Metformin for endometrial hyperplasia.

Endometrial cancer is one of the most common gynaecological cancers in the world. Rates of endometrial cancer are rising, in part because of rising obesity rates. Endometrial hyperplasia is a precancerous condition in women that can lead to endometrial cancer if left untreated. Endometrial hyperplasia occurs more commonly than endometrial cancer. Progesterone tablets that are currently used to treat women with endometrial hyperplasia are associated with adverse effects in up to 84% of women. A levonorgestrel intrauterine device may improve compliance, but it is invasive, is not acceptable to all women, and is associated with irregular vaginal bleeding in 82% of cases. Therefore, an alternative treatment for women with endometrial hyperplasia is needed. Metformin, a drug that is often used to treat people with diabetes, has been shown, in some human studies, to reverse endometrial hyperplasia. However, the effectiveness and safety of metformin for treatment of endometrial hyperplasia remain uncertain. This is an update of a review first published in 2017. To determine the effectiveness and safety of metformin in treating women with endometrial hyperplasia. We searched the Cochrane Gynaecology and Fertility Specialised Register, CENTRAL, MEDLINE, PubMed, Embase, Google Scholar, OpenGrey, LILACS, and two trials registers from inception to 5 September 2022. We searched the bibliographies of all relevant studies, and contacted experts in the field for any additional trials. We included randomised controlled trials (RCTs) and cross-over trials comparing metformin (used alone or in combination with other medical therapies) versus placebo, no treatment, any conventional medical treatment, or any other active intervention for women with histologically confirmed endometrial hyperplasia of any type. Two review authors independently assessed studies for eligibility, extracted data from included studies, assessed the risk of bias in the included studies, and assessed the certainty of the evidence for each outcome. We resolved disagreements by discussion or by deferring to a third review author. When study details were missing, review authors contacted the study authors. The primary outcome of this review was regression of endometrial hyperplasia histology (with or without atypia) towards normal histology. We included seven RCTs, in which a total of 387 women took part. In the comparison, Metformin plus megestrol versus megestrol alone, we rated the certainty of the evidence as low for the outcome, regression of endometrial hyperplasia. We rated the quality of the evidence as very low for the rest of the outcomes, in all three comparisons. Although there was a low risk of selection bias, there was a high risk of bias in the blinding of personnel and outcome assessment (performance bias and detection bias) in many studies. This update identified four new RCTs and six ongoing RCTs. Metformin versus megestrol We are uncertain whether metformin increases the regression of endometrial hyperplasia towards normal histology over megestrol (odds ratio (OR) 4.89, 95% confidence interval (CI) 1.56 to 15.32; P = 0.006; 2 RCTs, 83 participants; I² = 7%; very low-certainty evidence). This evidence suggests that if the rate of regression with megestrol is 61%, the rate of regression with metformin would be between 71% and 96%. It is unresolved whether metformin results in different rates of abnormal uterine bleeding or hysterectomy compared to megestrol. No study in this comparison reported progression of hyperplasia to endometrial cancer, recurrence of endometrial hyperplasia, health-related quality of life, or adverse effects during treatment. Metformin plus megestrol versus megestrol monotherapy The combination of metformin and megestrol may enhance the regression of endometrial hyperplasia towards normal histology more than megestrol alone (OR 3.27, 95% CI 1.65 to 6.51; P = 0.0007; 4 RCTs, 258 participants; I² = 0%, low-certainty evidence). This suggests that if the rate of regression with megestrol monotherapy is 54%, the rate of regression with the addition of metformin would be between 66% and 84%. In one study, 3/8 (37.5%) of participants who took metformin had nausea that settled without further treatment. It is unresolved whether the combination of metformin and megestrol results in different rates of recurrence of endometrial hyperplasia, progression of endometrial hyperplasia to endometrial cancer, or hysterectomy compared to megestrol monotherapy. No study in this comparison reported abnormal uterine bleeding, or health-related quality of life. Metformin plus levonorgestrel (intrauterine system) versus levonorgestrel (intrauterine system) monotherapy We are uncertain whether there is a difference between groups in the regression of endometrial hyperplasia towards normal histology (OR 0.29, 95% CI 0.01 to 7.56; 1 RCT, 46 participants; very low-certainty evidence). This evidence suggests that if the rate of regression with levonorgestrel monotherapy is 96%, the rate of regression with the addition of metformin would be between 73% and 100%. It is unresolved whether the combination of metformin and levonorgestrel results in different rates of abnormal uterine bleeding, hysterectomy, or the development of adverse effects during treatment compared to levonorgestrel monotherapy. No study in this comparison reported recurrence of endometrial hyperplasia, progression of hyperplasia to endometrial cancer, or health-related quality of life. Review authors found insufficient evidence to either support or refute the use of metformin, specifically megestrol acetate, given alone or in combination with standard therapy, for the treatment of women with endometrial hyperplasia. Robustly designed and adequately powered randomised controlled trials, yielding long-term outcome data are still needed to address this clinical question.

Shiwani H ，Clement NS ，Daniels JP ，Atiomo W ... -  《Cochrane Database of Systematic Reviews》

Metformin for endometrial hyperplasia.

Clement NS ，Oliver TR ，Shiwani H ，Sanner JR ，Mulvaney CA ，Atiomo W ... -  《-》

Levonorgestrel-releasing intrauterine system for endometrial hyperplasia.

