iWIP 

Full title: Effects of weight management interventions on maternal and fetal outcomes in pregnancy: Individual patient data (IPD) meta analysis of randomised trials and model based economic evaluation

Acronym: iWIP Individual Patient Data meta-analysis

Research Funder: NIHR - Health Technology Assessment 

Research status: Ongoing

OVERVIEW

Maternal obesity and excess weight gain in pregnancy are associated with maternal and foetal complications in pregnancy and in the long term. We recently completed an evidence synthesis project commissioned by the HTA on diet and lifestyle interventions that reduce or prevent obesity in pregnancy. The project identified the largest number of studies to date and showed that weight management interventions in pregnancy are effective in reducing maternal weight gain compared to standard care. Diet based interventions were more effective in reducing weight gain in pregnancy and improved pregnancy outcomes compared to physical activity based interventions. The findings were based on data published by the studies. We were limited in identifying if the effect of weight management interventions in pregnancy differed according to the weight in pre pregnancy (normal, overweight or obese), ethnicity, teenage pregnancies and socioeconomic status. Furthermore, the published data did not allow us to assess the relationship between the amount of weight change in pregnancy and risk of maternal and fetal complications. The only guidance on weight gain recommendations in pregnancy is provided by the Institute of Medicine (IOM) in the US. The UK policy makers do not recommend specific weight gain targets in pregnancy due to absence of robust evidence. Access to the data of individual patients in the published and unpublished studies will allow us to obtain detailed information to answer the above questions. The statistical technique of combining the data from individual patients in the studies to generate estimates of benefit of weight management interventions is known as Individual Patient Data (IPD) Meta-analysis. To address the above gaps in evidence, we have established collaboration (i-WIP International Weight Management in Pregnancy IPD Collaboration) of investigators who have conducted studies evaluating the effectiveness of interventions in pregnancy on maternal weight gain and complications to the mother and baby. We have access so far to over 4000 individual patient data through this collaborative network.
The proposed project will provide us the much needed funding to establish a data base of the individual data, facilitate access to the primary data including reformatting where necessary, strengthen the Network and for statistical support. We have shown that by pooling the individual data together, we have sufficient power to estimate with increased confidence, the differential effects if any of the weight management interventions in various groups, allowing us to target the population that needs the most support for a beneficial outcome. It will also generate recommendations on optimal weight gain in pregnancy to minimise maternal and fetal complications and the cost effectiveness of these interventions.

STAFF

Investigators

Chief investigator:

Shakila Thangaratinam, Professor of Maternal and Perinatal Health Women's Health Research Unit | Multidisciplinary Evidence Synthesis Hub (MESH) The Blizard Institute | Barts and the London School of Medicine and Dentistry | Queen Mary University of London 
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Co-investigator:

Khalid S Khan, Professor of Women's Health and Clinical Epidemiology, Women's Health Research Unit | The Blizard Institute | Barts and The London School of Medicine | Queen Mary University of London 
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ben Willem Mol, Professor of Obstetrics and Gynaecology and Clinical Epidemiology Academic Medical Centre, Amsterdam, The Netherlands 
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ari Coomarasamy, Professor of Gynaecology, Reproduction, Genes and Development School of Clinical and Experimental Medicine College of Medical and Dental Sciences, University of Birmingham 
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Statistical team

Richard Riley, Reader in Biostatistics Public Health, Epidemiology and Biostatistics College of Medical and Dental Sciences, University of Birmingham 
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Sally Kerry, Reader in Medical Statistics Centre for Primary Care and Public Health | The Blizard Institute | Barts and the London School of Medicine | Queen Mary University of London 
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Nadine Marlin, PCTU Statistician Centre for Primary Care and Public Health | The Blizard Institute | Barts and The London School of Medicine | Queen Mary University of London 
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Project Management

Ewelina Rogozinska, Acting Project Co-ordinator, Women's Health Research Unit | Multidisciplinary Evidence Synthesis Hub (MESH) | The Blizard Institute| Barts and The London School of Medicine and Dentistry | Queen Mary University of London 
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Julie Dodds, Senior Trials Co-ordinator, Women's Health Research Unit | The Blizard Institute | Barts and The London School of Medicine | Queen Mary University of London 
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Research Assistants

Anneloes Ruifrok, Researcher Academic Medical Centre, Amsterdam, The Netherlands 
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Girish Rayanagoudar, Clinical Research Fellow, Endocrinology and Diabetes, Queen Mary University of London

Emma Molyneaux, Researcher Health Services and Population Research, King's College London

Admin support

Anna Placzek, Women's Health Research Unit | The Blizard Institute, Barts and The London School of Medicine | Queen Mary, University of London

Tracy Holtham, Women's Health Research Unit | The Blizard Institute, Barts and The London School of Medicine | Queen Mary, University of London

