Different interventions to prevent preterm birth in pregnant women with cervical shortening during pregnancy: a protocol for a network meta-analysis

Abstract

Introduction

Premature birth is the leading cause of neonatal mortality. Cervical length shortening during pregnancy serves as the gold standard for predicting preterm birth. Although several interventions have been applied to reduce the incidence of preterm birth in short cervix pregnant women, the optimal intervention in clinical practice remains controversial. The aim of this study is to conduct a network meta-analysis to explore the optimal intervention for preventing preterm birth among pregnant women with a short cervix.

Methods and analysis

We will search electronic information databases including PubMed, Embase Ovid, Cochrane Library Ovid, Wanfang Data, China Science and Technology Journal Database(VIP) and clinical trial registry websites (US Clinical Trials Registry and China Clinical Trials Registry) until 1 January 2024. Randomised controlled trials (RCTs) comparing two or more interventions to prevent preterm birth in short cervix pregnant women will be included. The primary outcomes are preterm birth rate at <37 weeks and the composite neonatal adverse outcome, secondary outcomes include spontaneous preterm birth rate at <37 weeks, preterm birth rate at <34 weeks, spontaneous preterm birth rate at <34 weeks, week of gestation, birth weight of the newborn, perinatal mortality and neonatal admission rate. Risk of bias 2.0 (ROB 2.0） will be used to assess the risk of bias in the RCT, and the Confidence in Network Meta-Analysis software will be used to assess the certainty of the generated evidence. The network meta-analysis will be conducted using the gemtc package in R 4.2.2. Two investigators independently performed article screening, data extraction and quality assessment. In addition, subgroup analyses and sensitivity analyses will be used to assess the robustness of the findings.

Ethics and dissemination

Ethical considerations will not be required. Results will be published in a peer-review journal.

PROSPERO registration number

CRD42022315200.

Correspondence to Dr Jiyong Liu; [email protected]

STRENGTHS AND LIMITATIONS OF THIS STUDY

• We will include all available data from randomised controlled trials.

• Multiple subgroup analyses will be conducted to assess the applicability of the study findings to different populations and in different settings.

• Responding to the call of several researchers, we have made the first attempt to extend the length of cervical shortening in pregnancy to 30 mm, a measure that may benefit more patients.

• Heterogeneity between studies may affect the study findings.

Introduction

The WHO defines preterm birth as any delivery before 37 weeks of gestation. Statistical data indicate that approximately 15 million babies are born prematurely worldwide, accounting for 11% of all newborns.^{1 2} Premature birth is the leading cause of neonatal mortality.² Even among surviving premature infants, there are numerous short-term and lifelong health challenges, including respiratory distress syndrome, bronchopulmonary dysplasia, necrotizing enterocolitis, neurodevelopmental disorders, chronic lung disease, hypertension, glucose intolerance, learning disabilities, as well as issues related to vision and hearing.²

The shortening of cervical length (CL) is one of the primary risk factors for preterm birth.^{3 4} Providing interventions for pregnant women with a short cervix can reduce the incidence of preterm birth and neonatal mortality. It is an important way to reduce the burden of preterm labour.^{2 5} Numerous interventions have been employed to reduce the occurrence of preterm birth in pregnant women with a short cervix, including pharmacological interventions such as vaginal progesterone and intramuscular progesterone, as well as non-pharmacological interventions like cervical cerclage and cervical pessary.^6–8 Although current guidelines only recommend vaginal progesterone and cerclage for preventing preterm birth in pregnant women with a short cervix, emerging evidence in recent years suggests that interventions such as cervical pessary, cervical pessary combined with vaginal progesterone and cerclage combined with vaginal progesterone are also safe and effective preventive interventions.^9–11

