Nutritional advice for improving outcomes in multiple pregnancies
Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:
To assess the effects of special diets, or dietary advice, on the outcome of multiple pregnancies.
Background
Description of the condition
Multiple pregnancies occur when two or more fetuses are present in the uterus; this can be a result of implantation of two genetically different fertilised eggs by two different sperm (dizygotic twins), or due to the division of a one fertilised egg (monozygotic). Monozygotic twins can share the same placenta (monochorionic) or have separate placentas (dichorionic) (Fox 2006).
Twin pregnancies can be diagnosed by an ultrasound scan as early as six weeks. Women with twin pregnancies may have more severe nausea and vomiting than those with singleton pregnancies, as a result of higher levels of hCG (Rao 2004).
Since the 1970s there has been a steady increase in the number of twin pregnancies; from 9.6 per 1000 maternities in England and Wales (ONS 2006) in 1976 to 15.5 in 2008 (ONS 2009); in the USA, 18.8 per 1000 were twins in 1975 (Taffel 1992) rising to 32.1 per 1000 in 2006 (Martin 2009). This rise has been attributed to the increase in the use of assisted reproductive technology, and to some women deciding to have children later in life. An increase in maternal age increases the chances of twin pregnancy. In the USA it was found that 20% of twin pregnancies from 1980 to 1997 were due to spontaneous conception, 40% due to in vitro fertilisation and 40% due to ovulation induction (Nakhuda 2005). In Europe, spontaneously conceived twins comprise 1% of all deliveries; 20% to 30% of all twins are due to assisted reproductive technology (Hansen 2009).
Twin pregnancies are associated with higher rates of complications for both the mother and the fetuses. Gestational hypertension and pre‐eclampsia are twice as likely to occur as in singleton pregnancies with risk ratios of 2 and 2.6 respectively (Siddiqui 2007). There is a higher incidence of antepartum and postpartum haemorrhage, with the average blood loss after delivery being 500 ml higher than after singleton birth (Rao 2004).
Mulitple births have a substantial perinatal mortality rate (PMR) compared to singletons. In England and Wales, in 2006, there was an overall PMR of 8.2 per 1000 births with twins having a rate of 27.2 per 1000, and triplets and higher order births 81.8 per 1000 (CEMACH 2008). When infants are born long before term they are functionally immature, and are usually admitted to neonatal intensive care unit. They have poor thermoregulatory mechanisms, which is worsened by low brown fat levels and heat loss. Respiratory distress syndrome occurs as a result of lack of surfactant, and may be associated with chronic lung disease (bronchopulmonary dysplasia) (Keeling 2000). Multiple pregnancies have a strong association with preterm deliveries (before 37 weeks), ranging from 48.2% in Ireland to 68.4% in Austria; between 39.1 and 47.1% occur at 32 to 36 weeks, and 8.1 to 12.7% before 32 weeks (Blondel 2006).
Fifty percent of twins are small for gestational age (SGA) by 39 weeks using the 1991 US singleton live birth values. Ten per cent to 15% of multiple pregnancies (twins and triplets) are SGA by 22 to 30 weeks' gestation (Alexander 1998).
Cerebral palsy is also eight times more likely to occur in twins, and has a greater prevalence when the birthweight is less than 1000 g (Pharoah 2002). When infants are below 1500 g, 30% to 65% will have neurological problems ranging from motor to cognitive/behavioural deficits. Other severe neurological concerns are intraventricular haemorrhage and periventricular leukomalacia. Necrotising enterocolitis also affects low birthweight infants, and there is a high risk of infection due to an immature immune system. Prematurity is a risk factor for retinopathy of prematurity (ROP); the retina is vascularly immature as there is insufficient vascularisation due to poor production of angiogenic factors (Keeling 2000).
Depending on the chorionicity of the twins, there are specific complications. For example 10% to 15% of monochorionic twins have twin‐twin transfusion syndrome which can result in fetal death if left untreated. This occurs as a result of anastomoses on the placenta, resulting in a compromise of blood flow to one of the twins which results in poor growth while the other twin receives the redirected blood from the placenta (Rao 2004).
