Multicomponent perinatal breastfeeding support in women with BMI >25: The Latch On multi-centre randomised trial.

OBJECTIVE: To investigate the effectiveness of a multicomponent breastfeeding support intervention on breastfeeding prevalence at 3 months among women with a body mass index (BMI) >25 kg/m2 . DESIGN: Multicentre multicomponent randomised controlled trial. SETTING: Four maternity centres in Ireland. POPULATION: A total of 225 primiparous women and their nominated support partners. Participants were aged 18 years and over, with BMI ≥25 kg/m2 , carrying a singleton pregnancy and without contraindication for breastfeeding. METHODS: The intervention included an antenatal group breastfeeding education session for participants and their support partners, followed by a planned postnatal breastfeeding assessment and telephone support for up to 6 weeks by a lactation consultant. MAIN OUTCOME MEASURES: Any breastfeeding at 3 months postpartum. RESULTS: Any breastfeeding prevalence was 68.7% (n = 68) in the intervention group and 62.1% (n = 59) in the control group at 3 months postpartum (odds ratio 1.33, 95% confidence interval 0.72-2.46, p = 0.36). Any and exclusive breastfeeding rates did not significantly differ at any other time point. More women in the control group accessed support from private lactation consultants (intervention 23.5% [n = 12], control 45.3% [n = 24], p = 0.02). CONCLUSIONS: The control group had higher than expected breastfeeding rates, and the study found no evidence of effect on the primary outcome. Providing comprehensive education and support for women intending to breastfeed remains of paramount importance.

Pregnancy complications and later life women's health.

There has been increasing recognition of the association between various pregnancy complications and development of chronic disease in later life. Pregnancy has come to be regarded as a physiological stress test, as the strain it places on a woman's body may reveal underlying predispositions to disease that would otherwise remain hidden for many years. Despite the increasing body of data, there is a lack of awareness among healthcare providers surrounding these risks. We performed a narrative literature review and have summarized the associations between the common pregnancy complications including gestational hypertension, pre-eclampsia, gestational diabetes, placental abruption, spontaneous preterm birth, stillbirth and miscarriage and subsequent development of chronic disease. Hypertensive disorders of pregnancy, spontaneous preterm birth, gestational diabetes, pregnancy loss and placental abruption are all associated with increased risk of various forms of cardiovascular disease. Gestational diabetes, pre-eclampsia, early miscarriage and recurrent miscarriage are associated with increased risk of diabetes mellitus. Pre-eclampsia, stillbirth and recurrent miscarriage are associated with increased risk of venous thromboembolism. Pre-eclampsia, gestational diabetes and stillbirth are associated with increased risk of chronic kidney disease. Gestational diabetes is associated with postnatal depression, and also with increased risk of thyroid and stomach cancers. Stillbirth, miscarriage and recurrent miscarriage are associated with increased risk of mental health disorders including depression, anxiety and post-traumatic stress disorders. Counseling in the postnatal period following a complicated pregnancy, and advice regarding risk reduction should be available for all women. Further studies are required to establish optimal screening intervals for cardiovascular disease and diabetes following complicated pregnancy.

Breastfeeding duration is associated with favorable body composition and lower glycoprotein acetyls in later life.

OBJECTIVE: The aim of the present study was to investigate associations between lifetime breastfeeding behaviors and cardiovascular risk in later reproductive years. METHOD: This was a prospective 10-year longitudinal cohort study of 168 parous women. Health, lifestyle and infant feeding questionnaires, blood samples, anthropometry and body composition were collected. Cardiovascular risk was estimated using QRISK®3 and hierarchical multiple linear regression analysis performed. RESULTS: Mean age was 42.4 years (SD 3.8; range 31-50) and 98.7% (n = 156/158) were premenopausal. Ever breastfeeding rates were 72.6% (n = 122/168) and 37.5% (n = 63/168) lifetime ≥12 months breastfeeding duration. Median durations were 5.5 weeks for exclusive breastfeeding (IQR 35.8; range 0-190) and 30.5 weeks for any breastfeeding (IQR 84.0; range 0-488). Breastfeeding duration was not associated with QRISK®3 scores in adjusted models. Lower glycoprotein acetyls were associated with ever breastfeeding (P = 0.03), and lifetime breastfeeding ≥12 months (P = 0.001). Lifetime breastfeeding ≥12 months and longer exclusive breastfeeding were associated with lower fat mass index (P = 0.03, P = 0.01), tissue percentage fat (P = 0.02, P = 0.009) and visceral adipose tissue volume (P = 0.04, P = 0.025) after correcting for confounders including body mass index. CONCLUSION: Longer breastfeeding is associated with favorable body composition and lower glycoprotein acetyls, a novel inflammatory biomarker associated with cardiometabolic risk. Breastfeeding is a low-cost, health promoting behavior for women and infants. Pregnant women, especially those at higher risk of cardiovascular disease, should be counseled about the potential benefits of exclusive and longer breastfeeding duration.

Machine learning-based clinical decision support systems for pregnancy care: A systematic review.

BACKGROUND: Clinical decision support systems (CDSSs) can provide various functions and advantages to healthcare delivery. Quality healthcare during pregnancy and childbirth is of vital importance, and machine learning-based CDSSs have shown positive impact on pregnancy care. OBJECTIVE: This paper aims to investigate what has been done in CDSSs in the context of pregnancy care using machine learning, and what aspects require attention from future researchers. METHODS: We conducted a systematic review of existing literature following a structured process of literature search, paper selection and filtering, and data extraction and synthesis. RESULTS: 17 research papers were identified on the topic of CDSS development for different aspects of pregnancy care using various machine learning algorithms. We discovered an overall lack of explainability in the proposed models. We also observed a lack of experimentation, external validation and discussion around culture, ethnicity and race from the source data, with most studies using data from a single centre or country, and an overall lack of awareness of applicability and generalisability of the CDSSs regarding different populations. Finally, we found a gap between machine learning practices and CDSS implementation, and an overall lack of user testing. CONCLUSION: Machine learning-based CDSSs are still under-explored in the context of pregnancy care. Despite the open problems that remain, the few studies that tested a CDSS for pregnancy care reported positive effects, reinforcing the potential of such systems to improve clinical practice. We encourage future researchers to take into consideration the aspects we identified in order for their work to translate into clinical use.