In the absence of treatment, endometrial hyperplasia (EH) can progress to endometrial cancer, particularly in the presence of histologic nuclear atypia. The development of EH results from exposure of the endometrium to oestrogen unopposed by progesterone. Oral progestogens have been used as treatment for EH without atypia, and in some cases of EH with atypia in women who wish to preserve fertility or who cannot tolerate surgery. EH without atypia is associated with a low risk of progression to atypia and cancer; EH with atypia is where the cells are structurally abnormal, and has a higher risk of developing cancer. Oral progestogen is not always effective at reversing the hyperplasia, can be associated with side effects, and depends on patient adherence. The levonorgestrel-intrauterine system (LNG-IUS) is an alternative method of administration of progestogen and may have some advantages over non-intrauterine progestogens. To evaluate the effectiveness and safety of the levonorgestrel intrauterine system (LNG-IUS) in women with endometrial hyperplasia (EH) with or without atypia compared to medical treatment with non-intrauterine progestogens, placebo, surgery or no treatment. We searched the following databases: the Cochrane Gynaecology and Fertility Group (CGF) Specialised Register, CENTRAL, MEDLINE, Embase, CINAHL and PsycINFO, and conference proceedings of 10 relevant organisations. We handsearched references in relevant published studies. We also searched ongoing trials in ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry, and other trial registries. We performed the final search in May 2020. Randomised controlled trials (RCTs) and cross-over trials of women with a histological diagnosis of endometrial hyperplasia with or without atypia comparing LNG-IUS with non-intrauterine progestogens, placebo, surgery or no treatment. Two review authors independently performed study selection, risk of bias assessment and data extraction. Our primary outcome measures were regression of EH and adverse effects associated with the LNG-IUS device (such as pelvic inflammatory disease, device expulsion, uterine perforation) when compared to treatment with non-intrauterine progestogens, placebo, surgery or no treatment. Secondary outcomes included hysterectomy, hormone-related adverse effects (such as bleeding/spotting, pelvic pain, breast tenderness, ovarian cysts, weight gain, acne), withdrawal from treatment due to adverse effects, satisfaction with treatment, and cost or resource use. We rated the overall quality of evidence using GRADE methods. Thirteen RCTs (1657 women aged 22 to 75 years) met the inclusion criteria. Two studies had insufficient data for meta-analysis, thus the quantitative analysis included 11 RCTs. All trials evaluated treatment duration of six months or less. The evidence ranged from very low to moderate quality: the main limitations were risk of bias (associated with lack of blinding and poor reporting of study methods), inconsistency and imprecision. LNG-IUS versus non-intrauterine progestogens Primary outcomes Regression of endometrial hyperplasia The LNG-IUS probably improves regression of EH compared with non-intrauterine progestogens at short-term follow-up (up to six months) (OR 2.94, 95% CI 2.10 to 4.13; I² = 0%; 10 RCTs, 1108 participants; moderate-quality evidence). This suggests that if regression of EH following treatment with a non-intrauterine progestogen is assumed to be 72%, regression of EH following treatment with LNG-IUS would be between 85% and 92%. Regression of EH may be improved by LNG-IUS compared with non-intrauterine progestogens at long-term follow-up (12 months) (OR 3.80, 95% CI 1.75 to 8.23; 1 RCT, 138 participants; low-quality evidence), Adverse effects associated with LNG-IUS There was insufficient evidence to determine device-related adverse effects; only one study reported on expulsion with insufficient data for analysis. Secondary outcomes The LNG-IUS may be associated with fewer hysterectomies (OR 0.26, 95% CI 0.15 to 0.46; I² = 19%; 4 RCTs, 452 participants; low-quality evidence), fewer withdrawals from treatment due to hormone-related adverse effects (OR 0.41, 95% CI 0.12 to 1.35; I² = 0%; 4 RCTs, 360 participants; low-quality evidence) and improved patient satisfaction with treatment (OR 5.28, 95% CI 2.51 to 11.10; I² = 0%; 2 RCTs, 202 participants; very low-quality evidence) compared to non-intrauterine progestogens. The LNG-IUS may be associated with more bleeding/spotting (OR 2.13, 95% CI 1.33 to 3.43; I² = 78%; 3 RCTs, 428 participants) and less nausea (OR 0.52, 95% CI 0.28 to 0.95; I² = 0%; 3 RCTs, 428 participants) compared to non-intrauterine progestogens. Data from single trials for mood swings and fatigue had a similar direction of effect as for bleeding/spotting, nausea and weight gain. There was insufficient evidence to determine cost or resource use. LNG-IUS versus no treatment Regression of endometrial hyperplasia One study demonstrated that the LNG-IUS is associated with regression of EH without atypia (OR 78.41, 95% CI 22.86 to 268.97; I² = 0%; 1 RCT, 190 participants; moderate-quality evidence) compared with no treatment. This study did not report on any other review outcome. There is moderate-quality evidence that treatment with LNG-IUS used for three to six months is probably more effective than non-intrauterine progestogens at reversing EH in the short term (up to six months) and long term (up to two years). Adverse effects (device-related and hormone-related) were poorly and incompletely reported across studies. Very low quality to low-quality evidence suggests the LNG-IUS may reduce the risk of hysterectomy, and may be associated with more bleeding/spotting, less nausea, less withdrawal from treatment due to adverse effects, and increased satisfaction with treatment, compared to non-intrauterine progestogens. There was insufficient evidence to reach conclusions regarding device-related adverse effects, or cost or resource use.