The risk to mother and fetus is increased even more when the number of fetus increases. High order pregnancies such as triplets and quadruplets have higher rates of perinatal mortality and have an earlier gestation at delivery; the average gestation for triplets is 33.4 weeks. High order births are more likely to deliver earlier on in the pregnancy than twins, 16.9% triplets and 29.2% quadruplets were born before 29 weeks compared to 5.6% of twins. The number of fetuses also impacts on the chances of survival for both twins, with 6.3 % triplets, 8.0 % quadruplets pregnancies having one or more fetal deaths compared to 2.4 % of twins (Luke 2008).
In one series, 100% of triplets were admitted to the neonatal intensive care unit, and 54% had a major morbidity (retinopathy of the newborn, necrotising enterocolitis, ventilator support or intraventricular haemorrhage) (Luke 2006).
Description of the intervention
It has been suggested that improved diets may lead to improved outcomes in multiple pregnancies. This is based on observations that a particular range of maternal weight gain is optimally associated with good fetal outcome (Goodnight 2009). However, the converse is not necessarily true ‐ that outcome can be improved by boosting maternal weight gain through improved diet.
Dietary input consists of many different components that may be relevant to multiple pregnancy. These include quantity of calorific intake, the source of calories such as a fat or proteins, micronutrients such as vitamin C, vitamin E, thiamine, magnesium, calcium and zinc as well the inclusion of certain nutrients such as omega fats. The balance and amount of these components have the potential to impact on the health of the mother and fetuses.
Detailed dietary advice has been popular in the US. For example, Luke et al recommend a calorie intake of 4000 kcal for underweight mothers (body mass index (BMI) 19.8) with a total weight gain of 50 lbs to 62 lbs (23‐28 kgs); 3500 kcal for normal weight (BMI 19.8 to 26.0) with a weight gain of 40 lbs to 54 lbs (18‐25 kgs); 3250 kcal for overweight (BMI 26.1 to 29.0) 38 lbs to 47 lbs (17‐21 kgs) and 3000 kcal for obese mothers (BMI greater than 29.0) with a weight gain of 29 lbs to 38 lbs (13‐17 kgs) (Luke 2005).
The Institute of Medicine in the US has produced guidelines recommending a weight gain of 37 lbs to 59 lbs (17‐28 kgs) for normal weight, 31 lbs to 50 lbs (14‐23 kgs) for overweight, and 25 lbs to 42 lbs (11‐19 kgs) for obese women (Rasmussen 2009).
Published work also discusses the importance of a high carbohydrate diet as it has been linked to unwanted weight gain, poor glycaemic control and poor fetal growth, whereas a diet consisting of 20% protein, 40% carbohydrate, and 40% fat was found to have better fetal growth (Luke 2005).
Haematinic supplements such as folic acid and iron will not be considered in this review, as they are assessed elsewhere (Pena‐Rosas 2009).
How the intervention might work
In multiple pregnancies, the metabolic rate of the mother is 10% greater than in women with singleton pregnancies (Shinagawa 2005), as a result of an increasing demand on the mother’s energy expenditure due to a larger placenta producing higher quantities of hormones, and two fetuses requiring a continuous nutrient supply for growth (Goodnight 2009). As a result of the increased energy use, fasting glucose levels are lower with a resulting depletion of glycogen reserves. Fats are broken down as an alternative energy source (Luke 2005).
The theoretical basis for recommending high calorie diets is to increase the provision nutrients to prevent the likelihood of poor fetal growth. Poor fetal growth may have a number of adverse consequences and can also be associated with preterm birth. A high calorie diet may also help maintain the mother's nutritional state, which otherwise may be depleted by the needs of the babies, resulting in impaired maternal sense of well being. A retrospective study has shown that women with normal weights at the start of twin pregnancies are more likely to have larger neonates and less likely to deliver preterm if their weight gain during pregnancy met US Institute of Medicine recommendations (Fox 2010).
