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Gestational Diabetes Mellitus is linked to emotional well-being in a bi-directional manner. This study aims to map and summarize factors and proposed mechanisms contributing to this link

Group B streptococcus (GBS) is a group of naturally occurring bacteria that colonises the anogenital region of every third pregnant woman. From the anogenital region they can colonise the urine and cause bacteriuria. It is well documented that treatment of GBS-bacteriuria with more than 104 colony forming units per millilitre (CFU/ml) reduces the risk of maternal and neonatal morbidity. There is, however, no clear evidence as summarised in this review that GBS-bacteriuria more than 104 CFU/ml increases the risk of maternal and neonatal morbidity which is why no treatment is warranted.

Additional file 2. PICO Question and search strategy. Describing the participants, intervention, comparator and outcomes. Search terms for each search are presented.

Additional file 1. PRISMA IPD Checklist. Checklist of items to include when reporting an individual participant data meta-analysis.

Background: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. Objective : To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers. Design: This was an individual participant data meta-analysis of cohort studies. Setting: Source data from secondary and tertiary care. Predictors: We identified predictors from systematic reviews, and prioritised for importance in an international survey. Primary outcomes Early-onset (delivery at = 34 weeks' gestation) and any-onset pre-eclampsia. Analysis: We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration. We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of >= 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using l(2) and tau(2). A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals. Result The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia. Limitations: Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data. Conclusion: For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings. Future work: Recalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate.

BACKGROUND: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. METHODS: IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. RESULTS: Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%. CONCLUSIONS: The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice. TRIAL REGISTRATION: PROSPERO ID: CRD42015029349 .

Additional file 1: Supplementary methods: Additional details for handling missing data and evaluating predictive performance of models. Table S1: Search strategy for pre-eclampsia prediction models. Table S2: Predictors evaluated in the models externally validated in the IPPIC-UK cohorts. Table S3: Prediction models and equations identified from the literature search. Table S4: Study level characteristics of IPPIC-UK cohorts. Table S5: Patient characteristics of IPPIC-UK cohorts. Table S6: Number and proportion missing for each predictor in each cohort used for external validation. Table S7: Risk of bias assessment of the IPPIC-UK cohorts using the PROBAST tool. Table S8: Summary of linear predictor values and predicted probabilities for each model in each cohort. Table S9: Predictive performance statistics for models in the individual IPPIC-UK cohorts. Table S10: Predictive performance statistics for models in nulliparous women in all cohorts and in the POP cohort. Fig. S1: Decision curves for early pre-eclampsia models in SCOPE, UPBEAT and POP. Fig. S2: Decision curves for late pre-eclampsia models in SCOPE, Allen 2017, UPBEAT and POP.

Additional file 1. SPIRIT Checklist.

Proportion of women with gestational weight gain outside the Institute of Medicine recommendations (2009) by kilogram. Number and proportion of women by each kilogram of GWG above (A) or below (B) the Institute of Medicine recommendations (2009) - overall and by baseline BMI category (DOCX 19 kb)

Adverse pregnancy outcomes according to adherence to the Institute of Medicine recommendations (2009). Proportion of adverse pregnancy outcomes according to adherence to the Institute of Medicine recommendations (2009) overall and by baseline BMI category (DOCX 18 kb)

Additional file 1. Search string.

Characteristics of women classified according to the Institute of Medicine recommendations (2009). Table with baseline characteristics of women from the control arms of randomised trials used in the analyses classified by adherence to the Institute of Medicine (2009) recommendations (DOCX 21 kb)

Lists of potential confounders. Tables with confounders considered for individual models depending on the outcome of interest (DOCX 20 kb)