Mittermeier T ，Farrant C ，Wise MR 《Cochrane Database of Systematic Reviews》

Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.

Description of the condition Malaria, an infectious disease transmitted by the bite of female mosquitoes from several Anopheles species, occurs in 87 countries with ongoing transmission (WHO 2020). The World Health Organization (WHO) estimated that, in 2019, approximately 229 million cases of malaria occurred worldwide, with 94% occurring in the WHO's African region (WHO 2020). Of these malaria cases, an estimated 409,000 deaths occurred globally, with 67% occurring in children under five years of age (WHO 2020). Malaria also negatively impacts the health of women during pregnancy, childbirth, and the postnatal period (WHO 2020). Sulfadoxine/pyrimethamine (SP), an antifolate antimalarial, has been widely used across sub-Saharan Africa as the first-line treatment for uncomplicated malaria since it was first introduced in Malawi in 1993 (Filler 2006). Due to increasing resistance to SP, in 2000 the WHO recommended that one of several artemisinin-based combination therapies (ACTs) be used instead of SP for the treatment of uncomplicated malaria caused by Plasmodium falciparum (Global Partnership to Roll Back Malaria 2001). However, despite these recommendations, SP continues to be advised for intermittent preventive treatment in pregnancy (IPTp) and intermittent preventive treatment in infants (IPTi), whether the person has malaria or not (WHO 2013). Description of the intervention Folate (vitamin B9) includes both naturally occurring folates and folic acid, the fully oxidized monoglutamic form of the vitamin, used in dietary supplements and fortified food. Folate deficiency (e.g. red blood cell (RBC) folate concentrations of less than 305 nanomoles per litre (nmol/L); serum or plasma concentrations of less than 7 nmol/L) is common in many parts of the world and often presents as megaloblastic anaemia, resulting from inadequate intake, increased requirements, reduced absorption, or abnormal metabolism of folate (Bailey 2015; WHO 2015a). Pregnant women have greater folate requirements; inadequate folate intake (evidenced by RBC folate concentrations of less than 400 nanograms per millilitre (ng/mL), or 906 nmol/L) prior to and during the first month of pregnancy increases the risk of neural tube defects, preterm delivery, low birthweight, and fetal growth restriction (Bourassa 2019). The WHO recommends that all women who are trying to conceive consume 400 micrograms (µg) of folic acid daily from the time they begin trying to conceive through to 12 weeks of gestation (WHO 2017). In 2015, the WHO added the dosage of 0.4 mg of folic acid to the essential drug list (WHO 2015c). Alongside daily oral iron (30 mg to 60 mg elemental iron), folic acid supplementation is recommended for pregnant women to prevent neural tube defects, maternal anaemia, puerperal sepsis, low birthweight, and preterm birth in settings where anaemia in pregnant women is a severe public health problem (i.e. where at least 40% of pregnant women have a blood haemoglobin (Hb) concentration of less than 110 g/L). How the intervention might work Potential interactions between folate status and malaria infection The malaria parasite requires folate for survival and growth; this has led to the hypothesis that folate status may influence malaria risk and severity. In rhesus monkeys, folate deficiency has been found to be protective against Plasmodium cynomolgi malaria infection, compared to folate-replete animals (Metz 2007). Alternatively, malaria may induce or exacerbate folate deficiency due to increased folate utilization from haemolysis and fever. Further, folate status measured via RBC folate is not an appropriate biomarker of folate status in malaria-infected individuals since RBC folate values in these individuals are indicative of both the person's stores and the parasite's folate synthesis. A study in Nigeria found that children with malaria infection had significantly higher RBC folate concentrations compared to children without malaria infection, but plasma folate levels were similar (Bradley-Moore 1985). Why it is important to do this review The malaria parasite needs folate for survival and growth in humans. For individuals, adequate folate levels are critical for health and well-being, and for the prevention of anaemia and neural tube defects. Many countries rely on folic acid supplementation to ensure adequate folate status in at-risk populations. Different formulations for folic acid supplements are available in many international settings, with dosages ranging from 400 µg to 5 mg. Evaluating folic acid dosage levels used in supplementation efforts may increase public health understanding of its potential impacts on malaria risk and severity and on treatment failures. Examining folic acid interactions with antifolate antimalarial medications and with malaria disease progression may help countries in malaria-endemic areas determine what are the most appropriate lower dose folic acid formulations for at-risk populations. The WHO has highlighted the limited evidence available and has indicated the need for further research on biomarkers of folate status, particularly interactions between RBC folate concentrations and tuberculosis, human immunodeficiency virus (HIV), and antifolate antimalarial drugs (WHO 2015b). An earlier Cochrane Review assessed the effects and safety of iron supplementation, with or without folic acid, in children living in hyperendemic or holoendemic malaria areas; it demonstrated that iron supplementation did not increase the risk of malaria, as indicated by fever and the presence of parasites in the blood (Neuberger 2016). Further, this review stated that folic acid may interfere with the efficacy of SP; however, the efficacy and safety of folic acid supplementation on these outcomes has not been established. This review will provide evidence on the effectiveness of daily folic acid supplementation in healthy and malaria-infected individuals living in malaria-endemic areas. Additionally, it will contribute to achieving both the WHO Global Technical Strategy for Malaria 2016-2030 (WHO 2015d), and United Nations Sustainable Development Goal 3 (to ensure healthy lives and to promote well-being for all of all ages) (United Nations 2021), and evaluating whether the potential effects of folic acid supplementation, at different doses (e.g. 