The optimal prevention of preterm birth in pregnant women with a short cervix remains unclear due to the lack of evidence from clinical trials directly comparing the effectiveness and safety of all available interventions.^{12 13} A three-arm randomised controlled trial (RCT) that included cervical cerclage, cervical pessary and vaginal progesterone did not reach a conclusion due to an insufficient number of eligible participants.¹⁴ Another similar three-arm RCT is currently recruiting participants.¹⁵ A cohort study in 2016 indicated a similar efficacy in preventing preterm birth among cervical cerclage, cervical pessary and vaginal progesterone. However, the cohorts for the three interventions were from the UK, USA and Spain, which may have affected the study results due to population heterogeneity.¹⁶

In the absence of direct comparative RCTs, the use of network meta-analyses that combine direct and indirect comparisons is an effective strategy. However, existing network meta-analyses encounter issues like delayed updates, imprecise inclusion criteria for study populations and incomplete incorporation of studies for subgroup analysis.

A network meta-analysis published in 2017 suggested potential benefits of vaginal progesterone in preventing preterm birth in women with a short cervix during twin pregnancies. However, the search for this study was conducted more than 8 years ago, and many recent findings may alter the conclusions of the study.¹⁷ In 2018, a network meta-analysis evaluating progesterone, cervical pessary and cervical cerclage for preventing preterm birth in singleton pregnancies suggested that progesterone is the optimal intervention for preventing preterm birth in singleton pregnancies with short cervix.¹⁸ Like the previous study, the search for this study was conducted more than 6 years ago, and newly published RCTs might influence the study’s conclusions. In 2021, a network meta-analysis revealed that cervical pessary, progesterone and cervical cerclage showed no significant effects in reducing preterm birth rates or morbidity in women with a short cervix during twin pregnancies. However, this study exhibited significant inconsistency and heterogeneity, thereby undermining the confidence of healthcare decision-makers in its study’s findings.¹⁹ A comprehensive network meta-analysis which was published in 2022, incorporating various interventions such as cervical cerclage, cervical pessary, progesterone (both vaginal and intramuscular) and placebo/no treatment, assessed the efficacy and safety of preventing preterm birth in singleton pregnancies. The study suggested vaginal progesterone as the primary choice for preventive treatment. However, the population included in this network meta-analysis did not specifically focus on pregnant women with short cervix. Additionally, the study did not analyse specific subgroups within the high-risk population, such as women with a short cervix or those with a history of preterm birth.²⁰

Therefore, the optimal intervention to prevent preterm birth in pregnant women with a short cervix remains controversial. Considering that CL shortening during pregnancy serves as the gold standard for predicting preterm birth, there is an urgent need to conduct a network meta-analysis to explore the optimal preventive measures for pregnant women with a short cervix (CL＜30 mm).¹¹

Methods and analysis

This study will strictly adhere to the process outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the PRISMA extension for network meta-analyses. Furthermore, this study has been registered in the international prospective review database PROSPERO with the registration number CRD42022315200.

Type of patients

This study includes pregnant women diagnosed with a short cervix (＜30 mm) through ultrasound examination. The reason we chose to use a CL of less than 30 mm is that there have been many studies looking at this threshold. Hibbard’s study revealed that the 10th percentile CL for patients at 16–22 weeks’ gestation was 30 mm²¹; based on the results of a prospective observational cohort study with a sample size of 3449 cases, Guerby concluded that women with a CL between 25–30 mm were also at risk of preterm birth, and therefore, he considered the 30 mm threshold to be the optimal CL for the prediction of preterm birth by 37 weeks^{22 23}; in addition, Pacagnella’s study highlighted a risk of preterm birth among pregnant women with a CL of 25–30 mm, emphasising the lack of evidence-based treatment.²⁴

There are no restrictions based on age, gestational age, number of pregnancies, race, previous pregnancy history, previous preterm history, prior surgical history or whether there was threatened preterm birth. Ultrasound includes transvaginal and transabdominal scans.

Type of studies

We will include all published and unpublished RCTs, as long as they provide accessible outcome data. These RCTs should compare two or more interventions aiming to reduce the risk of preterm birth in women with a short cervix. We will exclude duplicate publications, publications lacking extractable outcome data and publications without clear specification of CL in pregnant women. There are no language restrictions for included publications.

