Results: All participants perceived, experienced, anticipated and/or internalised GDM-specific stigma. They perceived GDM-specific stigma to be driven by stereotypes and blame, and facilitated by societal norms regarding motherhood and pregnancy, media messaging, as well as inflexible and inconsistent healthcare policies. They reported social, emotional and self care impacts, as well as perceived loss of autonomy in clinical care. They proposed potential protective mechanisms, including social and healthcare support, self-belief and self-compassion, and community awareness. In cognitive debriefing interviews, women reported that the draft items comprehensively covered their experiences of GDM-specific stigma and offered suggestions for refinement, resulting in a 74-item pool. Conclusions: Findings provide rich insights into the experiences and impacts of stigma among women with GDM in Australia and resulted in a co-designed GDM-specific stigma item pool. The item pool is ready for psychometric assessment and item reduction, which will enable future quantification of the occurrence, impacts and mechanisms of GDM-specific stigma.

Metformin is widely prescribed for the management of type 2 diabetes mellitus, polycystic ovary syndrome, and gestational diabetes mellitus in pregnancy. Its use is driven by factors including oral administration, lower patient and health system burden and cost, and benefits including lower risk of excess gestational weight gain and hypoglycemia compared with insulin. Metformin use appears safe in pregnancy; however, there remain concerns regarding long-term effects of intrauterine metformin exposure on offspring health. Randomized controlled trial follow-up studies suggest that metformin-exposed offspring may have altered postnatal growth trajectories and increased adiposity in childhood, although data are limited. Whether this is a transient adaptation or a precursor to long-term metabolic dysfunction is unclear, as data on cardiometabolic and neurodevelopmental parameters, including glucose homeostasis, lipid metabolism, and cognitive function, are sparse and inconsistent. Methodological challenges include heterogeneous study designs, high attrition rates, and inadequate control for confounding variables. Given these uncertainties, further well-powered, long-term prospective studies and individual patient data meta-analyses, harmonizing data and adjusting for confounders, are needed to clarify risks and benefits of metformin use in pregnancy. Until such data are available, clinicians must weigh the benefits and advantages of metformin use in pregnancy against the unknowns regarding potential long-term impact on offspring health. PLAIN LANGUAGE SUMMARY: Metformin is a medicine often used during pregnancy to help manage conditions such as type 2 diabetes, gestational diabetes, and polycystic ovary syndrome (PCOS). It is commonly chosen because it is taken as a tablet rather than by injection, has a lower risk of causing low blood sugar, and is generally easier and less expensive to use than insulin. Research has shown that metformin is safe for use during pregnancy in the short term. However, there are still questions about whether it has any lasting effects on children who were exposed to it before birth. This review explores this topic in detail. Some studies have found that children exposed to metformin during pregnancy may have slightly different growth patterns, such as having more body fat or being heavier in early childhood. However, these results are inconsistent and most studies show no clear differences in overall health outcomes, including in heart health, metabolism, or brain development. The results are mixed, and many studies are small or have design limitations, which makes it difficult to draw strong conclusions. At this stage, there is no clear evidence that metformin causes harm to children in the long term. However, because some studies suggest there may be effects on childhood growth and development, researchers emphasize the need for further long-term research. These future studies should follow children into adolescence and adulthood to better understand any lasting impacts. Until more is known, doctors and patients will need to carefully consider the known benefits of metformin in pregnancy alongside the current uncertainties about long-term effects on child health.