0.4 mg, 1 mg, 5 mg daily), interferes with the effect of drugs used for prevention or treatment of malaria. To examine the effects of folic acid supplementation, at various doses, on malaria susceptibility (risk of infection) and severity among people living in areas with various degrees of malaria endemicity. We will examine the interaction between folic acid supplements and antifolate antimalarial drugs. Specifically, we will aim to answer the following. Among uninfected people living in malaria endemic areas, who are taking or not taking antifolate antimalarials for malaria prophylaxis, does taking a folic acid-containing supplement increase susceptibility to or severity of malaria infection? Among people with malaria infection who are being treated with antifolate antimalarials, does folic acid supplementation increase the risk of treatment failure? Criteria for considering studies for this review Types of studies Inclusion criteria Randomized controlled trials (RCTs) Quasi-RCTs with randomization at the individual or cluster level conducted in malaria-endemic areas (areas with ongoing, local malaria transmission, including areas approaching elimination, as listed in the World Malaria Report 2020) (WHO 2020) Exclusion criteria Ecological studies Observational studies In vivo/in vitro studies Economic studies Systematic literature reviews and meta-analyses (relevant systematic literature reviews and meta-analyses will be excluded but flagged for grey literature screening) Types of participants Inclusion criteria Individuals of any age or gender, living in a malaria endemic area, who are taking antifolate antimalarial medications (including but not limited to sulfadoxine/pyrimethamine (SP), pyrimethamine-dapsone, pyrimethamine, chloroquine and proguanil, cotrimoxazole) for the prevention or treatment of malaria (studies will be included if more than 70% of the participants live in malaria-endemic regions) Studies assessing participants with or without anaemia and with or without malaria parasitaemia at baseline will be included Exclusion criteria Individuals not taking antifolate antimalarial medications for prevention or treatment of malaria Individuals living in non-malaria endemic areas Types of interventions Inclusion criteria Folic acid supplementation Form: in tablet, capsule, dispersible tablet at any dose, during administration, or periodically Timing: during, before, or after (within a period of four to six weeks) administration of antifolate antimalarials Iron-folic acid supplementation Folic acid supplementation in combination with co-interventions that are identical between the intervention and control groups. Co-interventions include: anthelminthic treatment; multivitamin or multiple micronutrient supplementation; 5-methyltetrahydrofolate supplementation. Exclusion criteria Folate through folate-fortified water Folic acid administered through large-scale fortification of rice, wheat, or maize Comparators Placebo No treatment No folic acid/different doses of folic acid Iron Types of outcome measures Primary outcomes Uncomplicated malaria (defined as a history of fever with parasitological confirmation; acceptable parasitological confirmation will include rapid diagnostic tests (RDTs), malaria smears, or nucleic acid detection (i.e. polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), etc.)) (WHO 2010). This outcome is relevant for patients without malaria, given antifolate antimalarials for malaria prophylaxis. Severe malaria (defined as any case with cerebral malaria or acute P. falciparum malaria, with signs of severity or evidence of vital organ dysfunction, or both) (WHO 2010). This outcome is relevant for patients without malaria, given antifolate antimalarials for malaria prophylaxis. Parasite clearance (any Plasmodium species), defined as the time it takes for a patient who tests positive at enrolment and is treated to become smear-negative or PCR negative. This outcome is relevant for patients with malaria, treated with antifolate antimalarials. Treatment failure (defined as the inability to clear malaria parasitaemia or prevent recrudescence after administration of antimalarial medicine, regardless of whether clinical symptoms are resolved) (WHO 2019). This outcome is relevant for patients with malaria, treated with antifolate antimalarials. Secondary outcomes Duration of parasitaemia Parasite density Haemoglobin (Hb) concentrations (g/L) Anaemia: severe anaemia (defined as Hb less than 70 g/L in pregnant women and children aged six to 59 months; and Hb less than 80 g/L in other populations); moderate anaemia (defined as Hb less than 100 g/L in pregnant women and children aged six to 59 months; and less than 110 g/L in others) Death from any cause Among pregnant women: stillbirth (at less than 28 weeks gestation); low birthweight (less than 2500 g); active placental malaria (defined as Plasmodium detected in placental blood by smear or PCR, or by Plasmodium detected on impression smear or placental histology). Search methods for identification of studies A search will be conducted to identify completed and ongoing studies, without date or language restrictions. Electronic searches A search strategy will be designed to include the appropriate subject headings and text word terms related to each intervention of interest and study design of interest (see Appendix 1). Searches will be broken down by these two criteria (intervention of interest and study design of interest) to allow for ease of prioritization, if necessary. The study design filters recommended by the Scottish Intercollegiate Guidelines Network (SIGN), and those designed by Cochrane for identifying clinical trials for MEDLINE and Embase, will be used (SIGN 2020). There will be no date or language restrictions. Non-English articles identified for inclusion will be translated into English. If translations are not possible, advice will be requested from the Cochrane Infectious Diseases Group and the record will be stored in the "Awaiting assessment" section of the review until a translation is available. The following electronic databases will be searched for primary studies. Cochrane Central Register of Controlled Trials. Cumulative Index to Nursing and Allied Health Literature (CINAHL). Embase. MEDLINE. Scopus. Web of Science (both the Social Science Citation Index and the Science Citation Index). We will conduct manual searches of ClinicalTrials.gov, the International