Why it is important to do this review
Some recommended diets for women with multiple pregnancies have calorific contents similar or greater than those for frontline troops. As some women with multiple pregnancies are more sedentary than they would be when not pregnant, these diets have the potential to be associated with substantial weight gain. Excess weight gain is associated with increased complications in labour and higher rates of caesarean births, gestational diabetes, pre‐eclampsia and anaesthesia related risks (Reece 2008). Obese mothers have higher rates of fetal macrosomia, as well as increased chance of infection post surgery. By advocating a high calorie diet and a high weight gain it is important to consider the psychological impact this might have on the mother as well as future implications for her health. The mother will then have to face the difficulty of losing her extra weight postpartum, as obesity is linked to type 2 diabetes, cardiovascular disease, and thromboembolism (Castro 2002). It therefore needs to be established whether special diets do lead to improved outcomes for the babies and whether these cause unwanted weight gain in the mother.
It is also important to consider the financial implications, as a high calorie diet may be expensive.
Objectives
To assess the effects of special diets, or dietary advice, on the outcome of multiple pregnancies.
Methods
Criteria for considering studies for this review
Types of studies
We will include randomised controlled trials; 'quasi‐random' studies, and cluster‐randomised trials. We will not include crossover trials and studies presented only as abstracts.
Types of participants
Women with multiple pregnancies (two or more fetuses) either nulliparous or multiparous and their babies.
Types of interventions
Specialised diets or specific dietary advice for multiple pregnancies, whether or not demonstrated to have an impact on weight gain during pregnancy, compared with usual care or alternative diets/advice.
Types of outcome measures
Primary outcomes
-
Early preterm births (before 34 weeks)
-
Small‐for‐gestational age at birth (as defined by the trialists)
Secondary outcomes
Maternal outcomes
-
Weight gain
-
Caesarean births
-
Instrumental vaginal delivery
-
Maternal satisfaction/dissatisfaction/anxiety
Fetal outcomes
-
Preterm birth less than 37 weeks
-
Very early preterm birth less than 28 weeks
-
Respiratory distress syndrome
-
Intraventricular haemorrhage
-
Periventricular leucomalacia
-
Retinopathy of prematurity
-
Necrotising enterocolitis
-
Admission to neonatal intensive care unit
-
Perinatal death
Child outcomes
-
Learning difficulties
-
Developmental delay (as defined by the trialists)
-
Growth (weight, head circumference, height/length)
-
Cerebral palsy
Search methods for identification of studies
Electronic searches
We will contact the Trials Search Co‐ordinator to search the Cochrane Pregnancy and Childbirth Group’s Trials Register.
The Cochrane Pregnancy and Childbirth Group’s Trials Register is maintained by the Trials Search Co‐ordinator and contains trials identified from:
-
quarterly searches of the Cochrane Central Register of Controlled Trials (CENTRAL);
-
weekly searches of MEDLINE;
-
handsearches of 30 journals and the proceedings of major conferences;
-
weekly current awareness alerts for a further 44 journals plus monthly BioMed Central email alerts.
Details of the search strategies for CENTRAL and MEDLINE, the list of handsearched journals and conference proceedings, and the list of journals reviewed via the current awareness service can be found in the ‘Specialized Register’ section within the editorial information about the Cochrane Pregnancy and Childbirth Group.
Trials identified through the searching activities described above are each assigned to a review topic (or topics). The Trials Search Co‐ordinator searches the register for each review using the topic list rather than keywords.
Searching other resources
We will search the reference lists of retrieved articles.
We will not apply any language restrictions.
Data collection and analysis
Selection of studies
Two review authors will independently assess for inclusion all the potential studies we identify as a result of the search strategy. We will resolve any disagreement through discussion or, if required, we will consult a third person.