Background High Body Mass Index (BMI) and gestational weight gain (GWG) affect an increasing number of pregnancies. The Institute of Medicine (IOM) has issued recommendations on the optimal GWG for women according to their pre-pregnancy BMI (healthy, overweight or obese). It has been shown that pregnant women rarely met the recommendations; however, it is unclear by how much. Previous studies also adjusted the analyses for various women’s characteristics making their comparison challenging. Methods We analysed individual participant data (IPD) of healthy women with a singleton pregnancy and a BMI of 18.5 kg/m2 or more from the control arms of 36 randomised trials (16 countries). Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were used to describe the association between GWG outside (above or below) the IOM recommendations (2009) and risks of caesarean section, preterm birth, and large or small for gestational age (LGA or SGA) infants. The association was examined overall, within the BMI categories and by quartile of GWG departure from the IOM recommendations. We obtained aOR using mixed-effects logistic regression, accounting for the within-study clustering and a priori identified characteristics. Results Out of 4429 women (from 33 trials) meeting the inclusion criteria, two thirds gained weight outside the IOM recommendations (1646 above; 1291 below). The median GWG outside the IOM recommendations was 3.1 kg above and 2.7 kg below. In comparison to GWG within the IOM recommendations, GWG above was associated with increased odds of caesarean section (aOR 1.50; 95%CI 1.25, 1.80), LGA (2.00; 1.58, 2.54), and reduced odds of SGA (0.66; 0.50, 0.87); no significant effect on preterm birth was detected. The relationship between GWG below the IOM recommendation and caesarean section or LGA was inconclusive; however, the odds of preterm birth (1.94; 1.31, 2.28) and SGA (1.52; 1.18, 1.96) were increased. Conclusions Consistently with previous findings, adherence to the IOM recommendations seem to help achieve better pregnancy outcomes. Nevertheless, even in the context of clinical trials, women find it difficult to adhere to them. Further research should focus on identifying ways of achieving a healthier GWG as defined by the IOM recommendations. Electronic supplementary material The online version of this article (10.1186/s12884-019-2472-7) contains supplementary material, which is available to authorized users.

PRISMA-P 2015 Checklist. (DOCX 30Â kb)

AIM: Trials on diet and physical activity in pregnancy report on various outcomes. We aimed to assess the variations in outcomes reported and their quality in trials on lifestyle interventions in pregnancy.METHODS: We searched major databases without language restrictions for randomized controlled trials on diet and physical activity-based interventions in pregnancy up to March 2015. Two independent reviewers undertook study selection and data extraction. We estimated the percentage of papers reporting 'critically important' and 'important' outcomes. We defined the quality of reporting as a proportion using a six-item questionnaire. Regression analysis was used to identify factors affecting this quality.RESULTS: Sixty-six randomized controlled trials were published in 78 papers (66 main, 12 secondary). Gestational diabetes (57.6%, 38/66), preterm birth (48.5%, 32/66) and cesarian section (60.6%, 40/66), were the commonly reported 'critically important' outcomes. Gestational weight gain (84.5%, 56/66) and birth weight (87.9%, 58/66) were reported in most papers, although not considered critically important. The median quality of reporting was 0.60 (interquartile range 0.25, 0.83) for a maximum score of one. Study and journal characteristics did not affect quality.CONCLUSION: Many studies on lifestyle interventions in pregnancy do not report critically important outcomes, highlighting the need for core outcome set development.

Aim: Obese women undergoing caesarean section are at increased risk of surgical wound infection, which may lead to reduced quality of life, and increased health care cost. The aim is to evaluate the effect of incisional Negative Pressure Wound Therapy applied prophylactically in obese women undergoing caesarean section.Method: This study is designed as an unblinded, randomised trial. Women with a pre-gestational BMI ≥30 undergoing planned or emergency caesarean section are allocated into one of two groups (incisional Negative Pressure Wound Therapy vs. standard dressing), stratified by centre and type of caesarean section. The dressing is applied immediately following the surgical procedure. In the intervention group the therapy will be left in situ for five days. In the control group the dressing will be left in situ for at least 24 hours as standard procedure. Follow-up is 30 days.Conclusion: The study is on-going. We expect to find a 50% reduction of wound infection when using iNPWT compared to standard dressings in this high-risk subpopulation.

