Dopamine agonists for preventing future miscarriage in women with idiopathic hyperprolactinemia and recurrent miscarriage history
Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:
To assess the effectiveness and safety of different types of dopamine agonists versus a placebo in preventing future miscarriage given to women who had idiopathic hyperprolatinemia with a history of recurrent miscarriages.
Background
Description of the condition
Recurrent miscarriage is defined as three or more consecutive spontaneous ending of a pregnancy at a stage where the embryo or fetus is incapable of surviving, generally within the first 20 weeks of gestation (Katharina 2008). In the face of declining birth rates, more and more researchers tend to define recurrent miscarriage to be at least two spontaneous abortions (Toth 2010).Ten to fifteen per cent of all clinically recognized pregnancies end in a miscarriage (Regan 1989), and recurrent miscarriage affects 1%‐3% of all women (Stirrat 1990; Toth 2010). The most common symptoms of a miscarriage are vaginal bleeding, cramping and pain (Yip 2003).
Recurrent miscarriage is a heterogeneous condition, of which the etiology is not completely understood. Known risk factors include chromosomal abnormalities, endocrine disorders (luteal phase deficiency, thyroid disorders, diabetes mellitus, high androgen levels, hyperprolactinaemia, polycystic ovary syndrome, antiphospholipid syndrome, etc.), anatomic abnormalities (uterine synechiae, cervical incompetence, intrauterine adhesion, uterine malformation such as uterine septum, uterine fibroids, scar tissue, etc.), immunologic factors (humoral response abnormalities, cellular response abnormalities, etc), infections and endometriosis (Daya 2004; García‐Enguídanos 2002; Toth 2010). Increased age, tobacco, caffeine, alcohol, and administration of certain drugs may also increase a woman's risk for miscarriage (García‐Enguídanos 2002). Still, in nearly 50% of recurrent miscarriage patients, the underlying cause remains unknown (Toth 2010).
Prolactin is a peptide hormone, the structure of which is similar to that of growth hormone and placental lactogen, mainly produced by the lactotrope cells in the anterior pituitary gland (Mancini 2008). In normal condition, its secretion is regulated by the prolactin inhibitory factors (PIF) and prolactin‐releasing factors (PRF) from the pituitary. Dopamine, the main PIF, acts on surface membrane dopamine D2 receptors on lactotroph cells to decrease prolactin. Its secretion has a diurnal cycle with a nocturnal rise, peaking during rapid eye movement and in the early morning, and decreasing thereafter. With commonly used assays, normal prolactin levels in women are usually less than 25 μg/L (Chahal 2008). Appropriate amounts of prolactin are important for development of ovarian follicles and the corpus luteum. Other effects include stimulation of the mammary glands to produce milk, providing the body with sexual gratification after sex, inhibition of sex steroids and contribution to surfactant synthesis of the fetal lungs (Tyson 1973).
Hyperprolactinemia is the presence of abnormally high circulating levels of prolactin, which is defined to be above 25 μg/L (Chahal 2008). Conditions associated with it include pituitary tumors, hypothyroidism, renal failure, polycystic ovarian syndrome, physiological causes (pregnancy, breastfeeding, stress, etc.), pregnancy, breastfeeding, certain drugs (risperidone and metoclopramide, etc.) (Chahal 2008; Stirrat 1990). High levels of prolactin may cause production and spontaneous flow of breast milk. It also tends to inhibit the secretion of both follicle‐stimulating hormone (FSH) and gonadotrophin‐releasing hormone (GnRH), leading to hypogonadism, hypoestrogenism, menses changes (i.e., menstrual flow changes, irregular menses, amenorrhea), osteopenia and anovulatory infertility. When no cause of hyperprolactinemia can be identified, the condition is termed idiopathic hyperprolactinemia. Long term follow‐up has shown that microadenomas appear in about 10% of patients with idiopathic hyperprolactinemia (Chahal 2008).
Clinical observation found prolactin concentrations were significantly higher in women experiencing recurrent miscarriage, suggesting that hyperprolactinemia was causally related to the development of miscarriage, especially in women with recurrent miscarriage in whom no other cause for their repeated pregnancy loss was apparent (Ando 1992; Bussen 1999; Hirahara 1996; Hirahara 1998). Hirahara identified this situation as hyperprolatinemic recurrent miscarriage (Hirahara 1998). A possible mechanism is that high levels of prolactin affect function of the ovaries, resulting in luteal phase defect and miscarriage. In clinics, doctors tend to examine the patients' serum prolactin levels when no cause of recurrent miscarriages has been found, and treatment is given when hyperprolactinemia is found.
Description of the intervention
A dopamine agonist is a compound that activates signalling pathways through the dopamine receptor in the brain, decreasing the production of prolactin and treating hyperprolactinaemia. Examples of dopamine agonists include bromocriptine, cabergoline, and quinagolide. Bromocriptine has no effect on placenta or fetus, and could be used orally or vaginally. First‐pass metabolism by the liver can be avoided when bromocriptine is supplemented vaginally, while this administration has no affect on sperm activity (Carranza‐Lira 1999). Rat studies show cabergoline has a direct inhibitory effect on pituitary lactotroph cells, and cabergoline is frequently used as a second‐line agent in the management of hyperprolactinaemia when bromocriptine is ineffective. Quinagolide acts on D2 receptor and is also used for the treatment of elevated levels of prolactin. Side effects of dopamine agonists include nausea, vomiting, headache, hypotension, arrhythmia, and psychotic symptoms.