Clinical Trials Registry Platform (ICTRP), and the United Nations Children's Fund (UNICEF) Evaluation and Research Database (ERD), in order to identify relevant ongoing or planned trials, abstracts, and full-text reports of evaluations, studies, and surveys related to programmes on folic acid supplementation in malaria-endemic areas. Additionally, manual searches of grey literature to identify RCTs that have not yet been published but are potentially eligible for inclusion will be conducted in the following sources. Global Index Medicus (GIM). African Index Medicus (AIM). Index Medicus for the Eastern Mediterranean Region (IMEMR). Latin American & Caribbean Health Sciences Literature (LILACS). Pan American Health Organization (PAHO). Western Pacific Region Index Medicus (WPRO). Index Medicus for the South-East Asian Region (IMSEAR). The Spanish Bibliographic Index in Health Sciences (IBECS) (ibecs.isciii.es/). Indian Journal of Medical Research (IJMR) (journals.lww.com/ijmr/pages/default.aspx). Native Health Database (nativehealthdatabase.net/). Scielo (www.scielo.br/). Searching other resources Handsearches of the five journals with the highest number of included studies in the last 12 months will be conducted to capture any relevant articles that may not have been indexed in the databases at the time of the search. We will contact the authors of included studies and will check reference lists of included papers for the identification of additional records. For assistance in identifying ongoing or unpublished studies, we will contact the Division of Nutrition, Physical Activity, and Obesity (DNPAO) and the Division of Parasitic Diseases and Malaria (DPDM) of the CDC, the United Nations World Food Programme (WFP), Nutrition International (NI), Global Alliance for Improved Nutrition (GAIN), and Hellen Keller International (HKI). Data collection and analysis Selection of studies Two review authors will independently screen the titles and abstracts of articles retrieved by each search to assess eligibility, as determined by the inclusion and exclusion criteria. Studies deemed eligible for inclusion by both review authors in the abstract screening phase will advance to the full-text screening phase, and full-text copies of all eligible papers will be retrieved. If full articles cannot be obtained, we will attempt to contact the authors to obtain further details of the studies. If such information is not obtained, we will classify the study as "awaiting assessment" until further information is published or made available to us. The same two review authors will independently assess the eligibility of full-text articles for inclusion in the systematic review. If any discrepancies occur between the studies selected by the two review authors, a third review author will provide arbitration. Each trial will be scrutinized to identify multiple publications from the same data set, and the justification for excluded trials will be documented. A PRISMA flow diagram of the study selection process will be presented to provide information on the number of records identified in the literature searches, the number of studies included and excluded, and the reasons for exclusion (Moher 2009). The list of excluded studies, along with their reasons for exclusion at the full-text screening phase, will also be created. Data extraction and management Two review authors will independently extract data for the final list of included studies using a standardized data specification form. Discrepancies observed between the data extracted by the two authors will be resolved by involving a third review author and reaching a consensus. Information will be extracted on study design components, baseline participant characteristics, intervention characteristics, and outcomes. For individually randomized trials, we will record the number of participants experiencing the event and the number analyzed in each treatment group or the effect estimate reported (e.g. risk ratio (RR)) for dichotomous outcome measures. For count data, we will record the number of events and the number of person-months of follow-up in each group. If the number of person-months is not reported, the product of the duration of follow-up and the number of children evaluated will be used to estimate this figure. We will calculate the rate ratio and standard error (SE) for each study. Zero events will be replaced by 0.5. We will extract both adjusted and unadjusted covariate incidence rate ratios if they are reported in the original studies. For continuous data, we will extract means (arithmetic or geometric) and a measure of variance (standard deviation (SD), SE, or confidence interval (CI)), percentage or mean change from baseline, and the numbers analyzed in each group. SDs will be computed from SEs or 95% CIs, assuming a normal distribution of the values. Haemoglobin values in g/dL will be calculated by multiplying haematocrit or packed cell volume values by 0.34, and studies reporting haemoglobin values in g/dL will be converted to g/L. In cluster-randomized trials, we will record the unit of randomization (e.g. household, compound, sector, or village), the number of clusters in the trial, and the average cluster size. The statistical methods used to analyze the trials will be documented, along with details describing whether these methods adjusted for clustering or other covariates. We plan to extract estimates of the intra-cluster correlation coefficient (ICC) for each outcome. Where results are adjusted for clustering, we will extract the treatment effect estimate and the SD or CI. If the results are not adjusted for clustering, we will extract the data reported. Assessment of risk of bias in included studies Two review authors (KSC, LFY) will independently assess the risk of bias for each included trial using the Cochrane 'Risk of bias 2' tool (RoB 2) for randomized studies (Sterne 2019). Judgements about the risk of bias of included studies will be made according to the recommendations outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). Disagreements will be resolved by discussion, or by involving a third review author. The interest of our review will be to assess the effect of assignment to the interventions at baseline. We will evaluate each primary outcome using the RoB2 tool. The five domains of the Cochrane RoB2 tool include the following. Bias arising from the randomization process. Bias due to