OTHER INFORMATION

Scientific Summary

DESIGN: Individual patient data (IPD) meta analysis of randomised trials Our IPD meta-analytical approach will follow existing guidelines and our output will comply as a minimum with the PRISMA statement, and adhere to recent reporting guidelines for IPD meta-analysis. Our methods will be as follows:

SEARCH STRATEGY: As a first step in the IPD meta-analysis, we will update the literature search to identify new trials since completion of our systematic review (HTA No. 09/27/06) on effects of weight management interventions in pregnancy. The following databases will be searched: MEDLINE, EMBASE, BIOSIS, LILACS, Pascal, Science Citation Index, Cochrane Database of Systematic Reviews (CDSR), Cochrane Central Register of Controlled Trials (CENTRAL), Database of Abstracts of Reviews of Effects (DARE) and Health Technology Assessment Database (HTA). Language restrictions will not be applied to electronic searches. Authors of the included studies from the International Weight management In Pregnancy IPD collaboration (i-WIP) will also be asked to examine the included study list to identify any studies or data that might have been missed.

ESTABLISHMENT OF IPD COLLABORATIVE GROUP: We have already established the i-WIP (International Weight Management in Pregnancy) collaborative group that includes representatives from all the groups which have published trials on weight management interventions in pregnancy. We have provisional support to date from 22 study investigators for access to individual patient data for over 4000 women.

DATA COLLECTION, ENTRY AND CHECKING AND STUDY QUALITY: All variables recorded, even those not reported in the published studies, will be considered for collection and for planning subgroup analyses with sufficient statistical power. A bespoke database will be set up and authors will be allowed to supply data in whatever way convenient to them. The quality of each trial will be also be assessed at this stage, for example to evaluate the integrity of the randomisation and follow up procedure.

DATA SYNTHESIS

-Summarising overall effect of weight management interventions: Meta-analyses of the effectiveness of weight management interventions in pregnancy will be performed for the weight related and critically important pregnancy outcomes identified by the Delphi survey. Then, for each intervention type and outcome separately, we will perform either a one-step or a two-step IPD meta-analysis to obtain the pooled intervention effect.

-Examining heterogeneity and potential subgroup effects: To consider the causes of heterogeneity and factors that may modify intervention effect, for each weight management intervention we will perform pre-specified subgroup analyses by BMI, age, parity, ethnicity, underlying risk factors like diabetes and type of intervention. Examination of subgroup effects will be undertaken by extending the one-stage meta-analysis framework to include treatment-covariate interaction terms, which provide the change in intervention effect for a 1-unit change in the covariate.

-Examining optimal levels of weight gain in pregnancy that minimises adverse outcomes: For each BMI group separately and each outcome, we will fit a suitable regression model that accounts for clustering of patient within studies and quantifies how each
1-unit increase in weight gain changes the risk of a poor outcome.

-Evaluation of predictors of weight change in pregnancy: For all candidate predictors, we will perform separate analyses in each BMI cohort (normal, overweight and obese) and analyse on the whole meta-analysis database, adjusting again for the clustering of patients within studies.

-Exploration of sources of bias: unavailable data and publication bias: For each analysis containing 10 or more studies the likelihood of publication bias will be investigated through the construction of contour-enhanced funnel plots and appropriate statistical tests for 'small-study effects'.

HEALTH ECONOMIC AND DECISION ANALYTIC MODELLING:

The cost-effectiveness of weight management interventions in pregnancy will be evaluated using effect size estimates for these interventions obtained from the IPD meta-analysis to determine the characteristics of the weight management intervention that are most cost-effective. The cost and health-related quality of life implications of the interventions will be assessed using other relevant data in the published literature. In the base case, the outcome measure will be the cost per life year gained, taking into account only the cost of the intervention and health care costs saved as a result of reduced infant and maternal mortality. Value of information analyses will also be conducted in order to identify the most important parameters for further investigation, and the economic value of such research.

EXPECTED OUTPUT OF RESEARCH:

We currently have access to data from 4000 individual women. This is a work in progress. Thus there is about a 50-fold increase in the sample size for our IPD project compared to the median number, n=81 (smallest n=12; largest n=1000) in the trials and substantially increase the power to detect genuine interactions. This will enable intervention effects to be quantified for clinically relevant groups. It will also allow the magnitude of benefit due to weight change in pregnancy to be quantified for both the mother and baby. This will allow us to implement those weight management interventions that show clear benefit with specific weight gain targets in pregnancy. For a new trial to detect a 30% reduction in composite pregnancy outcome, thousands of patients are required. This will be practically difficult and expensive. In contrast, our IPD meta-analysis provides an efficient way to substantially increase the sample size, without the need for a new trial, to obtain the sufficient numbers required in most of the categories considered. The economic evaluation will determine the characteristics of the weight management intervention that are most cost-effective.