Evidence map›Paper›PMID 30357607›Full record

SynthesisClinical drug investigation2019

Is It Necessary to Perform the Pharmacological Interventions for Intrahepatic Cholestasis of Pregnancy? A Bayesian Network Meta-Analysis.

Yi Shen, Jie Zhou, Sheng Zhang, Xu-Lin Wang, Yu-Long Jia, Shu He, Yuan-Yuan Wang, Wen-Chao Li, Jian-Guo Shao, Xun Zhuang and 2 more

Abstract readSystematic ReviewNetwork Meta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Clinical drug investigation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Intrahepatic Cholestasis of Pregnancy: Toward Improving Perinatal Outcome.Reproductive sciences (Thousand Oaks, Calif.) · 2022
    Review
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 1 institution in 1 country.

Yi Shen *Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China.
Jie Zhou *Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China.ORCID http://orcid.org/0000-0003-3168-2828
Sheng ZhangDepartment of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China.
Xu-Lin WangDepartment of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China.
Yu-Long JiaDepartment of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China.
Shu HeDepartment of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China.
Yuan-Yuan WangDepartment of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China.
Wen-Chao LiDepartment of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China.
Jian-Guo ShaoCenter for Liver Diseases, Nantong Third People's Hospital, Nantong University, 60 Mid-Youth Road, Nantong, 226006, Jiangsu, China.
Xun ZhuangDepartment of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China.
Yuan-Lin LiuDepartment of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Nantong, China.
Gang QinCenter for Liver Diseases, Nantong Third People's Hospital, Nantong University, 60 Mid-Youth Road, Nantong, 226006, Jiangsu, China. tonygqin@ntu.edu.cn.ORCID http://orcid.org/0000-0002-4363-2572
Nantong University · CN

Funding

Jiangsu Provincial Department of Science and Technology BE2015655National Natural Science Foundation of China 81370520The Nantong Municipal Bureau of Science and Technology HS2016002The Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX17-1941
6 · The paper itself

Abstract

BACKGROUND AND

objectiveAlthough many meta-analyses have evaluated the pharmacotherapy of intrahepatic cholestasis of pregnancy (ICP) and recommended ursodeoxycholic acid (UDCA) as an effective treatment, the defect of the pair-wise analyses and the mixture of the control group made the outcome uncertain and unclear. We aimed to employ Bayesian network meta-analysis (NMA) to compare the maternal and fetal outcomes after UDCA, S-adenosylmethionine (SAMe) mono-therapy or the combination treatment of these two drugs for ICP patients.

methodsMultiple electronic database searches were conducted for articles published up to 1 September 2018. The relevant information was extracted from the published reports with a predefined data extraction sheet, and the risk of bias was assessed with the Cochrane risk-of-bias tool. Poisson Bayesian network meta-analysis was employed to identify the synthesized evidence from the relevant trials, with reporting hazard risks (HRs) and 95% credible intervals (CrIs).

resultsThe pooled outcomes of the 13 randomized controlled trials (RCTs) with 625 participants indicated that none of the three regimens can significantly improve maternal and fetal outcomes.

conclusionThis NMA of the RCTs clarified that the current intervention has no favorable effect on pruritus and other symptoms in ICP patients.

Indexed as

Pregnancy OutcomeBayes TheoremCholestasis, IntrahepaticFemaleHumansPregnancyPregnancy ComplicationsPruritusRandomized Controlled Trials as TopicTreatment OutcomeUrsodeoxycholic AcidUrsodeoxycholic Acid

Identifiers

PMID30357607
OpenAlexW2898484809

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.