deviations from intended interventions. Bias due to missing outcome data. Bias in measurement of the outcome. Bias in selection of the reported result. Each domain of the RoB2 tool comprises the following. A series of 'signalling' questions. A judgement about the risk of bias for the domain, facilitated by an algorithm that maps responses to the signalling questions to a proposed judgement. Free-text boxes to justify responses to the signalling questions and 'Risk of bias' judgements. An option to predict (and explain) the likely direction of bias. Responses to signalling questions elicit information relevant to an assessment of the risk of bias. These response options are as follows. Yes (may indicate either low or high risk of bias, depending on the most natural way to ask the question). Probably yes. Probably no. No. No information (may indicate no evidence of that problem or an absence of information leading to concerns about there being a problem). Based on the answer to the signalling question, a 'Risk of bias' judgement is assigned to each domain. These judgements include one of the following. High risk of bias Low risk of bias Some concerns To generate the risk of bias judgement for each domain in the randomized studies, we will use the Excel template, available at www.riskofbias.info/welcome/rob-2-0-tool/current-version-of-rob-2. This file will be stored on a scientific data website, available to readers. Risk of bias in cluster randomized controlled trials For the cluster randomized trials, we will be using the RoB2 tool to analyze the five standard domains listed above along with Domain 1b (bias arising from the timing of identification or recruitment of participants) and its related signalling questions. To generate the risk of bias judgement for each domain in the cluster RCTs, we will use the Excel template available at https://sites.google.com/site/riskofbiastool/welcome/rob-2-0-tool/rob-2-for-cluster-randomized-trials. This file will be stored on a scientific data website, available to readers. Risk of bias in cross-over randomized controlled trials For cross-over randomized trials, we will be using the RoB2 tool to analyze the five standard domains listed above along with Domain 2 (bias due to deviations from intended interventions), and Domain 3 (bias due to missing outcome data), and their respective signalling questions. To generate the risk of bias judgement for each domain in the cross-over RCTs, we will use the Excel template, available at https://sites.google.com/site/riskofbiastool/welcome/rob-2-0-tool/rob-2-for-crossover-trials, for each risk of bias judgement of cross-over randomized studies. This file will be stored on a scientific data website, available to readers. Overall risk of bias The overall 'Risk of bias' judgement for each specific trial being assessed will be based on each domain-level judgement. The overall judgements include the following. Low risk of bias (the trial is judged to be at low risk of bias for all domains). Some concerns (the trial is judged to raise some concerns in at least one domain but is not judged to be at high risk of bias for any domain). High risk of bias (the trial is judged to be at high risk of bias in at least one domain, or is judged to have some concerns for multiple domains in a way that substantially lowers confidence in the result). The 'risk of bias' assessments will inform our GRADE evaluations of the certainty of evidence for our primary outcomes presented in the 'Summary of findings' tables and will also be used to inform the sensitivity analyses; (see Sensitivity analysis). If there is insufficient information in study reports to enable an assessment of the risk of bias, studies will be classified as "awaiting assessment" until further information is published or made available to us. Measures of treatment effect Dichotomous data For dichotomous data, we will present proportions and, for two-group comparisons, results as average RR or odds ratio (OR) with 95% CIs. Ordered categorical data Continuous data We will report results for continuous outcomes as the mean difference (MD) with 95% CIs, if outcomes are measured in the same way between trials. Where some studies have reported endpoint data and others have reported change-from-baseline data (with errors), we will combine these in the meta-analysis, if the outcomes were reported using the same scale. We will use the standardized mean difference (SMD), with 95% CIs, to combine trials that measured the same outcome but used different methods. If we do not find three or more studies for a pooled analysis, we will summarize the results in a narrative form. Unit of analysis issues Cluster-randomized trials We plan to combine results from both cluster-randomized and individually randomized studies, providing there is little heterogeneity between the studies. If the authors of cluster-randomized trials conducted their analyses at a different level from that of allocation, and they have not appropriately accounted for the cluster design in their analyses, we will calculate the trials' effective sample sizes to account for the effect of clustering in data. When one or more cluster-RCT reports RRs adjusted for clustering, we will compute cluster-adjusted SEs for the other trials. When none of the cluster-RCTs provide cluster-adjusted RRs, we will adjust the sample size for clustering. We will divide, by the estimated design effects (DE), the number of events and number evaluated for dichotomous outcomes and the number evaluated for continuous outcomes, where DE = 1 + ((average cluster size 1) * ICC). The derivation of the estimated ICCs and DEs will be reported. We will utilize the intra-cluster correlation coefficient (ICC), derived from the trial (if available), or from another source (e.g., using the ICCs derived from other, similar trials) and then calculate the design effect with the formula provided in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). If this approach is used, we will report it and undertake sensitivity analysis to investigate the effect of variations in ICC. Studies with more than two treatment groups If we identify studies with more than two intervention groups (multi-arm studies), where possible we will combine groups to create a single pair-wise comparison or use the methods set out in the Cochrane Handbook to avoid double counting study participants (Higgins 2021). For the subgroup analyses, when the control