Types of interventions

The interventions include cervical cerclage (such as the McDonald or Shirodkar procedures, or any other unspecified technique), cervical pessary, vaginal progesterone, intramuscular progesterone or any combination of these, without restrictions on the timing, frequency, route or dosage of medication.

Types of outcome measures

The outcomes chosen for this study were taken from Janneke’s 2016 publication ‘A Core Outcome Set for Evaluation of Interventions to Prevent Preterm Birth’,²⁵ including nine outcomes in terms of safety and efficacy.

Primary outcomes

• Preterm birth rate at <37 weeks.

• The composite neonatal adverse outcome included Apgar score <5 at 5 min, ventilator support or cardiopulmonary resuscitation, seizure, hypoxic ischaemic encephalopathy, sepsis, bronchopulmonary dysplasia, persistent pulmonary hypertension, necrotizing enterocolitis, birth injury or perinatal death.

Secondary outcomes

• Spontaneous preterm birth rate at <37 weeks.

• Preterm birth rate at <34 weeks.

• Spontaneous preterm birth rate at <34 weeks.

• Week of gestation.

• Birth weight of the newborn.

• Perinatal mortality.

• Neonatal admission rate.

Information source and search strategy

We will search six biomedical databases, including PubMed, Embase Ovid, Cochrane Library Ovid, China National Knowledge Infrastructure, Wanfang Data and VIP. Furthermore, we will search RCT registration websites such as the US Clinical Trials Registry (http://clinicaltrials.gov/) and the China Clinical Trials Registry (http://www.chictr.org/cn/) to identify potentially unpublished trials. The search will contain all available data from the inception of these databases up to 1 January 2024. The search strategy will be specific for each database and will include a combination of medical subject headings and free text terms for ‘Premature Birth’, ‘Pregnancy’, ‘Pessaries’, ‘Cerclage’ or ‘Progesterone’. The search strategy for PubMed is detailed in box 1. Search strategies for all databases can be found in the online supplemental material.

Box 1

Search strategy (PubMed).

1. "progesterone"[MeSH Terms]

2. "progesteron*" [All Fields]

3. "progestins"[MeSH Terms]

4. "progestin*" [All Fields]

5. "progestogen*" [All Fields]

6. " progestagen*" [All Fields]

7. "17 alpha hydroxyprogesterone"[MeSH Terms]

8. "hydroxyprogesterone*" [All Fields]

9. "17-OHP*" [All Fields]

10. "17OHP*" [All Fields]

11. 1 OR 2 OR 3 OR 4 OR 5 OR 6 OR 7 OR 8 OR 9 OR 10

12. "cerclage, cervical"[MeSH Terms]

13. "cerclage*" [All Fields]

14. "Shirodkar" [All Fields]

15. "McDonald" [All Fields]

16. "MacDonald" [All Fields]

17.12 OR 13 OR 14 OR 15 OR 16

18. "pessaries"[MeSH Terms]

19. "pessar*"[All Fields]

20. "Arabin"[All Fields]

21.18 OR 19 OR 20

22. "pregnancy"[MeSH Terms]

23. "pregnan*"[All Fields]

24. "Premature Birth" [MeSH Terms]

25. "gravidity " [MeSH Terms]

26. "Maternal Health Services" [MeSH Terms]

27. "Infant, Newborn" [MeSH Terms]

28. "Infant, Premature" [MeSH Terms]

29. "Fetus" [MeSH Terms]

30. 22 OR 23 OR 24 OR 25 OR 26 OR 27 OR 28 OR 29 31. (11 OR 17 OR 21) AND 30 32. randomized controlled trial[pt] OR randomized controlled trials as topic[mh] OR random allocation [mh] OR double-blind method[mh] OR single-blind method[mh] OR random*[tw] OR "Placebos"[Mesh] OR placebo[tiab] OR ((singl*[tw] OR doubl*[tw] OR trebl*[tw] OR tripl*[tw]) AND (mask*[tw] OR blind*[tw] OR dumm*[tw])) NOT (animals[mh] NOT human[mh]) 33.31 AND 32

Study selection

The retrieved publications will be imported into ENDNOTES X9 (Thomson Corporation, Thomson ResearchSoft, USA）software for removing duplicates and selection. The two researchers will conduct back-to-back screening to determine the final included studies, with any disagreements resolved by a third researcher. The article selection process will involve two steps: initially, two researchers will exclude obviously irrelevant publications based on their titles and abstracts, and then will assess the remaining publications by examining their full texts.

