Evidence map›Paper›PMID 27757588›Full record

ReviewPediatric nephrology (Berlin, Germany)2017

A contemporary approach to the prevention of peritoneal dialysis-related peritonitis in children: the role of improvement science.

Allison Redpath Mahon, Alicia M Neu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pediatric nephrology (Berlin, Germany), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 31% 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

5 citing papers in PubMed, 15 citations in OpenAlex.

  1. Review
  2. Article
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  5. 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

2 authors at 2 institutions in 1 country.

Allison Redpath MahonPediatric Nephrology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. acredpath@pediatrics.wisc.edu.
Alicia M NeuPediatric Nephrology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Johns Hopkins University · USUniversity of Wisconsin–Madison · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Peritonitis is a leading cause of hospitalizations, morbidity, and modality change in pediatric chronic peritoneal dialysis (CPD) patients. Despite guidelines published by the International Society for Peritoneal Dialysis aimed at reducing the risk of peritonitis, registry data have revealed significant variability in peritonitis rates among centers caring for children on CPD, which suggests variability in practice. Improvement science methods have been used to reduce a variety of healthcare-associated infections and are also being applied successfully to decrease rates of peritonitis in children. A successful quality improvement program with the goal of decreasing peritonitis will not only include primary drivers directly linked to the outcome of peritonitis, but will also direct attention to secondary drivers that are important for the achievement of primary drivers, such as health literacy and patient and family engagement strategies. In this review, we describe a comprehensive improvement science model for the reduction of peritonitis in pediatric patients on CPD.

Indexed as

Anti-Bacterial AgentsAntibiotic ProphylaxisCatheters, IndwellingChildCross InfectionHumansKidney Failure, ChronicPatient Education as TopicPeritoneal DialysisPeritonitisPractice Guidelines as TopicQuality ImprovementRegistriesRisk FactorsStaphylococcal InfectionsStaphylococcus aureusAnti-Bacterial AgentsCare bundlesExit site infectionHealth literacyHome visitPatient engagementPeritonitisQuality improvement

Identifiers

PMID27757588
OpenAlexW2537485106

What OpenQuestion 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 OpenQuestion graph.