Aim: Postoperative wound complications make many surgical procedures unnecessarily complex, particularly in high-risk patients. Negative Pressure Wound Therapy is well recognized in the management of open wounds. In recent years, it has been introduced as well in the management of closed surgical incisions to reduce postoperative wound complications, though the evidence base to support this intervention is limited. The aim of this study was to assess if Negative Pressure Wound Therapy (NPWT) reduces postoperative complications when applied on closed surgical incisions.Method: A systematic review and meta-analysis of randomized controlled trials of NPWT compared to standard postoperative dressings on closed incisions. Results: Ten studies met the inclusion criteria, reporting on 1344 incisions (1121 patients). NPWT was associated with a significant reduction of wound infection (46%), and seroma formation (52%) compared to standard care. The reduction in wound dehiscence was not statistically significant. The numbers needed to treat were 3 (seroma), 17 (dehiscence), and 25 (infection). Methodical heterogeneity across studies led to downgrading quality of evidence to moderate for infection and seroma, and low for dehiscence. Conclusion: Compared with standard postoperative dressings, NPWT significantly reduces the rate of wound infection and seroma when applied to closed surgical wounds, but diversity in clinical and methodological aspects of the included studies means that no absolute or general recommendations can be made. Aim: Postoperative wound complications make many surgical procedures unnecessarily complex, particularly in high-risk patients. Negative Pressure Wound Therapy is well recognized in the management of open wounds. In recent years, it has been introduced as well in the management of closed surgical incisions to reduce postoperative wound complications, though the evidence base to support this intervention is limited. The aim of this study was to assess if Negative Pressure Wound Therapy (NPWT) reduces postoperative complications when applied on closed surgical incisions.Method: A systematic review and meta-analysis of randomized controlled trials of NPWT compared to standard postoperative dressings on closed incisions. Results: Ten studies met the inclusion criteria, reporting on 1344 incisions (1121 patients). NPWT was associated with a significant reduction of wound infection (46%), and seroma formation (52%) compared to standard care. The reduction in wound dehiscence was not statistically significant. The numbers needed to treat were 3 (seroma), 17 (dehiscence), and 25 (infection). Methodical heterogeneity across studies led to downgrading quality of evidence to moderate for infection and seroma, and low for dehiscence. Conclusion: Compared with standard postoperative dressings, NPWT significantly reduces the rate of wound infection and seroma when applied to closed surgical wounds, but diversity in clinical and methodological aspects of the included studies means that no absolute or general recommendations can be made.

Background: Obese women undergoing caesarean section are at increased risk of surgical wound complications, which may lead to delayed recovery, pain, reduced quality of life, and increased health care cost. The aim of this study is to evaluate the effect of incisional Negative Pressure Wound Therapy (iNPWT) applied prophylactically following caesarean section compared with standard postoperative dressings in obese women undergoing caesarean section.Method: This study is designed as an unblinded, pragmatic, parallel randomised multicenter trial, with a 1:1 allocation. A computer random number generator are used to allocate the participants into one of two groups (iNPWT vs. standard dressing), stratified by centre and type of CS. The study is conducted at five public hospitals located in three regions of Denmark. Interim analyses will be preformed along the trial using the group sequential method. The analyses will be performed when sample size reaches 179, 357, 535, 713, and 891, respectively. Population: women with a pre-gestational BMI ≥ 30 undergoing planned or emergency caesarean section. The iNPWT or standard dressings is applied immediately following operation. In the intervention group the therapy will be left in situ for five days. In the control group the dressing will be left in situ for at least 24 hours as standard procedure. Follow-up is 30 days.Results: The study is on going. The first 179 participants are about to be analysed. Overall we expect to find a 50% reduction of wound infection when using iNPWT compared to standard postoperative dressings in this high-risk subpopulation.

One third of the pregnant Danish women are overweight or obese. Maternal obesity is an independent risk factor for adverse maternal and foetal outcomes including infertility, miscarriage, congenital malformations, preeclampsia, gestational diabetes, complicated deliveries, caesarean section, macrosomia and childhood obesity. This article reviews the effect of maternal obesity on obstetric and neonatal outcomes and provides recommendations for management of obesity in pregnancy.

To study the effects of lifestyle intervention on gestational weight gain (GWG) and obstetric outcomes.

Udgivelsesdato: 10.06

Funder: NIHR School for Primary Care Research; doi: http://dx.doi.org/10.13039/501100013374, BACKGROUND: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. METHODS: IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. RESULTS: Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%. CONCLUSIONS: The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice. TRIAL REGISTRATION: PROSPERO ID: CRD42015029349 .

After publication of this work [1], we noted that we inadvertently failed to include the complete list of all coauthors and that sample sizes of some of the trials listed in Table two were incorrect.