Data extraction and management
We will design a form to extract data. For eligible studies, at least two review authors will extract the data using the agreed form. We will resolve discrepancies through discussion or, if required, we will consult a third person. We will enter data into Review Manager software (RevMan 2008) and check for accuracy.
When information regarding any of the above is unclear, we will attempt to contact authors of the original reports to provide further details.
Assessment of risk of bias in included studies
Two review authors will independently assess risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2009). We will resolve any disagreement by discussion or by involving a third assessor.
(1) Sequence generation (checking for possible selection bias)
We will describe for each included study the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it should produce comparable groups.
We will assess the method as:
-
adequate (any truly random process, e.g. random number table; computer random number generator);
-
inadequate (any non‐random process, e.g. odd or even date of birth; hospital or clinic record number); or
-
unclear.
(2) Allocation concealment (checking for possible selection bias)
We will describe for each included study the method used to conceal the allocation sequence and determine whether intervention allocation could have been foreseen in advance of, or during recruitment, or changed after assignment.
We will assess the methods as:
-
adequate (e.g. telephone or central randomisation; consecutively numbered sealed opaque envelopes);
-
inadequate (open random allocation; unsealed or non‐opaque envelopes, alternation; date of birth);
-
unclear.
(3) Blinding (checking for possible performance bias)
We will describe for each included study the methods used, if any, to blind study participants and personnel from knowledge of which intervention a participant received. We will consider that studies are at low risk of bias if they were blinded, or if we judge that the lack of blinding could not have affected the results. We will assess blinding separately for different outcomes or classes of outcomes.
We will assess the methods as:
-
adequate, inadequate or unclear for participants;
-
adequate, inadequate or unclear for personnel;
-
adequate, inadequate or unclear for outcome assessors.
(4) Incomplete outcome data (checking for possible attrition bias through withdrawals, dropouts, protocol deviations)
We will describe for each included study, and for each outcome or class of outcomes, the completeness of data including attrition and exclusions from the analysis. We will state whether attrition and exclusions were reported, the numbers included in the analysis at each stage (compared with the total randomised participants), reasons for attrition or exclusion where reported, and whether missing data were balanced across groups or were related to outcomes. Where sufficient information is reported, or can be supplied by the trial authors, we will re‐include missing data in the analyses which we undertake. We will assess methods as:
-
adequate (missing data < 20%);
-
inadequate;
-
unclear.
(5) Selective reporting bias
We will describe for each included study how we investigated the possibility of selective outcome reporting bias and what we found.
We will assess the methods as:
-
adequate (where it is clear that all of the study’s pre‐specified outcomes and all expected outcomes of interest to the review have been reported);
-
inadequate (where not all the study’s pre‐specified outcomes have been reported; one or more reported primary outcomes were not pre‐specified; outcomes of interest are reported incompletely and so cannot be used; study fails to include results of a key outcome that would have been expected to have been reported);
-
unclear.
(6) Other sources of bias
We will describe for each included study any important concerns we have about other possible sources of bias.
We will assess whether each study was free of other problems that could put it at risk of bias:
-
yes;
-
no;
-
unclear.
(7) Overall risk of bias
We will make explicit judgements about whether studies are at high risk of bias, according to the criteria given in the Handbook (Higgins 2009). With reference to (1) to (6) above, we will assess the likely magnitude and direction of the bias and whether we consider it is likely to impact on the findings. We will explore the impact of the level of bias through undertaking sensitivity analyses ‐ seeSensitivity analysis.
Measures of treatment effect
Dichotomous data
For dichotomous data, we will present results as summary risk ratio with 95% confidence intervals.
Continuous data
For continuous data, we will use the mean difference if outcomes are measured in the same way between trials. We will use the standardised mean difference to combine trials that measure the same outcome, but use different methods.