group was shared by two or more study arms, we will divide the control group (events and total population) over the number of relevant subgroups to avoid double counting the participants. Trials with several study arms can be included more than once for different comparisons. Cross-over trials From cross-over trials, we will consider the first period of measurement only and will analyze the results together with parallel-group studies. Multiple outcome events In several outcomes, a participant might experience more than one outcome event during the trial period. For all outcomes, we will extract the number of participants with at least one event. Dealing with missing data We will contact the trial authors if the available data are unclear, missing, or reported in a format that is different from the format needed. We aim to perform a 'per protocol' or 'as observed' analysis; otherwise, we will perform a complete case analysis. This means that for treatment failure, we will base the analyses on the participants who received treatment and the number of participants for which there was an inability to clear malarial parasitaemia or prevent recrudescence after administration of an antimalarial medicine reported in the studies. Assessment of heterogeneity Heterogeneity in the results of the trials will be assessed by visually examining the forest plot to detect non-overlapping CIs, using the Chi2 test of heterogeneity (where a P value of less than 0.1 indicates statistical significance) and the I2 statistic of inconsistency (with a value of greater than 50% denoting moderate levels of heterogeneity). When statistical heterogeneity is present, we will investigate the reasons for it, using subgroup analysis. Assessment of reporting biases We will construct a funnel plot to assess the effect of small studies for the main outcome (when including more than 10 trials). Data synthesis The primary analysis will include all eligible studies that provide data regardless of the overall risk of bias as assessed by the RoB2 tool. Analyses will be conducted using Review Manager 5.4 (Review Manager 2020). Cluster-RCTs will be included in the main analysis after adjustment for clustering (see the previous section on cluster-RCTs). The meta-analysis will be performed using the Mantel-Haenszel random-effects model or the generic inverse variance method (when adjustment for clustering is performed by adjusting SEs), as appropriate. Subgroup analysis and investigation of heterogeneity The overall risk of bias will not be used as the basis in conducting our subgroup analyses. However, where data are available, we plan to conduct the following subgroup analyses, independent of heterogeneity. Dose of folic acid supplementation: higher doses (4 mg or more, daily) versus lower doses (less than 4 mg, daily). Moderate-severe anaemia at baseline (mean haemoglobin of participants in a trial at baseline below 100 g/L for pregnant women and children aged six to 59 months, and below 110 g/L for other populations) versus normal at baseline (mean haemoglobin above 100 g/L for pregnant women and children aged six to 59 months, and above 110 g/L for other populations). Antimalarial drug resistance to parasite: known resistance versus no resistance versus unknown/mixed/unreported parasite resistance. Folate status at baseline: Deficient (e.g. RBC folate concentration of less than 305 nmol/L, or serum folate concentration of less than 7nmol/L) and Insufficient (e.g. RBC folate concentration from 305 to less than 906 nmol/L, or serum folate concentration from 7 to less than 25 nmol/L) versus Sufficient (e.g. RBC folate concentration above 906 nmol/L, or serum folate concentration above 25 nmol/L). Presence of anaemia at baseline: yes versus no. Mandatory fortification status: yes, versus no (voluntary or none). We will only use the primary outcomes in any subgroup analyses, and we will limit subgroup analyses to those outcomes for which three or more trials contributed data. Comparisons between subgroups will be performed using Review Manager 5.4 (Review Manager 2020). Sensitivity analysis We will perform a sensitivity analysis, using the risk of bias as a variable to explore the robustness of the findings in our primary outcomes. We will verify the behaviour of our estimators by adding and removing studies with a high risk of bias overall from the analysis. That is, studies with a low risk of bias versus studies with a high risk of bias. Summary of findings and assessment of the certainty of the evidence For the assessment across studies, we will use the GRADE approach, as outlined in (Schünemann 2021). We will use the five GRADE considerations (study limitations based on RoB2 judgements, consistency of effect, imprecision, indirectness, and publication bias) to assess the certainty of the body of evidence as it relates to the studies which contribute data to the meta-analyses for the primary outcomes. The GRADEpro Guideline Development Tool (GRADEpro) will be used to import data from Review Manager 5.4 (Review Manager 2020) to create 'Summary of Findings' tables. The primary outcomes for the main comparison will be listed with estimates of relative effects, along with the number of participants and studies contributing data for those outcomes. These tables will provide outcome-specific information concerning the overall certainty of evidence from studies included in the comparison, the magnitude of the effect of the interventions examined, and the sum of available data on the outcomes we considered. We will include only primary outcomes in the summary of findings tables. For each individual outcome, two review authors (KSC, LFY) will independently assess the certainty of the evidence using the GRADE approach (Balshem 2011). For assessments of the overall certainty of evidence for each outcome that includes pooled data from included trials, we will downgrade the evidence from 'high certainty' by one level for serious (or by two for very serious) study limitations (risk of bias, indirectness of evidence, serious inconsistency, imprecision of effect estimates, or potential publication bias).

Crider K ，Williams J ，Qi YP ，Gutman J ，Yeung L ，Mai C ，Finkelstain J ，Mehta S ，Pons-Duran C ，Menéndez C ，Moraleda C ，Rogers L ，Daniels K ，Green P ... -  《Cochrane Database of Systematic Reviews》

Levonorgestrel intrauterine system for endometrial protection in women with breast cancer on adjuvant tamoxifen.