How the intervention might work
The possible mechanisms of dopamine agonist on preventing recurrent miscarriage include (Lecomte 1997; Seppälä 1976):
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it acts on ovaries directly to promote synthesis of steroid hormones;
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it acts on pituitary to promote synthesis of steroid hormones;
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it acts on hypothalamus to promote secretion of luteinizing hormone‐releasing hormone (LHRH);
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it inhibits secretion of prolactin (PRL).
Why it is important to do this review
Although recurrent miscarriage affects only 1%–3% of women, it influences the well being and psychosocial status of patients. Bussen and Hirahara reported hyperprolactinemia was found in around 36% of recurrent miscarriage patients (Bussen 1999; Hirahara 1996). Due to the fact that prolactin levels are important in maintaining early pregnancy and hyperprolactinaemia is relatively common in women who miscarry (Ando 1992; Bussen 1999; Hirahara 1996; Hirahara 1998), hyperprolactinaemia may be linked to recurrent miscarriage. However, the pathophysiologic mechanisms are still unclear. Accordingly we set out to determine the benefits and harms from dopamine agonist in preventing future miscarriage given to women who had idiopathic hyperprolatinemia with a history of recurrent miscarriages.
Objectives
To assess the effectiveness and safety of different types of dopamine agonists versus a placebo in preventing future miscarriage given to women who had idiopathic hyperprolatinemia with a history of recurrent miscarriages.
Methods
Criteria for considering studies for this review
Types of studies
Randomized controlled trials (RCTs) in all languages which examine the effect of dopamine agonists on preventing future miscarriage, given to women who had idiopathic hyperprolatinemia with a history of recurrent miscarriages are eligible for inclusion in this review. We will not include quasi‐RCTs in this review. We will exclude studies published only in abstract form whereby no further or insufficient information could be procured from the authors.
Types of participants
This review will only include patients with primary idiopathic hyperprolactinemia.
Types of interventions
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Dopamine agonists alone versus placebo.
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Dopamine agonists combined with other therapy versus other therapy alone.
Types of outcome measures
Primary outcomes
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Rates of live births (term delivery or premature delivery).
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Rates of miscarriage.
Secondary outcomes
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Rates of conception.
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Serum prolactin levels.
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Safety: teratogenicity, developmental disabilities of fetus, etc.
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:
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quarterly searches of the Cochrane Central Register of Controlled Trials (CENTRAL);
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weekly searches of MEDLINE;
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handsearches of 30 journals and the proceedings of major conferences;
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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 contact known investigators in the relevant area to obtain data from any unpublished work and review reference lists of retrieved articles for any further studies of relevance to the review.
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 the Cochrane Pregnancy and Childbirth Group.
Data extraction and management
We will design a form to extract data. For eligible studies, two review authors will independently extract the data using the agreed form. We will resolve discrepancies through discussion or, if required, we will consult the Cochrane Pregnancy and Childbirth Group. 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 the Cochrane Pregnancy and Childbirth Group.
(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:
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adequate (any truly random process, e.g. random number table; computer random number generator);
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inadequate (any non‐random process, e.g. odd or even date of birth; hospital or clinic record number);
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unclear.
(2) Allocation concealment (checking for possible selection bias)
We will describe for each included study the method used to conceal the allocation sequence in sufficient detail 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:
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adequate (e.g. telephone or central randomization; consecutively numbered sealed opaque envelopes);
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inadequate (open random allocation; unsealed or non‐opaque envelopes, alternation; date of birth);
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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:
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adequate, inadequate or unclear for participants;
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adequate, inadequate or unclear for personnel;
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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 randomized 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:
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adequate (rates of loss to follow‐up 20% or less);
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inadequate (rates of loss to follow‐up greater than 20%);
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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:
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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);
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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);
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unclear.
(6) Other sources of bias
We will describe for each included study any important concerns we have about other possible sources of bias; for example, potential sources of bias related to the specific study design, early stop due to some data‐dependent process, or extreme baseline imbalance.
We will assess whether each study was free of other problems that could put it at risk of bias:
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yes;
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no;
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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 standardized mean difference to combine trials that measure the same outcome, but use different methods.
Unit of analysis issues
Cluster‐randomized trials
We will include cluster‐randomized trials in the analyses along with individually randomized trials. We will incorporate the data of cluster‐randomized trials using the generic inverse variance method in which we will use logarithms of risk ratio estimates, along with the inflated standard error of the logarithms of risk ratio estimates. We will adjust their inflated standard error using the methods described in the Handbook using an estimate of the intra cluster 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‐randomized trials and individually‐randomized trials, we plan to synthesize 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 randomization unit is considered to be unlikely.
We will also acknowledge heterogeneity in the randomization unit and perform a subgroup analysis to investigate the effects of the randomization unit.
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 randomized to each group in the analyses, and all participants will be analyzed 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 randomized 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 I² is greater than 30% and either T² is greater than zero, 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 among 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.
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Different types of dopamine agonists (e.g. comparisons between bromocriptine, carbegeline, and quinagolide)
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Routes of supplementation (e.g. oral versus vaginal)
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Dosage of supplementation (e.g. prolactin >100 mg/ml versus < 100 mg/ml).
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Level of serum prolactin on admission (e.g. for bromocriptine, < 7.5 mg/d versus > 7.5 mg/d).
We will use the following outcomes in subgroup analysis: rates of live births (term delivery or premature delivery); rates of miscarriage.
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
We will carry out sensitivity analyses to explore the effect of high risk of bias on the summary estimates. Outcomes in the sensitivity analysis will include rates of live births (term delivery or premature delivery) and rates of miscarriage.