Unit of analysis issues
Cluster‐randomised trials
We will include cluster‐randomised trials in the analyses along with individually randomised trials. We will adjust their standard errors using the methods described in the Handbook using an estimate of the intracluster correlation co‐efficient (ICC) derived from the trial (if possible), from a similar trial or from a study of a similar population. If we use ICCs from other sources, we will report this and conduct sensitivity analyses to investigate the effect of variation in the ICC. If we identify both cluster‐randomised trials and individually‐randomised trials, we plan to synthesise the relevant information. We will consider it reasonable to combine the results from both if there is little heterogeneity between the study designs and the interaction between the effect of intervention and the choice of randomisation unit is considered to be unlikely.
We will also acknowledge heterogeneity in the randomisation unit and perform a subgroup analysis to investigate the effects of the randomisation unit.
Multiple pregnancies
For neonatal outcomes, we will use the number of babies as the denominator, whereas for maternal outcomes we will use the number of women as the denominator.
Dealing with missing data
For included studies, we will note levels of attrition. We will explore the impact of including studies with high levels of missing data in the overall assessment of treatment effect by using Sensitivity analysis.
For all outcomes, we will carry out analyses, as far as possible, on an intention‐to‐treat basis, i.e. we will attempt to include all participants randomised to each group in the analyses, and all participants will be analysed in the group to which they were allocated, regardless of whether or not they received the allocated intervention. The denominator for each outcome in each trial will be the number randomised minus any participants whose outcomes are known to be missing.
Assessment of heterogeneity
We will assess statistical heterogeneity in each meta‐analysis using the T², I² and Chi² statistics. We will regard heterogeneity as substantial if T² is greater than zero and either I² is greater than 30% or there is a low P value (less than 0.10) in the Chi² test for heterogeneity.
Assessment of reporting biases
If there are 10 or more studies in the meta‐analysis we will investigate reporting biases (such as publication bias) using funnel plots. We will assess funnel plot asymmetry visually, and use formal tests for funnel plot asymmetry. For continuous outcomes we will use the test proposed by Egger 1997, and for dichotomous outcomes we will use the test proposed by Harbord 2006. If we detect asymmetry in any of these tests or by a visual assessment, we will perform exploratory analyses to investigate it.
Data synthesis
We will carry out statistical analysis using the Review Manager software (RevMan 2008). We will use fixed‐effect meta‐analysis for combining data where it is reasonable to assume that studies are estimating the same underlying treatment effect: i.e. where trials are examining the same intervention, and the trials’ populations and methods are judged sufficiently similar. If there is clinical heterogeneity sufficient to expect that the underlying treatment effects differ between trials, or if substantial statistical heterogeneity is detected, we will use random‐effects meta‐analysis to produce an overall summary if an average treatment effect across trials is considered clinically meaningful. We will treat the random‐effects summary as the average range of possible treatment effects and we will discuss the clinical implications of treatment effects differing between trials. If the average treatment effect is not clinically meaningful we will not combine trials.
If we use random‐effects analyses, we will present the results as the average treatment effect with its 95% confidence interval, and the estimates of T² and I².
Subgroup analysis and investigation of heterogeneity
If we identify substantial heterogeneity, we will investigate it using subgroup analyses and sensitivity analyses. We will consider whether an overall summary is meaningful, and if it is, use random‐effects analysis to produce it.
We plan to carry out the following subgroup analyses.
-
Twin versus higher order multiple pregnancies.
-
Primigravid versus parous women.
-
Underweight versus normal versus overweight women.
We will use the following outcomes in subgroup analysis.
-
Early preterm births (before 34 weeks)
-
Small‐for‐gestational age at birth (as defined by the trialists)
For fixed‐effect inverse variance meta‐analyses we will assess differences between subgroups by interaction tests. For random‐effects and fixed‐effect meta‐analyses using methods other than inverse variance, we will assess differences between subgroups by inspection of the subgroups’ confidence intervals; non‐overlapping confidence intervals indicate a statistically significant difference in treatment effect between the subgroups.
Sensitivity analysis
Should there be several included trials, we plan to carry out a sensitivity analysis based on trial quality. We will restrict this to the primary outcomes only.