Adjuvant tamoxifen reduces the risk of breast cancer recurrence in women with oestrogen receptor-positive breast cancer. Tamoxifen also increases the risk of postmenopausal bleeding, endometrial polyps, hyperplasia, and endometrial cancer. The levonorgestrel-releasing intrauterine system (LNG-IUS) causes profound endometrial suppression. This systematic review considered the evidence that the LNG-IUS prevents the development of endometrial pathology in women taking tamoxifen as adjuvant endocrine therapy for breast cancer. To determine the effectiveness and safety of the levonorgestrel intrauterine system (LNG-IUS) in pre- and postmenopausal women taking adjuvant tamoxifen following breast cancer for the outcomes of endometrial and uterine pathology including abnormal vaginal bleeding or spotting, and secondary breast cancer events. We searched the following databases on 29 June 2020; The Cochrane Gynaecology and Fertility Group specialised register, Cochrane Central Register of Controlled Trials, MEDLINE, Embase, PsycINFO and Cumulative Index to Nursing and Allied Health Literature. We searched the Cochrane Breast Cancer Group specialised register on 4 March 2020. We also searched two trials registers, checked references for relevant trials and contacted study authors and experts in the field to identify additional studies. We included randomised controlled trials (RCTs) of women with breast cancer on adjuvant tamoxifen that compared the effectiveness of the LNG-IUS with endometrial surveillance versus endometrial surveillance alone on the incidence of endometrial pathology. We used standard methodological procedures recommended by Cochrane. The primary outcome measure was endometrial pathology (including polyps, endometrial hyperplasia, or endometrial cancer), diagnosed at hysteroscopy or endometrial biopsy. Secondary outcome measures included fibroids, abnormal vaginal bleeding or spotting, breast cancer recurrence, and breast cancer-related deaths. We rated the overall certainty of evidence using GRADE methods. We included four RCTs (543 women analysed) in this review. We judged the certainty of the evidence to be moderate for all of the outcomes, due to imprecision (i.e. limited sample sizes and low event rates). In the included studies, the active treatment arm was the 20 μg/day LNG-IUS plus endometrial surveillance; the control arm was endometrial surveillance alone. In tamoxifen users, the LNG-IUS probably reduces the incidence of endometrial polyps compared to the control group over both a 12-month period (Peto odds ratio (OR) 0.22, 95% confidence interval (CI) 0.08 to 0.64, I² = 0%; 2 RCTs, n = 212; moderate-certainty evidence) and over a long-term follow-up period (24 to 60 months) (Peto OR 0.22, 95% CI 0.13 to 0.39; I² = 0%; 4 RCTs, n = 417; moderate-certainty evidence). For long-term follow-up, this suggests that if the incidence of endometrial polyps following endometrial surveillance alone is assumed to be 23.5%, the incidence following LNG-IUS with endometrial surveillance would be between 3.8% and 10.7%.  The LNG-IUS probably slightly reduces the incidence of endometrial hyperplasia compared with controls over a long-term follow-up period (24 to 60 months) (Peto OR 0.13, 95% CI 0.03 to 0.67; I² = 0%; 4 RCTs, n = 417; moderate-certainty evidence). This suggests that if the chance of endometrial hyperplasia following endometrial surveillance alone is assumed to be 2.8%, the chance following LNG-IUS with endometrial surveillance would be between 0.1% and 1.9%. However, it should be noted that there were only six cases of endometrial hyperplasia. There was insufficient evidence to reach a conclusion regarding the incidence of endometrial cancer in tamoxifen users, as no studies reported cases of endometrial cancer. At 12 months of follow-up, the LNG-IUS probably increases abnormal vaginal bleeding or spotting compared to the control group (Peto OR 7.26, 95% CI 3.37 to 15.66; I² = 0%; 3 RCTs, n = 376; moderate-certainty evidence). This suggests that if the chance of abnormal vaginal bleeding or spotting following endometrial surveillance alone is assumed to be 1.7%, the chance following LNG-IUS with endometrial surveillance would be between 5.6% and 21.5%. By 24 months of follow-up, abnormal vaginal bleeding or spotting occurs less frequently than at 12 months of follow-up, but is still more common in the LNG-IUS group than the control group (Peto OR 2.72, 95% CI 1.04 to 7.10; I² = 0%; 2 RCTs, n = 233; moderate-certainty evidence). This suggests that if the chance of abnormal vaginal bleeding or spotting following endometrial surveillance alone is assumed to be 4.2%, the chance following LNG-IUS with endometrial surveillance would be between 4.4% and 23.9%. By 60 months of follow-up, there were no cases of abnormal vaginal bleeding or spotting in either group. The numbers of events for the following outcomes were low: fibroids (n = 13), breast cancer recurrence (n = 18), and breast cancer-related deaths (n = 16). As a result, there is probably little or no difference in these outcomes between the LNG-IUS treatment group and the control group.  AUTHORS' CONCLUSIONS: The LNG-IUS probably slightly reduces the incidence of benign endometrial polyps and endometrial hyperplasia in women with breast cancer taking tamoxifen. At 12 and 24 months of follow-up, the LNG-IUS probably increases abnormal vaginal bleeding or spotting among women in the treatment group compared to those in the control. Data were lacking on whether the LNG-IUS prevents endometrial cancer in these women. There is no clear evidence from the available RCTs that the LNG-IUS affects the risk of breast cancer recurrence or breast cancer-related deaths. Larger studies are necessary to assess the effects of the LNG-IUS on the incidence of endometrial cancer, and to determine whether the LNG-IUS might have an impact on the risk of secondary breast cancer events.

Romero SA ，Young K ，Hickey M ，Su HI ... -  《-》