Data collection

The two researchers will extract data independently for the final included studies using a predesigned data extraction form, with any disagreements resolved by a third researcher. The extracted data include basic information (title, author, author’s institution, country, year of publication, journal), study methodology information (number and locations of centres, randomisation method, blinding method, allocation concealment method, study start and end time, study inclusion and exclusion criteria, number of people, time of ultrasound examination), patient information (age, gestational age, number of pregnancies, body mass index, previous pregnancy history/surgery history, CL, race, smoking or not, basic treatment), interventions information (intervention name, intervention route, intervention time, intervention dose, intervention frequency, intervention start and stop time) and data on each outcome. When data are missing, emails will be sent to the corresponding authors. In addition, we standardised the units, using grams for weight units, months for time units and weeks for gestation time in all data.

Risk of bias and evidence certainty assessment

Two researchers will independently use the Risk of Bias 2.0 (ROB 2.0) tool developed by the Cochrane Collaboration to evaluate the bias risk in the included RCTs,²⁶ with any disagreements resolved by a third researcher. ROB 2.0 includes five domains: bias arising from the randomisation process, bias due to deviations from intended interventions, bias due to missing outcome data, bias in measurement of the outcome and bias in selection of the reported result. Based on the result in each domain, ROB 2.0 categorises the risk of bias of each RCT as ‘low risk’, ‘some concerns’ or ‘high risk’.

Two researchers will independently use the Confidence in Network Meta-Analysis (CINeMA) software to assess the certainty of evidence.²⁷ CINeMA assesses the evidence certainty of network meta-analysis holistically, considering six domains: within-study bias, reporting bias, indirectness, imprecision, heterogeneity and incoherence. CINeMA categorises the evidence certainty of each outcome into four levels—‘high’, ‘moderate’, ‘low’ and ‘very low’—based on the result of each domain.

Trustworthiness assessment

Although we have used the CINeMA framework to assess the certainty of evidence for network meta-analyses, however, it is not designed to detect study aspects indicative of untrustworthy science. Cochrane Pregnancy and Childbirth Research Group has developed a trustworthiness screening tool that can be used to detect study aspects indicative of untrustworthy science.²⁸ The tool had four domains: (a) is the research governance trustworthy?; (b) are the baseline characteristics trustworthy?; (c) is the study feasible? and (d) are the results plausible? At the end of the evaluation process, each study was classified as: (a) included (YES to all questions); (b) excluded (retracted study) or (c) awaiting classification (any NO to the questions).²⁹

Data synthesis

Based on the Bayesian theory and Markov Chain Monte Carlo method, we will perform a network meta-analysis of the data using R 4.2.2 software.^{27 30} The parameters are set as follows: number of chains is 6; initial value is 2.5; amount of adaptation iterations is 50 000; amount of simulation iterations is 200 000 and thinning factor is 10. The selection of the effect model relies on the Deviance Information Criterion (DIC) calculation. If the DIC difference between the random-effects model and the fixed-effects model exceeds 3, the model with the smaller DIC will be chosen. Conversely, the random-effects model will be selected if the difference is less than 3.³¹ The network meta-analysis will be conducted using the gemtc package in R software.

OR and its 95% CI will be used to pool study data for dichotomous variables; mean difference and its 95% CI will be used to pool study data for continuous variables. When continuous variables are presented as median and IQR, their mean and SD will be estimated along with the sample size using an online tool developed by Xiang Wan and Dehui Luo.^{32 33} When continuous variables are reported as SE, they will be converted to SD using the formula SD=SE * √N (where N denotes the study population).

CINeMA software will be used to generate the network diagram of included studies, while the gemtc package will create league tables, forest plots and probability ranking plots for comparing different interventions in the network meta-analysis. Scatter plots of primary outcomes will be used to determine a comprehensive ranking of effectiveness and safety among different interventions. Surface Under the Cumulative Ranking (SUCRA) will be used to address the inability of scatterplots to be quantified. Overall heterogeneity will be assessed using the χ² test, with I² > 50% indicating high heterogeneity among studies.³⁴ The node-splitting method will examine the consistency between direct and indirect comparison results for interventions in closed-loop studies.³⁵ For outcomes with more than 10 included studies, funnel plots will be constructed using STATA 16.0 to assess publication bias.^{36 37}

Subgroup analysis

This study will conduct subgroup analyses based on identified high-risk factors for preterm birth, including the number of pregnancies (single or multiple), history of preterm birth (previous preterm birth or no previous preterm birth) and the preterm labour symptoms (asymptomatic and symptomatic preterm labours).^38–40 Additionally, we will perform subgroup analyses based on the income level of the countries where the study centres are located and categorised according to the World Bank’s classification (high-income, upper-middle-income, lower-middle-income and low-income countries). Given that previous studies have confirmed a correlation between shorter CL during pregnancy and an increased risk of preterm birth,^{41 42} we plan to conduct a subgroup analysis based on CL, dividing participants into the following groups: <25 mm, <20 mm and <15 mm. These analyses aim to investigate the effectiveness and safety of various interventions across different populations and settings.

Sensitivity analysis

Sensitivity analyses will be used to assess the stability of the study results. The following sensitivity analyses will be conducted.

• We will exclude studies one by one to assess whether a particular study will have a significant impact on the conclusions.

• We will exclude studies assessed as ‘high risk’ by the ROB2.0 tool.

• We will exclude studies that are assessed as untrustworthy.

Patient and public involvement

There was no patient or public involvement in this work.

Discussion

To the best of our knowledge, this study is the first network meta-analysis comparing different interventions to reduce the risk of preterm birth in pregnant women with cervical shortening during pregnancy, and we hope that the findings of this study may help in future clinical decision-making. Although similar studies have been published previously,^17–20 this study has the following advantages:

• It incorporates the latest large multi-centre RCTs, ensuring timeliness.⁴³

• It focuses on pregnant women with cervical shortening, a gold standard predictor of preterm birth, enhancing its clinical applicability.

• The selection of outcomes in this study refers to JANNEKE’s published core set of preterm birth outcomes, indicating the clinical importance and widespread attention given to this study’s outcomes.

• Unlike previous studies that defined a short cervix as <25 mm, this study responds to the call of many researchers to extend this range to <30 mm.

• We plan to use the trustworthiness assessment tool to assess the trustworthiness of the included studies, a measure that we believe will increase the credibility of our findings.

However, there are also limitations in this study. The primary limitation is the heterogeneity of the population. Our study is restricted only by CL during pregnancy and does not account for factors such as the number of fetuses and history of preterm birth, which could significantly increase the heterogeneity among the included studies. Additionally, we anticipate that many studies may not comprehensively report all outcome measures beyond the primary endpoint (preterm birth rate at <37 weeks). This incomplete reporting could reduce the reliability of conclusions drawn for secondary outcomes.

Ethiucs and dessimination

This study involved no study participants and was exempt from institutional review. All data analysed in this study will come from previously published articles.

Ethics statements

Patient consent for publication

Not applicable.

Footnote

Contributors JF Luo: conceptualisation, data curation, formal analysis, software, writing—original draft; DL: conceptualisation, validation, writing—review and editing, methodology; JY Liu: project administration, resources, supervision, writing—review and editing. The guarantor of the study is JF LuoL; accepts full responsibility for the finished work and/or the conduct of the study, has access to the data and controls the decision to publish.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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